Test-time few-shot object detection (FSOD) represents an innovative approach for identifying novel categories using a limited number of support examples, obviating the need for model fine-tuning. Despite advancements, existing FSOD methods, including our prior work, continue to grapple with challenges posed by domain/category shift and limited data availability. Building upon our previous research on test-time FSOD, this article proposes a novel dynamic prototype fusion network (PFN) to overcome these limitations. To mitigate the impact of the distribution shift, a dynamic prototype refinement method is introduced that updates prototypes from supporting images in an adaptive manner. Further, limited samples are mitigated through exhaustive exploitation of information within support images. Specifically, we design a dual-level multiscale information integration approach that effectively fuses information across different network layers and image scales, enhancing the model's discriminating capabilities. Additionally, a mask-based preprocessing technique harnesses segmentation labels on support samples, effectively suppressing the adverse impact of background noise on model accuracy. Notably, to align with the constraints of test-time scenarios, model parameters remain fixed during the configuration step, with only prototypes being updated each time users input novel supporting samples. As a result, our method achieves superior performance over existing state-of-the-art FSOD methods on multiple benchmarks, demonstrating remarkable potential in the realm of FSOD. The code is available at https://github.com/CatfishW/TIDEV2.

Artificial intelligence-generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC’s evolution through four developmental milestones, ranging from early rule-based systems to modern transfer learning (TL) models, within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, this article employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this article addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.

As the demand for disassembling end-of-life products grows, limitations in traditional disassembly line design, low efficiency, and high resource consumption become increasingly evident. Particularly in large-scale disassembly tasks, where the cost of conventional remanufacturing rises and the technologies fail to meet high-efficiency requirements. The integration of robots into disassembly lines is a promising solution to alleviate these issues. This work presents a multi-product hybrid disassembly line balancing problem that considers machine wear rates and establishes a mixed-integer programming model guided by profit maximization to address it. An improved fireworks algorithm is used in the proposed approach. The developed solution is compared with genetic and ant colony algorithms. Evaluation results and analysis demonstrated the competitive efficiency and stability of our approach.

Integrating artificial intelligence into educational technology presents great opportunities for automated educational systems. These systems could relieve teacher resources and support underperforming students. However, creating systems that are adaptive, scalable, and factually correct is resource intensive. Furthermore, there are many technologies that are prevalent, but lack systematic ways to integrate them into existing educational technologies. Building on reinforcement learning and large language models (LLMs), this paper introduces a multi-agent framework for adding both a reinforcement learning-based tutor and an LLM-driven peer to educational systems. The integrated architecture is unified with a central ontology, acting as a symbolic knowledge base and facilitating data transformation. We also detail a novel windowed experience sharing method for improving reinforcement learning training efficiency when dealing with similar environments and low-data situations. We present our architecture and simulated results to verify the reinforcement learning algorithm as an adaptive tutor, as well as the integration of an LLM-driven peer and educational outcomes from this integration.

Current Multi-Modal Large language Models (MMLMs) primarily rely on instance-level feature statistics for cross-modal alignment. However, they commonly suffer three inherent limitations including vulnerability to outlier perturbations, neglect of inter-feature covariance structures, and local optimum trapping. These limitations stem from a critical oversight—existing approaches disregard the global statistical structure of multi-modal data, treating cross-modal alignment as isolated feature-level alignment rather than systematic distribution-level alignment. To address these issues, this paper proposes Layer-wise Covariance Alignment (LCA), which first leverages distribution-level alignment for cross-modal alignment. The effectiveness of LCA is validated through the use of parameter-efficient Low-Rank Adaptation (LoRA) on CLIP architectures. Experimental validation across eight benchmarks demonstrates state-of-the-art performance, confirming the critical role of distribution-level alignment in overcoming sample-level optimization constraints for cross-modal learning.

In recent years, with the rapid development of artificial intelligence and social media, the influence of word-of-mouth (WOM) on the diffusion of online content has become increasingly evident. Video platforms can use artificial intelligence to collect WOM data of programs and formulate corresponding pricing strategies. Based on this background, considering the impact of online WOM effects on the diffusion of on-demand content exclusive to members, this study constructs a two-stage product provision model for online video platforms, consisting of the premiere and follow-up broadcast stage. Based on expected utility theory, this research explores the pricing strategies for member-exclusive on-demand content under two profit models and analyzes the influence of program WOM attributes and program quality on optimal decision-making. The findings reveal that: When the premiere stage WOM for a program is either highly positive or negative, video platform should adopt an "advertising-dominant strategy". When the premiere stage WOM is moderate, a " fee-dominant strategy" is preferable. Higher program quality increases the platform's inclination toward the "fee-dominant strategy". The better the premiere stage WOM and program quality, the more users tend to watch during the premiere stage. Accordingly, both the program price and the platform's expected profit will vary to different degrees depending on these conditions.

The internal gear power honing process is widely used in gear machining for electric vehicles because of the advantages of high-precision and high-efficiency machining. The gear honing process involves six axes and high spindle speeds, this process contributes a substantial amount of energy consumption but has less attention on energy saving. To improve the energy efficiency of gear honing process, this paper proposes an energy consumption modeling and process parameter optimization method under multi-axis coupling. The multi-axis coupling motion and energy consumption characteristics of gear honing process are analyzed. The energy consumption model of gear honing process under multi-axis coupling is then established, and the influence law of process parameters on honing energy consumption is investigated. Furthermore, a multi-objective process parameter optimization model for minimizing energy consumption and machining time is constructed. An improved multi-objective atomic orbital search (IMOAOS) algorithm is developed to solve the multi-objective optimization problem. The gear honing experiment results demonstrate that the optimized scheme reduces energy consumption by 39.75% and machining time by 8.48% compared to the empirical scheme. The proposed multi-objective optimization scheme also significantly balances the energy consumption and machining time of gear honing process compared with single-objective optimization.

