Industrial products' reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery ...efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.
Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good ...representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.
This paper presents an adaptive particle swarm optimization with supervised learning and control (APSO-SLC) for the parameter settings and diversity maintenance of particle swarm optimization (PSO) ...to adaptively choose parameters, while improving its exploration competence. Although PSO is a powerful optimization method, it faces such issues as difficult parameter setting and premature convergence. Inspired by supervised learning and predictive control strategies from machine learning and control fields, we propose APSO-SLC that employs several strategies to address these issues. First, we treat PSO with its optimization problem as a system to be controlled and model it as a dynamic quadratic programming model with box constraints. Its parameters are estimated by the recursive least squares with a dynamic forgetting factor to enhance better parameter setting and weaken worse ones. Its optimal parameters are calculated by this model to feed back to PSO. Second, a progress vector is proposed to monitor the progress rate for judging whether premature convergence happens. By studying the reason of premature convergence, this work proposes the strategies of back diffusion and new attractor learning to extend swam diversity, and speed up the convergence. Experiments are performed on many benchmark functions to compare APSO-SLC with the state-of-the-art PSOs. The results show that it is simple to program and understand, and can provide excellent and consistent performance.
This paper proposes an iterative synthesis approach to Petri net (PN)-based deadlock prevention policy for flexible manufacturing systems (FMS). Given the PN model (PNM) of an FMS prone to deadlock, ...the goal is to synthesize a live controlled PNM. Its use for FMS control guarantees its deadlock-free operation and high performance in terms of resource utilization and system throughput. The proposed method is an iterative approach. At each iteration, a first-met bad marking is singled out from the reachability graph of a given PNM. The objective is to prevent this marking from being reached via a place invariant of the PN. A well-established invariant-based control method is used to derive a control place. This process is carried out until the net model becomes live. The proposed method is generally applicable, easy to use, effective, and straightforward although its off-line computation is of exponential complexity. Two FMS are used to show its effectiveness and applicability
Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve computationally expensive problems with some success. However, traditional EAs are not suitable to deal with ...high-dimensional expensive problems (HEPs) with high-dimensional search space even if their fitness evaluations are assisted by surrogate models. The recently proposed autoencoder-embedded evolutionary optimization (AEO) framework is highly appropriate to deal with high-dimensional problems. This work aims to incorporate surrogate models into it to further boost its performance, thus resulting in surrogate-assisted AEO (SAEO). It proposes a novel model management strategy that can guarantee reasonable amounts of re-evaluations; hence, the accuracy of surrogate models can be enhanced via being updated with new evaluated samples. Moreover, to ensure enough data samples before constructing surrogates, a problem-dimensionality-dependent activation condition is developed for incorporating surrogates into the SAEO framework. SAEO is tested on seven commonly used benchmark functions and compared with state-of-the-art algorithms for HEPs. The experimental results show that SAEO can further enhance the performance of AEO on most cases and SAEO performs significantly better than other algorithms. Therefore, SAEO has great potential to deal with HEPs.
Time flies. Since I took over the Editor-in-Chief position from this journal's founding Editor-in-Chief, Professor Fei-Yue Wang in 2018, five years has past just like a second. Looking back from ...today, as a team, we, including editorial board members, editorial staff members, our early career advisory board members, all contributing authors and all reviewers, should be proud of what this journal has achieved. This journal is the first of its kind of journals, resulting from IEEE' s collaboration with Chinese Association of Automation-an outside-USA professional organization/institution. We have overcome many barriers and difficulties, and well proven that we, united and working together, can accomplish a challenging mission. We indeed set a great example for IEEE and its many societies to pursue more and more collaboration with other publishers, organizations and institutions from not only China but also such countries as India, Brazil, and Japan.
A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern ...industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.
In recent decades, primal-dual neural networks, as a special type of recurrent neural networks, have received great success in real-time manipulator control. However, noises are usually ignored when ...neural controllers are designed based on them, and thus, they may fail to perform well in the presence of intensive noises. Harmonic noises widely exist in real applications and can severely affect the control accuracy. This work proposes a novel primal-dual neural network design that directly takes noise control into account. By taking advantage of the fact that the unknown amplitude and phase information of a harmonic signal can be eliminated from its dynamics, our deliberately designed neural controller is able to reach the accurate tracking of reference trajectories in a noisy environment. Theoretical analysis and extensive simulations show that the proposed controller stabilizes the control system polluted by harmonic noises and converges the position tracking error to zero. Comparisons show that our proposed solution consistently and significantly outperforms the existing primal-dual neural solutions as well as feedforward neural one and adaptive neural one for redundancy resolution of manipulators.
This paper presents a Gaussian classifier-based evolutionary strategy (GCES) to solve multimodal optimization problems. An evolutionary technique for them must answer two crucial questions to ...guarantee its success: how to distinguish among the different basins of attraction and how to safeguard the already discovered good-quality solutions including both global and local optima. In GCES, multimodal optimization problems are regarded as classification ones, and Gaussian mixture models are employed to save the locations and basins of already and presently identified local or global optima. A sequential estimation technique for the covariance of a Gaussian model is introduced into GCES. To best adjust the global step size, a strategy named top-ranked sample selection is introduced, and a classification method instead of a common but problematic radius-triggered manner is proposed. Experiments are performed on a series of benchmark test functions to compare GCES with the state-of-the-art multimodal optimization approaches. The results show that GCES is not only simple to program and understand, but also provides better and consistent performance.
Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify ...their minority class is a critically important issue. Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index ( WGI ) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve ( ROC AUC ) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20 % or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.
