The draw ratios allocation critically determines the final performance of carbon fiber. However, the stochastic nature in component fluxes and concentrations variations introduces uncertainty into draw ratios, causing frequent parameter fluctuations. Therefore, we propose a robust operation optimization framework that formulates a biobjective model to minimize linear density and maximize strength. Within this framework, a causal forest-guided partitioning for robust operation optimization algorithm is developed to solve the model. Specifically, the algorithm uses perturbations on individual decision variables to form treatment groups from an initial control population, enabling causal forests to infer the overall causal effects of each decision variable on the objectives. Next, the computed mean conditional average treatment effects are clustered via K-means to partition the variables by their robustness relevance. Then, a tailored optimization strategy, augmented by an external archiving mechanism, is performed on each group to efficiently search for the robust Pareto optimal set. Finally, adaptive utopian point-based decision making is used to determine the optimal setpoint. The proposed framework is validated on benchmark problems and in simulation study, achieves a 29.93% reduction in linear density and 99.41% increase in strength, thereby confirming its practical applicability.

The pre-oxidation process of PAN fiber is an important step in the practical production of carbon fiber, which has a significant impact on the performance and production efficiency of carbon fiber. In this article, in order to solve the problem that it is difficult to obtain high-quality carbon fiber while reducing energy consumption in the practical production of the pre-oxidation process, we build a dynamic multi-objective model about energy consumption and PAN modulus. We then propose the modified robust optimization over time (mROOT) to solve it and optimize the process parameters, which aims to maximize PAN modulus and minimize energy consumption while decreasing the average switching cost of solutions by reducing the switching times of solutions, hoping to provide some theoretical guidance for the pre-oxidation process of carbon fiber in practical production. The experimental results show that compared with TMO and RPOOT, the solutions obtained by mROOT have longer average survival time and lower average switching cost, and compared with traditional time series prediction models, the combination of online predictor proposed in this article and mROOT is a more practical and effective method for solving the model.

Carbon fiber is an innovative strategic material in key fields such as national defense, etc. In recent years, the demand for carbon fiber in the entire international and domestic markets is in a period of rapid development. The polymerization process is one of the crucial processes in carbon fiber production. In order to improve the quality of precursor fiber and save costs, the optimization of process parameters is one of the important links. Considering the complexity of process parameter optimization, we improved an optimization method based on multi-fidelity model to solve the problem, which combined the advantages of multi-objective mechanism model and data-driven model of carbon fiber production polymerization process. On the basis of modified ordinal transformation and optimal sampling framework, we embedded a heuristic algorithm to search solution space and combined the clustering algorithm for grouping. The experimental results show that the improved multi-fidelity optimization method outperforms other multi-fidelity optimization methods. This paper can provide guidance for the optimization of process parameters of carbon fiber polymerization, and has a certain reference value.

Software vulnerabilities have posed huge threats to the cyberspace security, and there is an increasing demand for automated vulnerability detection (VD). In recent years, deep learning-based (DL-based) vulnerability detection systems have been proposed for the purpose of automatic feature extraction from source code. Although these methods can achieve ideal performance on synthetic datasets, the accuracy drops a lot when detecting real-world vulnerability datasets. Moreover, these approaches limit their scopes within a single function, being not able to leverage the information between functions. In this paper, we attempt to extract the function's abstract behaviors, figure out the relationships between functions, and use this global information to assist DL-based VD to achieve higher performance. To this end, we build a Behavior Graph Model and use it to design a novel framework, namely VulBG. To examine the ability of our constructed Behavior Graph Model, we choose several existing DL-based VD models (e.g., TextCNN, ASTGRU, CodeBERT, Devign, and VulCNN) as our baseline models and conduct evaluations on two real-world datasets: the balanced$\text{FFMpeg}+\text{Qemu}$dataset and the unbalanced$\text{Chrome} +\text{Debian}$dataset. Experimental results indicate that VulBG enables all baseline models to detect more real vulnerabilities, thus improving the overall detection performance.

Code clone detection aims to find similar code fragments and gains increasing importance in the field of software engineering. There are several types of techniques for detecting code clones. Text-based or token-based code clone detectors are scalable and efficient but lack consideration of syntax, thus resulting in poor performance in detecting syntactic code clones. Although some tree-based methods have been proposed to detect syntactic or semantic code clones with decent performance, they are mostly time-consuming and lack scalability. In addition, these detection methods can not realize fine-grained code clone detection. They are unable to distinguish the concrete code blocks that are cloned. In this paper, we design Tamer, a scalable and fine-grained tree-based syntactic code clone detector. Specifically, we propose a novel method to transform the complex abstract syntax tree into simple subtrees. It can accelerate the process of detection and implement the fine-grained analysis of clone pairs to locate the concrete clone parts of the code. To examine the detection performance and scalability of Tamer, we evaluate it on a widely used dataset BigCloneBench. Experimental results show that Tamer outperforms ten state-of-the-art code clone detection tools (i.e., CCAligner, SourcererCC, Siamese, NIL, NiCad, LVMapper, Deckard, Yang2018, CCFinder, and CloneWorks).

