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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

共 20366 条记录，以下是921-930 订阅

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Research on methods for evaluating the health condition of power batteries 9

Research on methods for evaluating the health condition of p...

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9th International conference on Energy Materials and Electrical engineering, ICEMEE 2023

作者： Pi, Xingwang Liu, Qian School of Vehicle Applications Hunan Automobile Engineering Vocational College No. 79 Wisdom Road Shifeng District Hunan Province Zhuzhou City China

ISBN: (纸本)9781510672765

With the increasing demand for electric vehicles and energy storage systems, the health condition evaluation of power batteries has become a critical research area. this paper presents a comprehensive study on methods for assessing the health condition of power batteries. the research focuses on both feature-based and data-driven approaches, aiming to provide accurate and efficient evaluations without requiring in-depth knowledge of battery reaction mechanisms. Various parameters and indicators are analyzed and utilized to quantify the health condition of power batteries. Additionally, advanced techniques such as machine learning and artificial intelligence are employed to develop predictive models for estimating the health condition. the proposed evaluation methods contribute to improving battery recycling rates and ensuring the proper disposal of lithium-ion power batteries. the results of this research provide valuable insights for the development of effective and reliable health condition evaluation methods for power batteries. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Batteries Resistance Neural networks Circuit switching Dielectric spectroscopy Education and training Machine learning Error analysis Industry Polarization

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Study on the Effectiveness of New Development Methods in software engineering based on Experiments with AB Testing 24

Study on the Effectiveness of New Development Methods in Sof...

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9th International conference on Cyber Security and Information engineering, ICCSIE 2024

作者： Chen, Lei Zhang, Difeng Li, Simu School of Computer Science and Engineering Wuhan Institute of Technology China Hubei Key Laboratory of Intelligent Robot Wuhan Institution of Technology China School of Electrical and Information Engineering Wuhan Institute of Technology China Economic and Business Department College of Post and Telecommunication of Wuhan Institute of Technology China

ISBN: (纸本)9798400718137

this study aims to evaluate the effectiveness of an emerging software engineering development method through AB testing experiments. As a commonly used online experimental method, AB testing is used to compare the performance of two or more solutions. It has been widely used in the software development process to optimize user experience and function implementation. this study proposes a new development framework that combines AB testing and software testing, which quantifies the impact of different development strategies on software quality by constructing a correlation matrix. In order to verify the effectiveness of this method, this paper designed a series of experiments and implemented AB testing in multiple software projects. Experimental results show that by accurately adjusting the variable combinations in AB testing, software stability and user satisfaction can be significantly improved. In addition, this paper also develops a set of computer algorithm models to simulate different development scenarios and predict the potential benefits of new development methods through simulation technology. through the analysis of simulation results, this article found that projects using new development methods have improved in key performance indicators, especially in shortening development cycles and reducing defect rates. the development method that combines AB testing and software testing not only improves the quality of software products, but also enhances the flexibility and response speed of the development process. © 2024 Copyright held by the owner/author(s).

关键词： software quality

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Multi-Modal Medical Image Fusion based on cGAN and the Curvelet Transform with 3D Imaging 9

Multi-Modal Medical Image Fusion Based on cGAN and the Curve...

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9th International conference on Communication and Electronics Systems, ICCES 2024

作者： Senthil Murugan, K.R. Harini, S. Jayabharathi, P. Shekkina Paulin, J. Vishal Oviya, S. Velammal College of Engineering and Technology Department of Cse Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

A novel Multi-modal Medical Image Fusion technique combining Conditional Generative Adversarial Networks (ConditionalGenerative Adversarial Networks (cGAN)) and the Curvelet Transform for 3D medical images. the Curvelet Transform decomposes input images into multi-scale and multi-directional features, capturing essential edges and textures, while the cGAN (Conditional Generative Adversarial Networks), conditioned on the modality type, intelligently fuses complementary information from different medical scans such as CT, MRI, and PET. By incorporating 3D imaging, the method enhances spatial coherence and volumetric accuracy, making it particularly effective for complex cases like brain imaging and tumor detection. Experimental results on multi-modal medical datasets demonstrate superior image quality with improved edge preservation, texture detail, and contrast, outperforming traditional fusion techniques. this method offers a powerful tool for enhancing diagnostic accuracy and supporting clinical decision-making through clearer and more comprehensive fused images. © 2024 IEEE.

