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2024 International Conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Kang, Yuxiu Zhu, Yaling Wang, Jundi Clinic of Logistics Management Lanzhou Institute of Technology Gansu Lanzhou730050 China School of Computer Science & Artificial Intelligence Lanzhou Institute of Technology Gansu Lanzhou730050 China

ISBN: (纸本)9781510685437

The few-shot intestinal polyp image semantic segmentation aims to segment unseen targets in the query image with only a few pixel-by-pixel annotated support images. However, existing few-shot intestinal polyp image semantic segmentation methods mainly mine valuable guidance information from the support branch, ignoring the role played by the task target information in the query branch in improving model performance. In this paper, a few-shot intestinal polyp image semantic segmentation method based on multi-scale cross fusion attention is proposed. Firstly, the pre-trained convolutional neural network is used to map the images on both branches into the same feature space, and the multi-scale information on different branches is extracted respectively. Then, cross attention is used to establish the scale fusion between the multi-scale information of the support branch and the query branch, promoting the semantic alignment of features between branches. Finally, the similarity values between the encoding features at each position on the prototype set and the query image are calculated using a parameter-free metric method, and the unseen target area in the query image is segmented according to the similarity value. Extensive experiments on open-source intestinal polyp image dataset demonstrate the superiority of the designed method. © 2024 SPIE.

关键词： Semantic Segmentation

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Software Architecture for Process Simulation in Biogas Plants 14

Software Architecture for Process Simulation in Biogas Plant...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Melnyk, Andriy Maslyiak, Yurii Popovych, Pavlo Sitkar, Taras Momotiuk, Evhenii Kostyk, Bohdan West Ukrainian National University Department of Computer Science Ternopil Ukraine West Ukrainian National University Department of Transport and Logistics Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine

ISBN: (纸本)9798350350036

The work substantiates the use of modern information technologies and mathematical modeling based on the analysis of interval data as effective process management tools in biogas plants. The use of interval modeling allows you to build models with guaranteed prognostic properties. The article also presents the features of building the software architecture for mathematical modeling of processes in biogas plants. The main aspects of the implementation of software for modeling processes in biogas plants are described, the feature of which is the software interpretation of interval discrete models and an open service-oriented architecture, which ensures their integration into existing control systems of biogas plants and allows to control biochemical processes in real time and thereby increase efficiency of functioning of biogas plants. A number of diagrams illustrating the features of the software implementation of the environment for simulating processes in biogas plants are given, and the specifics of its implementation into the SIMBA#biogas universal system of simulating and simulating the operation of biogas plants are described. © 2024 IEEE.

关键词： Service oriented architecture (SOA)

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Siamese Networks for RF-Based Vehicle Trajectory Prediction 17

Siamese Networks for RF-Based Vehicle Trajectory Prediction

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17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023

作者： Wijesinghe, Anjana Ambegoda, Thanuja D. Perera, Amal Shehan Sivakumar, Thillaiampalam University of Moratuwa Dept of Computer Science & Engineering Moratuwa Sri Lanka University of Moratuwa Dept of Transport & Logistics Management Moratuwa Sri Lanka

ISBN: (纸本)9798350323634

Traffic surveys monitor the traffic flow to generate data used in improving traffic management. These surveys were traditionally carried out solely by manpower. With the improvement of video analysis, traffic surveys have been shifting towards using automated processes. Surveys carried out by manpower are ineffective unless the surveyors are professionally trained while using video footage requires expensive systems, large digital storage space, and bandwidth for IoT applications. Smart city concepts rely heavily on real-time data from many systems to operate and would benefit from lower computational power, power to operate, and bandwidth requirements. This research investigates the use of radio frequencies (RF) to capture vehicles moving through an intersection. RF data has an inherent nature of being more privacy-preserving than video and requiring less digital storage space and bandwidth while having all the benefits of using video for this purpose. Multiple machine learning models were experimented with for vehicle detection where long short term memory neural networks achieved the best result and was used to detect the presence of vehicles. The vehicle trajectory prediction algorithm used the data of the detected vehicles to predict the trajectory using the similarity between records where siamese neural networks with triplet training outperformed other methods. The data generated can be used to compute metrics for the vehicle occupancy of an intersection. This research enables traffic surveys and real-time monitoring to be carried out with minimal manpower using low-cost low-powered devices that generate smaller sized data samples. © 2023 IEEE.