The integration of flexible process planning and cutting parameter optimization is of great significance to reduce energy consumption and shorten production time. Flexible manufacturing system brings great uncertainty to the flexible process planning and cutting parameter optimization. Most studies are conducted in a static manufacturing environment and lack of adaptive capacity to the uncertainty of the machining resources. To this end, an integrated decision-making method of flexible process plan and cutting parameter is proposed to improve energy efficiency. Specifically, the improved AND/OR network graph is employed to describe various types of process flexibility. Secondly, the coupling characteristics between energy consumption and machining resources, cutting parameters, and operation sequences are analyzed. Then, a Markov Decision Process is utilized to simulate the dynamic generation process of flexible process plans and cutting parameters, and the integrated decision-making method considering dynamic machining resources is designed with actor-critic framework. Finally, extensive comparative experiments are carried out to verify the validity of the proposed method. Experimental results indicate that: 1) the proposed method can determine flexible process plans and cutting parameters to adapt to the dynamics of machining resources. 2) The integrated optimization method reduces$E_{total} $and$T_{p}$by 3.59% and 3.45% compared to the two-stage optimization methodNote to Practitioners—Decision making of flexible process plans and cutting parameters relies on machining resources in the machining process. Dynamic changes in machining resources make the energy-aware decision of the flexible process plans and cutting parameters a challenging problem. To the best of our knowledge, this paper develops an integrated method of flexible process planning and cutting parameter optimization considering dynamic machining resources that can adapt to the change of machining resources. It may assist decision-makers to provide more practical flexible process plans and cutting parameters based on dynamic machining resources in the machining process.

Ecological environment and natural resource issues are becoming more and more prominent, which promotes the recycling of waste products for green economy. Disassembly plays a key role in the remanufacturing and reuse of waste products. However, with the rapid development of production automation, designers tend to ignore the fact that manual operation is more flexible. It is of great importance to consider human factors in a disassembly process. This work considers two human disassembly postures, namely standing and sitting. The multiobjective disassembly line balancing problem considering human posture changes is studied. A mathematical model with the objective functions of maximizing profit, minimizing the number of posture changes at a workstation, and minimizing the difference of maximum posture changes between any two workstations is established. The model is solved through a newly proposed Pareto-based discrete harmony search algorithm. Three neighborhood structures are designed to enlarge the search space for better solutions. Furthermore, an elite reserve strategy is used to improve the global optimization ability of the proposed algorithm. Finally, the proposed model and algorithm are applied to cases of different scales of complexities, and the effectiveness of the proposed model and algorithm is verified in comparison with four competitive algorithms.

Industry 5.0 enhances manufacturing ability through efficient human-machine interaction, combining human resources and robots to complete tasks more accurately and effectively. Artificial intelligence (AI) plays an essential role in Industry 5.0. As a branch in AI, multiagent deep reinforcement learning (MADRL) attracts vast attention in both academia and industry. However, there is a gap between virtual and physical environments in terms of howcleanan observed state is. In addition, state adversarial attacks can seriously impact the performance of MADRL. Hence, how to improve the robustness of MADRL algorithms is an important research topic. In this article, we propose an optimal policy-based state adversary attack method that would make the MADRL algorithm more robust when it is applied in the training process of agents. Two case studies related to Industry 5.0 and a general case study are presented in which robustness training against the optimal adversarial attack is tested. The MADRL algorithms involved in the experiments include centralized training and decentralized execution (CTDE) framework and shared experience actor-critic (SEAC) to demonstrate the universality of our method.

Formal verification becomes increasingly critical to ensure system functionality, reliability and safety as they grow in complexity. Existing methods tend to focus on a single dimension of system aspects—such as control flow, data flow or timing constraints—or, at most, consider two of these perspectives without integrating all three. In addition, data flow models generally represent high-level data abstraction without including operational details within underlying contexts. The inability of these models to capture system behavior undermines their reliability, ultimately increasing the likelihood of the corresponding systems malfunctioning. To address these issues, we propose a formal verification method based on a timed Petri net with database tables (TPDT-net). First, we model the system using TPDT-net and generate its state reachability graph (SRG). Next, we extend timed computation tree logic (TCTL) by introducing database-related data element operators, thus proposing a database-oriented TCTL (DTCTL) model checking method. In addition, we formalize the system correctness problem as corresponding DTCTL formulas, which are analyzed based on the SRG. This approach transforms correctness verification into a satisfiability problem of DTCTL formulas within the SRG. Finally, we validate the practicality and effectiveness of the proposed method through case studies and experiments.

With the rapid development of information technology and logistics technology, traditional centralized factories are transforming into distributed production systems, forming a multi-factory manufacturing model. This study uses Petri nets to model the disassembly processes of end-of-life (EOL) products, integrates the disassembly line balancing issue with the resource sharing over multiple factories, propose a hybrid layout for multi-factory remanufacturing, and establishes a linear programming mathematical model that optimizes the disassembly profit. Deep deterministic policy gradient(DDPG), a deep reinforcement learning algorithm, is employed to solve the model. Experimental results demonstrate the feasibility of the proposed approach.

In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students.

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning experience.We especially focus on Chat Generative Pre-trained Transformer (ChatGPT) owing to its considerable potential to supplement regular class learning.We address the strengths and weaknesses of learning with ChatGPT.Finally, we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

Product disassembly is critically important in recycling end-of-life products, reducing their negative impact on environmental pollution and minimizing resource waste. Disassembly line balancing problems have attracted much attention from researchers and industrial practitioners. Most of the existing studies, however, consider only human disassembly or robot disassembly alone. This work considers human-robot collaboration. It proposes an human-robot collaborative disassembly line balancing model considering stochastic task time, where an AND/OR graph is adopted to describe a product’s disassembly process. The objectives are to maximize the total profit and minimize energy consumption. A Pareto improved multi-objective shuffled frog leaping algorithm with a stochastic simulation strategy is proposed to solve the model. In addition, an elite strategy is introduced in global search to enhance the algorithm’s optimization capability. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of this algorithm are demonstrated. Its comparison with some most popular state-of-the-art methods is performed.Note to Practitioners—This paper is motivated by the benefits of human-robot collaboration in the disassembly systems. The presented approach is suitable for disassembly lines with multiple objectives, and the weight of each objective cannot be accurately grasped. Most of the existing operation allocation methods are based on the correlation between humans and robots and the factors affecting disassembly. This paper suggests the selection of humans and robots is completely random and decided by an optimization algorithm. This paper designs an improved multi-objective shuffled frog leaping algorithm based on Pareto’s rule. Experimental results show that this algorithm can be applied to solve practical disassembly line balancing problems.

Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain. Recent advances in FSOD focus on fine-tuning the base model based on a few objects via meta-learning or data augmentation. Despite their success, the majority of them are grounded with parametric readjustment to generalize on novel objects, which face considerable challenges in Industry 5.0, such as 1) a certain amount of fine-tuning time is required and 2) the parameters of the constructed model being unavailable due to the privilege protection, making the fine-tuning fail. Such constraints naturally limit its application in scenarios with real-time configuration requirements or within black-box settings. To tackle the challenges mentioned above, we formalize a novel FSOD task, referred to as test-time few-shot detection (TIDE), where the model is un-tuned in the configuration procedure. To that end, we introduce an asymmetric architecture for learning a support-instance-guided dynamic category classifier. Further, a cross-attention module and a multiscale resizer are provided to enhance the model performance. Experimental results on multiple FSOD platforms reveal that the proposed TIDE significantly outperforms existing contemporary methods. The implementation codes are available at https://github.com/deku-0621/TIDE.

This work-in-progress paper discusses the development of an educational game to provide integrated geotechnical engineering education modules that connect theoretical concepts, laboratory testing, field investigation, and engineering design. The game, Earth Trek, is developed based on the design of geothermal piles, which are an innovative and sustainable geotechnical engineering approach to combat climate change. Virtual reality is applied to visualize the field environments, laboratory conditions, and design components for structural simulation. The game uses a combination of storytelling and tasks to engage students with geotechnical concepts in an enjoyable way. With the newly developed game, geotechnical engineering instructors can provide students with exposure to laboratory testing and field environments, improving the quality of geotechnical engineering education. The use of multiphysics enriched mixed reality gaming allows for a visual representation of the connections between theoretical concepts, laboratory testing, field investigation, and engineering design. Additionally, this study discusses the challenges that geotechnical students face when dealing with worldwide concerns such as energy demand, environmental protection, infrastructure sustainability, and hazard reduction. Earth Trek allows students to apply geotechnical engineering knowledge to explore the underground space and the associated geothermal energy to tackle the engineering problems using only their smartphones. Through exploring the virtual environment and completing game tasks, students can obtain different testing tools used for geotechnical experiments, including thermal conductivity measurement and direct shear test. They are also trained to conduct parametric study to explore the influence of boundary conditions on thermal transfer efficiency of the geothermal pile. The key contribution of this work is to illustrate an educational paradigm based on mixed reality, moving towards creative engineering education in geotechnical engineering. The newly developed educational game and Earth Trek are expected to enhance geotechnical engineering education and provide students with an interdisciplinary knowledge to tackle worldwide concerns.

This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system's educational efficacy through early pre-post-test results from students who played the game with and without the personalized learning system integration.

This Research to Practice Work-in-Progress paper discusses a next-generation learning system for K-12 students to educate them on scientific concepts surrounding the human body. Specifically, our gamified learning system is designed to make learning more fun, engaging, and effective through game and experiment elements that align with science and math learning standards. It will also increase systematic problem-solving and algorithmic reasoning for K-12 students. Since the human body can be thought of as a combination of interacting systems, the game also introduces students to computational thinking by introducing internal body functions. To achieve these goals, this project has two components. First, an educational virtual reality game will be built according to the natural human body structure. During the game process, students will experience the same as the human body functions, travel along the blood circulation, help with the heartbeat, and participate in oxygen exchange. While students are playing the game, our gamified adaptive learning system tracks and controls the student's learning progress. As the AI component collects student data and uses this information, our system adjusts game content and addresses possible learner issues to refine the learning curriculum for a faster and more effective learning experience. Second, a series of hands-on activities will be conducted based on the functions of the human body (e.g., developing an artificial heart and experiencing how the heart works). Through this project, an attractive and efficient next-generation learning system will be developed and used to expose K-12 students to this learning system. The education of students will be accomplished in different dimensions through games and hands-on practice, respectively. Additionally, compared to traditional learning methods, our learning system not only increases students' interest in learning but also makes it more personalized compared to the conventional learning process. Likewise, we will refer to the results of self-efficacy surveys administered to students and teachers separately to test their perceptions of their abilities and the new system.

With higher education pushing toward larger class sizes, a large portion of current methodology focuses on one-size-fits-all approaches that can effectively educate a large class. However, when these approaches fail, students can be left behind and fail classes due to simple misunderstandings. Inspired by these issues, this paper proposes a modular reinforcement learning system that can be used in intelligent educational systems to inform personalized student support. Based on a similar method detailed in prior work, this paper proposes experience sharing with tutor agents as a computationally light approach to improve reinforcement learning training speed on the task of student support. We also provide preliminary results obtained from student simulations to demonstrate the effectiveness of the proposed method on reinforcement learning agent performance.

The adjustment effect of the anchoring effect and online reviews on consumer cognition has grown to be a significant element influencing business pricing. This study explores the effects of online reviews and price anchoring on company pricing and profits by building an online product pricing model based on expected utility theory from the perspective of consumer purchasing psychology. The findings show that firms must take consumer anchoring psychology into account when making decisions if they want to increase revenue. Different pricing strategies are used depending on the variables associated with the quality of online reviews. The higher the sensitivity coefficient of reviews, in particular when the quality of the reviews is higher than a specified value, the bigger the profit. In the anchor point, the optimal price is rising. When a company chooses a higher price policy, the optimal price steadily decreases with the degree of anchoring.

Price is an important index of consumers' purchase choice, and the price comparison behavior of consumers in the decision-making process also affects the profit and loss of their own purchase utility to varying degrees. Based on multinomial logit(MNL) model, the reference price is incorporated into product line development and design, and the pricing decision of product line and value-added services bundle is studied. The influence mechanism of reference price effect on optimal product pricing and maximum profit is analyzed, and the deviation of strategic decision-making caused by not considering reference price effect is discussed. The results show that the reference price effect has a positive impact on the lowest price products in the product line, but has a negative impact on the high price products in the product line, the total market share of the firm and the total profit. When the reference price effect is ignored, the pricing of different quality products and services in the product line will be higher or lower, and the total market share and total profit will be higher. The results can provide theoretical support for product line design and pricing decision.

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

Serious games (SGs) are a practice of growing importance due to their high potential as an educational tool for augmented learning. However, little effort has been devoted to address student learning optimization in an SG from a systematic point of view. This article tackles this challenge by developing a learning-embedded attribute Petri net (LAPN) model to represent game flow and student learning decision-makings. The dynamics of learner behaviors in game are then addressed through the incorporation of learning mechanisms (i.e., reinforcement learning (RL) and random forest classification) into the Petri net model for knowledge reasoning and learning. Finally, an algorithm based on LAPN is proposed, aiming to guide learners to achieve a faster and better solution to problem-solving in game. The benefit of the proposed model and algorithm is then demonstrated in the SG Gridlock.

