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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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software Performance engineering for Foundation Model-Powered software (FMware) 25

Software Performance Engineering for Foundation Model-Powere...

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16th ACM/SPEC international conference on Performance, ICPE 2025

作者： Hassan, Ahmed E. SAIL Queen's University Kingston ON Canada

ISBN: (纸本)9798400710735

this keynote examines the transformative impact of Foundation models (FMs), particularly Large Language models (LLMs), on software development, emphasizing the critical role of software Performance engineering (SPE) in ensuring FM-powered software (FMware) achieves essential performance goals such as throughput and latency. With the LLM market projected to reach 36.1 billion by 2030 [3], addressing SPE challenges has become increasingly urgent. Drawing extensively from comprehensive literature surveys, industry-academia interactions, customer feedback, and practical experience detailed in [6]. this keynote identifies four critical SPE challenges throughout the FMware lifecycle, discusses current state-of-practice solutions, proposes future research directions, and introduces a vision for an innovative SLA-aware runtime system designed to enhance the performance and efficiency of FMware. © 2025 Owner/Author.

关键词： aiware fm-powered software fmware foundation model llm-powered software software performance engineering

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Interpretable software Maintenance and Support Effort Prediction Using Machine Learning

Interpretable Software Maintenance and Support Effort Predic...

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international conference on software engineering - Companion, ICSE, Companion

作者： Susmita Haldar Luiz Fernando Capretz School of Information Technology Fanshawe College London Ontario Canada Department of Electrical and Computer Engineering Western University London Ontario Canada

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

software maintenance and support efforts consume a significant amount of the software project budget to operate the software system in its expected quality. Manually estimating the total hours required for this phase can be very time-consuming, and often differs from the actual cost that is incurred. the automation of these estimation processes can be implemented with the aid of machine learning algorithms. the maintenance and support effort prediction models need to be explainable so that project managers can understand which features contributed to the model outcome. this study contributes to the development of the maintenance and support effort prediction model using various tree-based re-gression machine-learning techniques from cross-company project information. the developed models were explained using the state-of-the-art model agnostic technique SHapley Additive Explanations (SHAP) to understand the significance of features from the developed model. this study concluded that staff size, application size, and number of defects are major contributors to the maintenance and support effort prediction models.

关键词： software maintenance Machine learning algorithms Costs Automation Estimation Machine learning predictive models

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Optimizing Heart Failure predictive Accuracy: An Effective Approach Using SMOTE Techniques 4

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Baber, Ahmed Ahmed, Faizan Rehman, Attique Ur Javaid, Sabeen Alshammeri, Menwa Mir, Azka Kumar, Deepak University of Sialkot Department of Software Engineering Sialkot Pakistan College of Computer and Information Sciences Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. the heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. the fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. this study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. this research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. the results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies. © 2024 IEEE.

关键词： Nearest neighbor search

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predictive current control of switched reluctance motor based on extended observer 6

Predictive current control of switched reluctance motor base...

引用

6th international conference on Automation, Control and Robotics engineering, CACRE 2021

作者： Yunsheng, Fan Yue, Liu Di, Liu College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (纸本)9781665435765

In order to solve the problem of current jitter caused by current not following the given value in the traditional deadbeat current predictive control system, a current predictive control method based on extended state observer was proposed. Based on the traditional deadbeat current predictive control algorithm, an extended state observer mathematical model including current and internal and external disturbances is established, and a weight factor is introduced. the reference current in the predictive control is modified by the current observation value, and then the reference voltage is compensated by the disturbance of the control system obtained from the observation, which effectively reduces the current loop disturbance of the system. In order to verify the feasibility of the proposed scheme, a three-phase 12/8 pole switched reluctance motor was used to build the simulation models of the traditional deadbeat current control and the current predictive control based on the extended state observer. the simulation results show that the proposed scheme can effectively reduce the current jitter and restrain the torque ripple. © 2021 IEEE.

