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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Rat, Cezara-Liliana Prostean, Octavian Filip, Ioan Vasar, Cristian Department of Automation and Applied Informatics Politehnica University of Timisoara Bvd. V. Parvan No. 2 Timisoara300223 Romania

ISBN: (纸本)9783030519919

This paper describes a SCADA system for a microgrid laboratory that simulates a grid-connected wind turbine. The SCADA application monitors the entire system, ensuring reliable and stable operation. It gathers information from all the elements of the system, including the embedded controller of the emulator and the grid-connected inverter. It can monitor real-time parameters such as voltages, currents, or powers, and can store measurements in a MySQL database. This system also contains an interface for remote viewing designed using LabVIEW Remote Panels embedded into a web page. In order to tighten the security features on the website, the default web server provided by NI is replaced with WAMP server, making it possible to control the content of the website using server-side technology. Only authorized users can view the SCADA application or access the database for historical data. © Springer Nature Switzerland AG 2021.

关键词： Wind turbines

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ALGORITHMS BASED ON ANALYTIC LEARNING NEURAL NETWORKS FOR FINAL EXAM SCHEDULING 16

ALGORITHMS BASED ON ANALYTIC LEARNING NEURAL NETWORKS FOR FI...

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16th International Symposium on Operational Research in Slovenia, SOR 2021

作者： Erdős, Szilvia Kővári, Bence Budapest University of Technology and Economics Department of Automation and Applied Informatics Magyar tudósok krt. 2 Budapest1117 Hungary

ISBN: (纸本)9789616165570

The automatic generation of schedules has been in the focus of researches for decades. The scheduling of oral final exams is a particular subtask of automatic scheduling problems, where special requirements constrain the state space. The problem is examined with analytic learning-based neural networks, and the results are compared with other solutions. The results show that there is an acceptable solution with neural network approaches for this complexity. Moreover, with some improvements, there can be solutions, which are better and fairer than the manually compiled schedules. © 2021 Samo Drobne – Lidija Zadnik Stirn – Mirjana Kljajić Borštnar – Janez Povh – Janez Žerovnik

关键词： Scheduling

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Sensor Node Fault Detection in Wireless Sensor Networks Utilizing Node-Level Diagnostics

Sensor Node Fault Detection in Wireless Sensor Networks Util...

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Reliable Distributed Systems

作者： Dominik Widhalm Karl M. Goeschka Wolfgang Kastner Department Electronic Engineering University of Applied Sciences Technikum Wien Vienna Austria Faculty of Informatics TU Wien Automation Systems Group Vienna Austria

Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. Most related fault detection approaches consider the sensor nodes as black boxes. They neglect vital information available on the node level. Consequently, most of these approaches can not distinguish between (i) irregular but correctly sensed data events and (ii) data corruption caused by soft faults. In contrast, our contribution integrates node-level diagnostics with the characteristics of the sensor data. We utilize this node-level diagnostic information to present our fault detection approach. Based on simulations and practical experiments, we show the correctness and efficiency of our approach. The results show that our approach offers a high fault detection rate and can differentiate between events and faults. Furthermore, it consumes a justifiably small overhead of resources and energy.

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Proceedings of the International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES 2022) 1

Proceedings of the International Conference on Artificial In...

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丛书名： Atlantis Highlights in Intelligent Systems

2023年

作者： Valentina E. Balas Ramesh C. Bansal Siva Kumar Mangipudi Subhojit Dawn

ISBN: (数字)9789462392663

ISBN: (纸本)9789462392656

The book invites submission of novel, recent area of innovation and previously unpublished research work/idea in the field of modern applications of artificial intelligence techniques to electrical engineering systems. The applications of artificial intelligence related to various fields of electrical engineering are mentioned in the conference tracks. The conference is meant to discuss the challenges and applications of latest evolutionary computing techniques, neural networks, fuzzy logic, machine learning and data analytics in the fields of power systems, power electronics, robotics, automation, instrumentation, control systems, mechatronics and photonics. It provides a platform to the students, researchers, scientists, faculty members, professionals and practitioners to interact, present and get innovative ideas in the field of electrical engineering. As a part of AITEES-2022, many keynote sessions are planned to enhance the research and innovation skills of participants. Eminent professors from academic institutions and world renowned industrial experts from India and abroad will deliver keynote sessions.

关键词： Artificial Intelligence Computer Science Machine Learning Big Data

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Simulation Environment Implementation for Generation of Training Samples

Simulation Environment Implementation for Generation of Trai...