关键词： Image fusion

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Multi-step Iterative Automated Domain Modeling with Large Language Models 24

Multi-step Iterative Automated Domain Modeling with Large La...

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ACM/IEEE 27th International conference on Model Driven engineering Languages and Systems: Companion proceedings (MODELS)

作者： Yang, Yujing Chen, Boqi Chen, Kua Mussbacher, Gunter Varro, Daniel McGill Univ Montreal PQ Canada Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798400706226

Domain modeling, which represents the concepts and relationships in a problem domain, is an essential part of software engineering. As large language models (LLMs) have recently exhibited remarkable ability in language understanding and generation, many approaches are designed to automate domain modeling with LLMs. However, these approaches usually formulate all input information to the LLM in a single step. Our previous single-step approach resulted in many missing modeling elements and advanced patterns. this paper introduces a novel framework designed to enhance fully automated domain model generation. the proposed multi-step automated domain modeling approach extracts model elements (e.g., classes, attributes, and relationships) from problem descriptions. the approach includes instructions and human knowledge in each step and uses an iterative process to identify complex patterns, repeatedly extracting the pattern from various instances and then synthesizing these extractions into a summarized overview. Furthermore, the framework incorporates a self-reflection mechanism. this mechanism assesses each generated model element, offering self-feedback for necessary modifications or removals, and integrates the domain model with the generated self-feedback. the proposed approach is assessed in experiments, comparing it with a baseline single-step approach from our earlier work. Experiments demonstrate a significant improvement over our earlier work, with a 22.71% increase in the F-1-score for identifying classes, 75.18% for relationships, and a 10.39% improvement for identifying the player-role pattern, with comparable performance for attributes. Our approach, dataset, and evaluation provide valuable insight for future research in automated LLM-based domain modeling.

关键词： domain modeling large language models few-shot learning prompt engineering

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Where is it? Tracing the Vulnerability-Relevant Files from Vulnerability Reports 24

Where is it? Tracing the Vulnerability-Relevant Files from V...

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44th ACM/IEEE International conference on software engineering, ICSE 2024

作者： Sun, Jiamou Chen, Jieshan Xing, Zhenchang Lu, Qinghua Xu, Xiwei Zhu, Liming Csiro SydneyNSW Australia Csiro & Australian National University CanberraACT Australia

ISBN: (纸本)9798400702174

With the widely usage of open-source software, supply-chain-based vulnerability attacks, including SolarWind and Log4Shell, have posed significant risks to software security. Currently, people rely on vulnerability advisory databases or commercial software bill of materials (SBOM) to defend against potential risks. Unfortunately, these datasets do not provide finer-grained file-level vulnerability information, compromising their effectiveness. Previous works have not adequately addressed this issue, and mainstream vulnerability detection methods have their drawbacks that hinder resolving this gap. Driven by the real needs, we propose a framework that can trace the vulnerability-relevant file for each disclosed vulnera-bility. Our approach uses NVD descriptions with metadata as the inputs, and employs a series of strategies with a LLM model, search engine, heuristic-based text matching method and a deep learning classifier to recommend the most likely vulnerability-relevant file, effectively enhancing the completeness of existing NVD data. Our experiments confirm that the efficiency of the proposed framework, with CodeBERT achieving 0.92 AUC and 0.85 MAP, and our user study proves our approach can help with vulnerability-relevant file detection effectively. To the best of our knowledge, our work is the first one focusing on tracing vulnerability-relevant files, laying the groundwork of building finer-grained vulnerability-aware software bill of materials. © 2024 ACM.

关键词： Open source software

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Research on the Architecture of an Intelligent Assistant System for Commanders 14

Research on The Architecture of an Intelligent Assistant Sys...

