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arXiv 2024年

作者： Shi, Lu Haseli, Masih Mamakoukas, Giorgos Bruder, Daniel Abraham, Ian Murphey, Todd Cortes, Jorge Karydis, Konstantinos Dept. of Electrical and Computer Engineering University of California Riverside United States Dept. of Mechanical and Aerospace Engineering University of California San Diego United States Dept. of Planning and Control Zoox Inc. United States Dept. of Mechanical Engineering University of Michigan United States Dept. of Mechanical Engineering Yale University United States Dept. of Mechanical Engineering Northwestern University United States Tsinghua University China

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics. © 2024, CC BY.

关键词： Adversarial machine learning

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia Ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 IEEE.

关键词： Feature Selection

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

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Application of a systematic approach to the analysis of requirements and testing of NPP automated process control systems in the field of information security using specialized software

Application of a systematic approach to the analysis of requ...

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2021 International Conference on Electrotechnical Complexes and systems, ICOECS 2021

作者： Kemkin, Vladislav Doroshenko, Boris Sakharov, Konstantin Salov, Igor Salova, Julia Chernyaev, Alexey National Research University 'Moscow Power Engineering Institute' Dept. of Automated Control Systems for Thermal Processes Moscow Russia Joint-stock Company 'Rasu' Dept. of Design of I&C Computer Security Systems Moscow Russia National Research University 'Moscow Power Engineering Institute' Joint-stock Company 'Rasu' Dept. of Automated Control Systems for Thermal Processes Moscow Russia

ISBN: (纸本)9781665407274

This article reports examines the prerequisites for development and the expected results of the application of a new systematic approach to testing Industrial control System of Nuclear Power Plants (NPP ICS) in the field of information security using specialized software. The regulatory and technical documentation containing the requirements for testing the NPP ICS in the field of information security is considered, the requirements are analyzed and criteria for systematization of these requirements are formed. The result of the research is the creation of a systematic list of requirements for testing of NPP ICS subsystems in terms of information security, as a basis for the software of formation of programs, methods of checking and testing of NPP ICS subsystems in terms of information security. © 2021 IEEE.

关键词： Application programs

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Feedforward control in the Presence of Input Nonlinearities: A Learning-based Approach

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IFAC-PapersOnLine 2022年第37期55卷 235-240页

作者： Jilles van Hulst Maurice Poot Dragan Kostić Kai Wa Yan Jim Portegies Tom Oomen Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology The Netherlands Center of Competency ASM Pacific Technology The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

关键词： Nonlinear system identification Identification for control Iterative learning control Data-based control Motion control Applications in semiconductor manufacturing

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SES: Smart Education System

SES: Smart Education System

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International Conference on computer and Applications (ICCA)

作者： Wadha Alyammahi Saed Alrabaee Dept. of Computer and Network Engineering Dept. of Information Systems and Security College of Information Technology United Arab Emirates University AlAin UAE

Responding to inefficiencies in traditional classroom management methods, this paper proposes an innovative, technology-driven approach to enhance educational processes. Central to our proposal is the development of a facial recognition system designed for efficient student attendance tracking, thus eliminating time-consuming roll calls. Additionally, we plan to introduce interactive whiteboard features to enrich classroom dynamics between students and faculty. However, our approach extends beyond mere attendance tracking and interactive learning. We aim to launch a Program Learning Outcomes (PLO) and Course Learning Outcomes (CLO) mapper. Leveraging Natural Language Processing (NLP) techniques, this tool will auto-align CLOs with PLOs, facilitating a more efficient curriculum development process. We also suggest implementing a feature powered by YOLOv5 to monitor and assess student attention in the classroom. Our comprehensive suite of tools is designed to equip educators with resources to refine their teaching strategies and boost student learning outcomes. By integrating facial recognition for attendance, interactive whiteboard features, NLP-based CLO/PLO mapping, and attention monitoring, we aspire to provide a robust solution enabling educators to adapt their teaching methods to students’ unique needs.

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Adaptation of PAM4 Serial Link Transceiver Equalizers using Eye Diagram Monitoring

Adaptation of PAM4 Serial Link Transceiver Equalizers using ...

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2021 International Conference on Microelectronics, ICM 2021

作者： Mubarak, Hisham M. Elkhweski, Mohsen F. Yehia, Yehia Hamdy El-Kharashi, M. Watheq Zaki, Ahmed M. Ain Shams University Dept. of Computer and Systems Engineering Cairo Egypt Ain Shams University Dept. of Electronics and Communications Engineering Cairo Egypt

ISBN: (纸本)9781665408394

This paper studies the possible gains from using a single adaptation algorithm in tuning multiple equalizers in a Pulse Amplitude Modulation 4-level (PAM4) serial link transceiver. A comparison with the typical approach of using an algorithm for tuning one of the many equalizers used in transceivers from the perspective of bit error rate (BER) is carried out. This work also presents a method of measuring the eye diagram using an Eye Diagram Monitor (EDM) to continuously feed a Figure of Merit (FOM) to the adaptation algorithm to update equalizers' settings. A demonstration on deploying our algorithm in adapting three major building blocks of a typical PAM4 transceiver is presented, showing its effectiveness in comparison with adapting only one equalizer at receiver (Rx) side. Simulation results show improvement in eye diagram measures in terms of achieving an eye height of 326 mV and a width of 97 ps compared to 67 mV and 12 ps respectively in case of adapting only one equalizer in Rx. © 2021 IEEE.

