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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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An NHPP-Based SRGM with Time Dependent Growth Process 6

An NHPP-Based SRGM with Time Dependent Growth Process

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6th IEEE international conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Haque, Md. Asraful Ahmad, Nesar Z.H. College Of Engineering Technology AMU Department Of Computer Engineering Aligarh202002 India

ISBN: (纸本)9781665425520

the mathematical model that is used to forecast how software reliability will improve over time when errors are identified and resolved, known as a 'software Reliability Growth Model' or in short SRGM. these models assist the management in determining how much time and effort should be dedicated to testing. this paper presents a new SRGM that analyzes the failure data with the help of nonhomogeneous Poisson process. the model assumes the linear increment in fault detection rate process. the proposed model is validated on a dataset collected from an industrial project. the comparison results with seven established models in terms of four criteria have been reported. © 2021 IEEE.

关键词： software reliability

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A Machine Learning Approach to Predict the Efficiency of the Semiconductor Neutron Detector 6

A Machine Learning Approach to Predict the Efficiency of the...

引用

6th IEEE international conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Malik, Parveen Parida, Manoj Kumar Kumar, Vikas School of Electronics Engineering Kiit Deemed to Be University Bhubeneswar India

ISBN: (纸本)9781665456982

In this research paper, we have developed a novel approach using the machine learning models to predict the efficiency of semiconductor neutron detector for boric acid based converter material. Considering the over head of computational resources and time to compute the efficiency of a neutron detector, it becomes imperative to find the models that can take much lesser resources and time. In this novel work, the model was trained from the earlier research work done on GEANT4 simulation software and the predicted data was also compared with the simulated data. the various machine learning regression model were designed for stacked detector. Further, comparison have been done to find the best fit for the experimental data. © 2022 IEEE.

关键词： Efficiency

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Real-Time Model predictive Control for Simultaneous Drift and Trajectory Tracking of Autonomous Vehicles 6

Real-Time Model Predictive Control for Simultaneous Drift an...

引用

6th CAA international conference on Vehicular Control and Intelligence, CVCI 2022

作者： Dong, Haotian Yu, Huilong Xi, Junqiang School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

Drifting vehicles can accomplish obstacle avoidance at the limits of handling. However, insufficient rear tire lateral force will not suppress the vehicle from spinning if the steering angle and driving torque are unreasonable during trajectory tracking. For this strongly coupled nonlinear system, resolving the contradiction between the real-time performance and the complexity of the controller is challenging. this work proposed a real-time drift trajectory tracking controller (DTTC) for autonomous vehicles under the framework of model predictive control. To improve the computational efficiency and ensure the control performance, DTTC adopts a hierarchical structure, where the upper controller figures out the desired kinematic control inputs and the lower controller swiftly follow these quantities. the multi-objective control law of the upper controller adopts a real-time linearization error model based on a series of nonlinear models, with effective feedback emendation in each control cycle. the high-fidelity simulation results of a typical scenario, figure-8 drift, demonstrate that DTTC has superior transient and steady-state performance to the compared controller, with a 44% reduction in lateral root mean square (RMS) error. the maximum single-step solution time of DTTC is less than 0.035 s. © 2022 IEEE.

关键词： Model predictive control

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Airfoil Self Noise Prediction Using Machine Learning and Explainable AI 6

Airfoil Self Noise Prediction Using Machine Learning and Exp...

引用

6th IEEE international conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Pandey, Honey Pandey, Prateek Gupta, Deepa Amrita School of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350381689

A major environmental challenge in the aerospace sector is presented by aircraft noise, particularly airfoil noise. Traditional methods for predicting airfoil noise involve complex and computationally intensive numerical simulations. this research explores the use of machine learning to enhance the efficiency and accuracy of airfoil noise prediction. By utilizing extensive datasets from wind tunnel experiments, encompassing a variety of airfoil geometries and flow conditions, supervised learning algorithms are employed to construct a predictive model. Diverse regression models, including random forests, support vector regression, CatBoost, XgBoost, Gradient Boosting and decision trees, are trained on the NACA 0012 airfoil dataset to uncover correlations between airfoil characteristics and noise emissions. Initial findings underscore the potential of machine learning, with Gradient Boosting regression achieving a noteworthy R2 score of 0.96625 and RMSE as 1.2622. this study not only equips engineers with a valuable tool for optimizing airfoil shapes to reduce noise but also establishes the groundwork for real-time noise prediction systems in aircraft operations. © 2024 IEEE.