The objective of disassembly lines is to disassemble end-of-life products in a remanufacturing field. The disassembly line balancing problem (DLBP) considers how to allocate disassembly operations to operators on the disassembly line to optimize predetermined goals, such as cycle time. In practice, various environmental uncertainties (e.g., uncertain product quality) exist in the disassembly line. These uncertainties entail DLBP essentially a dynamic multiobjective optimization problem (DMOP). This study presents a dynamic DLBP (D-DLB) to model the effect of environmental uncertainties on the assignment of disassembly operations. Furthermore, a prediction-based dynamic optimization algorithm, termed domain generalization-based dynamic multiobjective evolutionary algorithm (DG-DMOEA), combining meta-learning with multiobjective optimization, is proposed to solve D-DLB. In DG-DMOEA, a meta-learning algorithm is employed to learn the parameters of a solution-generative model from the Pareto-optimal sets (POSs) in all historical environments. Subsequently, the solution-generative model is applied to generate a high-quality initial population that can assist multiobjective optimization algorithms in finding the POS in the new environment faster. Since no information in the new environment is required, learning can begin before the new environment arrives, significantly reducing computational time. Moreover, different solution-generative models can be designed for different DMOPs. Therefore, DG-DMOEA can thoroughly combine real-world problem properties to represent knowledge. The experimental results show that, compared with state-of-the-art methods, DG-DMOEA can considerably improve the quality of solutions and significantly enhance the ability to react quickly to environmental changes.

Code clone detection is an active research domain of software engineering. There are two core demands for clone detection: scalable detection and complicated clone detection. For scalable detection, existing approaches treat the source code as a text or token sequence and then calculate their similarity. However, the text-based and token-based approaches are difficult to detect complicated clone types due to the lack of consideration of code structure. The methods based on intermediate representations of code can effectively achieve complex clone types detection but are limited by the complexity of representations to be scalable. In this paper, we proposeCode2Img, a tree-based code clone detector, which satisfies scalability while detecting complicated clones effectively. Given the source code, we first perform clone filtering by the inverted index to locate the suspected clones. For each suspected clone, we create the adjacency image based on the adjacency matrix of the normalized abstract syntax tree (AST). Then we design an image encoder to highlight the structural details further and refine pixels of the image. Specifically, we employ the Markov model to encode the adjacency image into a state probability image and remove its useless pixels. By this, the original complex tree can be transformed into a one-dimensional vector while preserving the structural feature of the AST. Finally, we detect clones by calculating the Jaccard Similarity of these vectors. We conduct comparative evaluations on effectiveness and scalability with eight other state-of-the-art clone detectors (SourcererCC,NIL,LVMapper,Nicad,Siamese,CCAligner,Deckard, andYang2018). The experimental results show thatCode2Imgachieves the best performance among all the comparative tools in terms of both detection effectiveness and scalability. It indicates thatCode2Imgcan be applicable to scalable complicated clone detection.

In order to enable multiple mobile robots to efficiently complete operations in a collision-free human-robot collaborative environment, this paper proposes a mathematical model and algorithm for multi-robot path planning in a disassembly cell. The algorithm takes the disassembly task assignment matrix and task attributes as input and uses the improved ant colony algorithm to carry out static path planning for robots. It is worth noting that due to the uncertainty brought by workers, this paper uses random numbers to simulate the range of worker activities. Under the premise of fully considering the safety of workers, combined with algebraic methods, we perform collision detection on the places where collisions may occur. The collision detection algorithm mainly relies on the Segre characteristic to determine the positional relationship between two three-dimensional quadric surfaces. According to the collision detection results and combined with the task priority, two obstacle avoidance strategies are proposed. The simulation results show the superiority of the improved ant colony algorithm and the feasibility of the overall algorithm.

Job shop scheduling always plays an important role in the manufacturing process and is one of the decisive factors influencing manufacturing efficiency. In the actual process of production scheduling, there exist some uncertain events, information asymmetry, and abnormal disturbance, which would cause the execution deviation and affect the efficiency and quality of scheduling execution. Traditional scheduling methods are not sufficient to solve the challenges well. Due to the rise of digital twin, which has the characters of virtual reality interaction, real-time mapping, and symbiotic evolution, a new job shop scheduling method based on digital twin is proposed to reduce the scheduling deviation. In this article, the architecture and working principle of the new job shop scheduling mode are introduced. Then, scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling. Finally, a prototype system is designed to verify the validity of this new job shop scheduling mode.