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

共 3308 条记录，以下是661-670 订阅

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Mobile Robot Manipulator Localization in Indoor Environments for Object Picking Tasks

Mobile Robot Manipulator Localization in Indoor Environments...

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international conference on Control and Robots (ICCR)

作者： Yunfan Xie Goragod Pongthanisorn Shin-ichiro Kaneko Genci Capi Dept. of Mechanical Eng. Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for mobile robots. Ensuring accuracy and adaptability is essential for their successful operation. this paper presents a new method that integrates RGB cameras with Contrastive Learning (CL) for self-localization estimation and advanced object picking for mobile manipulators. the effectiveness of the proposed method for location estimation in object picking is verified in indoor environments.

关键词： Adaptation models Accuracy Robot vision systems Estimation Contrastive learning predictive models Manipulators Robustness Indoor environment Mobile robots

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A Comparative Study of Photovoltaic Power Prediction models Based on BP and XGBoost

A Comparative Study of Photovoltaic Power Prediction Models ...

引用

Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Tao Li Yuanxi Zhang Zaixin Zhu Lu Bai Fei Meng Guohua Yang Dispatching & Control Center of State Grid Ningxia Power Co. Ltd Yinchuan Ningxia China School of Electronic and Electrical Engineering Ningxia University Yinchuan Ningxia China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Photovoltaic (PV) power generation is influenced by fluctuating environmental factors and exhibits significant non-linearity, making accurate prediction challenging. this paper constructs photovoltaic power prediction models based on BP neural network and XGBoost algorithms, and experimentally validates their predictive performance. the models are evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ). the experimental results indicate that the BP neural network demonstrates stable performance in handling complex nonlinear relationships, while XGBoost achieves higher prediction accuracy and better generalization ability when the dataset is relatively small. this study provides a reference for the selection of photovoltaic power prediction models and lays the foundation for improving prediction accuracy in renewable energy power systems.

关键词： Photovoltaic systems Measurement Accuracy Fluctuations predictive models Power system stability Prediction algorithms Stability analysis Forecasting Biological neural networks

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BEMAO: A novel adaptive and high-order BEM solver for steady-state acoustics - part 2: applications 30

BEMAO: A novel adaptive and high-order BEM solver for steady...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Hamiche, K. Atak, O. Li, Y. Bériot, H. Desmet, W. Siemens Digital Industries Software Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

this paper presents a novel adaptive and high order implementation of the Boundary Element Method (BEM) for steady-state acoustics, which we refer to as BEMAO. the method uses a priori error estimators to automatically adapt the p-order of the elements based on the target accuracy, edge lengths and input frequency. Using high order interpolation shape functions (up to order 6) compresses the model size substantially. In addition, BEMAO is also able to use a single non-homogeneous mesh to cover a wide frequency range while it provides optimal model size per frequency without the need for remeshing. the assembly cost is reduced via the use of multipole approximations. A direct solver is used to solve the compressed models. All these properties make BEMAO a novel and competitive BEM solver. the Part 2 of the BEMAO papers focuses on industrial applications: noise radiation from aircraft propellers, automotive pass-by noise, submarine TES (Target Echo Strength), loudspeaker sound. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Boundary element method

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Robust Learning of Deep predictive models from Noisy and Imbalanced software engineering Datasets 22

Robust Learning of Deep Predictive Models from Noisy and Imb...

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proceedings of the 37th IEEE/ACM international conference on Automated software engineering

作者： Zhong Li Minxue Pan Yu Pei Tian Zhang Linzhang Wang Xuandong Li Nanjing University China The Hong Kong Polytechnic University China

ISBN: (纸本)9781450394758

With the rapid development of Deep Learning, deep predictive models have been widely applied to improve software engineering tasks, such as defect prediction and issue classification, and have achieved remarkable success. they are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the effectiveness of deep predictive models in practice. this problem has become a major obstacle for deep learning-based software engineering. In this paper, we propose RobustTrainer, the first approach to learning deep predictive models on raw training datasets where the mislabelled samples and the imbalanced classes coexist. RobustTrainer consists of a two-stage training scheme, where the first learns feature representations robust to sample mislabelling and the second builds a classifier robust to class imbalance based on the learned representations in the first stage. We apply RobustTrainer to two popular software engineering tasks, i.e., Bug Report Classification and software Defect Prediction. Evaluation results show that RobustTrainer effectively tackles the mislabelling and class imbalance issues and produces significantly better deep predictive models compared to the other six comparison approaches.