Serious games (SGs) have emerged in recent years as a key method to augment education and training. SGs allow players to experience new concepts while exploring engaging virtual environments. To further extend the educational merit of SGs, adaptive SGs integrate games with adaptive systems to provide a more supportive education. Typically, these adaptive systems either focus on assisting or engaging the player, or on providing a more human-like tutoring experience. For appropriate adaptation, these games make use of player analytics and player modeling to determine what help to provide, predict future player actions, or otherwise model a player’s mental or physical state. To assist researchers in the development of future adaptive SGs, this article reviews and categorizes both player modeling and game adaptation methods from publications over the past ten years. We offer comparisons of various methods to achieve both modeling and adaptation, as well as comprehensive categories for both aspects. Furthermore, we also offer insights for future areas of study in adaptive SGs, as well as possible directions for SG developers.

The integration of artificial intelligence (AI) with cyber–physical–social systems (CPSS) creates new research opportunities and challenges with major societal implications. The behavioral and cognitive enhancement of intelligent systems promotes a productive and creative partnership and collaboration between humans and machines. Advancements in these areas enable adaptability, scalability, resiliency, safety, security, and usability that expand the horizons of CPSS.

Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

Transformational development in science and engineering rapidly integrates machine intelligence and human intelligence to form cyber–physical–social intelligence (CPSI), underpinning the growing interactions of cyberspace, physical space, and social space. A multidimensional reality is emerging and evolving with these interactions, leading to the creation of many new concepts and frameworks, such as parallel intelligence, digital twin, and metaverse. Meanwhile, new challenges continue to arise when we seek to reveal the fundamental principles for command and control of the new and extended reality by identifying and characterizing the relations of machines, humans, and nature. To meet these challenges, it is important for us to understand and explore algorithmic theories and processes that constitute CPSI, and to build cyber–physical–social systems (CPSS)[1]with the features of agility, focus, and convergence that are vital to ensure an inclusive, sustainable, equitable, and resilient future for people and our planet.

This Research to Practice Work-in-Progress Paper builds on prior developments of a gamified adaptive tutoring system that automates and personalizes a student’s learning process without instructor intervention. To address the continued expansion of general education, as well as the grand challenge of personalized learning, automated learning systems are becoming common within higher education. Our personalized learning system uses an uses a structured, general-purpose game model that enables us to both track and control student progress through the sections of the game. While students play through a system-integrated game, a back-end AI component adaptively chooses both where the student is directed and what help they receive to optimize their learning. The end result is a fully integrated game system that can measure student performance using integrated tests, leveraging that information to adjust game content, address learner misconceptions, and lead to a faster and more effective learning session. As part of continued research, we present results from comparison testing of our educational game system in tandem with relevant course material.With our preliminary results, we focus on demonstrating the system’s ability to provide appropriate content to players based on expert opinion. We show the educational utility of the game system, demonstrating an increase in student performance post-intervention on relevant content tests. We also show results from self-efficacy surveys administered to students to test their opinion of their own abilities. By sharing our testing and verification, we demonstrate the effectiveness of our intelligent educational game system.

Under platform economy, prices, reviews, and sales are the three most concerned purchase decision factors for consumers. However, different customers have different sensitivity to the same decision factors. Therefore, it would be an important to study the sensitivity of the consumers to reviews, price, and sales. Based on the Multinominal Logit Model (MNL Model), this paper constructs a two-stage pricing model for new products of platform enterprises, and studies the influence of price, review and sales on enterprise profit. Then it analyzes the influence mechanism of consumers’ sensitivity to price change, product cost and consumers’ valuation of product quality on product pricing and enterprise profit. After that, it further formulates the two-stage optimal pricing strategy for product sales. Research shows that enterprises should not only consider consumers’ sensitivity to comments, price and sales volume, but also learn from the previous sales experience when making pricing strategy. The research results can provide theoretical basis and decision support for product pricing and operation management of platform enterprises.

In the freemium business model, how to price value-added services and design effective strategy to achieve the sustainability of value-added services promotion is of great significance to enterprises. By constructing a monopolistic freemium enterprise, this paper uses a Multinational Logit model (MNL model) to analyze value-added services pricing and two kinds of value-added services promotion strategies (the quality reduction strategy of basic product and the price discount strategy of value-added services) with heterogeneous sticky-users demand, and then discusses the optimal promotion strategy. The results show that: Both the quality reduction strategy of basic product and the price discount strategy of value-added services can have positive impacts on the profit of enterprise. The sticky users demand valuation plays a positive role in promoting the optimal profit of enterprise. The optimal promotion strategy is the price discount strategy of value-added services. This study can provide a theoretical basis and decision support for the operation and management of the freemium enterprises.

With the advancement of society and technology, consumers are becoming more personalized and more willing to buy new products. To meet the diverse needs of consumers, the design and development of product lines have become an important strategic issue of enterprises. Based on the consumer choice model, this paper aims at the design and development of product lines and the purchase behavior of consumers. A two-stage pricing model is constructed under the condition of bundled sales of products and services. This paper analyzes the impact that enterprises should consider products with value-added services and consumers’ purchasing behavior on product line two-stage pricing. Research shows that the level of product value-added services and the degree of enterprise strategy will have an impact on the price of the product line and the enterprise’s profit. When the service level is higher, the enterprise’s product line price and profit will increase, and when the enterprise discount higher, the enterprise’s total profit and product line price will decrease.

The quality of durable consumer goods is more and more concerned by consumers, and the development of value-added services to improve product quality has become an important way for enterprises to obtain profits. Based on the multinomial logit (MNL) model, this paper establishes a product line optimization model considering value-added services, which helps find the optimal pricing, market share and maximum profit. Through numerical experiments, the effects of the ratio of service price to product price, product quality, service quality, utility loss caused by product failure on the optimal solutions are studied. The study finds that when developing a product line, increasing the relative price of services while reducing product pricing is the optimal strategy. The research results can provide theoretical basis and decision support for the pricing of durable consumer goods and value-added services.

