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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Jiang, Bo Liang, Xu Wang, Longkan Zhang, Zhifan Wang, Yuemei State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China CSSC Systems Engineering Research Institute Beijing100080 China China Ship Research and Development Academy Beijing100192 China

ISBN: (纸本)9783031687747

Nowadays, sailing in formation has been the major trend with the rise of unmanned ships. In the past, research about unmanned catamaran formation were more focused on algorithmic control, but the changes in hydrodynamic performance and energy are often overlooked. In nature, various collective behaviors indicate that followers would obtain significant energy consumption from beneficial interaction in certain formation configurations. Based on the previous research, the high-speed catamaran delft-372 was chosen as the basic numerical model in this paper. Four ship’s formation configurations (tandem, "trimaran", and rectangle formations) are proposed to analyze the influence on the hydrodynamic performance of each ship, based on the Reynolds Averaged Navier-Stokes (RANS) method in combination with the Shear Stress Transport (SST) k-ω turbulence model. Through comparative analysis, it can be seen that the resistance and motion of the following ships have been optimized under specific formation configurations, but the impact of various formation configurations is quite different. In terms of single ship’s energy preservation, the last following ship of "trimaran" formation has the best performance. For the whole, tandem formation is the optimal fleet configuration. In conclusion, the hydrodynamic performance of four catamarans in different formation configurations has been studied, which can provide some reference significance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Drag reduction

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Method for controlling phytoplankton distribution in fresh open water

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International Journal of Environmental Studies 2024年第5期81卷 2130-2147页

作者： Moldasheva, Raushan Ismailova, Aisulu Turmukhanova, Gulnur Mailybayeva, Aiman Yelezhanova, Shynar Zhanuzakova, Zulfiya Zhamangara, Aizhan Department of Information Systems Faculty of Computer Systems and Vocational Education S. Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Software Engineering Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Department of Computer Science Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Laboratory of Palaeobotany Astana Botanical Garden branch of the Institute of Botany and Phytointroduction Astana Kazakhstan

This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in fresh open water using a gradient descent approach with the search for a starting point for research. The paper also presents the reconstructed biomass indicators for changes in water temperature, with model calibration based on results from 5 years of surface sampling. A special feature of the proposed computer data processing is the cubic spline interpolation, which allows a higher calculation accuracy. This method reduces the sample size required, providing a comprehensive picture of the condition of the infrastructure and allowing for a more efficient response to emerging ecosystem issues. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Phytoplankton

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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Computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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ALIGNING FIRST, THEN FUSING: A NOVEL WEAKLY SUPERVISED MULTIMODAL VIOLENCE DETECTION METHOD

arXiv

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arXiv 2025年

作者： Jin, Wenping Zhu, Li Sun, Jing School of Software Engineering Xi’an Jiaotong University Xi’an China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Weakly supervised violence detection refers to the technique of training models to identify violent segments in videos using only video-level labels. Among these approaches, multimodal violence detection, which integrates modalities such as audio and optical flow, holds great potential. Existing methods in this domain primarily focus on designing multimodal fusion models to address modality discrepancies. In contrast, we take a different approach—leveraging the inherent discrepancies across modalities in violence event representation to propose a novel multimodal semantic feature alignment method. This method sparsely maps the semantic features of local, transient, and less informative modalities (such as audio and optical flow) into the more informative RGB semantic feature space. Through an iterative process, the method identifies the suitable non-zero feature matching subspace and aligns the modality-specific event representations based on this subspace, enabling the full exploitation of information from all modalities during the subsequent modality fusion stage. Building on this, we design a new weakly supervised violence detection framework that consists of unimodal multiple-instance learning for extracting unimodal semantic features, multimodal alignment, multimodal fusion, and final detection. Experimental results on benchmark datasets demonstrate the effectiveness of our method, achieving an average precision (AP) of 86.07% on the XD-Violence dataset. Our code is available at https://***/xjpp2016/MAVD. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Lamya Benaddi Charaf Ouaddi Lahbib Naimi Adnane Souha Abdeslam Jakimi Brahim Ouchao Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction.

关键词： Decision support systems Sentiment analysis Social networking (online) Reviews Soft sensors Transportation User interfaces User experience Driver behavior Usability

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Big Data Bot with a Special Reference to Bioinformatics

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Computers, Materials & Continua 2023年第5期75卷 4155-4173页

作者： Ahmad M.Al-Omari Shefa M.Tawalbeh Yazan H.Akkam Mohammad Al-Tawalbeh Shima’a Younis Abdullah A.Mustafa Jonathan Arnold Biomedical Systems and Informatics Engineering Department Yarmouk UniversityIrbid21163Jordan Department of Medicinal Chemistry and Pharmacognosy Yarmouk UniversityIrbid21163Jordan Department of Electrical Computer and Software EngineeringUniversity of Ontario Institute of TechnologyOshawaL1H7K4Canada Department of Mechanical Engineering University of MosulMosul41001Iraq Genetics Department University of GeorgiaAthens30602GAUSA

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential *** scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough *** data should go through several processes,including mining,data processing,analysis,and *** study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human *** software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line *** software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel ***,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data *** a result,data collection from the web will become faster and less expensive,with no need for manual data *** software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

关键词： Bioinformatics big data data extraction bot drug design

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On the Influence of Grid Cell Size on Taxi Demand Prediction 8th

On the Influence of Grid Cell Size on Taxi Demand Predict...

