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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Geometric Dilution of Precision for Far-Distance TDOA Location of Shortwave

Geometric Dilution of Precision for Far-Distance TDOA Locati...

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information Communication Technologies Conference (ICTC)

作者： Xin Yang Yun-Xia Ye No.36 Research Institute of CETC Jiaxing Zhejiang China No.36 Research Institute of CETC Science and Technology on Communication Information Security Control Laboratory Jiaxing Zhejiang China

A method of time difference of arrival (TDOA) location for shortwave signal source by four receiving stations is introduced. The precision of time difference and the Geometric Dilution of Precision (GDOP) of TDOA location are analyzed and simulated. The results show that TDOA estimation performance is positively correlated with signal bandwidth, signal cumulative time and signal-to-noise ratio (SNR), while TDOA location precision is positively correlated with TDOA estimation precision and distance between the receiving stations. At the same time, the simulation analysis of station arrangement among multiple stations is carried out, and the results show that Y-shaped station mode gets the best location effect.

关键词： Time difference of arrival Simulation Estimation Bandwidth Position measurement Reflection Communications technology

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Research on Improved Multiple Users Detection Algorithm in Pattern Division Multiple Access System

SSRN

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Han, Guofeng Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong The Guofeng Han is with the Tangshan employment service center Tangshan human resources and Social Security Bureau Tangshan063000 China The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation(MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms. © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Weapon Target Assignment for Surface-to-air Defense with an Improved Reinforcement Genetic Algorithm

Weapon Target Assignment for Surface-to-air Defense with an ...

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Chinese Automation Congress (CAC)

作者： Hua Gong Dalong Liu Yong Zhang Ke Xu School of Science Shenyang Ligong University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

ISBN: (纸本)9781665426480

Decision optimization in surface-to-air defense and coordinated weapon target assignment can improve the defense effect and the utilization rate of weapons. Considering the constraints of weapon capability, a multi-target optimization model is established to maximize the damage probability and minimize the weapon consumption cost. A dual population genetic algorithm improved by reinforcement learning is designed to solve this model. The chromosome exchange ratio is controlled by reinforcement learning to ensure population diversity. Experimental results verify the convergence speed and accuracy of the improved reinforcement genetic algorithm.

关键词： Costs Weapons Heuristic algorithms Sociology Reinforcement learning Statistics Optimization

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Dynamic Threat Assessment of Air multi-target Based on DBN-TOPSIS Method

Dynamic Threat Assessment of Air multi-target Based on DBN-T...

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Chinese Automation Congress (CAC)

作者： Hua Gong Xiaoye Yu Yong Zhang Fang Liu School of Science Shenyang Ligong University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

ISBN: (纸本)9781665426480

In the traditional air target threat assessment, only quantitative threat degree is considered, and the target threat range assessment is missing. Aiming at the problem, a comprehensive dynamic threat assessment method is designed in this paper. This comprehensive method considers two terms of the target threat: threat degree and threat range. In terms of threat degree assessment, a dynamic Bayesian network (DBN) and a technique for order preference by similarity to an ideal solution (TOPSIS) are fused. The multi-target fusion assessment method DBN-TOPSIS is proposed. This method maps the probability of multi-target threat level obtained by dynamic Bayesian network to TOPSIS evaluation matrix. This strategy solves the problem of multi-target threat ranking. In terms of threat range assessment, a grey prediction threat range assessment method GM(1,1) is obtained. This method solves the problem of small sample threat range assessment. The experimental results show that the multi-target fusion threat assessment method is reasonable, and the target threat range can directly reflect the change of target threat degree in airspace.

关键词： Automation Threat assessment Bayes methods

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EMO: Edge Model Overlays to Scale Model Size in Federated Learning

arXiv

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arXiv 2025年

作者： Wu, Di He, Weibo Feng, Wanglei Wen, Zhenyu Qian, Bin Varghese, Blesson School of Computer Science University of St Andrews United Kingdom Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology China University of Science and Technology of China China State Key Laboratory of Industrial Control Technology Zhejiang University China

