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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Li, Jianqi Liang, Yincong Du, Rui Wan, Jingying Cao, Binfang Liu, Huiting Hunan University of Arts and Sciences Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft Changde China Jishou University Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft Changde China Hunan University of Arts and Sciences College of Computer and Electrical Engineering Changde China

ISBN: (纸本)9798350346596

Aiming at the problem that the defects generated in the production and transportation of punched nickel-plated steel strips are not easy to be detected by deep learning methods, a lightweight, low-redundancy, and high-precision detection method is proposed in this paper. Firstly, a feature extraction network based on GhostNet is constructed, which reduces the amount of computation and feature redundancy while ensuring accuracy. Then the ECA module is applied to the detection head to perform weighted fusion of the features of different channels for better differentiation. Finally, the YOLO detection head is used for multi-scale detection. In the experiment, the mAP of 84.86% was obtained by this method, which proves that this method can be applied to the actual steel strip defect: detection. © 2023 IEEE.

关键词： Redundancy

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A Lightweight Multi-domain Multi-attention Progressive Network for Single Image Deraining 24

A Lightweight Multi-domain Multi-attention Progressive Netwo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhong, Junliu Li, Zhiyi Xiang, Dan Han, Maotang Li, Changsheng Gan, Yanfen School of Computer Science Guangzhou Maritime University Guangdong Guangzhou China Network and Information Service Center Guangdong University of Foreign Studies South China Business College Guangdong Guangzhou China School of Low Altitude Equipment and Intelligent Control Guangzhou Maritime University Guangdong Guangzhou China School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce Guangdong Guangzhou China

ISBN: (纸本)9798400706868

Currently, the information processing in a spatial domain alone has intrinsic limitations that hinder the deep network's effectiveness (performance) improvement in a single image deraining. Moreover, the deraining networks' structures and learning processes are becoming increasingly intricate, leading to challenges in structural lightweight, and training and testing efficiency. We propose a lightweight multi-domain multi-attention progressive network (M2PN) to handle these challenges. For performance improvement, the M2PN backbone applies a simple progressive CNN-based structure consisting of the S same recursive M2PN modules. This recursive backbone with a skip connection mechanism allows for better gradient flow and helps to effectively capture low-to-high-level/scales spatial features in progressive structure to improve contextual information acquisition. To further complement acquired spatial information for better deraining, we conduct spectral analysis on the frequency energy distribution of rain steaks, and theoretically present the relationship between the spectral bandwidths and the unique falling characteristics and special morphology of rain steaks. We present the frequency-channel attention (FcA) mechanism and the spatial-channel attention (ScA) mechanism to fuse frequency-channel features and spatial features better to distinguish and remove rain steaks. The simple recursive network structure and effective multi-domain multi-attention mechanism serve as the M2PN to achieve superior performance and facilitate fast convergence during training. Furthermore, the M2PN structure, with a small network component quantity, shallow network channels, and few convolutional kernels, requires only 168K parameters, which is 1 to 2 orders of magnitude lower than the existing SOTA networks. The experimental results demonstrate that even with such a few network parameters, M2PN still achieves the best overall performance. © 2024 ACM.

关键词： Image enhancement

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Mixed-Integer Optimal control With Disjunctive Characteristic Time Constraints for Energy-Optimal Speed Planning

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IEEE Transactions on Transportation Electrification 2025年

作者： Dong, Shiying Chen, Hong Luo, Xi Meng, Xiangyu Huang, Yanjun Liu, Qifang Gao, Bingzhao Jilin University Department of Control Science and Engineering Changchun China Tongji University College of Automotive Studies Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signalized intersections defines complex spatio-temporal constraints, and multiple available open time windows introduce the discontinuous feasible domains of crossing times. The CAVs should select an open time-window for each intersection, determine the crossing times, and optimize the speed profiles accordingly, which turns out to be a hard-to-solve optimization problem. To address this challenge, this paper proposes a unifying approach that transforms the energy optimization problem into a novel mixed-integer optimal control problem with characteristic time constraints (MIOCP-CT). This approach facilitates the simultaneous optimization of open time windows, crossing times, and speed profiles within a single optimization problem, which can be solved by our proposed novel numerical solution procedure. The application of the proposed method in the eco-driving of CAVs approaching multiple signalized intersections demonstrates its performance within real-world urban driving cycles. The experimental results of the hardware-in-the-loop (HiL) demonstrate a superior energy-saving performance of 5.83% compared to existing methods, while also achieving real-time feasibility within 100 milliseconds. © 2015 IEEE.

