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12th International Scientific and Practical conference "information Control systems and Technologies", ICST 2024

作者： Timchenko, victor Kreinovich, Vladik Kondratenko, Yuriy Admiral Makarov National University of Shipbuilding av. Geroev of Ukraine 9 Mykolaiv054025 Ukraine University of Texas at El Paso 550 W University El PasoTX79968 United States Petro Mohyla Black Sea National University 10 68th Desantnykiv Str. Mykolaiv054003 Ukraine Institute of Artificial Intelligence Problems of MES and NAS of Ukraine Kyiv01001 Ukraine

The article is devoted to the development of methods and architecture of optical color computing, techniques of transforming color information for textual representation and numerical calculation, including transmission by optical channels, and thus expanding the functionality of a promising approach to creating artificial intelligence components in decision support systems. This approach is based on the representation of fuzzy information as an information quantum of visible light of a certain color and the construction of an architecture of logical operations based on the additive and subtractive transformation of light radiation using color filters. The paper presents the principles of forming fuzzy input and output color information, algorithms for hybrid calculations with the transformation of optical data into numerical calculus, methods for converting text messages into color information and, further, into numerical encoding. Components of the optical architecture are presented that allow the implementation of transformation algorithms with high computation speed and wide possibilities for constructing parallel structures. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： optical data processing

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optical Character Recognition Based Answer Script Correction Using Deep Learning and Language processing algorithms 4

Optical Character Recognition Based Answer Script Correction...

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4th International conference on Ubiquitous Computing and Intelligent information systems, ICUIS 2024

作者： Sankara Mahalingam, M. Kumar, N. Suresh Reddy, Basireddy Sai Charan Reddy, Nalibili Venkata Suneel Kumar Govardhan, Adimulam Bontha, Harshith Sai Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhunagar India

ISBN: (纸本)9798331529635

The rapid advancements in digital assessment systems have highlighted the need for efficient, automated methods of evaluating written examination scripts. The paper presents a new framework that leverages optical Character Recognition (OCR), deep learning, as well as Natural Language processing (NLP) to automate the correction and evaluation of handwritten answer scripts. The suggested system begins by utilizing OCR to digitize handwritten responses, converting them into machine readable text. Subsequently, advanced learning frameworks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed to classify and recognize written characters with high accuracy, even in varying handwriting styles. Once digitized, the responses undergo semantic analysis and content evaluation using advanced NLP algorithms. These algorithms, integrated with transformer models like BERT (Bidirectional Encoder Representations from Transformers), evaluate the coherence, relevance, and correctness of answers by comparing them to model answers and predefined keywords. The system incorporates both syntactic and semantic matching techniques to ensure a comprehensive assessment of the answer content, while also analyzing grammar and language use. The proposed system can significantly streamline the educational assessment process, paving the way for intelligent and automated grading systems in academic and professional environments. © 2024 IEEE.

关键词： Convolutional neural networks

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A Real-time Implementation of Object Tracking algorithms Fusion for Thermal Imaging Sequences

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IEIE Transactions on Smart processing and Computing 2022年第4期11卷 262-269页

作者： Hung, Nguyen Ngoc Tinh, Cao Huu Thuan, Nguyen vi Van, Pham Ngoc Department of Aerospace Control Systems Le Quy Don Technical University Hanoi Viet Nam

Object tracking is a widely used algorithm in image processing. When tracking objects on thermal images, however, issues, such as changes in size, temporary occlusion, lack of prominent features, and active thermal noise, are frequently encountered. This article proposes using Multi-Processor System on Chip (MPSoC) technology to implement new tracking algorithms on programmable hardware platforms to increase the computational speed. This real-time algorithm will combine the superiority of existing object tracking algorithms, such as Camshift, Kalman, and optical Flow, to overcome the difficulties mentioned above. © 2022 Institute of Electronics and information Engineers. All rights reserved.

关键词： Infrared imaging

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Decentralized Target Localization in Distributed MIMO Radars 16

Decentralized Target Localization in Distributed MIMO Radars

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16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Qinhui Li, Jie Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781510689251

This paper addresses the problem of target estimation in distributed MIMO radars, focusing on the challenge of requiring a robust fusion center to process all the collected data. To overcome this limitation, we propose a solution based on the Alternating Direction Method of Multipliers (ADMM) that eliminates the need for a centralized fusion center. By leveraging the sparsity of target location information within the detection range, the target localization problem is reformulated as a sparse recovery problem, which is then efficiently solved using ADMM. The key innovation of the proposed approach is the transformation of the average value computation, typically required in centralized methods, into a consensus averaging problem, and subsequently into a consensus optimization problem. This transformation allows for fully decentralized processing, where each node only communicates with its neighboring nodes to reach a consensus on the estimation of target location. Simulation results demonstrate that the proposed distributed algorithm achieves performance comparable to that of semi-distributed algorithms while fully exploiting the advantages of decentralization. © 2025 SPIE.

关键词： Consensus algorithm

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Field weed classification based on modified LBP and local sparse representation

Field weed classification based on modified LBP and local sp...

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2024 International conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Yang, Huan College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Accurately identifying field weeds is crucial for selecting appropriate agricultural machinery and herbicides. This paper focuses on nine common weed species during the seedling stage in natural backgrounds of field vegetables, proposing a novel weed foreground segmentation method based on image processing and a new weed classification model using sparse representation classification (K_SRC). Initially, K-means clustering and threshold segmentation are applied to the a component of Lab color space to obtain binary weed images, followed by area filtering to remove interference. The binary weed image is then masked with the original image to extract clean weed images. Color features and mLBP texture features are extracted from segmented weed images to form a training sample set. KNN computes a local dictionary, and sparse coefficients are calculated using the OMP algorithm. Experimental results demonstrate a classification accuracy of 95.22% compared to four other algorithms. Additionally, this research provides new insights into the application of sparse representation in agriculture. © 2024 SPIE.

