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IEEE Conference on Decision and control

作者： Dejan Milutinović Alexander Von Moll Satyanarayana G. Manyam David W. Casbeer Isaac E. Weintraub Meir Pachter Dept. University of California Santa Cruz Electrical and Computer Engineering Santa Cruz CA USA Control Science Center Aerospace Systems Directorate Air Force Research Laboratory Dayton OH USA DCS Corporation Dayton OH USA Dept. of Electrical Engineering Air Force Institute of TechnologyWright-Patterson AFB OH USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In planar pursuit-evasion differential games considering a faster pursuer and slower evader, the interception points resulting from equilibrium strategies lie on the Apollonius circle. This property is instrumental for leveraging geometric approaches for solving multiple pursuit-evasion scenarios in the plane. Here, we study a pursuit-evasion differential game on a sphere and generalize the planar Apollonius set to the spherical domain. We find that the interception point from the equilibrium strategies can leave the Apollonius set boundary and present a condition to keep the intercept point on the boundary. This condition allows for generalizing planar pursuitevasion strategies to the sphere.

关键词： Geometry Instruments Differential games Games

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Equivalence of Dubins Path on Sphere with Geographic Coordinates and Moving Frames

arXiv

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arXiv 2024年

作者： Kumar, Deepak Prakash Darbha, Swaroop Manyam, Satyanarayana G. Casbeer, David W. Pachter, Meir Mechanical Engineering 3123 TAMU College StationTX77843 United States DCS Corporation 4023 Col. Glenn Hwy DaytonOH United States Control Science Center AFRL AIAA United States Department of Electrical and Computer Engineering Air Force Institute of Technology United States

In this article, two methods of addressing path planning for a Dubins vehiclemoving on a sphere are considered, wherein either spherical coordinates or amoving frame are considered to describe the vehicle's motion. The primarycontribution of this article is to show the equivalence of these twoapproaches, which in turn shows that the results known for the movingframe-based description transfer to the model utilizing spherical coordinates. Copyright © 2024, The Authors. All rights reserved.

关键词： Spheres

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Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks

arXiv

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arXiv 2022年

作者： Davydov, Alexander Jafarpour, Saber Proskurnikov, Anton V. Bullo, Francesco The Center for Control Dynamical Systems and Computation University of California Santa Barbara93106-5070 United States The School of Electrical and Computer Engineering Georgia Institute of Technology United States The Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces l1 and l∞. We first establish properties of mappings which are monotone with respect to the non-Euclidean norms l1 or l∞. In analogy with their Euclidean counterparts, mappings which are monotone with respect to a non-Euclidean norm are amenable to numerous algorithms for computing their zeros. We demonstrate that several classic iterative methods for computing zeros of monotone operators are directly applicable in the non-Euclidean framework. We present a case-study in the equilibrium computation of recurrent neural networks and demonstrate that casting the computation as a suitable operator splitting problem improves convergence rates. Copyright © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Enabling Smart Agriculture in Nigeria: Application of Digital-Twin Technology 1

Enabling Smart Agriculture in Nigeria: Application of Digita...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Elijah, Olakunle Rahim, Sharul Kamal A. Emmanuel, Abioye Abiodun Salihu, Yahaya Otuoze Usman, Zubair Gatugel Jimoh, Adinoyi Momoh Wireless Communication Center School of Electrical Engineering University of Technology Malaysia Johor Bahru Malaysia School of Electrical Engineering University of Technology Malaysia Control and Mechatronics Engr. Dept. Johor Bahru Malaysia Kaduna Polytechnic Computer Engineering Department Kaduna Nigeria Saudi Telecommunication Company Riyadh Saudi Arabia

ISBN: (纸本)9781665434935

The digital twin (DT) is an emerging technology and one of the industry 4.0 technologies. The research in the application of DT in agriculture is currently attracting research interest. In this paper, we provide an overview of DT and discuss possible areas in which it can enable smart agriculture in Nigeria. The benefits and challenges of deploying the DT are also presented. Finally, methods that can be adopted in the application of DT towards solving the problems facing Nigeria's agriculture sector are proposed. © 2021 IEEE.

关键词： Agriculture

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Kamal Shahid Muhammad Majid Gulzar Muhammad Ali Raza Salman Habib Hasnain Ahmad Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings.

