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IAENG International Journal of computer science 2024年第7期51卷 791-800页

作者： Gu, Zhan-Xin Wu, Jie Jin, Wei Han, Guo-Jing School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Anshan Meteorological Service Anshan114004 China

Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence prediction task and propose MotionUVRNN, a recurrent neural network model suitable for wind field correction. The model consists of three elements: a feature decoupling module, an encoder, and a corrector. The feature decoupling module decouples the wind field into U-Wind (Wind component in x/longitude-direction) feature, V-Wind (Wind component in y/latitude direction) feature, and spatiotemporal feature. The encoder unifiedly models the motion trend and transient variation of the wind field. A novel long short-term memory dynamic capture unit (RS-LSTM) is proposed as the core part of the encoder to capture the different dynamic patterns of U-Wind and V-Wind. The corrector extracts and integrates multi-scale wind field information to capture regional features. By correcting the 3-12 hour wind field forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF;EC for short), the proposed model reduced the wind speed prediction error by 53.37% to 46.52%, and lowered the wind direction error by 21.99% to 13.26%. Moreover, this model surpasses the existing leading spatiotemporal sequence prediction models in wind field correction. © (2024) International Association of Engineers. All rights reserved.

关键词： Signal encoding

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Revolutionizing Image Captioning: Integrating Attention Mechanisms with Adaptive Fusion Gates

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 212-221页

作者： Sheng, Shou-Jun Zhou, Zi-Wei School of Computer and Software Engineering University of Science and Technology Liaoning AnShan114051 China

In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image. However, a prevalent issue with current attention mechanisms is their tendency to overlook essential elements within the image, prioritizing contextual aspects of the object when generating descriptive text. This constraint results in a decrease in the precision of the textual descriptions produced. To address this issue and improve the accuracy of image interpretation, a proposed model for image description utilizes an attention-based approach and includes a multi-layer decoder and a fusion gate. This model is based on an encoder-decoder architecture and utilizes Residual Network (ResNet) framework for feature extraction during the encoding phase, thereby extending the encoder-decoder structure into the decoding phase. Within this framework, an adaptive fusion gate mechanism is introduced and combined with multi-layer cascade decoders to facilitate the generation of utterances. This allows decoders from lower layers to actively contribute to the final text prediction phase, thereby incrementally improving the accuracy of the predicted text and generating more precise descriptions. MS COCO 2014 dataset has been utilized for the purpose of training and validating the effectiveness of this model in understanding images. The results clearly and unequivocally establish the model’s capacity to generate exceptional predictions. When compared with the top-performing models, it has demonstrated significant enhancements, as indicated by a 0.096 rise in BLEU_1 metric, a 0.153 improvement in ROUGE_L metric, and a remarkable 0.32 increase in CIDEr metric on MS COCO dataset. The overall improvement in performance across all evaluation criteria highlights the model’s alignment with the requirements of image understanding applications. © (2024), (International Associ

关键词： computer vision

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Hybrid concurrency control protocol for data sharing among heterogeneous blockchains

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Frontiers of computer science 2024年第3期18卷 1-12页

作者： Tiezheng GUO Zhiwei ZHANG Ye YUAN Xiaochun YANG Guoren WANG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100029China School of Computer Science and Engineering Northeastern UniversityShenyang 110167China

With the development of information technology and cloud computing,data sharing has become an important part of scientific *** traditional data sharing,data is stored on a third-party storage platform,which causes the owner to lose control of the *** a result,there are issues of intentional data leakage and tampering by third parties,and the private information contained in the data may lead to more significant ***,data is frequently maintained on multiple storage platforms,posing significant hurdles in terms of enlisting multiple parties to engage in data sharing while maintaining *** this work,we propose a new architecture for applying blockchains to data sharing and achieve efficient and reliable data sharing among heterogeneous *** design a new data sharing transaction mechanism based on the system architecture to protect the security of the raw data and the processing *** also design and implement a hybrid concurrency control protocol to overcome issues caused by the large differences in blockchain performance in our system and to improve the success rate of data sharing *** took Ethereum and Hyperledger Fabric as examples to conduct crossblockchain data sharing *** results show that our system achieves data sharing across heterogeneous blockchains with reasonable performance and has high scalability.

关键词： data sharing blockchain concurrency control protocol crosschain

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Design and Optimization of Self-Supporting Surfaces with Arch Beams

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 1-14页

作者： Wei, Guangshun Ma, Long Zhou, Yuanfeng Wang, Chen Zheng, Jianmin He, Ying School of Software Shandong University China School of Computer Science and Engineering Nanyang Technological University Singapore

The paper presents a new method for constructing self-supporting surfaces using arch beams that are designed to convert their thrust into supporting force, thereby eliminating shear stress and bending moments. Our method allows for the placement of the arch beams on the boundary or within a surface and partitions the surface into multiple self-supporting parts. The use of arch beams enhances stability and durability, adds aesthetic appeal, and allows for greater flexibility in the design process. We develop an iterative algorithm for designing selfsupporting surfaces with arch beams that enables the user to control the shape of the beams and surface through intuitive parameters and specify the desired location of the arch beams. We verify the physical stability of the structure using finite element analysis. Experimental results show that our method can produce visually pleasing self-supporting surfaces that satisfy the equilibrium equation with high accuracy. IEEE

关键词： Surface treatment

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic science and Technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

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computers, Materials & Continua 2024年第2期78卷 2067-2086页

作者： Sherly Alphonse Harshit Verma School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia

Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging *** innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed *** combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network *** cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection *** seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,*** results demonstrate the advantage of the proposed work over cutting-edge techniques.

关键词： Emotion classification CNN network HR-LDP

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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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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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X-ray Security Inspection Prohibited Items Detection Model based on Improved YOLOv7-tiny

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1279-1287页

作者： Teng, Wenzhao Zhang, Haigang Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Polytechnic University Shenzhen China Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational complexity, most current object detection models are challenging to deploy on portable mobile security inspection devices. Therefore, this paper proposes an improved YOLOv7-tiny model for application in prohibited item detection tasks. Firstly, the feature extraction backbone network is replaced with the lightweight GhostNet network to reduce computational complexity and improve detection speed. Secondly, the FPN in the Neck is replaced with BiFPN, further reducing computational complexity and memory access through skip connections. Finally, a lightweight CA attention mechanism is embedded between the Backbone and Neck layers, and the Focal-EIoU Loss function is employed to enhance the detection capability for small-sized items. Experimental results on the SIXray public dataset show a 14.8% reduction in model parameters, a 19.7% reduction in computational complexity, and a 15.9% reduction in volume after the improvements. The detection speed increases from 82.4 to 90.2, and the detection accuracy for prohibited items reaches 90.3%. The experimental results demonstrate that the improved model achieves overall lightweighting while maintaining a high detection rate and improving detection speed. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Computational complexity

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