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International Carpathian Control Conference (ICCC)

作者： Sándor Tihamér Brassai Norbert Szántó Adorján Bajka Olivér Bárdi András Németh Attila Hammas Department of Electrical Engineering Sapientia Hungarian University of Transylvania Corunca Romania Automation and Applied Informatics Sapientia Hungarian University of Transylvania Corunca Romania Department of Electronic Warfare National University of Public Service Budapest Hungary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Since their invention, drones have been used in many areas, both civil and military. Among these uses, some of the more important ones are reconnaissance, patrolling and object recognition. In most of the applications, objects in images are detected using neural networks. To carry out this process, datasets with a lot of training samples should be collected. Unlabeled data takes a lot of time and human resources to process. Carrying out real drone measurements is a long process and is affected by many external factors, such as weather conditions. A simulation environment can provide a solution to this problem, where different drone measurements can be simulated in varied environments. One of the advantages of the simulation environment is that the objects on the acquired images can be automatically labeled, which is a very time-consuming process in the case of manually labeled images. An additional advantage is that it can perform measurements on diverse terrains with realistic objects with the help of specified configurations, thus generating a large and diverse set of training samples with minimal human intervention. The paper contains information on how to generate different terrains, navigate them with a virtual drone, taking pictures and labeling them at the same time. Development possibilities include dynamically changing the appearance of objects, as well as introducing moving objects into the simulation. It has been proven that it is possible to generate a diverse training set with the help of the proposed simulation.

关键词： Training Technological innovation Reconnaissance Software Labeling Software measurement Object recognition

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Simulating Human Physical Behavior using Unreal Engine Full Body Inverse Kinematics

Simulating Human Physical Behavior using Unreal Engine Full ...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Alexandru-Mihai Pescaru Razvan Adrian Bertici Traian-Radu Plosca Mihai Victor Micea Department of Computer and Information Technology Politehnica University Timisoara Timisoara Romania Department XIII Pulmonology “Victor Babes” University of Medicine and Pharmacy Timisoara Timisoara Romania Department of Automation and Applied Informatics Politehnica University Timisoara Timisoara Romania

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

Extracting, analyzing, and simulating human physical behavior have proven to be challenging tasks for researchers all over the world for many years. If developed successfully, they can generate important results that will contribute to progress in many fields. These results can change many people's lives, aiding in a handful of domains, such as security, medicine, autonomous robots, and many more. A direct outcome is to obtain the data necessary to train and validate advanced neural network models implied by many of today's solutions. We propose here an approach that uses Unreal Engine 5 to reproduce the movements characteristic to normal human behavior in a simulated environment, considering restrictions of body joints. This method can be used to aid in creating flawless body animation, both procedural and AI generated. It can be included in healthcare-related body simulations, video game development, and in some other fields involving research on physical human or animal behavior.

关键词： Visualization Video games Neural networks Kinematics Games Data models Telecommunications Security Engines Autonomous robots

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Comparison of RSSI-Based Fingerprinting Methods for Indoor Localization

Comparison of RSSI-Based Fingerprinting Methods for Indoor L...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Dominik Csík Ákos Odry Peter Sarcevic Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

ISBN: (纸本)9781665489898

The widespread of Internet of Things (IoT) has increased the need of accurate indoor localization methods. The indoor localization problem aims to determine the object position in technology-deficiated environments., where the solution requires the application of both alternative sensors and efficient algorithms. This paper addresses three fingerprinting techniques and provides a comparative analysis based on real measurements. Namely, the performance of Weighted K-Nearest Neighbor (WKNN), Random Forest (RF) and Artificial Neural Network (ANN) fingerprinting approaches are evaluated. First, a database is generated of Received Signal Strength Indication (RSSI) values of five access points (APs) in laboratory environment. Then, heatmap-based fingerprinting is elaborated., and a comprehensive analysis is conducted in two important cases. In the first case all points are line of sight (LOS), while in the second case, the modules are covered by a column. The obtained results show that the ANN-based approach outperforms the WKNN and RF methods, thereby proving its efficient applicability in indoor localization problems.

关键词： Radio frequency Location awareness Heating systems Databases Area measurement Artificial neural networks Fingerprint recognition

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Word-final phoneme segmentation using cross-correlation

Word-final phoneme segmentation using cross-correlation

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EFMI 2020 Special Topic Conference on Integrated Citizen Centered Digital Health and Social Care: Citizens as Data Producers and Service co-Creators

作者： Mahmut, Emilian-Erman Nicola, Stelian Stoicu-Tivadar, Vasile Department of Automation and Applied Informatics Politehnica University Timisoara Romania

ISBN: (纸本)9781643681443

The goal of this paper is to present a word-final target phoneme automated segmentation method based on cross-correlation coefficients computed between a reference sound wave and a sample sound wave. Most existing Speech Sound Disorder (SSD) Screening solutions require human intervention to a greater or lesser extent and use segmentation methods based on hard-coded time frames. Moreover, existing solutions extract features from the frequency domain, which entails large amounts of computational power to the detriment of real-time feedback. The pre-processing algorithm proposed in this paper, implemented in a Python version 3.7 script, automatically generates 2 *** files corresponding to the phonemes found in word-final position in the initial sound waves. The *** files are meant to be used as valid and homogeneous input in a subsequent classification stage aimed at rigorously discriminating mispronunciations of the target phoneme and assist Speech-Language Pathologists (SLPs) with the SSD screening. © 2020 The European Federation for Medical informatics (EFMI) and IOS Press.

关键词： Frequency domain analysis

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. Control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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