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Outlier Exposure-Based Anomalous Sound Detection Using Deep learning 11

Outlier Exposure-Based Anomalous Sound Detection Using Deep ...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Abdelhady, Doha Abdelrahman, Youssef Othman, Hisham German University in Cairo MET Department Egypt

ISBN: (纸本)9798350322101

Automatic Anomalous Sound Detection (ASD) systems play an important role nowadays. the objective is to automatically determine whether a machine, such as a slider, is making a normal, or an abnormal sound even if there are domain shifts such as different factory noise, or different recording conditions. the Outlier exposure approach is applied by incorporating in the training data outliers which are other machine types in order to expose the model to both samples of the target machine which is the slider, and outliers. To further improve the results of our proposed system, experiments were conducted including trying different activation functions, different loss functions, or adding a dropout layer in the Autoencoder with data augmentation. Furthermore, a Convolutional Autoencoder is implemented to replace the simple dense Autoencoder. We also tried using a pre-trained MobileNetV2 with data augmentation. Good performance has been achieved by using data augmentation techniques. the experiments' results were compared with the original autoencoder results in terms of the AUC. © 2023 IEEE.

关键词： Deep learning

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automated Modeling Verification Complex of the intelligent Instrument System 11

Automated Modeling Verification Complex of the Intelligent I...

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11th international conference "Information Control Systems and Technologies", ICST 2023

作者： Kvasnikov, Volodymyr Kuzmych, Lyudmyla Yehorova, Svitlana Kuzmych, Andriy Guryn, Vasyl Pennsylvania State University University ParkPA16802 United States Institute of Water Problems and Land Reclamation NAAS 37 Vasylkivska Str Kyiv03022 Ukraine National aviation University 1 Liubomyra Huzara ave Kyiv03058 Ukraine Armed Forces of Ukraine the tactical group "Bakhmut" Bakhmut Donetsk region 84500 Ukraine National University of Water and Environmental Engineering 11 Soborna Str Rivne33000 Ukraine

automated Model Verification Complexes are an important tool for research, testing, and verification of information and measuring instrument control systems, navigation, mechanical engineering, and other branches of technology. the model of the automated verification and simulation complex of the instrument system was developed based on the method of calibrated signals using a software-controlled measure. the calculation of the mathematical methodical error and the mean square deviation of the methodical error during quantization of the sum of input influences and additive interference are given. © 2023 Copyright for this paper by its authors.

关键词： Errors

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Task Scheduling for Energy Efficiency in data Center: Machine learning Approach 11

Task Scheduling for Energy Efficiency in Data Center: Machin...

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11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Abdul Rachman, Arif Haidlir Murti, Muhammad Ary Suratman, Fiky Yosef School of Electrical Engineering Telkom University Bandung Indonesia Bandung Indonesia

ISBN: (纸本)9798350389289

Energy efficiency in data Center has becoming crucial issue in recent decades, considering the high energy consumption in data Centers along with the development of digital era. Cooling system is a part of data Center with highest energy consumption. this fact explains the need for long-lasting systems that manage energy consumption while ensuring optimal system performance. the aim of this paper is to develop and design a Task Scheduling to improve energy efficiency using machine learning approach. the goal is to maintain temperature in the range of 22°C - 26°C and reduce Power Usage Effectiveness (PUE) below 1.8. To support this goal, machine learning is used to obtain temperature prediction values. this research compares three types of algorithms: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Prophet to get the best prediction result. LSTM shows the lowest Root Mean Squared Error (RMSE) value (0.1617720) compared to ARIMA (0.111928) and Prophet (0.087384). It can be concluded that LSTM algorithm give more accurate prediction than the others. the task scheduling results then creates 24 best actions (schedules) that used as a reference for agents to carry out simulations in December to maximize the rewards that will be obtained for 30 days or 30 episodes per 24 actions/episodes. By using those methods, it can reduce the PUE become 1.76 from 1.9 and the temperature below 26°C. It means that the using of machine learning to reduce PUE by creating best schedule action space can increase the energy efficiency (DCiE) by 4%. © 2024 IEEE.

关键词： Adversarial machine learning

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11th international conference on Mechatronics and Control engineering, ICMCE 2023

11th International Conference on Mechatronics and Control En...