关键词： Random forests

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MULTITERMINAL IMPEDANSE-BASED FAULT LOCATION thROUGH SYNCHRONIZED PHASOR MEASUREMENTS 6

MULTITERMINAL IMPEDANSE-BASED FAULT LOCATION THROUGH SYNCHRO...

引用

6th international Scientific and Technical conference Relay Protection and Automation, RPA 2023

作者： Yablokov, A.A. Tychkin, A.R. Ivanov, I.E. Department of Electric Power Engineering Ivanovo State Power Engineering University Ivanovo Russia

ISBN: (纸本)9798350382334

Switching off an overhead transmission line can have a dramatic impact on the electric power system: interruption of power transmission, increasing the loading of adjacent power grid components, loss of load supply, islanding, etc. Improving the accuracy of transmission line fault location (FL) is a key aspect to restore the line back in service as soon as possible. Impedance-based FL is implemented with power system quantities measured under fault conditions and put into closed-form expressions, specialized tables or simulation models. software for FL [1-3] utilizes either single-end or two-end current and voltage measurements obtained from digital fault recorders (DFRs) or digital relays with a FL function. the FL error relative to the line length can sometimes be greater than 10% as a consequence of various uncertainties such as a fault resistance (for single-end FL methods), loss of good-quality synchronization (for two-end expressions), current transformer (CT) saturation [4], incorrect transmission line data, and so on. Some of these issues may be tackled by employing measurements captured at other points in a grid, outside of the faulty line. However, there are no clear rules and unique protocols to transmit data from DFRs and relays for FL (especially in a real-time environment). Also, the DFRs are not necessarily synchronized via GPS/GLONASS and similar precise time sources, which creates a need for "post"-synchronization of measurements recorded under a line fault. © 2023 IEEE.

关键词： Electric power transmission

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

引用

6th Parul international conference on engineering and Technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. this research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. the dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. this study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. the insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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Interpretable Fake News Detection on Social Media 23

Interpretable Fake News Detection on Social Media

引用

6th international conference on software engineering and Information Management, ICSIM 2023

作者： Xu, Xiwei Qin, Ke School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781450398237

With the development of information technology, public opinion can quickly spread to all over the world, permeate every corner of social life, and have a great impact on human's lives. Extracted from large-scale and multi-mode social media, user-generated information is anonymous and noisy. It is found that users' social interaction helps to detect fake news. At present, most methods focus on effectively detecting fake news with potential characteristics, but most models are lack of interpretability. therefore, this paper studies the interpretable detection of fake information in public social media platform, and proposes a model based on in-depth sentence-comment interactive reasoning network. the model uses fake information content and user comments to capture the most worth checking information sentences from user comments, in order to detect fake information and provide some explanation. this paper solves the following challenges: (1) how to detect fake news while improving the detection performance and interpretability;(2) how to extract the correlation between false content and user comments in the social media platform. © 2023 ACM.

关键词： Social aspects

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Simulation of variable thickness aluminum alloy plate casting 6

Simulation of variable thickness aluminum alloy plate castin...

引用

6th international conference on Advancements in Materials and Manufacturing, ICAAMM 2024