关键词： State estimation

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Study Analyse the Functionality of Photovoltaic Simulation software: PVsyst, PVSOL, PVWatts, Energyplus, IBE, PKPM and Candela PV 18

Study Analyse the Functionality of Photovoltaic Simulation S...

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18th IBPSA conference on Building Simulation, BS 2023

作者： Liu, Kangyong Zhang, Wenjie Yin, Wenlong Li, Xiaoming School of Energy and Power Engineering Nanjing University of Science and Technology Nanjing China

Photovoltaic (PV) software is important for PV project design, and there are few studies on the comparison of features and functions of different photovoltaic software. We compared some PV simulation software, namely PVsyst, PVSOL, PVWatts, Energyplus, IBE, PKPM and Candela PV. A detailed table is created for comparison including geographic information, meteorological information, PV system, PV installation, PV modules, inverter, shading occlusion, solar irradiance calculation model, power generation calculation model, economic analysis and carbon emission calculation. It is concluded that PVsyst and PVSOL etc. software is feature-rich and have various calculation models, while IBE，PKPM and Candela PV software are more closely aligned with Chinese standards and policies. this study can provide guidance for PV engineers in software selection. © 2023 *** rights reserved.

关键词： Economic analysis

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A Comprehensive Review and Research Trends of Large Language models (LLMs) in Artificial Intelligence 6

A Comprehensive Review and Research Trends of Large Language...

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6th international conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Widartha, Vandha Pradwiyasma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Pukyong National University Department of Information System Busan Korea Republic of

ISBN: (纸本)9798331533243

the emergence of Artificial Intelligence (AI) has significantly impacted the development of computational models for social systems, providing new opportunities for feedback-rich simulations. To achieve human-level language understanding, the incorporation of language models into comprehensive understanding systems is a crucial advancement. the use of Large Language models (LLMs) in various fields, such as geomorphology and physics, highlights the necessity for robust policies and frameworks to regulate their use. Data selection from the Scopus database yielded a total of 2149 documents from 1173 sources. the most cited article from 2023 is 799 citations. Average citations per doc is 6.221. USA, China, and United Kingdom appear to dominate in terms of the number of published. By conducting a scientometric review and bibliometric analysis using R studio and VOSviewer software, this study aims to identify key focus areas, challenges, and opportunities in the implementation of IoT technologies in urban environments. As AI technologies, including LLMs, progress, their influence on diverse sectors like education, engineering, and healthcare is increasingly evident, emphasizing the importance of thorough assessments of their capabilities and constraints. By synthesizing insights from existing literature and exploring emerging trends, this study contributes to the advancement of Large Language models (LLMs) in Artificial Intelligence and the development of a more connected and intelligent urban future. © 2024 IEEE.

关键词： engineering education

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Agility in software 2.0-Notebook Interfaces and MLOps with Buttresses and Rebars 6th

Agility in Software 2.0-Notebook Interfaces and MLOps with B...

引用

6th international conference on Lean and Agile software Development (LASD)

作者： Borg, Markus RISE Res Inst Sweden Lund Sweden Lund Univ Dept Comp Sci Lund Sweden

ISBN: (纸本)9783030942380;9783030942373

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

关键词： Machine learning

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Renewable Energy Source Optimization Based on Pumped-Storage Hydroelectricity 6

Renewable Energy Source Optimization Based on Pumped-Storage...

