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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711-720 订阅

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Events-Based Model predictive Control for Polytopic Uncertain Systems Under False Data Injection Attacks

Events-Based Model Predictive Control for Polytopic Uncertai...

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Industrial Cyber-Physical Systems (ICPS)

作者： Fan Wei Chaoling Zhangabc Xiongbo Wan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

In this paper, the events-based model predictive control (MPC) problem is studied for systems under false data injection (FDI) attacks. A time-varying event-triggered mechanism (ETM) is proposed to manage measurement data packet releases and a static ETM is used to reduce the influence of the FDI attacks on the controller. By using the properties of the defined robust positive invariant set, a solvable auxiliary optimization problem (OP) is proposed to design the controller. the recursive feasibility of the auxiliary OP and the input-to-state stability of the closed-loop system are guaranteed. the validity of the developed ETMs-based anti-attack MPC algorithm is shown by an example.

关键词： Uncertain systems predictive models Prediction algorithms Stability analysis Data models Numerical models Closed loop systems

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ANFIS-Based Deployment for Precise Electricity Load Forecasting: A Netherlands Case Study

ANFIS-Based Deployment for Precise Electricity Load Forecast...

引用

international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Fatemeh Marzbani Ahmed Osman Mohamed S. Hassan Department of Industrial Engineering American University of Sharjah Sharjah UAE Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Over the past years, electricity demand has ex-hibited a consistent upward trend. the time series data for electricity load are distinguished by their intermittent nature. Hence, accurate prediction of electricity demand is essential for the efficient operation of the electricity grid. this paper employs a two-step prediction framework for short-term predictions of electricity load for a case study in the Netherlands. In the first phase, historical data set is preprocessed and subjected to a feature engineering process. Subsequently, an Adaptive Neuro-Fuzzy Inference System (ANFIS) predictive model is applied to the preprocessed data to provide 15 minutes ahead electricity load forecast. In this stage, two different learning algorithms, namely hybrid and backpropagation, are used to train ANFIS and a comparison between the obtained results is conducted. the results indicate that the hybrid algorithm outperform the backpropagation in terms of prediction accuracy.

关键词： Backpropagation Accuracy Load forecasting Electricity Time series analysis Signal processing algorithms predictive models Prediction algorithms Data models Load modeling

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Towards Zero Accidents: Innovative Safety Strategies in Last Mile Vehicle engineering

Towards Zero Accidents: Innovative Safety Strategies in Last...

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ASME/IFToMM international conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Qi Kong Haiyan H. Zhang Linda L Naimi Purdue Polytechnic Institute Purdue University West Lafayette IN USA Zelos Technologies Inc. San Jose CA USA Purdue Polytechnic Institute and Center for Technology Development Purdue University West Lafayette IN USA Department of Technology Leadership and Innovation Purdue University West Lafayette IN USA

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

this study presents pioneering safety strategies in last-mile vehicle engineering, focusing on autonomous delivery vehicles (ADV s). It delves into the integration of advanced sensor technologies, predictive algorithms, and vehicle design optimizations aimed at mitigating the inherent risks associated with urban delivery systems. the research encompasses the development of neural network-based models for dynamic risk prediction and the application of Hardware-in-the-Loop (HIL) testing for enhanced safety evaluation. the findings demonstrate significant potential in reducing accidents, contributing to safer urban environments, and paving the way for future advancements in the realm of autonomous vehicle safety. the study emphasizes the importance of a holistic approach to vehicle engineering, where safety is considered at every stage of design and operation, underscoring our commitment to the ambitious goal of achieving ‘zero accidents’ in urban delivery scenarios.

关键词： Urban areas Focusing Standardization predictive models Robot sensing systems Safety Reliability

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Multi-stream Online Transfer Learning for software Effort Estimation: Is It Necessary? 17

Multi-stream Online Transfer Learning for Software Effort Es...

