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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Yifu Pan Jinglong Cheng Xinghua Lu Yajing Li Chenhao Wang Zhanjie Guo Institute of computer control technology Guangzhou Huali College Guangzhou China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In order to realize the visual detection and location search of shipwrecked at sea, a method for detecting shipwrecked at sea based on multi-beam local scene robust location is proposed. A visual image feature detection model of shipwrecked at sea is constructed, the multi-beam local scene robust feature analysis of shipwrecked at sea is realized by fusion matching and outlier parameter decomposition, the scene tracking of shipwrecked at sea is realized by combining the spatial beamforming method, and the super-pixel clustering processing of the wave beam is realized, and the robust positioning of shipwrecked is realized by considering the super-pixel gray information. According to the similarity between the pixels in the center of each cluster and the iterative search area, the multi-beam local scene is focused, the dynamic tracking and identification of real-time ships at sea and the focus of the map are realized by using the dual-mode verification method, the difference map is calculated by using the single-pixel logarithmic ratio operator and the neighborhood logarithmic ratio operator, and the wrecked ship is detected by weighted fusion according to the difference map. The simulation results show that this method can improve the positioning ability of the shipwrecked area, avoid the background interference, and get a clear picture of the detection result of the shipwrecked state change.

关键词： Seminars Visualization Feature detection Simulation Noise Interference Real-time systems

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Weighting Attributes Based on the Greedy Algorithm Properties 28th

Weighting Attributes Based on the Greedy Algorithm Propertie...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Zielosko, Beata Stanczyk, Urszula Jablonski, Kamil University of Silesia in Katowice Institute of Computer Science Bedzinska 39 Sosnowiec41-200 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules. © 2024 The Authors.

关键词： Consensus algorithm

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Research on Surveillance technology Based on Channel State Information technology Combined with Group Intelligence Cooperative Computing

Research on Surveillance Technology Based on Channel State I...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Guohua Luo Jiaming Lu Junjie Lin Wangxu Chen Kaiwen Zheng Xinghua Lu Institute of computer control technology Guangzhou Huali College Guangzhou China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Traditional visual surveillance systems have blind spots, resulting in a lack of information about the location and behavior of the surveillance target, while the lack of collaboration of a single visual sensor in traditional surveillance systems limits the acquisition of critical information. To solve this problem, this study proposes a surveillance technology scheme based on channel state information and group intelligence technology. The scheme utilizes CSI technology to design an indoor surveillance cooperative mechanism that can operate in smoke and dark conditions, even through barriers, to effectively monitor non-LoS targets, thus overcoming the blind spot problem of conventional visual surveillance systems. The system does not require the monitoring target personnel to wear sensors or configure specialized monitoring equipment, but rather realizes intelligent cooperative computing of multi-device groups through the breadth coverage characteristics of Wi-Fi information, combined with the changes in the intensity of CSI information caused by target motion, thus realizing real-time monitoring of the target, and improving safety and efficiency.

关键词： Wireless communication Visualization Wireless sensor networks Accuracy Surveillance Collaboration Video surveillance

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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The Preparation Method, Resistive Switching Mechanism and Application Prospects of Memristor 7

The Preparation Method, Resistive Switching Mechanism and Ap...

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7th International Conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Li, Jiao Zhao, Ximei Lei, Tengfei Chai, Lin Wang, Shi Yu, Yuna Collaborative Innovation Center of Memristive and Computing Application Qilu Institute of Technology Jinan250200 China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan250200 China College of Computer and Communication Engineering Qilu Institute of Technology Jinan250200 China