关键词： Pulse amplitude modulation

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Effect of climatological variations on Eco-efficiency of Wastewater Treatment Plants operation

Effect of climatological variations on Eco-efficiency of Was...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Silvana Revollar Montse Meneses Mario Francico Pastora Vega Ramón Vilanova Dept. Computer Science and Automation University of Salamanca Salamanca Spain Dept. Telecommunications and Systems Engineering Universitat Autonoma Barcelona Barcelona Spain

This study addresses the effect of climate conditions on treatment effectiveness and energy consumption in a conventional Wastewater Treatment Plant (WWTP). It has been observed that winter temperatures below 12°C produce a deterioration of pollutants elimination and energy efficiency in the activated sludge process (ASP). Then, in this work, variations of climatological conditions are considered, and ASP control parameters are modified, to evaluate their effect into the eco-efficiency of the WWTP operation. The eco-efficiency of the operation is analyzed from a plant-wide perspective considering the effects on different units of the plant. The Benchmark Simulation Model 2 (BSM2), that represents a typical WWTP, is selected for simulations. Introduction of seasonal temperature effects on ASP control strategy are contemplated for future work.

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Thermal Satellite Imagery Analysis and Emissivity Characteristics for the Prediction of Oil Reservoirs Existence 7

Thermal Satellite Imagery Analysis and Emissivity Characteri...

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7th International Conference on Space Science and Communication, IconSpace 2021

作者： Tarish, Ali Badri Sali, Aduwati Mohamed Shariff, Abdul Rashid Hanafi, Marsyita Ismail, Alyani Mohammed, Intehaa A. Upm Dept. Of Computer & Communication Systems Engineering Serdang Selangor Malaysia Upm WiPNET Research Centre Dept. Of Computer & Communication Systems Engineering Serdang Selangor Malaysia Upm Dept. Of Biological & Agricultural Engineering Serdang Selangor Malaysia Uob College Of Education For Pure Sciences Dept. Of Physics Babylon Iraq

ISBN: (纸本)9781665425230

Simultaneous transformation of natural land cover contributes significantly to changing the surface phenomena making accurate forecasting difficult. Ground surveys would permit Land Use Land Cover (LULC) classification, but they are burdensome, expensive, and time-consuming and lacks accuracy in the exploration for all target areas at the same time, which highlights satellite imagery an evident and preferred alternative. A new mathematical method has been used for calculating the highest and lowest Land Surface Temperature (LST) to improve model accuracy for a particular location and detect oil seep-induced alteration from Landsat Data Continuity Mission (LDCM). Two sites within different patterns were chosen to represent a diversity of thermal anomalies positions in the study area. The results show that there are no traces of oil in either zone 1 lies which in the Kanoya province and zone 2 located east of the coast of Kagoshima which is overlooking the Pacific Ocean. In the end, the night-time scenes of the Thermal Infrared Sensor (TIRS) achieve a high gain of the thermal image with radiance 3.2-12.65 (Watts/m2 sr μm). Moreover, the analysis of the (TIRS) image is an optimal explanation for geomorphology and subsurface geology in southern Japan via the potentiality of the simulators set. © 2021 IEEE.

关键词： Satellite imagery

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Improving ATSC 3.0 Channel Estimation using AI Techniques

Improving ATSC 3.0 Channel Estimation using AI Techniques

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IEEE International Symposium on Broadband Multimedia systems and Broadcasting (BMSB)

作者： E. Iradier I. Bilbao J. Montalban P. Angueira S-I. Park S. Kwon N. Hur Dept. Computer Languages and Systems UPV/EHU Bilbao Spain Dept. of Communications Engineering UPV/EHU Bilbao Spain Dept. Electronics Technology UPV/EHU Bilbao Spain ETRI Daejeon South Korea

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

Over the past several years, ATSC has continued to develop new technologies that can be integrated into the standard, making it a leading innovator in the broadcasting industry. The spectrum efficiency of the waveforms and coding, combined with new infrastructure based on the integration of a core network, has proposed ATSC 3.0 as a strong candidate for implementing new multimedia-related use cases. However, the requirements for certain use cases (e.g., AR/VR, 360-degree video) are very harsh, for instance, regarding the signal quality needed at the receiver end. A set of ATSC 3.0 physical layer configurations cannot be implemented with the current channel estimation and equalization solutions. Consequently, Artificial Intelligence (AI) is presented as the necessary enabler for these cases. Specifically, AI has demonstrated better use of the knowledge of the propagation channel than traditional processing methods. Therefore, this paper aims to propose AI-based channel estimation techniques for ATSC. Specifically, two solutions are proposed. The first is a super-resolution estimation enhancer, whereas the second performs the interpolation process within the AI. The results show that both techniques improve the performance of current processing techniques by around one order of magnitude.

关键词： Interpolation Superresolution Channel estimation Estimation Broadcasting Proposals Multimedia communication

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