引用

17th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Minku, Leandro L. Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9781450386807

software Effort Estimation (SEE) may suffer from changes in the relationship between features describing software projects and their required effort over time, hindering predictive performance of machine learning models. To cope with that, most machine learning-based SEE approaches rely on receiving a large number of Within-Company (WC) projects for training over time, being prohibitively expensive. the approach Dycom reduces the number of required WC training projects by transferring knowledge from Cross-Company (CC) projects. However, it assumes that CC projects have no chronology and are entirely available before WC projects start being estimated. Given the importance of taking chronology into account to cope with changes, it may be beneficial to also take the chronology of CC projects into account. this paper thus investigates whether and under what circumstances treating CC projects as multiple data streams to be learned over time may be useful for improving SEE. For that, an extension of Dycom called OATES is proposed to enable multi-stream online learning, so that both incoming WC and CC data streams can be learnt over time. OATES is then compared against Dycom and five other approaches on a case study using four different scenarios derived from the ISBSG Repository. the results show that OATES improved predictive performance over the state-of-the-art when the number of CC projects available beforehand was small. Learning CC projects over time as multiple data streams is thus recommended for improving SEE in such scenario. When the number of CC projects available beforehand was large, OATES obtained similar predictive performance to the state-of-the-art. therefore, CC data streams are unnecessary in this scenario, but are not detrimental either.

关键词： software effort estimation cross-company learning concept drift transfer learning data streams ensembles

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An Identification and Control Framework for Optimizing the Energy Consumption of a Wastewater Treatment Plant

An Identification and Control Framework for Optimizing the E...

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international IEEE conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Teo Protoulis Ioannis Kalogeropoulos Ioannis Kordatos Aristotelis Kapnopoulos Panos L. Zervas George Vangelatos Haralambos Sarimveis Alex Alexandridis Department of Electrical and Electronic Engineering University of West Attica Athens Greece School of Chemical Engineering National Technical University of Athens Athens Greece Systemica – G. Vangelatos & Co L.P. Athens Greece Entrade SA Athens Greece

Wastewater treatment plants (WWTPs) play a key role in circular economies, as they are capable of recycling large volumes of polluted water. One of the biggest challenges, though when considering such large-scale plants, is the fact that they consume a great amount of power in order to operate properly. In this work, the problem of optimizing the energy efficiency of WWTPs is addressed by presenting an economic dynamic matrix control (EDMC) formulation for industrial-scale WWTPs. To derive predictive models that are accurate and at the same time suitable for integration in EDMC without the need to perform cumbersome tests on the real plant, we first make use of a cooperative particle swarm optimization solver in order to identify a number of important parameters included in a detailed first principles model of the process; the identified detailed model is then used to derive the much simpler step-response predictive models needed for designing the EDMC scheme. the proposed approach is shown to provide higher energy efficiency when compared against a standard DMC controller, while also satisfying environmental regulations.

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EnuwaJGX: Machine Learning Gene Prediction software Application Model - An Innovative Method to Precision Medicine and predictive Analysis of Visualising Mutated Genes Associated to Neurological Phenotype of Diseases 14

EnuwaJGX: Machine Learning Gene Prediction Software Applicat...

引用

14th international conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Onah, Daniel F.O. Department of Information Studies University College London London United Kingdom

ISBN: (纸本)9789897586149

this research investigates an aspect of precision medicine related to genes and their association with diseases. Precision medicine is a growing area in medical science research. By definition precision medicine is an approach that allows the selection of treatments that are most likely to help treat patients based on the genetic understanding of their diseases. this approach proposes the customization of a medical model for healthcare, treatment, medical decision making about genetic diseases and develop models that are tailored to individual patient. there are readily available datasets provided by Genomics England related to diseases and the genes that cause these diseases. this research presents a predictive technique that scores the possibilities of a mutated gene causing a neurological phenotype. there are over a thousand genes associated with 26 subtypes of neurological diseases as defined by Genomics England capturing genetic variation, gene structure and co-expression network. the gene prediction was performed with search algorithms and methods that sequentially looped through the database for true match. Linear search algorithm was applied along index search method to perform the prediction matching of gene(s) that are associated to the disease(s). the prediction algorithm was formulated based on a Mathematical/probabilistic concept that was used to design the model for processing the data-set ready for gene prediction. It became apparent that over half a million (> 500,000) genes were predicted in this study that were associated to the neurological phenotype of the diseases in this research work. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Forecasting

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Modeling of NHPP-Based SRGM with Two Types of Faults

Modeling of NHPP-Based SRGM with Two Types of Faults

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Asheesh Tiwari Ashish Sharma Dept of CEA GLA University Mathura (UP) India

the various formulations for estimating the reliability of software are relying upon reliability growth models. Such modeling is accomplished furthermore with the consideration that all available faults are instantly detected. During our investigation, faults are labeled into two types based on easiness and difficulty in detection. the Fault detection process (FDP) for two types of faults is explicitly modeled. the applicability of the proposed SRGM is proved using a real dataset. Model parameters are calculated through (LSE) least square estimation, rather the measure of accuracy is the sum of (SSE) squared error,(RMSE) root mean squared error, R-square, and (MSE)mean squared error are evaluated by employing a valid dataset. the estimation with criteria and goodness of fit is also justified.