In multi-stage automotive production lines (MSAPLs), unforeseen disturbance events such as emergency order can disturb the initial production plan, leading to higher production cost and order delivery delay. To address this issue, an emergency order-oriented collaborative resequencing optimization method for MSAPL based on improved multi-objective particle swarm optimization algorithm (MOPSO) is proposed in this paper. First, a resequencing strategy is proposed for automotive orders based on their production status. Then, a collaborative resequencing mathematical model for MSAPL that selects the production cost and order delivery delay as the objectives is established, and an improved MOPSO is developed to optimize the mathematical model. Finally, a case study is implemented by citing a MSAPL as the example, which verifies the effectiveness and superiority of the proposed method.

In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach.Note to Practitioners—This article, for the first time, to the best of our knowledge, proposes an integrated approach to reduce both electrical and embodied energy consumption of a machining process through optimizing process plan and cutting parameters. Such broader consideration makes this integrated optimization approach more applicable to real industry settings and contributes to the comprehensive improvement of energy efficiency in the machining process. To better use this approach, the following three steps should be highlighted: 1) the energy footprint characteristics of the machining process should be comprehensively analyzed and modeled; 2) the integrated optimization model for minimizing energy footprint and production time needs to cooperate with machining constraints, such as process centralization, machining sequence, and process requirements; and 3) solving the proposed model is a hybrid programming process since there are discrete decision variables and continuous variables. A proper algorithm should be used to solve the proposed model.

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach.Note to Practitioners—Disassembly line balancing has been widely recognized as the most ecological way of retrieving EOL products. Through in-depth research, we present a Stochastic Multi-product Multi-objective Disassembly-sequencing-line-balancing Problem. Furthermore, we consider that the uncertainty of products might cause disassembly failure. To solve this problem effectively and quickly, we combine the simulated annealing algorithm with the Grey Wolf Optimizer. The results show that the algorithm can effectively solve the proposed problem. The disassembly scheme provided by the obtained solution set offers a variety of options for decision-makers.

With the surging energy cost and environmental impacts, strategies to achieve energy-efficient production have attracted increasing concerns of the manufacturing enterprises. For the fact that most manufacturing systems operate in a dynamic and nondeterministic environment, rescheduling strategies may be beneficial as it serves for adaption of initial schedule to dynamic events. Besides that, the development of modern information technology in manufacturing practice enables the flexibility of production toward alternative process plans. However, very little research has focused on the rescheduling problem integrated with process planning for energy saving. Hence, this work undertakes this challenge by proposing rescheduling decision mechanisms in response to two typical dynamic events with alternative process plans for energy-efficient flexible job shops. More specifically, by modeling the energy consumption of the flexible manufacturing system, the problem is first formulated as a mixed-integer programming optimization model. Rescheduling mechanisms for both new job arrivals and machine tool breakdowns are then designed, based on which a rescheduling algorithm is proposed in the form of a heuristic framework. The significance of the proposed algorithm is exemplified by a comparative case study under various scenarios. Note to Practitioners—The complex process plan selection and dynamic events in flexible job shops make the energy-aware schedule decision a challenging problem. Rescheduling addresses this issue, however, most existing rescheduling algorithms assume that only one process plan is given with a predetermined process route and machine tool allocation. This reduces the effectiveness of energy-saving for such a dynamic and flexible manufacturing system. This article, for the first time, to the best of our knowledge, proposes rescheduling decision mechanisms to generate schedules that can adapt to dynamic events and are energy-efficient with process plan flexibility. It may assist decision-makers to provide more practical and applicable energy-efficient schedules for flexible job shops, particularly when dynamic variations of the production environment occurred frequently.

OBJECTIVE: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.

As one of the crucial tasks of natural language processing, machine reading comprehension has gained increased attention in recent years. In this paper, we propose a four-way bidirectional attention network for a multiple-choice reading comprehension task, where every question comes with a set of candidate options and only one correct answer. Current methods on such tasks usually judge options independently and ignore their relations. Thus, this work designs a four-way bidirectional attention strategy to formulate the interactions among the passage, questions and candidate options. In particular, the relations among options are well represented. This enables the model to leverage the option correlation information for inferring the final answer accurately. The experimental evaluations on the CosmosQA dataset demonstrate the competitive performance of our model, and confirm the effectiveness of the option comparison strategy.

To meet the needs of the Internet of Things, every edge device is equipped with the functions of data collection, analysis, calculation, communication, and intelligence. Based on the consumption pattern of offline experience and online purchase, and considering the impact of product quality differences, product defects, and offline service level on customers' purchasing behavior, this paper uses the model (Multinominal Logit Model) to research customers' choice behavior and online product pricing. This paper takes the pricing of dual-channel retailers in different channels as the background, and how to maximize the retailer's profit as the goal, establishes the loss cost model of customer returns, and analyzes the influence of quality problem returns on the optimal pricing and profit of retailers in different channels. The study found that the offline service level remains at 0.24 and retailers can obtain the best profit; the optimal price decreases with the online product quality and the optimal profit increases. In the omni-channel environment, customers can buy products according to their own utility and preferences freely switch between various channels, retailers in the face of customer return this situation, can start from their own interests, provide appropriate service level, reasonable control product quality, make the optimal pricing, maximize their own profits. This study expands the theory of online product pricing from the perspective of customers behavior, provides a more flexible pricing mechanism for enterprises, and speeds up the development and application of intelligent edge computing systems.

A reasonable disassembly line structure and layout are particularly important in advancing disassembly technology. In this work, destructive and non-destructive disassembly modes are considered in multiple-product partial U-shaped disassembly-line-balancing. A mathematical model is established to maximize disassembly profit and minimize disassembly energy consumption for a U-shaped disassembly line. A multi-objective discrete chemical reaction optimization algorithm is then proposed to solve it. A crowded distance mechanism and elitist strategy are designed to obtain non-dominated solutions to accelerate its the convergence speed. The established model and proposed algorithm are applied in a ballpoint pen and radio set cases, and its superiority on a U-shaped disassembly line is verified by comparing it with two commonly used optimization methods.

Disassembly Line Balancing Problems have received much attention from practitioners and researchers due to their importance in sustainable economic development. This work focuses on a U-shaped disassembly line balancing problem and establishes its mathematical model by considering multiple optimization objectives, disassembly task priority relationship, staff training cost, and the cycle time of disassembly workstations. Considering the characteristics of the problem, it proposes a collaborative resource allocation strategy for a multi-objective evolutionary algorithm based on decomposition, resulting a new method called MOEA/D-CRA for short. It allocates corresponding computing resources according to the importance of each subproblem. Four cases are used to compare the MOEA/D-CRA with two well-known algorithms. Experimental results prove that it is significantly better than its two peers.

