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An algorithm of sensor optimization in controlled discrete event systems

Journal of computational Information Systems 2014年第20期10卷 8677-8684页

作者： Hao, Shuang Ouyang, Dantong Li, Lin Zhang, Liming Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Sensor optimization is the problem of minimizing sensor activation in a controlled discrete event system. During the evolutionary process, the available resources are supposed to be limited. Therefore, sensors are activated by the agent when it is necessary. Sensor activation policies are the functions that determine which sensors are to be activated. One policy is considered to minimal, if any strictly less activation decided by the agent satisfies the feasibility. In this paper, a new algorithm is proposed to compute the minimal sensor activation policy. The algorithm, based on the operation of Reverse Change and the property of the Label-reached, calculates the minimal solution of sensor activation and achieves a lower complexity of computation relatively.

关键词： Chemical activation

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A prediction approach based on long short-term memory networks for dynamic multiobjective optimization

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Expert Systems with Applications 2025年 283卷

作者： Xu, Biao Rang, Gejie Xie, Ruijie Li, Wenji Gong, Dunwei Fan, Zhun Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Shenzhen institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Dynamic multiobjective optimization problems (DMOPs) present significant challenges to conventional evolutionary optimization methods because of the continuous changes in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-driven approaches have demonstrated potential in rapidly adapting to these changes. However, many existing methods depend on linear models to forecast the evolving PSs, which may be restrictive. To counteract this limitation, this research presents a novel dynamic multiobjective evolutionary optimization algorithm that incorporates predictions from long short-term memory (LSTM) networks. Initially, in our methodology, the PS for each problem is segmented into multiple clusters, and the centroid of each cluster is identified. These cluster centroids, representing the PSs across various environmental conditions, are then transformed into a series of time series data. The LSTM network models are subsequently trained on this time series data as input samples. Utilizing these refined models, the centroids of the evolving PSs are predicted with improved precision. Moreover, to enhance the performance of the algorithm, an innovative population-generation strategy is also introduced that guarantees a well-converged and diverse starting population. Our proposed algorithm undergoes rigorous testing using benchmark functions, and the outcomes validate its proficiency in tackling DMOPs, showing superior performance compared to existing state-of-the-art algorithms. © 2025 Elsevier Ltd

关键词： Multiobjective optimization

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Document compression scheme based on integer data

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Journal of computational Information Systems 2014年第12期10卷 5203-5210页

作者： Liu, Yang Te, Rigen Li, Xiongfei Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China

Data compression does not only save space for data storage, but also improve its safety and efficiency during data transport. As any data can be saved in the form of an integer directly or indirectly, it is a meaningful work for us to extend integer data compression technique to the application of any arbitrary data compression. In this paper we propose a new compression algorithm (CNA), which does not need additional data support and data compression and reduction are lossless, for integer data based on Godel numbers. In the meantime, we will test the approach and show the uniqueness of the compression algorithm result and the correctness of the decompression data. By contrastive experimentation, the CNA compression algorithm proves good result for any documents, especially for integer documents. 1553-9105/Copyright © 2014 Binary Information Press.

关键词： Data compression

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Improve the quality of XML clustering by boosting theory

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Journal of computational Information Systems 2013年第11期9卷 4247-4254页

作者： Liu, Zhaojun Li, Xiongfei Li, Wei Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China

XML documents cluster analysis is a hot research topic. Researchers proposed a number of methods to cluster XML document collections. Boosting is successful well-known methods for improving the quality of clustering. This paper proposed a method named ICBQ which improve the clustering quality by boosting theory. The experiment runs on the both real dataset and synthetic dataset, we choose the Nierman's method, Dalamagas's method and Flesca's method to evaluate the ability of ICBQ. The experimental result shows that our approach is more precise than no boosting methods. © 2013 by Binary Information Press.

关键词： XML

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Applying functional dependencies to improve the performance and robustness of hidden naive bayes

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Journal of computational Information Systems 2013年第19期9卷 7967-7974页

作者： Qin, Shidong Wang, Limin Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Hidden Naive Bayes (HNB) has demonstrated remarkable progress in classification accuracy, accurate class probability estimation and ranking. Since HNB is based on one-dependence estimators to get the approximate value of conditional probability, in this paper we try to get more accurate approximations of Bayesian classifier by applying the data dependence theory of relational database. Originally HNB uses a hidden parent of an attribute to represent the influences of all other attributes, while the influence is directed in the Bayesian network. In this paper, we proposed to use the chain rule to estimate the influence among attributes. Compared to HNB, experimental results show that our algorithm has both advantages in classification accuracy and ranking performance which is measured by the area under the ROC curve (AUC). © 2013 Binary Information Press.

关键词： Bayesian networks

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Verifying Diagnosability of Discrete Event System with Logical Formula

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Chinese Journal of Electronics 2020年第2期29卷 304-311页

作者： GENG Xuena OUYANG Dantong HAN Cheng College of Computer Science and Technology Changchun University of Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable *** algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.

关键词： Fault diagnosis Diagnosability Discrete event system Finite state machine Conjunctive normal form

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Task-oriented Multi-domain Adversarial Network for fake news detection

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Applied Soft Computing 2025年 177卷

作者： Guo Zeqi Ouyang Jihong Li Ximing Li Changchun College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

The proliferation of fake news on online social media has severely misled public perception of event authenticity. To combat this, various Fake News Detection (FND) methods have been developed for specific domains, typically requiring large amounts of labeled data for effective training. However, in some domains, labeled data remains scarce. Multi-domain Fake News Detection (MFND) provides a solution by training a model with data from multiple domains to identify news veracity across domains. Despite its potential, discrepancies in feature distributions across domains, known as domain shift, can negatively impact the effectiveness of joint training. To mitigate this, we propose the Task-oriented Multi-domain Adversarial Network ( TMDA-Net ), a novel MFND model designed to align feature distributions across domains within a unified feature space using domain adversarial learning. This approach enhances generalization in data-scarce domains. Given that FND is domain-dependent, we also propose a fusion mechanism with adjustable weights to incorporate domain-specific characteristics. Additionally, we introduce two task-oriented auxiliary classification tasks to efficiently align and integrate features, ultimately improving FND accuracy. Extensive experiments on publicly available Chinese and English datasets validate the effectiveness of our proposed model.

关键词：

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Are Your Apps Accessible? A GCN-based Accessibility Checker for Low Vision Users

arXiv

引用

arXiv 2025年

作者： Zhang, Mengxi Liu, Huaxiao Song, Shenning Chen, Chunyang Huang, Pei Zhao, Jian College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Department of Computer Science Technical University of Munich Heilbronn85354 Germany Department of Computer Science Stanford University CA94305 United States College of Computer Science Changchun University Jilin Changchun130012 China

Context: Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective: In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method: ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results: Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion: To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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