关键词： Mixed-integer linear programming

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An Enhanced Analysisof Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Mary, S.J. Jereesha Anitha, C. Logeshwaran, Jaganathan Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Annai Velankanni College of Engineering Potalkulam Kanyakumari629401 India Saveetha School of Engineering Chennai600124 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Center University of Anbar Baghdad55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk Iraq

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating theserich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets. © 2024, CC BY-NC-ND.

关键词： Radial basis function networks

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LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation

arXiv

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arXiv 2024年

作者： Sun, Jingyun Dai, Chengxiao Luo, Zhongze Chang, Yangbo Li, Yang College of Computer and Control Engineering Northeast Forestry University Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman College of Architecture and Arts Design Shijiazhuang Tiedao University

Legal Large Language Models (LLMs) have shown promise in providing legal consultations to non-experts. However, most existing Chinese legal consultation models are based on single-agent systems, which differ from real-world legal consultations, where multiple professionals collaborate to offer more tailored responses. To better simulate real consultations, we propose LawLuo, a multi-agent framework for multi-turn Chinese legal consultations. LawLuo includes four agents: the receptionist agent, which assesses user intent and selects a lawyer agent;the lawyer agent, which interacts with the user;the secretary agent, which organizes conversation records and generates consultation reports;and the boss agent, which evaluates the performance of the lawyer and secretary agents to ensure optimal results. These agents’ interactions mimic the operations of real law firms. To train them to follow different legal instructions, we developed distinct fine-tuning datasets. We also introduce a case graph-based RAG to help the lawyer agent address vague user inputs. Experimental results show that LawLuo outperforms baselines in generating more personalized and professional responses, handling ambiguous queries, and following legal instructions in multi-turn conversations. Our full code and constructed datasets will be open-sourced upon paper acceptance. © 2024, CC BY.

关键词： Modeling languages

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Unsupervised Time Series Anomaly Detection Based on Adversarial Interpolation and Pseudo-anomaly Calibration

Unsupervised Time Series Anomaly Detection Based on Adversar...

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International Conference on control, Automation and Robotics (ICCAR)

作者： Xinwei Chen Xiaohui Lin Zuoyong Li Haoyi Fan Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Allocation and Scheduling of the Colorado River System

Allocation and Scheduling of the Colorado River System

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作者： Ziyang Zhang Kaisheng Wang Ruifeng Zeng College of Computer Science and Software Engineering Shenzhen University College of Mechatronics and Control Engineering Shenzhen University

Generally,dams serve as reservoirs for water *** Colorado River Basin is currently in a state of continuous drought,and if the five states of AZ,CA,WY,NM and CO maintain the agreements made hundreds of years ago,there will not be enough water to supply in the near *** propose a multi-objective programming algorithm under inequality constraints,under the condition that the sum of the competition coefficients is minimized,we solve the proportion of water resources that should be cut for general water usage and hydropower production at different water scarcity *** develop some policies to deal with water *** use the univariate analysis method to rationally analyze and reconstruct our model according to three different scenarios,and finally predict different situations,like additional water supply is needed or whether there will be more water flowing to the Gulf of California.

关键词： Differential equations kinetic equations entropy method competition coefficients multiobjective programming algorithms univariate analysis

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Three matrix conditions for the reduction of finite automata based on the theory of semi-tensor product of matrices

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Science China(Information Sciences) 2020年第2期63卷 227-229页

作者： Jumei YUE Yongyi YAN Zengqiang CHEN College of Agricultural Engineering Henan University of Science and Technology College of Information Engineering Henan University of Science and Technology College of Computer and Control Engineering Nankai University

Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of states of FSMs. Existing results for reducing FA can roughly be classified into four categories:merging of states [1], refining of the state

关键词： STP Sr Three matrix conditions for the reduction of finite automata based on the theory of semi-tensor product of matrices

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Preprocessing Techniques for Rectal Cancer Diagnosis using MR Images 24

Preprocessing Techniques for Rectal Cancer Diagnosis using M...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： D'Almeida, Sonal Patreena Kamath, Surekha Rajagopal, K.V. Manjunath, K.N. Department Of Instrumentation And Control Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Radiodiagnosis And Imaging Kasturba Medical College Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Computer Science And Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9798400708329

The use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable course of action. Image pre-processing is used to improve the quality of image and making it better for analysis and further processing. The study carried out in this paper is concentrated on pre-processing methods like Gaussian and Sobel filtering. The filtering techniques were implemented on Pelvic MR images obtained from Kasturba Medical college (KMC), Manipal. The findings suggest that Sobel filtering had a superior output image quality, indicating its potential as a preferred preprocessing technique for enhancing Pelvic MR images in rectal cancer diagnosis and treatment planning. The output image efficiency is calculated by Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE). © 2024 Owner/Author.

关键词： Mean square error

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