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Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

computers, Materials & Continua 2023年第3期74卷 5507-5525页

作者： Subhajit Chatterjee Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-si63243Korea Department of Computer Engineering Major of Electronic EngineeringJeju National UniversityInstitute of Information Science&TechnologyJeju63243Korea

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education-centric *** the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is *** to its freefloating nature,the shared electric kickboard is a common and practical means of *** plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is *** demand accurately with small data is *** data is necessary for training machine learning algorithms for effective *** generation is a method for expanding the amount of data that will be further accessible for *** this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original *** proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction *** modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster ***,we applied a regression-based blending ensemble technique that can help us to improve performance of demand *** used various evaluation criteria and visual representations to compare our proposed model’s *** data generated by our suggested GAN model is also *** TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.

关键词： Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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A Multi-label Classification Method for Positive and Unlabeled Dataset. 4

A Multi-label Classification Method for Positive and Unlabel...

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4th Asia Service Sciences and Software engineering Conference, ASSE 2023

作者： Takahashi, Hikaru Qiangfu, Zhao University of Aizu Computer Science and Engineering Department Major in Computer Information System Japan University of Aizu Computer Science and Engineering Department Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning. © 2023 ACM.

关键词： Machine learning

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YOLO-CRD:A Lightweight Model for the Detection of Rice Diseases in Natural Environments

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Phyton-International Journal of Experimental Botany 2024年第6期93卷 1275-1296页

作者： Rui Zhang Tonghai Liu Wenzheng Liu Chaungchuang Yuan Xiaoyue Seng Tiantian Guo Xue Wang Tianjin Key Laboratory of Intelligent Breeding of Major Crops Tianjin Agricultural UniversityTianjin300392China College of Computer and Information Engineering Tianjin Agricultural UniversityTianjin300392China

Rice diseases can adversely affect both the yield and quality of rice crops,leading to the increased use of pesticides and environmental *** detection of rice diseases in natural environments is crucial for both operational efﬁciency and quality *** learning-based disease identiﬁcation technologies have shown promise in automatically discerning disease ***,effectively extracting early disease features in natural environments remains a challenging *** address this issue,this study proposes the YOLO-CRD *** research selected images of common rice diseases,primarily bakanae disease,bacterial brown spot,leaf rice fever,and dry tip nematode disease,from Tianjin *** proposed YOLO-CRD model enhanced the YOLOv5s network architecture with a Convolutional Channel Attention Module,Spatial Pyramid Pooling Cross-Stage Partial Channel module,and Ghost *** former module improves attention across image channels and spatial dimensions,the middle module enhances model generalization,and the latter module reduces model *** validate the feasibility and robustness of this method,the detection model achieved the following metrics on the test set:mean average precision of 90.2%,accuracy of 90.4%,F1-score of 88.0,and GFLOPS of *** the speciﬁc diseases,the mean average precision scores were 85.8%for bakanae disease,93.5%for bacterial brown spot,94%for leaf rice fever,and 87.4%for dry tip nematode *** studies and comparative analyses veriﬁed the effectiveness and superiority of the proposed *** researchﬁnd-ings can be applied to rice disease detection,laying the groundwork for the development of automated rice disease detection equipment.

关键词： Convolutional neural network one stage training rice disease deep learning

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Review Rating Prediction Using BiLSTM 14th

Review Rating Prediction Using BiLSTM

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14th International Conference on Frontier Computing, FC 2024

作者： Kim, Yongjun Lee, Sang-Joon Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

User review ratings are crucial for assessing the quality of restaurants, movies, products, and services online. Traditionally, predicting these ratings involved analyzing the content of user reviews using natural language processing techniques. While this approach allows for the consideration of diverse data by aggregating reviews from all users, it's hindered by the fact that users have unique tendencies and expressions, resulting in varying ratings for the same review. To address this challenge, a novel method is proposed in this paper: a BiLSTM model that incorporates the user's average rating alongside their review content. By doing so, this method aims to enhance prediction accuracy by accounting for individual user characteristics. This approach considers the textual content of reviews and integrates the user's typical rating behavior, resulting in more personalized and precise rating predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Multi-Fault Classification Method for Wind Turbine with Class Imbalance Data 14th

Multi-Fault Classification Method for Wind Turbine with Clas...

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14th International Conference on Frontier Computing, FC 2024

作者： Chatterjee, Subhajit Gil, Joon-Min Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Data-driven fault classification is critical for wind turbine condition monitoring systems. However, imbalanced class distributions, where certain fault types are significantly less frequent than others, pose a challenge for traditional classifiers. This work addresses this issue by investigating the combined effect of oversampling with Synthetic Minority Oversampling Technique (SMOTE) and undersampling with Tomek Links for data cleaning, followed by a stacking ensemble learning approach. We evaluate the proposed method on a multiclass wind turbine fault dataset and demonstrate its effectiveness in improving classification performance compared to baseline methods. Our findings highlight the potential of the proposed approach for enhancing fault classification accuracy in wind turbine systems plagued by imbalanced data. The classification performance is evaluated using metrics like confusion matrix, precision, recall, and F1-score, demonstrating significant improvements compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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Enhancing Tomato Leaf Disease Classification with Augmentation Techniques in Deep Learning 14th

Enhancing Tomato Leaf Disease Classification with Augmentati...

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14th International Conference on Frontier Computing, FC 2024

作者： Debnath, Anik Gil, Joon-Min Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Inistitute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Tomato leaf diseases pose a significant threat to crop yield and agricultural productivity. Addressing the challenge of automatic detection and classification of diseases found in tomato leaves through the utilization of Convolutional Neural Networks (CNNs), a subset of deep neural networks. Initially, the dataset is augmented to address the issue of limited labeled data, employing traditional data augmentation methods to balance and enlarge the dataset. Exploring the effectiveness of various fine-tuned pre-trained models, including VGG-16, VGG-19, InceptionV3, EfficientNetB7, and ResNet-50 for classifying tomato leaf diseases. Ten common diseases affecting tomato leaves are classified, with the dataset expanded fourfold through data augmentation. Experiments demonstrate that the ResNet-50 model achieves the highest accuracy of 96.15% in disease classification. Through a detailed analysis, evaluating the performance of each technique and providing insights into their effectiveness for enhancing disease classification in tomato plants. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in engineering & Sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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The Performance Analysis of Model Parallelism in Deep Residual Network 3

The Performance Analysis of Model Parallelism in Deep Residu...

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3rd International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2022

作者： Feng, Yuqin Major in Computer Science Department of Engineering Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9781510661301

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore, the paper summarises the Residual Network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16. © 2023 SPIE.

关键词： Graphics processing unit

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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