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检索条件"丛书名=Lecture Notes in Networks and Systems"

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An Evaluation of the Performance of the ODE Solvers Implemented in Matlab for Simulating Multibody systems 10th

An Evaluation of the Performance of the ODE Solvers Implemen...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Genel, Omer Ekim Carrera, Carlos Perez Lok, Sefika Ipek Pappalardo, Carmine Maria Guida, Domenico Istanbul Tech Univ Dept Mech Engn TR-34437 Istanbul Turkiye Univ Salerno Dept Ind Engn Via Giovanni Paolo II132 I-84084 Fisciano Italy Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Mechatron Engn Izmir Turkiye

ISBN: (纸本)9783031662676;9783031662683

Ease of modeling and producing accurate results in an acceptable time are two key expectations from an environment used for the simulation of multibody systems. The latter is closely related to the solver used by the simulation environment and plays an essential role in evaluating the quality of the simulation process. In this study, the performances of some solvers used in MATLAB, SIMULINK, and SIMSCAPE MULTIBODY environments are investigated and preliminary results are presented. In this vein, three different models of the benchmark problem, which is a two mass-spring system, are developed in MATLAB, SIMULINK, and SIMSCAPEMULTIBODY environments,and the performances of solvers under consideration are studied in terms of accuracy and computational time. In the study, the effect of tolerance used in simulations on the integral of the absolute error and computational time is considered.

关键词： performance evaluation solver multibody system simulation MATLAB SIMULINK SIMSCAPE MULTIBODY

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Mapping of WEDM Output from Designed Experiments to Binary Symmetric Channel 9th

Mapping of WEDM Output from Designed Experiments to Binary S...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Lautre, Nitin Kumar Visvesvaraya Natl Inst Technol VNIT Dept Mech Engn Nagpur 440010 Maharashtra India

ISBN: (纸本)9789819735617;9789819735624

Wire-electro discharge machining (WEDM) has become an important non-traditional machining process, widely used in the aerospace, nuclear, and automotive industries. The electrode that is used forWEDM is a tiny flexible brass wire;therefore in the WEDM machining process, the wire behaviors and the influence of parametric conditions on the workpiece need to be well estimated. The investigations and simulations of Wire-EDM are useful for different machining conditions in practical scenarios. The channel coding methodology-based approach is utilized to simulate the machining parameters for a better material removal rate (MRR) of the workpiece of E0300 die steel. In the present work, a binary symmetric channel (BSC) is used for the information that can be transmitted through the channels with encoding and decoding techniques. The variousWEDmachining parameters selected are TON, TOFF, IP, VP, WF, WT, and SV. Mathematical models are also developed to correlate the interrelationship of important factors which invariably helps for prediction of MRR during machining. The objective of this development is to estimate the MRR based on the channel coding approach and verification of the results with the experimental machining parametric settings. The paper also highlights the various test results that confirm the ability of the proposed system to analyze the effect of the various process parameters of WEDM during the machining of E0300 alloy steel. The investigation also describes a simulating procedure, developed algorithms, and a few analyzed results ofWire-EDM. The simulated values of the machining parameters obtained through the approach can be recorded and organized for a better and more effective database system.

关键词： WEDM Channel coding MRR Machining parameters Alloy steel

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Intelligent Healthcare systems: Enhancing Performance with Smart CI/CD Pipelines

Intelligent Healthcare Systems: Enhancing Performance with S...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Mcheick, Hamid Zahre, Zahraa Fatima Mahmod Achouh, Pamela Jean Univ Chicoutimi Dept Comp Sci Chicoutimi PQ Canada Lebanese Univ Fac Sci Beirut Lebanon

ISBN: (纸本)9783031671944;9783031671951

The implementation of CI/CD, Continuous Integration/Continuous Deployment, practices in the healthcare sector represents an essential technique to improving the quality of healthcare systems. This paper sets its primary objective to explore the transformative impact of integrating CI/CD methodologies with smart sensors and machine learning algorithms in the realm of healthcare. It aims to highlight the significant benefits and overcome the limitations of existing solutions within the CI/CD landscape. Focusing on the deployment of smart sensors and intelligent algorithms, this study delves into their utility to monitor, analyze, and enhance system performance, maintenance, accuracy, and various important parameters. Through the integration and processing of real-time data alongside machine learning algorithms, this approach facilitates predictive maintenance, minimizes system downtime, and boosts operational efficiency. The overarching goal is to demonstrate how CI/CD practices can be tailored to meet the unique needs of healthcare environments, thereby ensuring high-quality patient care through advanced system reliability and performance. This exploration also sheds light on the potential for scalable, secure, and interoperable healthcare solutions, addressing key challenges in data privacy, system integration, and regulatory compliance.

