The dual-drive H-gantry is widely used for high-speed, high-precision Cartesian motion. Compared with the conventional rigid-linked design, the flexure-linked counterpart is able to prevent the damage of joints for its smaller interaxial coupling force. However, there are still barriers to further push up its precision, such as parametric uncertainties due to the inaccurate dynamical model, the possible induced vibration during high-speed motion, and the decentralized control structure required by industries. To maintain the tracking precision of carriages and minimize the vibration of the end effector, we aim to optimize parameters in decentralized controllers with choices of flexure pieces. We find that such decentralized feedback structure yields some uncontrollable but stabilizable states in the closed-loop system, and no direct solution from solving the algebraic Riccati equation is available in this case. Such structural constraint, together with constraints due to stability requirement and model uncertainties facilitates us to formulate an H2guaranteed cost control problem within a projected convex domain. From here, efficient numerical procedures are developed to obtain the global optimum by iterative linear programming. The real-time experiment validates the optimality and the robustness of the proposed method.

Freezing of gait (FoG) is a sudden and episodic inability to generate effective stepping among Parkinson's disease patients. It poses a risk for falls and deteriorates a patient's quality of life. Aid is sought after by the implementation of wearable systems that detect FoG and provide biofeedback cues in real-time. Detection is predominantly attempted with patient-independent classifiers which have difficulties to account for some patient's inimitable walking styles. Such gait peculiarities can be addressed with patient-adaptive classifiers. However, the patient-specific adaptations proposed thus far are retrospective and require a patient's labeled data. We propose to provide patient adaptivity in real-time through semi-supervised neural networks which exploit the stream of unlabeled data generated during usage. Using supervised learning, a patient-independent neural network is designed to serve as a base model. Upon a new patient's utilization of the system, the base model's parameters are adapted in real-time through unsupervised learning from the generated stream of unlabeled data. On average, patient adaptivity augmented sensitivity by 4.58% for the price of 0.59% in specificity. Moreover, it accounted for inimitable walking styles of patients that had been inadequately classified by the patient-independent base model. For such patients, sensitivity increased up to 42.01%. The overall patient-adaptive classifier resulted in 95.90% and 93.05% in sensitivity and specificity, respectively.

Linear direct feed drives are widely used in machine tools, but an abrupt counter force from the secondary part will induce the jerk to the metro frame contacted with the linear motor and cause the vibration of auxiliary devices on it. The jerk-decoupling cartridge (JDC) provides a buffer to reduce such an impact. To systematically take care of both the tracking error and the jerk induced to the metro frame, this paper presents an integrated design approach to determine parameters in the JDC and the position controller of the feed drive. The initial formulated non-convex optimization problem is converted to convex constrained gradient optimization problem and linear step searching problem. Thus, fast convergence of parameters is achieved within first few iterations. Through a series of simulation, the effectiveness of proposed methodology is verified.

This paper describes our ongoing development of a roll-to-roll (R2R) web handling system for thin and flexible membrane inspection using machine vision. Leakproof membrane is essential in many industries, e.g. food packaging and medical applications. Our primary objective is to achieve full quality control check for through-hole defects in membrane, and mark them if any. To achieve 100% inspection of the 400 mm wide and 1 km long web, a laterally movable camera stage is coordinated with web handling subsystem that uses basic trapezoidal motion control for repositioning the web so as to overcome the small area of inspection of the two line scan cameras mounted on stage, each with 80 mm field of view. As there is no open-source publication on web handling for R2R membrane inspection, we hope our methodology and findings through this work would be useful to other related research. Besides detailing the system design and setup, this paper also discusses the challenges faced, highlights the system performance, its shortcomings and areas of improvement.

The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.

Games are played by a wide variety of audiences. Different individuals will play with different gaming styles and employ different strategic approaches. This often involves interacting with nonplayer characters that are controlled by the game AI. From a developer's standpoint, it is important to design a game AI that is able to satisfy the variety of players that will interact with the game. Thus, an adaptive game AI that can scale the difficulty of the game according to the proficiency of the player has greater potential to customize a personalized and entertaining game experience compared to a static game AI. In particular, dynamic game difficulty scaling refers to the use of an adaptive game AI that performs game adaptations in real time during the game session. This paper presents two adaptive algorithms that use ideas from reinforcement learning and evolutionary computation to improve player satisfaction by scaling the difficulty of the game AI while the game is being played. The effects of varying the learning and mutation rates are examined and a general rule of thumb for the parameters is proposed. The proposed algorithms are demonstrated to be capable of matching its opponents in terms of mean scores and winning percentages. Both algorithms are able to generalize well to a variety of opponents.

The rapid reduction in critical dimension of integrated circuits has lead to substantial mask data expansion for mask design based on traditional model-based full-chip optical proximity correction (OPC). Conventional EPE-OPC is mainly based on edge placement error (EPE) without consideration of its effect on circuit performance; often resulting in an overcorrected OPC mask with little improvement in circuit performance at the expense of much higher cost. In this paper, a performance-based OPC (PB-OPC) methodology is proposed taking into account both performance and cost. A less complex mask is generated based on the performance matching criteria. The framework exploits thein situestimated postlithography performance deviation error to drive the customized mask design algorithm. In particular, device PB-OPC (DPB-OPC) was deployed to systematically synthesize both polysilicon and diffusion masks by using mean drive current deviation as the controlled performance index. The proposed approach is validated via detailed simulation using 65-nm foundry libraries and IEEE International Symposium on Circuits and Systems 1985 (ISCAS'85) benchmark circuits. When compared to conventional performance-aware EPE-OPC approach, the proposed DPB-OPC achieved 34% average reduction in mask size and up to 13.5% reduction in mean drive current deviation within reasonable run time.