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14th IEEE International conference on software engineering and Service Science, ICSESS 2023

作者： Jin, Xin Wang, Yu Wu, Wei Li, Xiaodong Science and Technology on Information System Engineering Laboratory Nanjing China

ISBN: (纸本)9798350336269

Battle commanders in modern war are facing new challenges such as massive information and short time windows. there is an urgent demand of intelligent assistant system to communicate with the commanders and staff officers, think about what they think, answer their questions, offer advises to their decision making, learn their experiences, and significantly improve their performance. based on capability requirements analysis of such system, its system architecture was proposed, which shares data, knowledge, functions and computing power with the command information system currently in use. An AI framework based technical architecture was proposed, which has five characteristics: natural human-computer interaction, flexible function integration, knowledge system driven, continues ability evolution, and powerful computing support. © 2023 IEEE.

关键词： Decision making

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the dynamic coastal landscape of Crete between the 7th and early 9th century AD 10

The dynamic coastal landscape of Crete between the 7th and e...

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10th International conference on Remote Sensing and Geoinformation of the Environment, RSCy 2024

作者： Roussos, Konstantinos Eratosthenes Centre of Excellence 82 Franklin Roosevelt Limassol3012 Cyprus

ISBN: (纸本)9781510681491

this paper intends to shed light on the changes that occurred in the coastal landscape of Crete during the period of Transformations (7th - 9th centuries AD) with a special focus on ports and harbors. It discusses how these changes reflect the changing social, economic and political conditions in the Aegean Archipelago between the mid-7th and early 9th centuries AD. It tests questions concerning how ports and harbors interacted with one another, with fortified sites, with other places across water, and with maritime navigation. Overall, considering recent historical and archaeological evidence, it aims to shed more light on the period of Transformations, highlighting certain aspects of the resilience and adaptability of the insular communities of Crete. Ports and harbors are not regarded as isolated dots on a map, but instead, they are viewed as parts of various networks with different types of interaction and as cultural products of political, social and economic circumstances. this human-centered approach puts at the center of attention people, their actions and experiences, as well as their engagement with natural environment. Maritime mobility and interaction are considered as key factors of island life. Due to the complexity of the topic, this research adopts an interdisciplinary framework which includes historical and archaeological approaches, combined with spatial analytical tools offered by the Digital Humanities (Geographical Information Systems). © 2024 SPIE.

关键词： Miocene

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A Cutting-Edge Implementation of IoT-based Monitoring for Floating Solar Cells with Dual-Axis Tracker 9

A Cutting-Edge Implementation of IoT-Based Monitoring for Fl...

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9th IEEE International Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Alam, Imtiaz Ikram, Arafat Ibne Ahmed Himu, Sheik Erfan Khandaker, Tahmid Abrar, Fahim Farzana, Niger International Islamic University Chittagong Department of Electrical and Electronic Engineering Chattogram Bangladesh Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya Japan

ISBN: (纸本)9798350319651

Solar power has emerged as a significant and vital renewable energy sector, finding diverse applications such as in commercial settings, solar heaters, and solar pumps. Its adoption has contributed to mitigating the shortage of integrated grid electricity. However, the key challenge remains in optimizing energy extraction from solar sources. A promising solution lies in the utilization of Dual Axis tracker-based solar panels, which facilitate efficient solar energy utilization. Presently, conventional solar power plants necessitate vast land areas and exhibit certain limitations. Considering the context of overpopulated countries like Bangladesh, floating solar power plants offer a more viable alternative to land-based counterparts. this paper delves into the investigation and implementation of Floating Solar Cells (FPV) equipped with dual-axis tracker technology to maximize solar energy capture. the dual-axis tracking capability allows for precise sun tracking, resulting in a significantly higher energy yield. To analyze the photovoltaic (PV) and current-voltage (IV) curves, a MATLAB Simulink model is employed, while meteorological data is collected using Homer software. the research focuses on the implementation of the project at the Teesta Barrage in Lalmonir Hat Zila. Additionally, an Internet of things (IoT) integration enables remote plant monitoring and real-time data acquisition. Our investigation concludes that dual-axis trackers represent the optimal solution for maximizing energy harvesting from available resources, and the adoption of Floating Solar Power Plants stands as an efficient means of sustainable energy generation, particularly in densely populated nations like Bangladesh. © 2023 IEEE.