关键词： Deep Learning Imbalanced Data Mislabelling predictive models

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Proactive maintenance strategy for submarine pipelines based on stochastic modelling of deterioration due to corrosion

Proactive maintenance strategy for submarine pipelines based...

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System Reliability and Safety engineering (SRSE), international conference on

作者： Hao Zhang Chizhi Chris Zhang Jian Jiang Xinan Chen Jing Dong Dongfang Liang Advanced Computer and Digital Technology Research Center Changchun Institute of Optics Fine Mechanics and Physics CAS Changchun China University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China International Business School Xi’an Jiaotong-Liverpool University Suzhou China Department of Engineering University of Cambridge Cambridge UK

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

this study presents a stochastic model for corrosion deterioration in submarine pipelines, a major threat to structural integrity and operational efficiency. Using Bayesian updating, the model dynamically adjusts its parameters with new inspection data, improving predictions of corrosion growth and optimizing maintenance intervals to minimize failures. A numerical example demonstrates the model’s effectiveness in forecasting corrosion progression and enhancing maintenance decisions based on reliability and failure probability using the gamma process.

关键词： Corrosion Pipelines Stochastic processes predictive models Inspection Numerical models Maintenance Safety Underwater vehicles Optimization

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Commodity Demand Forecasting of E-Commerce Merchants Based on the Stacking Fusion Model

Commodity Demand Forecasting of E-Commerce Merchants Based o...

引用

international conference on Communications, Information System and Computer engineering (CISCE)

作者： Jiayao Guo Northwestern Polytechnical University Xi'an Shaanxi China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

With the rise of e-commerce platforms and the continuous expansion of market scale, how to supply and distribute and manage the goods in e-commerce supporting warehouses is a factor that needs to be considered by every merchant. In order to reduce merchants' inventory costs and fulfill commitments on time, this paper establishes a forecasting model based on the Stacking fusion model combined with grid search. With “merchant - warehouse - commodity” as a time series about commodity demand on the time axis, the Stacking fusion model is established. It integrates XGBoost, LishtGBM and Catboost machine learning models, and adopts grid search and cross-validation methods for parameter optimization. Prediction accuracy, recall rate, goodness of fit coefficient, root-mean-square error and 5% error accuracy were used to evaluate the prediction performance.

关键词： Adaptation models Analytical models Accuracy Computational modeling Stacking Machine learning predictive models

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Prediction of Spherical Equivalent with Vanilla ResNet 1

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Myopic Maculopathy Analysis Challenge, MMAC 2023, which was a part of the 26th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023

作者： Li, Huayu Zhu, Wenhui Chen, Xiwen Wang, Yalin Department of Electrical and Computer Engineering University of Arizona TucsonAZ85721 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States School of Computing Clemson University ClemsonSC United States

ISBN: (数字)9783031548574

ISBN: (纸本)9783031548567

Recently, an increasing of deep learning models has been introduced to address various fundus image tasks, e.g. segmentation, classification, and enhancement. Concurrently, this emergence has been accompanied by the development of increasingly intricate model architectures and a parameter surge. However, these methods have overlooked the crucial significance of the intrinsic characteristics of the data. In this regard, we posit that a vanilla ResNet, when coupled with appropriate data augmentation, can also achieve a favorable performance. this study assesses the performance of the foundational deep learning architecture, the standard Residual Network (ResNet), in predicting Spherical Equivalent (SE) values. Spherical Equivalent (SE) is a crucial parameter in optometry employed for prescribing eyeglasses to correct vision. Our study intends to demonstrate the effectiveness of ResNet with proper data augmentations in achieving precise SE predictions without additional enhancements. Utilizing a vanilla ResNet50 model, we secured the third position in the Myopic Maculopathy Analysis Challenge 2023, task 3. Remarkably, our findings reveal that the unaltered deployment of ResNet yields exceptional predictive performance for estimating SE, highlighting the architectural prowess of simple models in a vital yet frequently demanding optometric context. Our software package is available at https://***/HuayuLiArizona/Prediction-of-Spherical-Equivalent-With-Vanilla-ResNet. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Power engineering Cost Prediction Based on Data Feature Analysis and Neural Networks

Power Engineering Cost Prediction Based on Data Feature Anal...