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11th international conference on Mechatronics and Control engineering, ICMCE 2023

ISBN: (纸本)9789819965229

the proceedings contain 13 papers. the special focus in this conference is on Mechatronics and Control engineering. the topics include: Using Wheel Slippage for Improved Maneuverability of 4 Wheel Steering Vehicles;design and Construction of a Prototype for the Lyophilization Process;three-Dimensional Finite Element Analysis and Experiment of Temperature Rise of Permanent Magnet Linear Synchronous Motor;Modeling and Control of BLDC Motor for Scaled Autonomous Vehicle Application;FedMSPC: A Federated Multivariate Statistical Process Control Framework For Privacy-Preserving Process Modeling Across Company Borders;LPV Modeling for Control Scheme Design of a Compound Helicopter;piecewise Modeling Algorithms Using Numerical data;Scaling of a Modular Production System (MPS) with Manufacturing Execution System (MES) and Multiple Agents;an Effective Methodology for Imbalanced data Handling in Predictive Maintenance for Offset Printing;Design and Experimental Validation of RL-Based Decision-Making System for Autonomous Vehicles;performance Evaluation of Machine learning App Approach to Modular Arrangement of Predetermined Time Standard.

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A Machine learning Framework for the Classification of ECG Signals 18

A Machine Learning Framework for the Classification of ECG S...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mahajan, Madhavi Kadam, Sonali Kulkarni, Vinaya Gujar, Jotiram Naik, Sanah Bibikar, Suruchi Ochani, Ankita Pratap, Sakshi Bharati Vidyapeeth University College of Ayurveda Maharashtra Pune India Bharati Vidyapeeth's College of Engineering for Women Maharashtra Pune India Sinhgad College of Engineering Maharashtra Pune India

ISBN: (纸本)9789380544519

the escalating volume of medical data underscores the critical need for efficient methods in classifying electrocardiogram (ECG) signals. Current approaches, while valuable, face limitations in achieving both high sensitivity and specificity, hampering their effectiveness in timely cardiac diagnosis. these challenges highlight the urgency for more robust methodologies to enhance the accuracy of ECG signal classification. To address these issues, this study proposes a comprehensive approach utilizing machine learning techniques. Our framework integrates diverse algorithms including Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression and an ensemble classifier. this ensemble approach aims to capitalize on the strengths of individual models, optimizing the overall classification performance. the application of this approach demonstrates promising outcomes, with improved sensitivity and specificity in ECG signal categorization. the versatility of the proposed framework holds significant potential for widespread applications, contributing to advancements in cardiovascular health monitoring and diagnosis. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Electrocardiogram (ECG) Ensemble Classifier K-Nearest Neighbors (KNN) Logistic Regression Machine learning Signal Classification Support Vector Machines (SVM) XGBoost

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Research on intelligent Application of Bridges Based on Big data 11th

Research on Intelligent Application of Bridges Based on Big ...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Xiong, Jun Zhu, Yiyang Department of Civil Engineering School of Physics Nanjing University of Science and Technology Jiangsu Nanjing210094 China College of Tourism and Social Management Nanjing Xiaozhuang University Jiangsu Nanjing211171 China

this article provides an in-depth analysis of how data processing technology is used and the various challenges faced in the process of bridge maintenance and upgrading. the management of overpasses, an important urban infrastructure, is moving from the traditional model to the road of automation and intelligence innovation. With the help of these advanced technologies, the efficiency of construction and maintenance work has been greatly improved, and the management model has also moved towards modernization. Although huge data resources have brought convenience and high accuracy, the insufficient total amount of data and limitations in the scope of application are still the main problems. problem. Research shows that in the whole cycle of bridge planning, construction, maintenance and operation, the use of large amounts of data is crucial, especially in terms of automated detection and intelligent maintenance. through the integration of key data, a complete database has been created, which It not only strengthens the continuous monitoring of the health status of the bridge, but also improves the accuracy of condition prediction. In future work, researchers will work on building a standardized intelligent maintenance system to improve the accuracy of prediction of potential problems through technology., and develop a more thorough maintenance strategy to promote the in-depth evolution of bridge maintenance towards data-driven and predictive maintenance. © 2024 the Authors. Published by Elsevier B.V.

关键词： data accuracy

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EXPLICITLY MINIMIZING thE BLUR ERROR OF VARIATIONAL AUTOENCODERS 11

EXPLICITLY MINIMIZING THE BLUR ERROR OF VARIATIONAL AUTOENCO...

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11th international conference on learning Representations, ICLR 2023

作者： Bredell, Gustav Flouris, Kyriakos Chaitanya, Krishna Erdil, Ertunc Konukoglu, Ender Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase the generative capabilities by creating more flexible models but often flexibility comes at the cost of higher complexity and computational cost. Several works have focused on altering the reconstruction term of the evidence lower bound (ELBO), however, often at the expense of losing the mathematical link to maximizing the likelihood of the samples under the modeled distribution. Here we propose a new formulation of the reconstruction term for the VAE that specifically penalizes the generation of blurry images while at the same time still maximizing the ELBO under the modeled distribution. We show the potential of the proposed loss on three different data sets, where it outperforms several recently proposed reconstruction losses for VAEs. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： learning systems

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Enhanced CNN Architecture for Accurate Waste Classification in Smart Cities 5

Enhanced CNN Architecture for Accurate Waste Classification ...