作者： Reddy, G.S. Hafeez, Md. Department of Mechanical Engineering Sreenidhi Institute of Science and Technology Yamnampet Ghatkesar Telangana Hyderabad501301 India

the simulation of variable thickness aluminum alloy plate casting is investigated to understand the solidification process and mitigate macro shrinkage in castings. Aluminium alloys offer versatile properties suitable for diverse applications, gaining prominence, especially in electric vehicle manufacturing. Incorporating rare earth elements like Nd into aluminum alloys enhances corrosion resistance, while grain refinement treatments further augment this attribute. this study utilizes sand molds with varying thicknesses to cast Al-13%Si alloy into variable-thickness plate casting. Simulation outcomes reveal the absence of shrinkage porosity, with the last solidification events occurring in risers and sprue. Silicon's role in reducing shrinkage porosity is highlighted, alongside advancements in grain refinement techniques for enhanced mechanical properties. the temperature distribution and cooling curves affirm directional solidification, aligning with desired temperature gradients. the manuscript underscores the dependence of simulation results on quality input data, emphasizing the critical role of temperature-dependent properties and initial boundary conditions. the continuous audit of simulation process data, focusing on fundamentals such as turbulence, modulus ratios, and directional solidification, is identified as essential for refining predictive models. this study contributes to the continuous improvement philosophy in manufacturing by providing insights into optimizing casting processes through simulations. By bridging the gap between simulation and real-world outcomes, this research enhances the reliability and applicability of solidification models, paving the way for advancements in aluminum alloy casting simulations. Notably, the automotive industry's shift towards aluminum components underscores the importance of these findings in manufacturing practices. © Published under licence by IOP Publishing Ltd.

关键词： Corrosion resistance

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Overhead Transmission Lines Early Warning and Decision Support System with predictive Analytics 5

Overhead Transmission Lines Early Warning and Decision Suppo...

引用

5th international conference on Advanced Electronic Materials, Computers and software engineering, AEMCSE 2022

作者： Yin, Weihua Gumabay, Maria Visitacion N. Lin, Haili Tu, Chengen Ao, Chun St. Paul University Philippines Tuguegarao Philippines Shaoyang University Shaoyang China Beijing Century Lily Technology Co. Ltd. Beijing China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

the electric power system is the largest artificial system so far. Many overhead transmission lines in the power grid are exposed to the natural environment for long periods, while the frequent occurrence of extreme weather events poses a severe threat to the power system. the data in this study came from multi-source dynamic data sets collected in real-time. these data are matched with previous overhead transmission lines disaster occurrences by coupling multiple to disaster factors through a decision support system based on predictive analysis. then data mining techniques are used to establish the data models for transmission lines disaster to identify correlation models for factors such as transmission lines faults and meteorological data and to model transmission lines in HN Province, China, through analysis of disaster probability for prediction early warning. the results showed that the disaster model of overhead transmission lines matched the actual situation and had a high accuracy of early warning. © 2022 IEEE.

关键词： Decision support systems

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Application of Simulink for the Study of Dual-Slope ADC 6

Application of Simulink for the Study of Dual-Slope ADC

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6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Serov, Andrey N. Dolgacheva, Ekaterina A. Antipov, Gennady V. National Research University Moscow Power Engineering Institute Diagnostic Information Technologies Department Moscow Russia

ISBN: (纸本)9781665405775

Currently, the number of digital measuring transducers of various physical quantities is growing in measurement and control tasks. the functioning of digital converters presupposes the presence of analog-To-digital converters (ADC) in their structure. Among the ADC architectures used, the principle of dual-slope integration can be characterized by high noise immunity and relative ease of implementation. In the tasks of studying the characteristics of dual-slope ADCs and educational tasks, the use of real chips or their prototypes in a large number of cases turns out to be inconvenient. Its especially related to the study of metrological characteristics, which, due to the smallness of their values against the background of random signal fluctuations, is extremely difficult to determine on a real prototype. this paper is devoted to the development of a dual-slope ADC model using the Simulink simulation package. the proposed model allows us to evaluate the static and dynamic characteristics of the dual-slope ADC. Most ADC parameters (sampling frequency, input signal range, reference signal value, duration of the first integration cycle) are configurable via a dialog box with the user. the issues of suppression of stationary and random external interference are considered. Recommendations are given for choosing the duration of the first integration cycle depending on the type of noise interference in the input signal. the obtained analytical expressions are confirmed by the results of simulation modeling of the developed model. the resulting model can be widely applied in the educational process, especially in the format of online education. © 2022 IEEE.

关键词： Performance evaluation Interference suppression Analytical models Transducers software packages Shape Current measurement

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