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SSRN 2023年

作者： Wu, Yali Dong, Ang Ren, Yuanguang Jiang, Qiaoyong Department of Information and Control Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China The School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

Identifying influential nodes is a recognized challenge for the tremendous number of nodes in complex networks. Most of proposed methods detect the influential nodes based on their degree or topological location, which only consider the local or global information of the network causing inaccuracy. In this paper, we propose a k-orders entropy-based method to identify influential nodes. The influence of node is determined by its entropy with local and global information. The entropy reflecting local information is measured by the different order neighbors' information of nodes while the entropy reflecting global information by the betweenness centrality. The experiments conducted on real-world networks demonstrate the proposed method is more accurate than other methods. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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Deep identification of nonlinear systems in Koopman form

arXiv

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arXiv 2021年

作者： Iacob, Lucian Cristian Beintema, Gerben Izaak Schoukens, Maarten Tóth, Roland Control System Group Eindhoven University of Tehcnology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep statespace encoders. Through this method, the usual drawback of needing to choose a dictionary of lifting functions a priori is circumvented. The encoder represents the lifting function to the space where the dynamics are linearly propagated using the Koopman operator. An input-affine formulation is considered for the lifted model structure and we address both full and partial state availability. The approach is implemented using the the deepSI toolbox in Python. To lower the computational need of the simulation error-based training, the data is split into subsections where multi-step prediction errors are calculated independently. This formulation allows for efficient batch optimization of the network parameters and, at the same time, excellent long term prediction capabilities of the obtained models. The performance of the approach is illustrated by nonlinear benchmark examples. © 2021, CC BY-NC-SA.

关键词： Signal encoding

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Optimal Action Strategy approach for Desert Crossing Based on Dynamic Programming

Optimal Action Strategy approach for Desert Crossing Based o...

引用

CQVIP Conference on Data Driven Intelligence and Innovation, CCDDII 2021

作者： Fan, Xingrong Meng, Dian Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing400067 China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China

It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing based on dynamic programming. According to the game rules, a multi-stage decision optimization model was first established to maximize the remaining funds of players within given game time. Then, a simulation-based dynamic programming algorithm was designed to solve the optimization model, and the optimal action strategy was obtained by backtracking crossing routes. Finally, two experimental case studies with different initial conditions were used to validate the model. In comparison with the benchmark exhaustive search algorithm, the proposed approach has higher performance and efficiency. The case studies confirm that the method can obtain the optimal action strategy under different game maps and initial resources. © 2021 Elsevier B.V.. All rights reserved.

关键词： Dynamic programming

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior

arXiv

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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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Matching Based Intelligent Edge Resource Allocation 3rd

Matching Based Intelligent Edge Resource Allocation

引用

3rd International Conference on Artificial Intelligence, 2022

作者： Qin, Peng Wang, Miao Zhao, Xiongwen Liu, Jiayan Wang, Shuo He, Haoting Xie, Yuanbo State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789811694226

With the rapid development of the Internet of Things, multi-access edge computing (MEC) has emerged as a key technique and intelligent edge resource allocation has become one of the important research issues in MEC. In this paper, we utilize advantages of matching game to integrate users’ needs and the position of edge computing nodes in IoT networks. The proposed GS-based two-sided matching approach simulates the competition and negotiation relationship between different user sets in IoT edge nodes, and provides feasible options based on the preference between different entities. Simulations show that it not only solves the problem of edge resource allocation in a semi-distributed manner, but also effectively improves SP’s revenue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Synergistic Optimization of Power & Capacity Configuration Considering control Strategy for Flywheel Energy Storage Participating in Primary Frequency Regulation

SSRN

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SSRN 2024年

作者： Liang, Lu Hong, Feng Ji, Weiming Jiang, Hui Hao, Junhong Fang, Fang Liu, Jizhen The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China

The intermittent and stochastic nature of renewable energy sources poses stern challenges to the frequency stability of new power system. The flywheel energy storage system (FESS), with its rapid response and high power density, plays a pivotal role in mitigating frequency fluctuations. To improve the performance of the FESS assisting thermal power unit (TPU), a collaborative optimization methodology of the capacity configuration and control strategy for FESS is presented in this paper. Aiming to enhance the primary frequency control (PFC) performance and reduce the life-cycle cost of the FESS, this paper established a capacity configuration model based on a dynamic adaptive control strategy, in which FESS compensates for the lack of TPU frequency regulation capability and recovery SOC actively. A case study demonstrated that the optimization model proposed is suitable for different frequency scenarios. It has been verified that the control strategy which is based on the SOC of FESS and frequency regulation capability of the TPU is economically more attractive compared to traditional strategies. In addition, this study revealed that a FESS with a charge/discharge rate of 7.5 C and rated power of 7.6 MW was appropriate for assisting a 600MW TPU in PFC by the optimal capacity configuration and control strategy. © 2024, The Authors. All rights reserved.

关键词： Wheels

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Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv

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arXiv 2024年

作者： Cong, Runmin Xiong, Hang Chen, Jinpeng Zhang, Wei Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a support-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Intelligent Built-in Test Design of controller Module by Improved Biologically Inspired Neural Network 10

Intelligent Built-in Test Design of Controller Module by Imp...

引用

10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Xie, Zhenmeng Hou, Guolian Zhang, Jianhua Huang, Congzhi School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing China

ISBN: (纸本)9781665424233

Built-in test (BIT) technology is widely employed in heavy-duty gas turbine control systems for fault recognition. However, it is difficult to obtain an excellent fault diagnostic ability by using the conventional BIT technology, and the false alarm rate is high. In this paper, a design of intelligent BIT based on improved biologically inspired neural network (BINN) is proposed to reduce false alarm. Firstly, massive historical measurement data of controller module is collected and used as training dataset and test dataset. Secondly, intelligent BIT based on improved BINN is designed to deal with the issue of module state identification and reduce false alarm rate. Finally, the effectiveness of proposed approach is validated by the given extensive numerical simulation results and experimental results. © 2021 IEEE.

关键词： Statistical tests

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