关键词： Healthcare Technology Continuous Integration Continuous Deployment Healthcare sector Smart sensors DevOps Predictive Maintenance Machine Learning System Quality Operational Efficiency

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Crime Prediction Using Machine Learning 1

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Ling, Hneah Guey Jian, Teng Wei Mohanan, Vasuky Yeo, Sook Fern Jothi, Neesha INTI Int Coll Penang Bayan Lepas Malaysia Multimedia Univ Fac Business Jalan Ayer Keroh Lama Melaka 75450 Malaysia Daffodil Int Univ Dept Business Adm Dhaka 1207 Bangladesh Univ Tenaga Nasl UNITEN Coll Comp & Informat Dept Comp Putrajaya Campus Kajang Malaysia

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

The widespread occurrence of criminal activities poses a substantial threat to public safety and property. Hence, the proactive prediction of crimes is vital as it empowers law enforcement agencies to make decisions on resource allocation and targeted interventions based on the data, ultimately leading to a more secure and protected community. Additionally, such initiatives raise public awareness, encouraging vigilance during periods of heightened criminal activity. In this project, machine learning techniques are leveraged to forecast the crime rate in the city of Chicago. This research introduces a more efficient data preparation method, optimizing data representation to enable machine learning models to capture patterns and learn from the information provided effectively. After training the models using LightGBM, XGBoost, CatBoost, and Gradient Boosting, the models achieved R-2 scores of 0.8086, 0.8088, 0.8094, and 0.8084, respectively. An ensemble method combining these individual models was implemented to improve the prediction performance. Through the voting ensemble method, the final R2 score for crime rate prediction was enhanced to 0.8104.

关键词： Machine Learning Time-Series Forecasting Crime Prediction

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Exploring Data Distributions in Machine Learning Models with SOMs 15th

Exploring Data Distributions in Machine Learning Models with...

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Konig, Caroline Vellido, Alfredo Univ Politecn Catalunya UPC Barcelona Tech Intelligent Data Sci & Artificial Intelligence ID Jordi Girona 1-3 Barcelona 08034 Spain

ISBN: (纸本)9783031671586;9783031671593

Data quality control is fundamental in data-driven analysis with machine learning (ML) models. In the domain of drug research, there is an increasing interest in the prediction of relevant biocompounds physicochemical properties with ML. In order to build predictive models of good quality, it is important to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage.

关键词： Self-Organizing Maps ADME properties Random Forest Interpretable Machine Learning Data Quality Bioinformatics

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Generate Unnoticeable Adversarial Examples on Audio Classification Models with Multi-perspective Objectives

Generate Unnoticeable Adversarial Examples on Audio Classifi...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Ma, Bin Li, Weixun Li, Huifeng Sun, Guanghui Gao, Minghui Wu, Dexin Xu, Jian State Grid Hebei Elect Power Co Ltd Shijiazhuang Hebei Peoples R China State Grid Hebei Elect Power Res Inst Shijiazhuang Hebei Peoples R China NARI Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China Beijing Kedong Elect Power Control Syst Co Ltd Beijing Peoples R China Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9783031744426;9783031744433

In recent years, there has been notable progress in the development of audio classification models, yet they remain vulnerable to adversarial attacks. While research into attack methodologies pertaining to images has experienced significant advancements, comparatively less attention has been directed towards attacks specifically targeting audio classification models. Furthermore, prevailing techniques for compromising audio models often yield adversarial examples that are readily identifiable due to discernible noise artifacts, thereby distinguishing them from clean samples. To address this disparity, we propose Multi-perspective Unnoticeable Audio Attack (MU2A for short), an innovative framework designed for crafting imperceptible adversarial examples for both untargeted and targeted assaults on audio classification models. Concretely, we frame our attack strategy as an optimization dilemma and incorporate the Multi-Scale Spectral (MSS) score, Decibels score, and Mutual Information within the objective function. This formulation aims to minimize the disparity between clean audio signals and their adversarial counterparts across frequency and time domains, as well as probabilistic perspectives. Empirical evaluations demonstrate that the magnitude of the adversarial perturbation generated by MU2A represents only 0.625% of the clean example, rendering the resulting adversarial examples acoustically indistinguishable to human perception.

关键词： Audio Classification Adversarial Attack Deep Learning

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The Influence of Electric Vehicle Technologies on Environmental Sustainability in Industrial Reporting: A Bibliometric Study 3rd

The Influence of Electric Vehicle Technologies on Environmen...

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3rd International Symposium on Industrial Engineering and Automation

作者： Ben Ali, Marwa Rauch, Erwin Free Univ Bozen Fac Engn Inst Ind & Energy Engn Sustainable Mfg Lab Piazza Univ 1 I-39100 Bolzano Italy

ISBN: (纸本)9783031704642;9783031704659

This research aimed to investigate the implications of electric vehicle (EV) technologies on the environmental sustainability of industrial reporting, given the critical role that vehicles play in all enterprises. To achieve this objective, this study employed bibliometric and systematic reviews as well as quantitative and qualitative indicators to evaluate the current state of research on EVs. Specifically, the study examined the influence of new energy technologies on supply chain and logistics operations, focusing on the European Sustainability Reporting Standards (ESRS) and the environmental aspect of sustainability. The transportation sector is a significant contributor to greenhouse gas emissions, and the widespread adoption of EVs has the potential to mitigate these emissions. However, research on the impact of EVs on the sustainability of industrial reporting is limited, especially in light of the new ESRS. The central research question is "What is the influence of new electric technologies on environmental sustainability in industrial reporting?" The study argues that the integration of EV technologies into enterprise supply chain ecosystem will lead to reduced greenhouse gas emissions, enhanced environmental sustainability in industrial reporting, and the achievement of the ninth Sustainable Development Goal (SDG), which includes the establishment of resilient infrastructure, promotion of inclusive and sustainable industrialization, and fostering innovation.

