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A Generative Model-Based Network Framework for Ecological Data Reconstruction

Computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

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Journal of Systems engineering and electronics 2023年第3期34卷 641-649页

作者： YU Yongbin TANG Haowen FENG Xiao WANG Xiangxiang HUANG Hang School of Information and software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China

Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge *** MCM-CNN is designed by adopting a memristor crossbar composed of a pair of ***-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and *** of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection *** results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

关键词： edge detection figure of merit(FOM) memristor crossbar synaptic circuit memristor crossbar-based cellular neural network(MCM-CNN)

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IPFA-Net: Important Points Feature Aggregating Net for Point Cloud Classification and Segmentation

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Chinese Journal of electronics 2025年第1期34卷 322-337页

作者： Jingya Wang Yu Zhang Bin Zhang Jinxiang Xia Weidong Wang School of Information and Software Engineering University of Electronic Science and Technology of China National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University

This paper focuses on the problems of point cloud deep neural networks in classification and segmentation tasks, including losing important information during down-sampling, ignoring relationships among points when extracting features, and network performance being susceptible to the sparsity of point cloud. To begin with, this paper proposes a farthest point sampling-important points sampling method for down-sampling, which can preserve important information of point clouds and maintain the geometry of input data. Then, the local feature relation aggregating method is proposed for feature extraction, improving the network's ability to learn contextual information and extract rich local region features. Based on these methods, the important points feature aggregating net(IPFA-Net) is designed for point cloud classification and segmentation tasks. Furthermore, this paper proposes the multi-scale multi-density feature connecting method to reduce the negative impact of point cloud data sparsity on network performance. Finally, the effectiveness of IPFA-Net is demonstrated through experiments on ModelNet40, ShapeNet part, and ScanNet v2 datasets. IPFA-Net is robust to reducing the number of point clouds, with only a 3.3% decrease in accuracy under a 16-fold reduction of point number. In the part segmentation experiments, our method achieves the best segmentation performance for five objects.

关键词： Point cloud compression Representation learning Geometry Fuses Semantics Artificial neural networks Feature extraction Sampling methods Robustness Data mining

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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Computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

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Computers, Materials & Continua 2024年第2期78卷 1977-1993页

作者： Zhiqing Dong Zhao Zhang Hongyan Zhou Xuebo Chen School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicated on 2D compressed sensing(CS)and the hyperchaotic ***,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong ***,the processed images are con-currently encrypted and compressed using 2D *** them,chaotic sequences replace traditional random measurement matrices to increase the system’s ***,the processed images are re-encrypted using a combination of permutation and diffusion *** addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct *** with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational ***,it has better *** experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

关键词： Image encryption image compression hyperchaotic system compressed sensing

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Compound facial expressions recognition approach using DCGAN and CNN

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Multimedia Tools and Applications 2024年第38期83卷 85703-85723页

作者： Ullah, Sana Ou, Jie Xie, Yuanlun Tian, Wenhong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu61173 China

Facial expression recognition (FER) technology has numerous applications in various fields such as health, entertainment and gaming, transportation, advertising and marketing, education, and many more. The recognition system based on seven basic expressions of emotion cannot satisfy the requirement for compound expression recognition as compound expressions have complex features due to a combination of basic emotions. Deep learning-based Recognition of compound emotions has recently emerged as a significant subject of academic research. Employing deep learning models to compound emotion recognition is facing imbalanced images in the dataset. To overcome the problem of imbalanced images in the dataset, the deep convolutional generative adversarial network (DCGAN) architecture has been utilized to balance the imbalanced dataset. The study proposes a novel approach for compound emotion recognition based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN), which has been validated using the Compound Facial Expression Emotions (CFEE) and Real-world Affective Faces Database (RAFDB) datasets. Through utilizing the proposed approach, it achieved a significantly high accuracy of 72.0% for the CFEE dataset and 65.4% for RAF-DB for compound facial expressions of emotions. The AU-ROC/ AU-PR curves and ablation experiments also validated the performance of the proposed approach. The performance of the proposed model is compared with the state-of-the-art advanced models on the balanced datasets, and for both the CFEE and RAF-DB datasets, the accuracy of compound emotion recognition improved considerably. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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VG-DOCoT:a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition

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Frontiers of information technology & electronic engineering 2024年第11期25卷 1497-1514页

作者： Yanping ZHU Lei HUANG Jixin CHEN Shenyun WANG Fayu WAN Jianan CHEN School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China

Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical *** solve this problem,a novel electroencephalogram(EEG)emotion recognition network named VG-DOCoT is proposed,which is based on depthwise over-parameterized convolutional(DO-Conv),transformer,and variational automatic encoder-generative adversarial network(VAE-GAN)***,the differential entropy(DE)can be extracted from EEG signals to create mappings into the temporal,spatial,and frequency information in *** enhance the training data,VAE-GAN is employed for data augmentation.A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network.A transformer structure is introduced into the network framework to reveal the global dependencies from EEG *** the proposed model,a binary classification on the DEAP dataset is carried out,which achieves an accuracy of 92.52%for arousal and 92.27%for ***,a ternary classification is conducted on SEED,which classifies neutral,positive,and negative emotions;an impressive average prediction accuracy of 93.77%is *** proposed method significantly improves the accuracy for EEG-based emotion recognition.

关键词： Emotion recognition Electroencephalogram(EEG) Depthwise over-parameterized convolutional(DO-Conv) Transformer Variational automatic encoder-generative adversarial network(VAE-GAN)

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Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

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Chinese Journal of electronics 2024年第1期33卷 175-184页

作者： Xichao ZHAN Zhongwen SUN Feng SHU Yiwen CHEN Xin CHENG Yuanyuan WU Qi ZHANG Yifan LI Peng ZHANG School of Information and Communication Engineering Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology

For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.

关键词： Hybrid analog and digital Direction of arrival Massive hybrid multiple-input multiple-output Phase ambiguity Low computational complexity

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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Swarm Intelligent Optimization Algorithms to Solve Feature Selection Problem

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第1期52卷 158-170页

作者： Liu, Yu Wang, Yu-Cai Wang, Jie-Sheng Zhang, Ya-Ni School of Electronic and Information Engineering University of Science and Technology Liaoning An-shan14051 China School of Electronic and Information Engineering University of Science and Technology Liaoning An-shan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

The objective of feature selection is to identify the optimal subset of features from all non-empty subsets of the attribute set that best approximates the distribution of the original dateset or is optimal according to a certain evaluation criterion, such as achieving the highest classification accuracy. In wrapper-based feature selection, swarm intelligence optimization algorithms are widely used. This paper addresses the feature selection problem using a wrapper-based approach, employing nine swarm intelligence optimization algorithms to solve it and compare their performance. The algorithms include Crow Search Algorithm (CSA), Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Arithmetic Optimization Algorithm (AOA), Butterfly Optimization Algorithm (BOA), Ant Lion Optimizer (ALO), Prairie Dog Optimization (PDO), and Sparrow Search Algorithm (SSA). Performance testing was conducted on 12 standard TJCI datasets to validate the performance of these algorithms. Convergence curves and box plots of accuracy values for the nine swarm intelligence optimization algorithms across the 12 datasets are presented. The simulation results are compared based on the mean and standard deviation of fitness, the number of selected features, and accuracy. © (2025), (International Association of Engineers). All rights reserved.

关键词： Optimization algorithms

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