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8th EAI International Conference on Smart Objects and Technologies for social Goods, GOODTECHS 2022

作者： Korth, Merlin Schleibaum, Sören Müller, Jörg P. Ehlers, Rüdiger Department of Informatics Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany Institute for Software and Systems Engineering Clausthal University of Technology Julius-Albert-Straße 4 Clausthal-Zellerfeld38678 Germany

ISBN: (纸本)9783031288128

Accurate taxi demand prediction has the potential to increase customer satisfaction and hence the usage of ride-sharing by predicting the number of taxis needed at a certain place and time. When reviewing the related work on demand prediction, we observed that in taxi demand prediction different grid topologies – e.g. rectangular subdivisions of an area – and sizes are applied. However, it is not clear how and why the grid cells are configured the way they are and a systematic comparison of different topologies and sizes as regards their influence on urban demand prediction is lacking. In this paper, we compare the influence of different grid cell sizes – 250 m, 500 m, and 1000 m – on the prediction accuracy of different types of deep learning-based taxi demand prediction approaches, such as convolutional neural networks, recurrent neural networks, and graph neural networks. Therefore, we select five deep learning-based approaches from related work and evaluate their performance on the New York City TLC taxi trip dataset and three different evaluation metrics. Our results show that approaches with a grid cell of size 1000 m and 500 m achieve a higher prediction accuracy. Furthermore, we propose to consider the grid cell size as a tunable parameter in demand prediction models. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Recurrent neural networks

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MRUIE: Multi-Reference Mapping for Underwater Image Enhancement

MRUIE: Multi-Reference Mapping for Underwater Image Enhancem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tonglin Cheng Juan Li Jintao Luo Zhihui Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Engineering Research Center for loT Software and Systems Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In complex underwater environments, the image quality will be damaged due to light absorption, water quality and other factors, which brings certain challenges to the underwater object detection task. In order to improve the quality of under-water images, as well as the effectiveness of underwater detection tasks, we proposed a multi-reference mapping based image enhancement network (MRUIE). Previous image enhancement methods set only one reference image for the original image to be learned, ignoring the diversity of real underwater images. We optimized for the salient problems of underwater images by allowing the network to learn multiple reference images to capture the uncertainty of real underwater images. First, for the prominent problems of color distortion, contrast imbalance, and low brightness and darkness in underwater images, we designed three optimization paths to generate three reference images respectively. Then, the three reference images are fed into the feature extraction network to construct the statistical distribution of features and generate a series of potential enhancement distributions. Finally, based on the enhancement distributions, Monte Carlo likelihood estimation is used to determine the final enhancement results. Experiments conducted on two datasets demonstrate that our proposed image enhancement algorithm can effectively enhance the restoration of the original underwater images and provide significant performance improvement in underwater vision tasks.

关键词： Image quality Monte Carlo methods Image color analysis Statistical distributions Water quality Feature extraction Image restoration

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Intelligent Recognition of Geometric Tolerance Symbol Annotation Blocks

Intelligent Recognition of Geometric Tolerance Symbol Annota...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Congxin Li Yuanping Xu Chaolong Zhang Chao Kong Jin Jin Weiye Wang Zhijie Xu Benjun Guo Dan Tang School of Software Engineering Chengdu University of Information Technology Chengdu China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In previous mechanical design drawings, the geometric tolerance symbol annotation block was directly annotated on the mechanical design drawing by engineers, resulting in the symbol annotation block being non-editable and non-shareable. To achieve digitization, this study proposes an improved Swin_YOLOv5_GE model based on YOLOv5 to realize the positioning of the symbol annotation block. Compared with the original YOLOv5, the accuracy is increased by 4%. Then, image processing is performed on the located and cropped symbol annotation block to achieve denoising and tilt correction of the symbol annotation block. Finally, it is input into CRNN for the recognition of the symbol annotation block. The final experimental results prove that the method in this paper can obtain an overall recognition accuracy of 94%, proving the feasibility of this method.

关键词： YOLO Accuracy Annotations Noise reduction Noise Symbols Mechanical variables measurement Mechanical products Manufacturing Mechanical engineering

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Charaf Ouaddi Lamya Benaddi Adnane Souha Lahbib Naimi El mahi Bouziane Abdeslam Jakimi Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism.

关键词： Decision support systems Codes Source coding Medical services User interfaces Syntactics Real-time systems Complexity theory DSL software engineering

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