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6th international conference on Green Technology and Sustainable Development, GTSD 2022

作者： Huy thinh, Tran Ngoc Chau Minh, Nguyen Huu Cat Tien, Lam Hoang Nam, Vo Hoai Lac, Ly Phuc Nguyen Tat Thanh University Faculty of Engineering and Technology Ho Chi Minh City Viet Nam Vietnam Aviation Academy Ho Chi Minh City Viet Nam Cao Thang Technical College Faculty of Electrical and Electronic Engineering Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology and Education Faculty of High-Quality Training Ho Chi Minh City Viet Nam Power Transmission Company 4 National Power Transmission Corporation Ho Chi Minh City Viet Nam

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

In 2019-2020, with encouragement from the Government in promoting the development of renewable energy sources, Vietnam has witnessed the rapid growth of solar and wind power sources. By the end of 2020, there were 16,700MW of solar power capacity connected to the national grid and accounted for 24 percent of the capacity of the whole national grid. these renewable energy sources are mainly concentrated in the Ninh thuan and Binh thuan provinces of Vietnam. the transmission power grid has not developed in time in this area to relieve all power sources, so there has been an overcapacity situation that has led to a reduction in the generating capacity of power plants, causing a waste of resources. In this paper we introduce renewable energy source storage technology based on pumped-storage hydroelectricity (PSH) technology, evaluating the role of a PSH plant in balancing the capacity of the power system when the system is connected to unstable power sources such as solar and wind power. Also, in this research, we propose Model predictive Controller (MPC) built through LabView software to control the power balance on the grid. the results of the research show that the solution to building a PHS plant to optimize renewable energy sources in the central provinces of Vietnam is the right solution for Geographically favorable conditions, PHS also brings economic profit and stabilizes Vietnam's electricity system © 2022 IEEE.

关键词： Solar energy

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Performance of multi-fidelity multi-output Kriging for problem with high-dimensional inputs

Performance of multi-fidelity multi-output Kriging for probl...

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6th Chinese international Turbomachinery conference (CITC 2024)

作者： Youwei He Linzhi Liu Jinliang Luo School of Mechanical Engineering University of South China Hengyang People's Republic of China

ISBN: (数字)9781837242153

Recently, multi-fidelity multi-output Kriging have been proposed to take the advantages of both the multi-fidelity and multi-output modelling methods. the predictive performance of multi-fidelity multi-output Kriging model has been evaluated with the modelling efficiency being neglected. In this paper, a multi-fidelity multi-output Kriging model is developed and compared with Kriging, multi-fidelity Kriging, and multi-output Kriging w.r.t. both the modelling accuracy and efficiency. Furthermore, the efficient modelling method for high-dimensional problems is incorporated into the various Kriging models. A 144-dimension engineering problem is established and collected with analytic problems to investigate the modelling accuracy and efficiency. Results show that the multi-output Kriging or multi-fidelity multi-output Kriging model can provide more accurate predictions than the single- or multi-fidelity single-output models on problems with low-dimensional inputs. While, for problems with high-dimensional inputs, the predictive capability of the multi-output Kriging or multi-fidelity multi-output Kriging degenerates significantly with the explosive growth of the time for model construction. With the efficient modelling method, the predictive capability and the modelling efficiency of the multi-output Kriging or multi-fidelity multi-output Kriging model are improved remarkably. However, their performance is still outperformed by single-output multi-fidelity Kriging model throughout the empirical experiments for problems with more than 10 inputs.

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Power Quality Analysis of Grid-To-Vehicle & Vehicle-To-Grid Technology

Power Quality Analysis of Grid-To-Vehicle & Vehicle-To-Grid ...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Abhigyan Baruah Devarshi Talukdar Nibir Sarma Rajarshi Sharma Kumari Nutan Singh Electrical Engineering Assam Engineering College Guwahati India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Power quality analysis is crucial in Grid-to- Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies, ensuring grid stability, reliable power flow, and equipment health. It enhances system efficiency, communication, and battery health in electric vehicles (EVs). It also ensures compliance with power quality standards, and customer satisfaction, and facilitates the integration of renewable energy. there are some common controllers used for quality improvement or harmonic reduction, such as the PI controller, LC filter, etc. this work proposes a model predictive controller for enhancement of the power quality with Electric vehicle application. the investigation of the proposed model has been developed in MATLAB Simulink and successfully tested the results.

关键词： Vehicle-to-grid Systematics software packages Power quality Stability criteria Power grids Mathematical models

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