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2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020

作者： Semenova, Olena Semenov, Andriy Voitsekhovska, Olha Kozin, Dmytro Vinnytsia National Technical University Dept. of Telecommunication Systems and Television Vinnytsia Ukraine Vinnytsia National Technical University Dept. of Radio-Frequency Engineering Vinnytsia Ukraine Vinnytsia National Technical University Faculty for Computer Systems and Automation Vinnytsia Ukraine

ISBN: (纸本)9781728191775

The paper suggests to apply a neural network in cellular networks in order to improve the vertical handover process. The proposed neural network is a multilayer perceptron. The paper presents the proposed architecture of the neural network. The operation of the perceptron has been described. Simulation of the neural network operation in Matlab software was performed. © 2020 IEEE.

关键词： MATLAB

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GAS TURBINE VIBRATION MONITORING BASED ON REAL DATA AND NEURO-FUZZY SYSTEM

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Diagnostyka 2024年第1期25卷

作者： Nail, Bachir Djaidir, Benrabeh Tibermacine, Imad Eddine Napoli, Christian Haidour, Nabil Abdelaziz, Rabehi Faculty of Science and Technology University of Djelfa Djelfa17000 DZ Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa Poland L'École Supérieure de Physique et de Chimie Industrielles Paris France

The gas turbine is considered to be a very complex piece of machinery because of both its static structure and the dynamic behavior that results from the occurrence of vibration phenomena. It is required to adopt monitoring and diagnostic procedures for the identification and localization of vibration flaws in order to ensure the appropriate operation of large rotating equipment such as gas turbines. This is necessary in order to avoid catastrophic failures and deterioration and to ensure that proper operation occurs. Utilizing an approach that is based on spectrum analysis, the purpose of this study is to provide a model for the monitoring and diagnosis of vibrations in a GE MS3002 gas turbine and its driven centrifugal compressor. This will be done by utilizing the technique. Following that, the collection of vibration measurements for a model of the centrifugal compressor served as a suggestion for an additional method. This method is based on the neuro-fuzzy approach type ANFIS, and it aims to create an equivalent system that is able to make decisions without consulting a human being for the purpose of detecting vibratory defects. In spite of the fact that the compressor that was investigated has flaws, this procedure produced satisfactory results. © 2024 by the Authors.

关键词： Vibration analysis

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Application of a differentiated instruction approach in computer systems education 11

Application of a differentiated instruction approach in comp...

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11th National Conference with International Participation, ELECTRONICA 2020

作者： Kostadinov, Atanas Nikolov Kolev, Krassimir Iliev Faculty of Electronics and Automation Technical University of Sofia Department of Computer Systems and Technologies Plovdiv4000 Bulgaria Technical Faculty University of Food Technologies Department of Computer Systems and Technologies Plovdiv4002 Bulgaria

ISBN: (纸本)9781728175317

Sometimes there is a difference in students' level of knowledge on the topics included in their university courses. The lecturer should present material to all participants regardless of their background. We have selected a differentiated instruction approach to cope with mentioned problem. This method has been used in Reconfigurable Logic, VLSI Design as well as computer Modeling subjects. The obtained results show that all students benefit from this approach. © 2020 IEEE.

关键词： Integrated circuit design

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Backtesting quantum computing algorithms for portfolio optimization

TechRxiv

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TechRxiv 2023年

作者： de las Heras, Ginés Carrascal Manso, Paula Hernamperez Botella, Guillermo del Barrio, Alberto Antonio IBM Quantum IBM Consulting España Madrid28830 Spain Department of Informatic Systems and Computation Faculty of Informatics Complutense University of Madrid Madrid28040 Spain CARTIF Valladolid47151 Spain Department of Computer Architecture and Automation Faculty of Informatics Complutense University of Madrid Madrid28040 Spain

The development of quantum computers represents a breakthrough in the evolution of computing. Their graceful processing capacity will help to solve some problems impossible until now because the algorithms that calculate their solution require too much amount of memory or processing time. In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. In this work we analyze the Variational Quantum Eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. We compare the results with three other classical algorithms for the same problem, running one equivalent condition. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work explores the backtesting of quantum and classical computing algorithms for portfolio optimization and compares the results. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer. © 2023, CC BY.