Most successful fittings detectors are anchor-based, which is challenging to meet the lightweight and real-time requirements of the edge computing system. We propose a high-resolution real-time network HRM-CenterNet. Firstly, the lightweight MobileNetV3 is used to extract multi-level features from images. Then, to improve the resolution of the feature maps and reduce the spatial semantic information loss during the image downsampling process, a high-resolution feature fusion network based on iterative aggregation is introduced. Finally, we conduct experiments on the PASCAL VOC dataset and fittings dataset. The results show that HRM-CenterNet improves accuracy as well as robustness, and meets the performance requirements of real-time edge detection.

Existing projector-camera calibration methods typically warp keypoints from a camera image to a projector image using estimated homographies and often suffer from errors in camera parameters and noises due to imperfect planarity of the calibration target. This article proposes a practical and robust projector-camera calibration system that explicitly deals with these challenges. First, a graph-theory-based correspondence algorithm is built on top of a color-coded spatial structured light (SL) pattern. Such SL correspondences are then used for a coarse projector-camera calibration. To gain more robustness against noises from an imperfect planar calibration board, we develop a bundle adjustment algorithm to jointly optimize the estimated projector-camera parameters and the correspondences’ coordinates. Moreover, our system requires only one shot of an SL pattern for each calibration board pose, which is much more practical than multishot solutions. Comprehensive experimental validation is conducted on both synthetic and real data sets, and our method clearly outperforms the existing methods in all experiments. For the benefit of the society, a practical open-source software with graphical user interface (GUI) of the developed system is publicly available athttps://github.com/bingyaohuang/single-shot-pro-cam-calib.Note to Practitioners—The proposed method is motivated by two challenges in industrial structured light (SL) system calibration: 1) robustness against imperfect planarity of the calibration target and 2) the number of SL projections per pose. In many industrial SL-based 3-D reconstruction systems, the calibration accuracy greatly affects the reconstruction reliability. Our SL calibration system explicitly deals with calibration target’s imperfect planarity and thus outperforms the existing methods in terms of system accuracy. Another advantage of our SL calibration system is single-shot-per-pose, allowing fast recalibration and reducing the decoding error due to slight pattern misalignment in multishot methods[37]. In addition, we release the open-source calibration software with a graphical user interface (GUI), with which calibration and sparse 3-D reconstruction can be easily performed without any further instructions. Moreover, considering the complex calibration environment and setup, we make the camera and projector imaging parameters, such as exposure, brightness, and contrast, adjustable through widgets and preview. Finally, a limitation of our color-coded SL system is its sensitivity to environment lighting and target texture. This problem may be solved by projector photometric compensation[16],[18],[19],[39].

Energy places an important role in a new scale of urbanization, digitization, and industrialization. Going “energy-efficient” then becomes a major component of the missions for manufacturers and service providers to stay globally competitive. In recent years, the newly emerging intelligent technologies are enhancing the production process and control management in an energy-effective and -efficient manner. In order to apply and implement these innovations, many new challenges and opportunities have emerged and significantly expanded the scopes of typical production and service automation.

Energy efficiency modeling is of great importance to energy management and conservation for machinery enterprises. To improve the generalization ability, this article combines the machining parameters and the configuration parameters into energy efficiency models, for which machine-learning (ML) algorithms are used considering the lack of theoretical formulas. Based on the three-year data collected in a shop floor, a comparative study for two different cases is conducted with a particular focus on prediction accuracy, stability, and computational efficiency. In Case 1, only cross-sectional data are used to predict energy efficiency, ignoring the deterioration of spindle motors and cutting tools. Three traditional ML algorithms, i.e., artificial neural networks, support vector regression, and Gaussian process regression, are evaluated with the help of five error metrics. In Case 2, we construct the models in a more realistic situation that considers the dynamic aspects of spindle motor aging and tool wear. A convolutional neural network, a stacked autoencoder, a deep belief network and the aforementioned traditional ML algorithms are investigated. The comparison shows that all the models in Case 1 suffer from performance degradation, while deep learning achieves the long-term improvement in accuracy. Note to Practitioners-Energy efficiency models deliver many advantages, ranging from energy-aware machine design to process optimization. Although a large amount of works in the past focused on physics-based and experimental modeling for specific machining configurations, it can be more effective to improve the applicability of the modeling methods by involving the configuration variables into the models. Due to the uncertainties in both the machine and the operation environment, machine learning is adopted to fit the high-dimensional and high-nonlinear energy system. To the best of our knowledge, this is the first article that provides a comprehensive survey on ML-based modeling in terms of data sizes, temporal granularities, feature selection, and algorithm performance. Such a survey helps engineers quickly justify the appropriate ML methods to meet the actual requirements.

Energy-efficient machining has become imperative for energy conservation, emission reduction, and cost saving of manufacturing sectors. Optimal machining parameter decision is regarded as an effective way to achieve energy efficient turning. For flexible machining, it is of utmost importance to determine the optimal parameters adaptive to various machines, workpieces, and tools. However, very little research has focused on this issue. Hence, this paper undertakes this challenge by integrated meta-reinforcement learning (MRL) of machining parameters to explore the commonalities of optimization models and use the knowledge to respond quickly to new machining tasks. Specifically, the optimization problem is first formulated as a finite Markov decision process (MDP). Then, the continuous parametric optimization is approached with actor-critic (AC) framework. On the basis of the framework, meta-policy training is performed to improve the generalization capacity of the optimizer. The significance of the proposed method is exemplified and elucidated by a case study with a comparative analysis.