This paper presents an evolutionary, game theoretic approach to simulate and study the collective outcome of public goods (PG) provisioning in an agent-based model. Using asymmetric information as the basis for decision making, distinct groups are configured to interact in an iteratedNplayer PG game, where coevolutionary learning is used as the adaptation tool to the dynamic environment. Impact of information type, number of players, group size, rate of interaction, number of available choices, nature of PG provision, and selection schemes are studied under a variety of settings. Simulation results reveal interesting dynamics of strategy and usage profiles, level of derived welfare, and the evolution of cooperation. Analysis of simulated attributes offers a more holistic understanding into the nature of collective action and insights of how the effects of social dilemma can be mitigated. This might provide a good guide to achieve efficient PG provision in the practical context.

An adaptive game AI has the potential of tailoring a uniquely entertaining and meaningful game experience to a specific player. An online adaptive AI should be able to profile its opponent efficiently during the early phase of the game and adapts its own playing style to the level of the player so that the player feels entertained playing against it. This paper presents an online adaptive algorithm that uses ideas from evolutionary computation to match the skill level of the opponent during the game. The proposed algorithms demonstrated using a car racing simulator is capable of matching its opponents in terms of both mean score and winning percentages.

Along the continued shrinking of critical dimension (CD), the optical proximity correction (OPC) scheme inevitably becomes more aggressive and results in greater mask complexity and cost. Conventional OPC are edge-placement-error (EPE) driven without considering the effect of correction on circuit performance at all. We propose a performance-based OPC (PB- OPC) framework that employs a much simpler mask correction algorithm based on the real-time estimated device performances. When compared to the conventional OPC approach, our PB-OPC achieved 33 to 93% reduction in mask MEBES size and closer circuit performance matching to the design intent.

The paper presents an agent-based, computational model to simulate and analyze the collective outcome of public goods provisioning under asymmetric information. Agents that embrace different information types are conceptualized and configured to interact in an N-player public goods game where each agent group adapts to the dynamic environment using co- evolutionary learning. The impact of information type, number of players, rate of interaction, group size, and the scheme of game play are studied under different settings. The simulated results reveal interesting dynamics of strategy profiles, level of public goods provisioned, and the evolution of cooperation. Analysis of these simulated attributes provides a more holistic understanding of collective action and insights into how the effects of social dilemma can be mitigated.

Coevolutionary architectures have been shown to be effective ways to improve the performance of multiobjective (MO) optimization problems. This paper presents a cooperative coevolutionary algorithm for multiobjective particle swarm optimization (COMOPSO), which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subswarms. Representatives from each evolving subswarm are combined to form the solution to the whole system. The fitness of each individual is related to its ability to collaborate with individuals from other species, thereby encouraging the development of cooperative strategies. An adaptive niche sharing algorithm is introduced to handle the selection of the niche radius in a dynamic manner. Coupled with the adaptive niche sharing algorithm COMOPSO demonstrates its effectiveness and efficiency in evolving highly competitive solution sets against various MO algorithms on benchmark problems characterized by different difficulties with consistent results.

Wafer warpage is common in microelectronics processing. Warped wafers can affect device performance, reliability and linewidth control in various processing steps. We proposed in this paper an in situ fault detection technique for wafer warpage in microlithography. Early detection would minimize cost and processing time. Based on first principle thermal modeling, we are able to detect warpage fault from available temperature measurements. Experimental results demonstrate the feasibility of the approach. The proposed approach is applicable to other semiconductor substrates.

DNA microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design probes with high specificities, i.e. uniqueness, and also sensitivities, i.e., suitable melting temperature and no secondary structure. We make use of available biology tools to gain necessary sequence information of human chromosome 12, and combined with evolutionary strategy (ES) to find unique subsequences representing all predicted exons. The results are presented and discussed.

Although evolutionary algorithm is a powerful optimization tool, its computation cost involved in terms of time and hardware resources increases as the size or complexity of the problem increases. One promising approach to overcome this limitation is to exploit the inherent parallelism of evolutionary algorithms by creating an infrastructure necessary to support distributed evolutionary computing using existing Internet and hardware resources. This paper presents a Java-based distributed evolutionary computing software (Paladin-DEC), which enhances the concurrent processing and performance of evolutionary algorithms by allowing inter-communications of subpopulations among various computers over the Internet. Such a distributed system enables individuals to migrate among multiple subpopulations according to some patterns to induce diversity of elite individuals periodically, in a way that simulates the species evolve in natural environment. The Paladin-DEC software is capable of keeping data integrity throughout the computation, and is incorporated with the features of robustness, security, fault tolerance, and work balancing. The effectiveness and advantages of the Paladin-DEC are illustrated upon two case studies of drug scheduling in cancer chemotherapy and searching probe sets of yeast genome.

Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.