关键词： Optimization

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz 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 Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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RIEMANNIAN GEOMETRY-BASED EEG APPROACHES: A LITERATURE REVIEW

arXiv

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arXiv 2024年

作者： Tibermacine, Imad Eddine Russo, Samuele Tibermacine, Ahmed Rabehi, Abdelaziz Nail, Bachir Kadri, Kamel Napoli, Christian Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Department of Psychology Sapienza University of Rome Rome Italy Department of Computer Science University of Biskra Biskra Algeria Faculty of Science and Technology University of Djelfa Djelfa Algeria Polytechnic Military School Algiers Algeria Institute for Systems Analysis and Computer Science Italian National Research Council Italy Department of Computational Intelligence Czestochowa University of Technology Poland

The application of Riemannian geometry in the decoding of brain-computer interfaces (BCIs) has swiftly garnered attention because of its straightforwardness, precision, and resilience, along with its aptitude for transfer learning, which has been demonstrated through significant achievements in global BCI competitions. This paper presents a comprehensive review of recent advancements in the integration of deep learning with Riemannian geometry to enhance EEG signal decoding in BCIs. Our review updates the findings since the last major review in 2017, comparing modern approaches that utilize deep learning to improve the handling of non-Euclidean data structures inherent in EEG signals. We discuss how these approaches not only tackle the traditional challenges of noise sensitivity, non-stationarity, and lengthy calibration times but also introduce novel classification frameworks and signal processing techniques to reduce these limitations significantly. Furthermore, we identify current shortcomings and propose future research directions in manifold learning and riemannian-based classification, focusing on practical implementations and theoretical expansions, such as feature tracking on manifolds, multitask learning, feature extraction, and transfer learning. This review aims to bridge the gap between theoretical research and practical, real-world applications, making sophisticated mathematical approaches accessible and actionable for BCI enhancements. © 2024, CC BY.

关键词： Geometry

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Design and Analog Implementation of Synchronization Controllers for Feedback Instruments’ Servo Trainer Modules

Design and Analog Implementation of Synchronization Controll...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Syed Zulqarnain Haider Muhammad Shafran Khurram Karim Qureshi Muhammad Usman Asad Jason Gu Umar Farooq Intelligent Systems Lab and Automation Facility (ISLAF) IEECE PU Lahore Pakistan Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Faculty of Open Learning and Career Development Dalhousie University Halifax Canada Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

This paper describes the detailed implementation of synchronization controllers using operational amplifiers for the servo trainer modules manufactured by Feedback Instruments Inc. The existing curriculum provided along with these trainer modules lacks discussion and implementation on synchronization of servo systems. Synchronization of dynamical systems is of great significance and finds many applications in a diversity of disciplines. This work is directed to upgrade the curriculum of servo trainer modules with synchronization controllers such as proportional, proportional-integral, and state feedback controllers. Various plants’ states are read from the sensors on-board the servo trainers and provided to the controllers, which are implemented externally on breadboards. The controllers generate actuating signals which are fed to the servo trainer plants through the on-board motor amplifiers, thereby closing the feedback loop around the servo plants. The efficacy of the controllers is tested by exciting the master servo trainer system and recording the resulting master and slave servo trainer system trajectories through ESPIAL data acquisition software. It is experimentally found that the slave servo system follows the master servo system. In this way, synchronization of the master and slave servo plants is achieved. The proposed study will be a useful addition to the laboratory curriculum of linear control systems.

关键词： Training State feedback Instruments Main-secondary Software Trajectory Sensors Synchronization Servomotors Dynamical systems

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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