关键词： Solar energy

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AI-Enhanced Endometrial Cancer Diagnosis System 9

AI-Enhanced Endometrial Cancer Diagnosis System

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9th International conference on Computer Science and engineering, UBMK 2024

作者： Kürkçü, Raziye Aslihan Baylan, Isin Yeşim Yeşilkaya Attar, Erkut Selçuk, Burcu Şerif, Tacha Yeditepe University Department of Computer Engineering Istanbul Turkey Yeditepe University Hospital Department of Obstetrics and Gynecology Istanbul Turkey

ISBN: (纸本)9798350365887

Endometrial cancer is the most prevalent type of uterine cancer, and early diagnosis is important for effective treatment. Traditional methods like biopsy and histopathological examination, are often time-consuming and prone to human error. Accordingly, this study proposes, implements and evaluates an Artificial Intelligence (AI)-based prototype utilizing deep learning algorithms for endometrial cancer diagnosis, which would increase the accuracy and the speed of cancer detection by acting as an assistant to the physicians. the proposed system leverages advanced object detection models, making use of multiple You Only Look Once (YOLO) versions, trained on a comprehensive dataset of hysteroscopy images annotated by physicians. Specialized AI models demonstrate high precision and recall rates, significantly enhancing diagnostic capabilities. Moreover, the developed system features a user-friendly interface that enable clinicians to easily upload images and employ existing trained models to detect endometrial abnormalities. After training the models, the results show that the YOLOv9c model achieves the highest mAP of 0.906 at IoU=0.5 and the highest precision of 0.894, and the YOLOv8s model achieves the best recall of 0.906. Also, expert evaluations confirm t he system's reliability, noting accuracy of models ranging from 94% to 98% and marking a substantial advancement in endometrial cancer diagnostics. © 2024 IEEE.

关键词： Image annotation

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Human Abnormal Activity Prediction of Long Short-Term Memory using CNNs and RNNs in Deep Learning 9

Human Abnormal Activity Prediction of Long Short-Term Memory...

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9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Prathima, Ch. Suhel, Shaik Muhammed Rajeti, Roje Spandana Naik, Mude Sai Kumar Chaithanya, Singamsetty Krishna Reddy, Nallabathini Prabhakar Dept. of Ds A.Rangampet Tirupati India Sree Vidyanikethan Engineering College Dept. of Information Technology A.Rangampet Tirupati India JNTUGV-College of Engineering Department of Information Technology Vizianagaram India

ISBN: (纸本)9798350365092

this research presents a unique method of predicting aberrant activity in humans by integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN). By using the advantages of CNNs for frame-by-frame spatial analysis and LSTM networks for temporal dependency modeling inside the frame sequence, our hybrid model seeks to efficiently capture both spatial and temporal characteristics in video sequences. Combining these two designs, we get a strong model that can identify complex patterns suggestive of anomalous human behavior. the CNN LSTM hybrid model that has been suggested performs better in terms of accuracy and resilience when predicting aberrant behaviors than conventional approaches and standalone models, as shown by experimental validation on a variety of benchmark datasets. the integration of both spatial and temporal information advances our knowledge of the dynamics at play in the identification of aberrant behavior. Furthermore, our approach is flexible enough to be used to real-world surveillance situations, demonstrating encouraging outcomes in scenarios with different camera viewpoints and environmental circumstances. the use of CNN and LSTM not only improves prediction accuracy but also makes the model more interpretable by shedding light on the spatiotemporal characteristics of aberrant behaviors. To sum up, this study advances the field of intelligent video analysis by providing a useful tool for predicting anomalous human behavior. this tool has potential uses in security monitoring, public safety, and anomaly detection systems. © 2024 IEEE.

关键词： Long short-term memory

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