Going “energy efficient” has been one of the missions for manufacturers to stay globally competitive. Considering machining as a major manufacturing activity, how to effectively model and control its energy consumption becomes critical. Although researchers have analyzed the energy consumption of machining from the machine, process planning, or shop-floor perspective individually, very litter work has comprehensively studied the concurrent interactions among energy-aware decisions for machine parameter settings, process planning, and shop-floor control. Hence, the work presented in this article undertakes this challenge in the context of a resource-constrained machining system. In particular, the energy characteristics of machining are first analyzed with the consideration of various machine tools, cutting tools, cutting parameters, operation sequences, as well as machine availability. A multiobjective optimization model is then developed to minimize both energy consumption and makespan. The solution is provided through honey bee mating optimization algorithm (HBMOA) combined with shop-floor simulation. In addition, the significance of the proposed approach is exemplified and elucidated by a case study. Note to Practitioners-A well-informed decision made in cutting parameter optimization or process planning relies heavily on the accurate data delivered from the shop floor. In other words, the decision made without the consideration of the shop-floor situation might not achieve the original goal or even fail to materialize. As these factors exist in a real manufacturing cycle, how to integrate shop-floor scheduling with process planning and machining parameter optimization becomes essential for energy-efficient manufacturing. This article undertakes this challenge and develops a multiobjective optimization model, where energy consumption of machining, for the first time to the best of our knowledge, is comprehensively analyzed at both the machine level and shop-floor level. Such broader integration makes this approach more practical and applicable to real industry settings, particularly when the shop-floor resource is limited.

Technological advancement has given education a new definition-parallel intelligent education-resulting in fundamentally new ways of teaching and learning. This article exemplifies an important component of parallel intelligent education-artificial education system in a narrative game environment to offer personalized learning. The system collects data on the player's actions while they play, assessing their concept knowledge via k-nearest-neighbor (kNN) classification, and provides tailored feedback to that student as they play the game. Based on an empirical evaluation, the kNN-based game system is shown to accurately provide players with differentiated instructions to guide them through the learning process based on the estimation of their knowledge levels.

This paper investigated a dynamic rescheduling problem for a flexible machining system with random machine breakdown and urgent job arrivals. The energy consumption characteristics of the machining system is explicitly analyzed by considering multiple flexibilities with related to process routes and machine tool selection as well as dynamic events. Then a multi-objective optimization model of dynamic rescheduling is presented to take minimum energy consumption and minimum makespan as objectives, which is solved by a MOGSA algorithm. Case studies with random urgent job arrival and machine breakdown are implemented and the experimental results show that the proposed approach is effective for energy saving through rescheduling.

We are making good progresses in our impact and reputation over the last year. According to the latest data released by Scopus on February 11, 2019, our CiteScore hits its historical high to 3.94, and TCSS ranks 8th out of the 226 journals (top 3.54%) in the field of social sciences. This is a solid improvement compared with the corresponding data in 2017 (CiteScore: 2.36, Rank: 17/226, top 8%). Thanks and congratulations to our authors, reviewers, and members of our editorial board. The current issue includes 17 regular papers and a brief discussion on social education.

Traditionally process planning is concerned with reducing energy consumption at a machining process level and is done on the assumption that the manufacturing resources at the shop floor are sufficient and available all the time. This paper presents an energy-efficient process planning approach for resource-constrained machining systems. The interactive effects of process routes and cutting parameters on energy consumption at machining process level and at shop floor level are explicitly analyzed. A multi-objective optimization model of process planning is presented to take minimum energy consumption and minimum makespan as objectives, which is solved by a HBMOA algorithm. The energy saving performance of the proposed process planning approach is demonstrated through case studies.

This paper studies a predictive modeling for cutting energy consumption in CNC turning process by using deep learning methods. An analysis of energy consumption in cutting period is firstly presented, based on which the impact factors of energy are clarified. Then the data collection platform and data pre-processing are introduced, followed by a brief review of Convolutional Neural Network (CNN), Stacked Auto-Encoder (SAE) and Deep Belief Network (DBN). These modeling methods are tested by k-fold cross-validation. The obtained results show that SAE is the most suitable method to model the relationship between process parameters, machining configuration and cutting energy.

This paper studies the energy characteristics with respect to process parameters from a systematic point of view, in terms of electricity and materials. A detail analysis of energy characteristics of a CNC machining system is firstly presented, based on which the calculation models of energy efficiency are formulated. Then the effects of process parameters on energy and processing time are investigated by using S/N analysis. The results show different optimization trends for two kinds of specific energy consumption considered in this work and detail explanations of the trends are given afterwards.

Job start-time delay often occurs in the steelmaking and continuous casting (SCC) production that changes constraints or assumptions on which the initial scheduling plan is based. The development of effective rescheduling method that allows the system to promptly react to such disruption becomes essential to improve productivity, reduce production costs, and enable efficient material and energy utilization. This paper tackles this challenge and presents a comprehensive analysis of start-time delay disturbance, its consequences, and strategies to resolve conflicts. A heuristic rescheduling algorithm is then proposed to allow the system to remain alert to this type of disruption in real SCC production, and to quickly react it with an optimal rescheduling plan that has the minimum total waiting time. The proposed methodology and algorithm are applied to and illustrated through both a simulated prototypical SCC system and Shanghai BaoSteel plant, a real industrial setting.

Structured light gains its popularity in 3D reconstruction applications due to its robustness against outliers. In the last few decades, a number of high-accuracy temporally encoded structured light emerged to solve the 3D reconstruction problem. However their applications are mainly limited to scanning stationary objects. When dealing with dynamic scenes and real-time data acquisition, one-shot spatial multiplexed structured light has the speed advantage. In this paper, we propose a fast 3D reconstruction method using one-shot special structured light. It works by projecting a static two-dimensional 8-color De Bruijn spatial grid pattern onto the scene, analyzing the deformation of the observed light pattern with respect to the projected one, and identifying their correspondence. Several local optimization strategies are used to offer a confident solution, including special vote majority for color detection and correction, and De Bruijn-based Hamming distance minimization to improve intersection neighborhood information. The effectiveness of the proposed method is verified through 3D reconstruction of a complicated bust.

Sensors and actuators are two indispensable parts in the paradigm of feedback control. Their implementation cost should be properly evaluated and constrained. In the previous work, a Petri net monitor with the least cost is synthesized through integer programming formulation. Despite its technical correctness, the existing method may lead to undesirable results when the net structure contains some shared or unshared resource places of a manufacturing-oriented net model. A necessary and sufficient condition is established to show that certain structures can lead to deadlock-prone supervisors. An efficient algorithm is developed to identify such structures. Furthermore, it is shown that if one can identify such structures at the initial stage, it is possible to achieve desirable controllers for the original systems. The theoretical correctness of the proposed algorithm is discussed. A manufacturing example is provided to illustrate the proposed approach.