关键词： Image segmentation Education and training Feature extraction Image classification Detection and tracking algorithms Binary data Image processing algorithms and systems Reconstruction algorithms Image processing

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Design of photovoltaic radio energy transmission system based on NSGA-II

Design of photovoltaic radio energy transmission system base...

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2024 International conference on Signal processing and Communication Security, ICSPCS 2024

作者： Zhou, Jiacheng Ma, Jun Wang, Hongxiang Chen, Bohang Institute of Computer Science Qinghai Normal University Qinghai China Institute of Physics and Electronic Information Engineering Qinghai Normal University Xining Qinghai China

ISBN: (纸本)9781510681699

In order to efficiently apply Wireless Power Transfer (WPT) technology in photovoltaic (PV) systems, mathematical modeling and mechanism analysis of Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) systems were conducted. By employing the multi-objective genetic algorithm (NSGA-II) and an adjustable coupling distance LCC-S topology, the reliability of power supply was ensured. The results demonstrate stable charging efficiency, strong adaptability, and high potential for widespread application. © 2024 SPIE.

关键词： Genetic algorithms

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A Novel Environment Reconstruction Method based on Distributed NLOS Signals 16

A Novel Environment Reconstruction Method based on Distribut...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Yu, Fangrui Li, Junlin Xie, Wei Zhang, Kehao Wan, Qun School of Information and Communication Engineering Nation Key Laboratory on Blind Signal Processing University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

Integrated Sensing and Communication (ISAC) is a rapidly evolving field with extensive application prospects. Environmental reconstruction (ER) is an important content of ISAC. Existing ER techniques often require large sample sizes or specific signals, which significantly limit their application. User Equipments (UEs) widely distributed in the environment and their communication signals provide an adequate measurement basis. However, there are challenges in utilizing communication signals due to limited research, and difficulties in identifying and extracting Non-Line-of-Sight (NLOS) signals. This paper proposes a distributed ER algorithm based on Angle of Arrival (AOA) and Time of Flight (TOF) information of NLOS signals, enabling identification and utilization of various order NLOS signals under complex NLOS conditions. A series of simulation delays validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Non line of sight propagation Reflection Reconstruction algorithms Mirror pointing Signal processing Environmental sensing Wireless communications

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Machine Learning for Signal processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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Application of artificial intelligence algorithm model in fault diagnosis of interactive information 6

Application of artificial intelligence algorithm model in fa...

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6th International conference on Image, video processing, and Artificial Intelligence, IVPAI 2024

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk International Institute of Engineering Psychology DenverCO80202 United States Foothill Preparatory School Temple CityCA91780 United States Department of Computer Engineering Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9781510681781

Interactive information fault diagnosis technology is a new type of fault diagnosis technology which is integrated by information fusion, artificial intelligence, computer science and other disciplines. It can extract interactive information data of equipment in real time, analyze the characteristics of fault information, and then find the change and trend of equipment operating status. However, there are still some shortcomings in the practical application of this technology, for example, it is difficult to deal with a large number of complex interactive information data, and it is difficult to carry out effective fault diagnosis. Therefore, an intelligent fault diagnosis method with improved grey relational degree is proposed in this paper. This method can effectively extract the interactive information feature data of the equipment, and carry out the correlation analysis of the fault feature data through the grey correlation analysis algorithm, which provides a new idea for the fault diagnosis of the equipment. © 2024 SPIE.

关键词： Correlation coefficients Evolutionary algorithms Matrices Artificial intelligence Fuzzy logic algorithms Data processing Algorithm development Feature extraction Intelligence systems

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Design and research of electric vehicle DC charging pile monitoring system based on LabviEW 3

Design and research of electric vehicle DC charging pile mon...

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3rd International conference on Electronic information Engineering and Data processing, EIEDP 2024

作者： Yang, Aihua Fang, Tianke Serrano, Elcid A. Chi, Yusen Liu, Fucai Chen, Zhenxiang School of Information Technology Mapua University Manila Philippines School of Information Media Zhangzhou Vocational College of Science and Technology Fujian Zhangzhou China School of Computing and Information Engineering Xiamen University of Technology Fujian Xiamen China Information Technology Teaching and Research Group Fujian Economic School Fujian Fuzhou China Dean's office Zhangzhou Vocational College of Science and Technology Fujian Zhangzhou China

ISBN: (纸本)9781510680531

With the rise in environmental awareness and the promotion of new energy vehicle policies, the electric vehicle (EV) market is rapidly expanding. Direct current (DC) charging stations, as a crucial infrastructure for electric vehicle charging, have made the design and optimization of their monitoring systems especially important. This study aims to design and implement an efficient and reliable DC charging station monitoring system for electric vehicles using the LabviEW platform. The system primarily achieves real-time data collection, signal processing, State of Charge (SoC) estimation, and user interface optimization. Through Geographic information systems (GIS) and big data analysis, this study also assesses the spatial distribution and accessibility of charging stations to promote the rational layout of charging infrastructure. Experimental results show that the developed monitoring system can effectively improve charging efficiency, reduce system response time, and enhance user experience. Moreover, system performance is further improved through optimization using genetic algorithms and simulated annealing algorithms. The research results provide technical support and practical guidance for the development of electric vehicle charging infrastructure, playing a significant role in promoting the sustainable development of the electric vehicle industry. © 2024 SPIE.

关键词： Genetic algorithms

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