关键词： Machine learning

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Advancing Plant Identification Through Leaf Image Analysis: A Comprehensive Review of Literature and Techniques

Advancing Plant Identification Through Leaf Image Analysis: ...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Prasad, Pulicherla Siva Senthilrajan, A. Alagappa University Dept. of Computer Science Tamil Nadu India Alagappa University Dept. of Computational Logistics Tamil Nadu India

ISBN: (纸本)9798350329186

Advent of the machine learning and computer vision practices has enabled significant growth in the arena of plant prediction from leaf images. The ability to accurately predict plant species and health status based on leaf characteristics has numerous applications in agriculture, environmental monitoring, and biodiversity conservation. This study mainly concentrates on exploring the recent advancements in machine learning for plant prediction using leaf images. This paper begins by discussing the importance of plant prediction and the challenges associated with it. Various image acquisition techniques and datasets commonly used in this domain are presented, highlighting their strengths and limitations. Subsequently, a comprehensive analysis of the various phases of plant prediction, including leaf image preprocessing, feature extraction, and classification is provided. Key techniques and algorithms utilized in each stage are discussed, ranging from traditional computer vision methods to deep learning approaches. The survey then delves into the evaluation metrics and benchmark datasets commonly employed to assess the performance of plant prediction models. Comparative analyses of existing studies are presented, highlighting their strengths and limitations. Furthermore, challenges and open research problems in accurate plant prediction are identified, such as the need for larger and more diverse datasets, interpretability of deep learning models, and robustness to varying environmental conditions. At the end, this survey concludes with a discussion of potential future research directions and emerging trends in the field. These include the integration of multi-modal data sources, such as hyper-spectral and thermal imaging, the application of transfer learning techniques for domain adaptation, and the use of explainable AI methods to enhance model interpretability and trustworthiness. Indeed, this survey paper serves as a comprehensive guide for researchers, practitione

关键词： Forecasting

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Semantic-Driven Video Question Answering with Fine-Grained Spatio-Temporal Interaction

Semantic-Driven Video Question Answering with Fine-Grained S...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Zhang, Jiawei Yang, Qiuxiang Pang, Shurong Shen, Yuanyuan Wang, Si Zhang, Fuwei School of Software North University of China China School of Computer Science and Technology North University of China China Anc Global Logistics Pty Ltd BardiaNSW2214 Australia

ISBN: (纸本)9798331509873

Research has been conducted on a critical issue in Video Question Answering (VideoQA): how to effectively capture spatiotemporal information from videos for accurate reasoning while understanding natural language questions. Previous studies predominantly employed pretraining networks for images and actions to extract global features. However, due to the lack of fine-grained features, VideoQA performance was constrained. To address this limitation, object pretraining networks were introduced to capture fine-grained spatiotemporal features by extracting object features from video frames. Nevertheless, the current approach still neglects sufficient consideration of semantic interactions between objects and actions in videos, limiting spatiotemporal reasoning capabilities. To overcome this challenge, a semantic-driven fine-grained spatiotemporal interaction approach for VideoQA has been proposed. This method significantly enhances VideoQA performance by supplementing semantic interaction information between objects and actions with image description information. Additionally, to effectively address cross-modal semantic alignment issues, a Bottleneck Transformer has been introduced. It achieves the fusion of different modality features through learnable parameters and backpropagation, bringing new perspectives and methods to the field of VideoQA. © 2024 IEEE.

关键词： Spatio-temporal data

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Analysis of Extreme Diurnal Temperature Range: Based on Generalized Extreme Value Model 21

Analysis of Extreme Diurnal Temperature Range: Based on Gene...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yinqi, Luo Liyin, Liu Guoyi, Xie Sui, Luo Suhang, Li Graduate School University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Logistics Support Department Chengdu611731 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China

ISBN: (纸本)9798331519254

Cengong County is a traditional agricultural county in Guizhou province. Extreme temperature difference between day and night has a significant impact on the development of local agriculture. In order to analyze the distribution characteristics of extreme day-night temperature difference in the local area, a Generalized Extreme Value(GEV) model was constructed. Firstly, according to the relevant theory of GEV modeling, this paper extracts the daily temperature difference between day and night in Cengong County from 1960 to 2022, takes one year as the grouping unit, and calculates the maximum value in each group, so as to obtain the sample data needed for modeling. Then, the Maximum Likelihood Estimation(MLE) and Profile Likelihood Estimation(PLE) are used to solve the model parameter estimates. Secondly, the grouping length has a significant impact on the accuracy of the model, so the stability of the model under different grouping intervals is analyzed. The last, the return level is calculated under the different return period. The results showed that the maximum temperature difference between day and night in Cengong County obeyed Weibull distribution with an upper limit, and the theoretical upper limit was 26.819C°. The model had good extrapolation ability. © 2024 IEEE.