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, little research focuses on stochastic remanufacturing process routings (RPR). This paper presents an analytical method, where four Graphical Evaluation and Review Technique (GERT)-based RPR models are proposed to mathematically represent and analyze the variability of remanufacturing task sequences. In particular, with the method, the probability of individual processes being taken in a remanufacturing system and the time associated with them can be efficiently determined. The proposed method is demonstrated through the remanufacturing of used lathe spindles and telephones, and verified by Arena simulation. Numerical experiments that investigate the relationships between RPR dynamics and other system parameters (such as inventory control for due-time performance and time buffer size for bottleneck control) are included.

In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to liveness enforcing supervisory control investigate AMS with either flexible routes or assembly operations, whereas little work investigates them with both. In this paper, we propose a novel class of systems, which can well deal with both features so as to facilitate the control of more complex AMS. Using structural analysis, we show that liveness of their Petri net model can be attributed to the absence of undermarked siphons, which is realizable by synthesizing a proper supervisory controller. Moreover, an efficient method is developed and verified via AMS examples.

In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to liveness-enforcing supervisory control study AMS with either flexible routes or assembly operations, whereas little work investigates them with both. In this paper, we propose a novel class of systems, which can well deal with both features so as to facilitate the investigation of more complex systems. Using structural analysis, we show that liveness of such systems can be attributed to the absence of undermarked siphons, which is realizable by synthesizing a proper supervisory controller.

The growing concerns for material resources, energy conservation and landfill capacity have put much pressure on manufacturers, charging them with the responsibility for their outdated products. However, obstacles arise when introducing product/material recovery in the economic landscape due to much uncertainty inherent in the process (e.g., prevailing condition of reclaimed products and the level of human intervention). This paper presents a rigorous model that accounts for such system dynamics in disassembly process planning (DPP), a critical stage to the efficiency of product/material recovery. In particular, this model with the learning capability will be able to: (1) mathematically represent the operational planning of disassembly in the light of uncertainty (i.e., the quality of reclaimed products and the impact of human intervention); (2) accumulate and exploit "knowledge" of system performance via the observation of the process behavior; and (3) dynamically derive a cost-effective disassembly plan.

This paper presents a systematic approach to the disassembly line (DL) design in meeting the requirement of variant orders for multiple used parts with different due dates. An extended disassembly Petri net model is proposed for the hierarchical modeling in order to derive the disassembly path with the maximal benefit in the presence of some defective components. An algorithm for balancing DLs to maximize the productivity of a disassembly system is presented. The results of simulation runs of the proposed methodology and algorithms applied to a simplified personal computer disassembly are provided. This work lays a foundation for designing efficient industrial automatic and semiautomatic disassembly systems. Note to Practitioners-Disassembly is rapidly growing in importance as manufacturers face increasing pressure to deal with obsolete products in an environmentally responsible and economically sound manner. This process can be performed at a single workstation or on a disassembly line (DL) that is organized as a sequence of workstations, each with one or more machines/operators to handle a certain type of disassembly task. Compared to a single workstation, DL provides higher productivity and greater potential for disassembly automation. However, it still faces serious scheduling and inventory problems because of a high degree of uncertainty in discarded products and disparity between demands for certain parts and their yield from disassembly. To address this challenge, this paper proposes a two-level systematic approach, aiming to maximize system throughput and system revenue by dynamically configuring the disassembly system into many DLs, while considering line balance, different process flows, and meeting different order due dates. The research results can help engineers build better disassembly systems.

Disassembly, as the process of systematic removal of desirable constituent parts from an assembly, is of growing importance due to the increasing environmental and economic pressure. Although disassembly in practice is manual and labor intensive, little attention has been paid to the human intervention in the disassembly process. This paper addresses this deficiency by developing a fuzzy attributed Petri net (FAPN) model to mathematically represent uncertainty in disassembly due to a large amount of human intervention. An algorithm based upon this model is further proposed for optimal disassembly planning with a view to making the technique more applicable to real industry settings. The benefit of the proposed model and algorithm is illustrated through the disassembly of a personal computer (PC) in a prototypical disassembly system.

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.

Due to expensive, highly complex and time-consuming processes, semiconductor manufacturing systems have been given special attention. In Tang et al. (2000), a heuristic algorithm to design the reconfigurable automated production system was proposed. However, machine breakdowns and planned and unplanned maintenance were not considered. This paper extends that work and addresses the related design issues in reconfigurable back-end semiconductor manufacturing systems with failures and maintenance. Considering different conditions of machines, queuing network approaches are used to derive the throughput of machines to enhance the proposed virtual production line design methodology in which the throughput was originally computed using the deterministic-machine processing time. A priority is assigned to each idle machine according to its past performance and adaptive algorithms for reconfiguration are proposed.

Industrial demanufacturing is a practice of growing importance due to increasing environmental and economic pressures. However, very little research focuses on it from a system perspective. This paper presents a disassembly planning and demanufacturing scheduling method for an integrated flexible demanufacturing system. Workstation Petri net and Product Petri net are proposed for its hierarchical and modular modeling in order to derive the disassembly path with the maximal end-of-life value. Scheduling Petri net is introduced to schedule the demanufacturing resources. The proposed methodology and algorithms are demonstrated through the disassembly of personal computers in an integrated flexible demanufacturing system.

Industrial recycling and remanufacturing is practice of growing importance due to the increasing environmental and economic pressures. It involves product disassembly to retrieve the desired parts and/or subassemblies by separating a product into its constituencies. The disassembly process modeling and planning is more challenging than assembly since its termination goal is not necessarily fixed depending on the system status. Moreover, it is uncontested that disassembly is charged with more uncertainty in system structures and component conditions than assembly. The paper reviews methods for modeling and process planning in disassembly. Its purpose is to survey the state-of-the-art of this emerging area to supply important information for future study.

Due to the mammoth needs of the semiconductor market, semiconductor manufacturing systems have been given special attention. Their operations require a manufacturing execution system (MES) that functions as the representative of an enterprise resource planner on the shop floor. However, MES integration with a shop-floor control system, while guaranteeing system agility, is difficult to implement. The paper introduces the concept of virtual production lines (VPL) as a solution to this implementation. A methodology is proposed to facilitate the VPL design, which dynamically configures a large system into many production lines according to system status, guarantees the line balance and efficiency, and predicates the completion time of each group of products accurately. The proposed methodology and algorithms are demonstrated through a simplified back-end semiconductor manufacturing system.