关键词： software testing Fault detection Computational modeling Measurement uncertainty Estimation Reliability engineering software

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Ship Fuel Consumption Prediction Model Based on LSTM

Ship Fuel Consumption Prediction Model Based on LSTM

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Menghao Shi Tiankai Yang Yongliang Liang Chao Wang College of Marine Electrical Engineering Dalian Maritime University Dalian Liaoning China College of Electrical Engineering Shandong University Jinan Shandong China Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd Shenyang Liaoning China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Recently, the European Commission and the international Maritime Organization (IMO) have implemented various operational measures and policies to reduce ship fuel consumption and related emissions. Considering that the accurate prediction of ship fuel consumption is crucial for controlling ship emissions, this paper proposes a ship fuel consumption prediction model based on long short-term memory (LSTM) technology. Firstly, the data preprocessing of the real ship data is performed by using the Chauvenet criterion. Secondly, the basic framework of the ship fuel consumption prediction model is established. then, the LSTM algorithm is utilized to establish the prediction model. the accuracy of the proposed method is verified by the simulation of actual ship data.

关键词： Accuracy predictive models Prediction algorithms Data models Energy efficiency Scheduling Real-time systems Fuels Marine vehicles Long short term memory

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Brain Stroke Prediction Using Deep Learning: A CNN Approach 4

Brain Stroke Prediction Using Deep Learning: A CNN Approach

引用

4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Reddy, Madhavi K. Kovuri, Karthik Avanija, J. Sakthivel, M. Kaleru, Shivaprasad Sree Vidyanikethan Engineering College Department of Cse Andhra Pradesh Tirupati India Cse B v Raju Institute of Technology Telangana Narsapur India Juniper Networks Inc Software Engineer Sr Staff SunnyvaleCA United States

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. It's a medical emergency;therefore getting help as soon as possible is critical. Seeking medical help right away can help prevent brain damage and other complications. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome, and supporting doctors in prescribing disease treatment. Using a deep learning model on a brain disease dataset, this method of predicting analytical techniques for stroke was carried out. In this model, the goal is to create a deep learning application that identifies brain strokes using a convolution neural network. In addition, three models for predicting the outcomes have been developed. this suggested study uses a CT scan (computed tomography) image dataset to predict and classify strokes. © 2022 IEEE.

关键词： Forecasting

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Simulating Product Costs Under Stochastic Conditions Using the ABC Matrix Model 1

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17th international conference on Interdisciplinarity in engineering, INTER-ENG 2023

作者： Henriques, Marcelo F. N. Dias, Luís Silva Afonso, Paulo Ferreira, Luís Pinto Department of Production and Systems School of Engineering University of Minho Campus of Azurém Guimarães4804-533 Portugal ALGORITMI Research Centre/LASI University of Minho Campus of Azurém Guimarães4804-533 Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto4249-015 Portugal Rua Dr. Roberto Frias 400 Porto4200-465 Portugal

ISBN: (数字)9783031546716

ISBN: (纸本)9783031546709

Costing models tend to follow a top-down or, alternatively, a bottom-up approach, following, respectively, a financial/accounting or an engineering/production perspective. Regardless of the approach used, cost models tend to be based on past information and be deterministic, contributing little to decision making at the organization’s operational and strategic levels. Moreover, the current business environment is characterised by conditions of high variability, uncertainty, complexity, and ambiguity. As such, predictive models for decision making which consider the stochastic nature of certain variables are beneficial. In this paper, we present a matrix activity-based costing model which allows a combined bottom-up and top-down approach to predict the resource requirements of a production system and to compute the cost of products and other relevant cost objects, considering the processes as deterministic or stochastic. the developed costing model includes the main elements which characterise a production system. therefore, it can be easily generalized to different processes and industries. this proposed costing model was replicated using the SIMIO simulation software, including parameters to both make the test process efficient and allow to take full advantage of experimentation. Moreover, tallies were modelled to exhibit custom results, suited to this matrix costing model. We have concluded that discrete event simulation is a well suited tool to cost modelling, especially given its stochastic capabilities and the ease of scenario creation. In future work, different system aspects can be assessed, different resource combination, and the cost per resource call;can be estimated and the simulation model can be improved to facilitate scenario creation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Discrete event simulation

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