关键词： Weibull distribution

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YOLOv7 versus YOLOv8: A Comparative Study on Traffic Sign Detection Accuracy in Real-World Images 8

YOLOv7 versus YOLOv8: A Comparative Study on Traffic Sign De...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Wojciechowski, Witold Nowakowska, Leslawa M. Zajac, Zofia G. Kaczmarek, Walenty J. Boz, Ilayda University of Radom Department of Transport & Logistics Masovian Voivodeship Poland University of Radom Department of Computer Science Masovian Voivodeship Poland Tarsus University Aerospace Engineering Department Mersin Turkey

ISBN: (纸本)9798331531492

Advancements in technology have spurred the development of autonomous driving systems, necessitating new innovations to enhance road safety and address transportation challenges. This study evaluates and compares the performance of YOLOv7 and YOLOv8 models from the YOLO family in detecting traffic signs, which are crucial for road safety. Using a comprehensive dataset sourced from the Google Earth platform, the research examines the limitations and successes of both models. YOLOv8 demonstrates notable performance in traffic sign detection under challenging conditions, with precision, recall, and mAP values of 0.866,0.815, and 0.807, respectively. While YOLOv7 shows competitive results, it falls short of YOLOv8 in difficult scenarios. The study provides a detailed analysis of both algorithms, exploring their strengths, weaknesses, and areas for potential improvement in traffic sign detection. © 2024 IEEE.

关键词： Contrastive Learning

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LOCAL OPTIMA IN DIVERSITY OPTIMIZATION: NON-TRIVIAL OFFSPRING POPULATION IS ESSENTIAL

arXiv

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arXiv 2024年

作者： Antipov, Denis Neumann, Aneta Neumann, Frank Optimisation and Logistics School of Computer Science The University of Adelaide Adelaide Australia

The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such tasks, since they are naturally designed to optimize populations of solutions. This approach to diversity optimization, called EDO, has been previously studied from theoretical perspective, but most studies considered only EAs with a trivial offspring population such as the (µ + 1) EA. In this paper we give an example instance of a k-vertex cover problem, which highlights a critical difference of the diversity optimization from the regular single-objective optimization, namely that there might be a locally optimal population from which we can escape only by replacing at least two individuals at once, which the (µ + 1) algorithms cannot do. We also show that the (µ + λ) EA with λ ≥ µ can effectively find a diverse population on k-vertex cover, if using a mutation operator inspired by Branson and Sutton (TCS 2023). To avoid the problem of subset selection which arises in the (µ + λ) EA when it optimizes diversity, we also propose the (1µ + 1µ) EAD, which is an analogue of the (1 + 1) EA for populations, and which is also efficient at optimizing diversity on the k-vertex cover problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Towards a Unified Flow Description Language for CPPSs: An Example with Material Flows 29

Towards a Unified Flow Description Language for CPPSs: An Ex...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Gödeke, Jana Hörstrup, Maximilian Detzner, Peter Fraunhofer Institute for Material Flow and Logistics Department for Ai and Autonomous Systems Dortmund Germany Ruhr University Bochum Faculty of Computer Science Distributed and Networked Systems Group Bochum Germany

ISBN: (纸本)9798350361230

This paper presents a unified approach for repre-senting multiple domains alongside production in cyber-physical production systems (CPPSs) through domain-specific languages (DSLs). The approach is illustrated using material flows (MFs) as an example. The paper identifies requirements for DSLs in CPPSs and MFs, including concurrency, synchronization, constraints, and heterogeneity support. Subsequently, a plugin system is in-troduced for the existing Production Flow Description Language (PFDL), which allows users to incorporate additional domain-specific functionality in the form of plugins. To demonstrate this approach, we present the MF plugin, resulting in the combined PFDLMF. This showcases the reusability of such an approach and the easy integration of new CPPS domains. We envision the convergence of the PFDL with plugins towards a unified flow description language for CPPSs. A practical CPPS use-case demonstrates the expressiveness of the PFDLMF in modeling and executing complex MFs in an agent-based architecture. © 2024 IEEE.

关键词： Reusability

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