引用

Energy Systems and Electrical Power (ICESEP), international conference on

作者： Beixue Liu Wenxuan Qiao Zhewei Shen Lei Wang Zhengyang Li Yi Zhang Hebei Electric Power Engineering Supervision Co. Ltd. Shijiazhaung Hebei China Shanghai Electric Power Design Institute Co. LTD Shanghai China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

In order to achieve more accurate analysis and processing of power engineering data in a shorter time frame, and to accurately predict the cost of power engineering, this study proposes a novel data processing method that integrates data mining and neural network technologies. this method firstly utilizes data mining algorithms to collect and organize historical data, then performs data preprocessing, and finally relies on neural network algorithms to construct prediction models. Based on specific application analyses of data mining and neural network technologies in the field of power engineering cost, this paper further conducts case studies to explore the effectiveness of this method in practical applications. the research results demonstrate that this integrated approach is not only applicable to different types of power engineering cost data, but also capable of providing accurate and efficient cost predictions, showing promising potential for analysis and application.

关键词： Power engineering Analytical models Costs Accuracy Neural networks Estimation predictive models

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Impact of COVID-19 on Crude Oil Price Prediction: A Comparative Performance Analysis of LSTM and ANN models

Impact of COVID-19 on Crude Oil Price Prediction: A Comparat...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Adilakshmi Yannam Nikitha Matta Pradeep Kumar Dunga Srinivas Peddireddi Leela Vamsi Tadisetti Hari Naga Lakshmi Pandraju Dept. of CSE(AIML) S R Gudlavalleru Engineering College Gudlavalleru India Dept.of CSE S R Gudlavalleru Engineering College Gudlavalleru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Crude oil is a key component in the industrial production, and its price fluctuations often leads to economic instability. this research compares the performance of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models in predicting crude oil prices across pre-pandemic period (2010–2019) and post-pandemic (2020–2024) period. Analysis reveals that models performed strongly before COVID-19, with LSTM achieving higher accuracy (R-squared: 0.933) compared to ANN (R-squared: 0.912). However, during the pandemic period, both models showed decreased performance, with LSTM maintaining superior results (R-squared: 0.901) compared to ANN (R-squared: 0.865). Despite challenges in capturing extreme market volatility, both models demonstrate resilience, with LSTM consistently outperforming ANN due to its superior capability in handling temporal dependencies.

关键词： COVID-19 Analytical models Pandemics Biological system modeling Oils Decision making predictive models Forecasting Long short term memory Resilience

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A Brief Review Particle Swarm Optimization on Intrusion Detection System 1

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Nandana, G.M. Yadav, Ashok Kumar Department of Computer Science and Engineering ASET Amity University Noida India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

there has been an improvement in recent years towards increase in volume for remarkable data in the field of Internet and computer networks. this brings up significant security-related challenges. In the past, a number of systems that provide host or network-level security have emerged. Many conventional security measures, such as, firewalls, spyware, authentication methods and antivirus software provide some way towards ensuring security, but they are still vulnerable to cyber-attacks and other system vulnerabilities. Some noteworthy alternatives are discovered, namely Intrusion Detection & Prevention Systems are an intriguing development, but they also have significant drawbacks, such as the inability to identify new assaults and respond to them in real time. Overtime, many researchers have proposed to develop a robust as well as intuitive infrastructure intrusion detection systems (IDS) utilising a variety of neural network models, i.e. support vector machines (SVM), rough sets, Particle Swarm Optimization (PSO) and many other versions. In this paper, we discuss all proposed models have been implemented by researchers in improving IDS performance with PSO model. Also, considering different IDS implemented, we can derive various IDS datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Network security

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