Federated Learning (FL) trains machine learning models on edge devices with distributed data. However, the computational and memory limitations of these devices restrict the training of large models using FL. Split Federated Learning (SFL) addresses this challenge by distributing the model across the device and server, but it introduces a tightly coupled data flow, leading to computational bottlenecks and high communication costs. We propose EMO as a solution to enable the training of large models in FL while mitigating the challenges of SFL. EMO introduces Edge Model Overlay(s) between the device and server, enabling the creation of a larger ensemble model without modifying the FL workflow. The key innovation in EMO is Augmented Federated Learning (AFL), which builds an ensemble model by connecting the original (smaller) FL model with model(s) trained in the overlay(s) to facilitate horizontal or vertical scaling. This is accomplished through three key modules: a hierarchical activation replay cache to decouple AFL from FL, a convergence-aware communication controller to optimize communication overhead, and an ensemble inference module. Evaluations on a real-world prototype show that EMO improves accuracy by up to 17.77% compared to FL, and reduces communication costs by up to 7.17× and decreases training time by up to 6.9× compared to SFL. © 2025, CC BY.

关键词： Federated learning

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Ground-to-air-Defense Firepower Assignment Decision Based on Non-cooperative Game

Ground-to-air-Defense Firepower Assignment Decision Based on...

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Chinese Automation Congress (CAC)

作者： Hua Gong Mengya Cui Yong Zhang Wenjuan Sun School of Science Shenyang Ligong University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

ISBN: (纸本)9781665426480

The key problem of ground-to-air defense is how to make accurate and effective firepower assignment decision for attacking targets. A multi-objective optimization model of ground-to-air defense firepower assignment is established with the aim of maximizing the damage probability and minimizing the weapon consumption, and non-cooperative game theory is used to solve the problem. A non-cooperative game model is established by mapping the two objectives to players, and weapon assignment schemes to strategy profiles. An improved tabu genetic algorithm based on Nash equilibrium is designed to get the assignment scheme. Experimental results show that the proposed method has better convergence and stability than the improved genetic algorithm based on tabu search for multi-objective optimization model.

关键词： Automation Weapons Games Nash equilibrium Search problems Minimization Stability analysis

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Interference Resource Scheduling Algorithm Based on Potential Game for UAV Cooperative Combat

Interference Resource Scheduling Algorithm Based on Potentia...

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International Conference on Communication technology (ICCT)

作者： Min Deng Zhigao Wu Yongqi Chen School of Automatic and Electronic Information Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China School of Automatic and Electronic Information Xiangtan University Xiangtan China Science and Technology on Communication Information Security Control Laboratory JiaxingChina

ISBN: (纸本)9781665432078

Interference resource scheduling is crucial in UAV (unmanned aerial vehicle) cooperative combats of all scales. In view of the simple constraints of the existing scheduling models and their small-scale application situations, a resource scheduling model with constraint of the minimum number of jammed targets is proposed to maximize the benefit of interference and minimize the cost. The analytic hierarchy process is used to weight the indicators of benefit and cost. And the interference resource scheduling model is constructed by the game theory. Besides, the feasibility and existence of the Nash equilibrium are proved. Then, a potential game theory algorithm combined with the tabu algorithm was proposed to solve the interference resource allocation problem. The simulation results show that the designed interference resource scheduling algorithm can achieve higher integration benefit than the other compared algorithms with less iterations.

关键词： Costs Scheduling algorithms Heuristic algorithms Simulation Interference Dynamic scheduling Autonomous aerial vehicles

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RSRP-Based CID Outdoor Positioning via LTE Networks 20

RSRP-Based CID Outdoor Positioning via LTE Networks

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3rd International Conference on Big Data Technologies, ICBDT 2020

作者： Li, Da Lei, Yingke Chen, Qianlin Zhang, Huan Science and Technology on Communication Information Security Control Laboratory College of Electronic Engineering National University of Defense Technology 230037 China 77526 Troop of the People's Liberation Army China 63768 Troop of the People's Liberation Army China

ISBN: (纸本)9781450387859

Precise positioning of outdoor user equipment (UE) is a hot spot for mobile communication. Satellite navigation and positioning technology has low positioning accuracy in complicated scenarios. In this paper, we propose a Cell-ID (CID) based positioning method via Long-Time-Evolution (LTE) network using reference signal received power (RSRP). By using the generalized regression neural network (GRNN) algorithm, precise positioning of UE is realized with a small amount of data. The performance of the proposed method is acquired by the means of cumulative distribution function (CDF). The experiment compares the effects of data preprocessing, CID information and training methods on positioning accuracy. And the experimental results prove the feasibility and effectiveness of the positioning method in complicated environments, with the positioning accuracy of 12.7m. © 2020 ACM.

关键词： Neural networks

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