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A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

computers, Materials & Continua 2024年第8期80卷 2363-2385页

作者： Rong Wang Xinxin Xu Zijia Wang Fei Ji Nankun Mu School of Electronic and Information Engineering South China University of TechnologyGuangzhou510641China School of Computer Science and Technology Ocean University of ChinaQingdao266100China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China School of Computer Science Chongqing UniversityChongqing400044China

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics ***,the efficiency of resource scheduling significantly influences the operation performance of *** solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm ***,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated *** new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the ***,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection *** the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible *** the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality *** evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is *** results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

关键词： Resource scheduling problem(RSP) ant colony system(ACS) marine container terminal(MCT) pre-selection strategy

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DFaker++: high-fidelity face swapping technology based on attention mechanism 3

DFaker++: high-fidelity face swapping technology based on at...

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3rd International Conference on Electronic information engineering, Big Data, and computer technology, EIBDCT 2024

作者： Shao, Wenhua Wang, Xiaodong Zeng, Zhiqiang Wu, Longpei School of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781510680449

In the era of artificial intelligence, face swapping technology has demonstrated its unique application value and broad prospects. Nevertheless, current research methods often struggle to achieve both rapidity and high quality while pursuing high-fidelity facial synthesis. In view of this, our study innovates on traditional models and integrates attention mechanisms, proposing a novel high-fidelity face swapping method1/4DFaker++. In terms of implementation, we first optimized and improved existing algorithms;secondly, by introducing an attention mechanism, the model can automatically focus on key facial areas in both source and target images, thereby more effectively preserving and blending facial features. Additionally, we employ the Huber loss function to guide model training, which shows better performance in cases with high noise or imperfect inputs. We propose a novel evaluation method for face swapping that merges objective metrics with subjective assessments, focusing on the quality of facial generation. Our experiments demonstrate that this approach outperforms existing models in both quality and efficiency, marking a significant step forward in facial synthesis technology. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Hindi Text Summarization: Using BERT

Hindi Text Summarization: Using BERT

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： More, Mukesh Yevale, Pallavi Mandwale, Abhang Agrawal, Kundan Mahale, Om Rajput, Sahilsing International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350360660

This paper points out a vacuum in the literature on text summarization: not enough attention has been paid to methods for Indian languages. This research investigates the current state-of-the-art techniques for summarizing Indian languages. The review reveals a bias in current research towards extractive summarization, which involves choosing important lines, whereas abstractive summary, which involves rewriting text, is more common. The paper advocates for a more balanced examination of both abstractive and extractive summarizing strategies specifically adapted for Indian languages by examining current methodologies and identifying their advantages and disadvantages. In certain situations, the suggested system outperforms models such as SummaRuNNer, Lead-3, NeuSumHD. These improvements improve the resilience of the model as well as the quality of the generated summaries with a ROUGE-1 score of 41.43 and ROUGE-2 score of 24.54. © 2024 IEEE.

关键词： Text mining

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Asynchronous Energy-to-peak Control for 2D Roesser-type Markov Jump Systems

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第11期51卷 1761-1768页

作者： Chen, Qing Zhang, Yu Han, Chengyi Chen, Ling Zhou, Jianping The School of Computer Science and Technology Anhui University of Technology Ma’anshan243032 China The School of Electrical and Information Engineering Wanjiang University of Technology Ma’anshan243032 China

This paper studies asynchronous energy-to-peak control for 2D Roesser-type Markov jump systems (RTMJSs). Given the practical challenge of obtaining the system state, output-feedback is utilized for closing the control loop instead of state feedback. The asynchronous behavior between the controlled plant and the controller is represented by a hidden Markov model. A sufficient condition for the stochastic stability and energy-to-peak disturbance attenuation (EPDA) of the closed-loop RTMJS is established using a Lyapunov function and Schur’s complement. Under fixed conditional probability, a scheme for asynchronous output-feedback controller design is proposed, employing variable replacement and matrix transformation to cope with the nonlinear terms. The present work is then expanded to the uncertain conditional probability scenario and a related controller design method is developed. Finally, the effectiveness of the proposed design methods is demonstrated through a thermal process. © (2024), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Efficient Steganography GAN Model with Residual Structures and GCSA Attention 2

Efficient Steganography GAN Model with Residual Structures a...

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2nd IEEE International Conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Xie, Yuhang Li, Jianmin Li, Yan College of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350373646

In the field of image steganography, the key issue has always been how to enhance the capacity and security of information hiding while maintaining image quality. This paper introduces an innovative model based on SteganoGAN that employs a Global Channel-Spatial Attention Mechanism (GCSA) and a residual structure in the decoder, significantly enhancing the effectiveness of steganography techniques. By integrating channel and spatial attention, the GCSA mechanism optimizes the recognition and processing of key features, greatly improving the precision and concealment of information hiding. Additionally, the model incorporates a residual structure in the decoder, enhancing learning capabilities and the quality of information recovery, ensuring good image quality even at high embedding rates. © 2024 IEEE.

关键词： Image quality

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, par

关键词： Magnetic resonance

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Graph convolutional network combined with random walks and graph attention network for node classification

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The Journal of China Universities of Posts and Telecommunications 2024年第3期31卷 1-14页

作者： Chen Yong Xie Xiaozhu Weng Wei College of Computer and Information Engineering Xiamen University of TechnologyXiamen 361024China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of TechnologyXiamen 361024China

Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor ***,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant *** this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise ***,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting *** model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message *** show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.

关键词： graph neural network(GNN) graph convolutional network(GCN) semi-supervised classification graph analysis

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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SiC JFETs Modular Cascaded Method with Active Clamp Control Strategy for DC Solid-state Circuit Breaker

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2435-2446页

作者： Hong Duan Wei Wang Zhikang Shuai Dong He Daixin Chen Z.John Shen the College of Electrical and Information Engineering Hunan UniversityChangsha 410006China the Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616 USA.

Power semiconductor devices cascaded topology is one of the most common solutions for solid-state circuit breakers(SSCBs)working in medium-voltage DC ***,when the number of cascaded devices is large,current series technologies of power semiconductor devices will be difficult to be applied since with uneven voltage sharing ***,this paper proposes a novel cascade method of multi-SiC JFETs based on modules *** method consists of two parts:one is a single-gate driver cascaded SiC JFETs topology which is used to form the module,the other one is an active clamp control strategy which ensures that each module is protected from overvoltage when modules are *** proposed cascade method can effectively suppress voltage overshoot of cascading power devices in the switching *** on the proposed cascade method,a 5kV/63A SSCB prototype is ***,an experiment is conducted based on the designed *** results validate the effectiveness of the proposed cascaded method for SSCB.

关键词： Active control clamp DC solid-state circuit breaker modular SiC JFETs series connection

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