ISBN: (纸本)9798350382877

Memristors have also become one of the most promising research hotspots for solving data storage and computing problems faced in the big data era. As an emerging electronic device, memristor has potentially important applications in information storage and processing. This paper constructs a rod-shaped TiO2-x nanoscale memristor with FTO/TiO2-x/Cu structure, whose reliability is based on the memory behavior and its conversion mechanism. Research progress including materials, preparation methods, resistive switching mechanism and application prospects are described. First, the constituent materials of memristor are introduced, focusing on metal oxides and organic materials such as titanium oxide (TiO2). The choice of these materials is critical to memristor performance and applications. Secondly, the advantages and limitations of the preparation methods are discussed. Then the resistance switching mechanism of memristor was studied in depth, focusing on the generation principle and adjustment mechanism of memristor effect. These include important concepts such as charge migration, Schottky barrier formation and oxygen vacancy drift, which help to understand the electrical properties of memristors. In addition, we explore the prospects of memristors in practical applications. Finally, past and current work in this field is summarized, and the prospects and challenges of memristorrelated research are prospected. © 2023 IEEE.

关键词： Memristors

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A Reachability-decidable Petri Net Modeling Method and its Application to Flexible Manufacturing Systems

A Reachability-decidable Petri Net Modeling Method and its A...

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2023 International Conference on Automation in Manufacturing, Transportation and Logistics, iCaMaL 2023

作者： Su, Yue Qi, Liang Zhou, MengChu Wiśniewski, Remigiusz Faculty of Innovation Engineering Macau University of Science and Technology China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Electrical and Computer Engineering New Jersey Institute of Technology Newark United States Institute of Control and Computation Engineering University of Zielona GÓra Poland

ISBN: (纸本)9781839539954

Petri nets (PNs) are graphical and mathematical tools used to model various discrete event systems and analyze their properties. Reachability is their fundamental property. When we use a state equation to determine a state's (marking's) reachability, the existence of legal firing sequences (LFSs) corresponding to nonnegative integer solutions (NISs) of the state equation needs to be determined. Our previous work has proposed an algorithm to determine the LFSs' existence in polynomial time. However, when a state equation has an infinite number of NISs, we cannot check all NISs to determine a marking's reachability. Hence, this work studies the relationship between the structure of an ordinary PN and the number of NISs of its state equation. Then, a method is proposed to modify the structure of the PN such that the state equation has no more than one NIS. The modified PN is used as the final model of the system, which maintains the functions of its modeled system. Its reachability can be determined in polynomial time. This work shows the application of the proposed method to a flexible manufacturing system's modeling and analysis. © The Institution of Engineering & technology 2023.

关键词： Polynomial approximation

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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A DRL-Based Cluster Scheduling Algorithm Configuration Optimization Approach for Artificial Intelligence Jobs

A DRL-Based Cluster Scheduling Algorithm Configuration Optim...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Shilin Wen Wei Liang Siyan Zhu Ke Qiu Xiaoning Wu Department Three of Command and Control North Automatic Control Technology Institute Taiyuan China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the widespread application of machine learning technology across multiple domains, artificial intelligence (AI) jobs have become significant workloads for cloud service providers. However, current cloud platforms rely on pre-set scheduling algorithms, making it difficult to adjust configurations in real-time according to characteristics of AI jobs, thereby limiting job completion efficiency. Additionally, the heterogeneity and dynamism of AI jobs increase the difficulty of resource prediction and scheduling. In order to address this issue, this paper proposes an effective approach based on Deep Reinforcement Learning (DRL), by using DRL technology to adjust scheduling algorithm configurations online, converting the optimization process into a Markov process for model training, so as to optimize configurations in real-time and reduce job completion time. Simultaneously, to shorten the data sampling time for DRL training, this paper develops a cluster simulation scheduler that can efficiently simulate cluster running states, which can accelerate DRL training. Experimental results demonstrate that the simulation scheduler exhibits fast simulation rates and high simulation accuracy, and our proposed approach effectively reduces job completion time in small-scale clusters, thereby enhancing cluster resource utilization.

关键词： Training Electrical engineering Limiting Scheduling algorithms Clustering algorithms Markov processes Real-time systems Resource management Artificial intelligence Optimization

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MASS FLOW DETECTION technology FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

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INMATEH - Agricultural Engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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