关键词： Sustainable Mobility Electric Vehicles Supply Chain Logistics European Sustainability Reporting Standards Bibliometric Analysis

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A Survey on Anomaly Detection in Network with ML Techniques 4th

A Survey on Anomaly Detection in Network with ML Techniques

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4th Congress on Intelligent systems (CIS)

作者： Karthika, L. Gunasundari, S. Gojan Sch Business & Technol Chennai Tamil Nadu India Velammal Engn Coll Chennai Tamil Nadu India

ISBN: (纸本)9789819990399;9789819990405

Anomaly detection is the process of finding malicious data that may occur in computer networks. There are multiple anomaly detection techniques available in practice. As the new technologies are growing fast in networking because of the connecting devices. A novel approach can be introduced using machine learning, which may be flexible enough to detect intrusions in a network structure. Nowadays, the dataset is imbalanced, so the anomaly detection model results are average. In this paper, anomaly detection is summarised based on different machine learning algorithms. We also reviewed the implementation and challenges faced in the ML technique to detect anomalies like DoS and DDoS attacks. The performance is reviewed from the survey only through the ML metrics. Due to the high range of multimedia data, it is essential to concentrate on Software Defined networks rather than traditional networks. The advantages and disadvantages of various ML approaches to detecting all kinds of general anomalies are discussed in this paper.

关键词： Machine learning Anomaly detection Denial of service DDoS

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Developing a Serious Game for Acute Pain Detection by Utilizing Virtual Reality and Brain-Computer Interfaces 9th

Developing a Serious Game for Acute Pain Detection by Utiliz...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Taheri, Meisam Aske, Adam Emile Tan, Kevin Inland Norway Univ Hamar Norway

ISBN: (纸本)9789819732883;9789819732890

Brain-computer interface (BCI) technology can be used to measure pain. Brain activity is recorded and analyzed using BCI devices. The use of brain-computer interface (BCI) technology for pain measurement is an area of ongoing research and development. While there is no single definitive proof at this time, there is a growing body of evidence and research that supports the idea that BCI technology can provide objective and precise measurements of the pain. BCI enables researchers to observe and evaluate pain-related neural activity real-time. This could lead to the identification of neural patterns linked with pain, which could then be used to develop personalized therapy strategies for each patient. Furthermore, we could combine this technology with VR to make a simulation that can monitor the patient's pain experience and assist with further therapy. This will provide a safe and controlled environment for patients to better understand their pain by allowing patients to interact simulation elements using their brain activity and get real-time feedback. Overall, the combination of brain-computer interface and virtual reality technology has immense potential to revolutionize the way we detect and ease pain. The aim is to develop a simulation that provides patients with an immersive and interactive environment, allowing them to engage in activities that can help manage their pain, such as meditation, relaxation exercises, and cognitive-behavioral therapy. In this study, authors worked on a method to show how pain can be detected and visualized and used in the simulation.

关键词： BCI VR Pain Acute pain Game Simulation

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Metrics for Local Market of Cloud Services 13th

Metrics for Local Market of Cloud Services

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13th Computer Science Online Conference

作者： Golovkin, Pavel Peter Great St Petersburg Polytech Univ Politekhnicheskaya St 29 St Petersburg 195251 Russia

ISBN: (纸本)9783031705946;9783031705953

The research helped on measuring the dynamics and concentration of cloud services markets to highlight market localization models. Research hypotheses were as follows: the dynamics and concentration of the market for technological services can act as indirect indicators of technology development;resources for providing cloud infrastructure services can be classified as non-shared resources;possible models for localizing cloud services markets include deepening autonomy or entering limited external markets. Materials of the research included data from analytical reviews of cloud services markets: Gartner, MCKinsey, CNews Analytics, Synergy, Canalys;We used ICT development indices from the Global Innovation Index - 2022: access to ICT, use of ICT, development of online government services, electronic participation, as well as volumes of import and export of services ICT for 132 countries of the world. Metrics for local market of cloud services were proposed as follows: ICT access, ICT use, market shares of top cloud providers, growth rate of total revenues, total revenues, value of Herfindahl-Hirschman Index (HHI). The ICT usage index increases (power function) as ICT availability increases. The ICT use index in China and Russia is quite high, above the average expected value for the current level of access to ICT. The protectionist measures of the Chinese government are limiting access to the country's market for global cloud providers. The strengthening position of Chinese providers in Southeast Asian markets is hampering competition from global providers. The generally positive dynamics of the cloud infrastructure services market in Russia and the low market concentration are prerequisites for the development of the local market. The small size of the Russian market and the lack of prospects for domestic providers in global markets limits their growth potential in the long term.

关键词： Cloud Services Market Market Concentration Global Innovation Index Indicators Regression Dependence

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