引用

5th international conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2024

作者： Kabilan, B. Sairam, R. Praveen, M. Saveetha Engineering College Dept. of ECE Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

Waste management is one of the biggest challenges of the present world as the generation of waste has risen tremendously. In this paper we introduce an automated waste classification system that uses Convolutional Neural Networks to effectively sort waste into four categories which are biodegradable, non-biodegradable, biomedical and electronic waste. the framework that has been suggested in this paper incorporates several new preprocessing techniques including data augmentation as well as noise removal for enhancing the robustness of the model. the CNN architecture has the convolutional, pooling and dense layers that are well tuned to identify essential features and enhance the accuracy of classification. the model was trained and validated using a large data set and the accuracy of the model was 91.87%, while the F1 score, recall and precision were also high for all the waste categories. In contrast to other methods for machine learning like SVM, RF, CNN was found to be better in dealing with high dimensional image data, owing to its flexibility and efficiency. the system can be implemented on edge devices which makes it possible to sort waste in real time and thus do not require much reliance on central systems. this approach has potential for application in cities, smart industries and healthcare units with a view of environmental conservation and optimal resource management. In the future, the work will be extended to improve the dataset, use transfer learning approaches and develop new applications including smart waste bins and hazardous material separation systems to enhance the field of automated waste management. © 2024 IEEE.

关键词： Convolutional neural networks

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E-Bike Energy Needs Estimation based on Route Characteristics and Rider Behavior 11

E-Bike Energy Needs Estimation based on Route Characteristic...

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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Ouf, Karim Soubra, Hassan Mazhr, Ahmed MET German University in Cairo Computer Engineering Cairo Egypt ECE École Centrale d'Électronique Computer Engineering Lyon France German University in Cairo Computer Engineering Cairo Egypt

ISBN: (纸本)9798350322101

Environmental awareness triggered the popularity of electric bicycles (E-Bikes) which has seen a significant rise in recent years due to technological advancement. Nevertheless, some limitations affect this continuous growth notably Range Anxiety i.e., the fear of running out of energy mid-ride. While numerous studies have investigated energy prediction in electric vehicles, research on electric bikes remains relatively scarce. Hence, this paper proposes an energy prediction machine learning model that considers route characteristics and rider behavior based on data collected from different sensors mounted on the E-Bike. Different machine learning models were used: Multiple Linear Regression, Random Forest, Light Gradient Boosting Machine, and Extreme Gradient Boosting Machine, and then evaluated using multiple metrics such as Mean Squared Error, Mean Absolute Error, Latency, CPU Utilization, and R2 Score. According to the evaluation metrics, the random forest model outperformed in terms of its R2 score, Mean Squared Error, and Mean Absolute Error, achieving the highest accuracy and lowest errors. However, it exhibited the poorest performance in terms of latency and CPU utilization. © 2023 IEEE.

关键词： Mean square error

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A data-driven scheduling module for electric vehicle charging 11

A data-driven scheduling module for electric vehicle chargin...

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11th international conference on Internet of things: Systems, Management and Security, IOTSMS 2024

作者： Chen, Henry Lambrinos, Lambros Grammenos, Ryan Karagiannis, Konstantinos Kfoury, Elie University College London Department of Electronic and Electrical Engineering London United Kingdom Cyprus University of Technology Department of Communication and Internet Studies Limassol Cyprus General Technology Ltd Athens Greece IntelliCasa Ltd London United Kingdom

ISBN: (纸本)9798350366501

the continuing uprise in the number of electric vehicles in many countries is undoubtedly contributing to the reduction of transportation-related emissions. However, a worrying consequence of this uprise is the additional demand placed on the electricity grid for vehicle charging which often coincides with the time period where household demand for electricity is high. To alleviate this effect, we propose an intelligent data-driven scheduling module for electric vehicle charging. Our goal is to satisfy the needs of electric vehicle owners in terms of the predicted vehicle usage and charging cost while limiting the impact of electric vehicle charging on the electricity grid. One of the most significant inputs to such a system is the availability of vehicle and user drive cycle data. Utilising such data, the scheduling module proposed in this paper demonstrates that deploying intelligent electric vehicle charging recommendation systems can enhance user experience and contribute to a more efficient and sustainable energy ecosystem. © 2024 IEEE.

关键词： State of charge

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