关键词： Meters Load monitoring Reactive power Artificial neural networks Smart homes Machine learning User interfaces

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Deep Security Detection Framework Based on ATT&CK

引用

International Journal of Network Security 2023年第1期25卷 161-170页

作者： Qiu, Rixuan Fu, Yu Le, Jian Zheng, Fuyong Qi, Gan Peng, Chao Zhang, Yi Li, Yuancheng Liu, Yan Dispatch Monitoring Center State Grid Jiangxi Information & Telecommunication Company Nanchang China School of Electrical Engineering and Automation Wuhan University Wuhan430072 China School of Control and Computer Engineering & North China Electric Power University Beijing102206 China

It is urgent to establish an efficient and comprehensive security system architecture facing the increasingly severe network threat. Aiming at the security problems in information systems, a deep security detection framework based on ATT&CK is proposed. There are three modules: information collection, analysis engine, and security response. The anomaly detection results of the technical layer based on ATT&CK knowledge base association analysis are mapped to the attack links of the tactical layer, thus realizing the bottom-up system security detection. An improved transformer anomaly detection method based on FLOATER positional coding is applied in the security detection framework to realize the early detection of network threats. Based on the continuous dynamic system, the positional coding function is learned to capture the temporal features of the sequence. Then multi-head attention is used to encode the input sequence. Moreover, the model training is optimized by the adversarial method. The experimental results demonstrated that the method applied in this work outperforms traditional deep learning models in terms of detection accuracy and F1 score. © 2022,International Journal of Network Security. All Rights Reserved.

关键词： Anomaly detection

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers

Biological Influence of Low-Frequency Electromagnetic Field ...

引用

ELEKTRO

作者： Robert Cichorowski Olga Kocikowska Marek Bajtoš Roman Radil Ladislav Janoušek Daria Gendosz De Carrillo Sebastian Student Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology liwice Poland Department of Engineering and Systems Biology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Department of Physiology Faculty of Medical Sciences Silesian University of Medicine Katowice Poland Department of Electromagnetic and Biomedical Engineering Faculty of Electrical Engineering and Informational Technology Žilina Slovakia Department of Histology Faculty of Medical Sciences in Zabrze Silesian University of Medicine Zabrze Poland Biotechnology Center Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week.

关键词： Proteins Biological system modeling Estimation Electromagnetic fields

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Brain-inspired feedback for spatial frequency aware artificial networks

Brain-inspired feedback for spatial frequency aware artifici...

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Asilomar Conference on Signals, Systems & computers

作者： Ramin Toosi Mohammad Ali Akhaee Mohammad-Reza A. Dehaqani School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran

Spatial frequency (SF) is a characteristic of an image that could dissociate course and fine shape information. Physiological and psychophysical studies widely investigated the role of various SF contents in image processing. Inspired by the primate brain structure, deep neural networks improved various computer vision tasks such as image classification. Physiological studies show that low SF (LSF) contents of an image could be processed faster to provide feedback to facilitate object recognition. However, this knowledge has not been considered in designing neural network structures. This study introduces SFNet, a new neural network structure that employs an LSF-based feedback mechanism. SFNet is a two-stream structure where one stream is used for LSF processing to provide feedback for image classification. The other stream combines the LSF-based feedback and the HSF processing to form the final decision. The role of the proposed LSF-based feedback in image classification is investigated utilizing the CIFAR100 dataset. The results show that SFNet improves the performance in the presence of SF filtering compared to the equivalent structures.

关键词： Band-pass filters Visualization Shape Filtering Streaming media Visual systems Physiology

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Communication-Efficient Personalized Federated Learning for Green Communications in IoMT

Communication-Efficient Personalized Federated Learning for ...

引用

IEEE International Conference on Communications (ICC)

作者： Ziqi Chen Jun Du Chunxiao Jiang Yunlong Lu Zhu Han Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

关键词： Training Adaptation models Data privacy Costs Accuracy Federated learning computational modeling

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Kartalol: A New Deep Neural Network Framework Based on Transfer Learning for Iris Segmentation and Localization Task: New Dataset for Iris Segmentation

SSRN

引用

SSRN 2022年

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Malek-Ashtar University of Technology Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature, each of which has somehow mitigated some of the mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture that has been initialized with a pre-trained MobileNetV2 model. In addition, we have collected 1000 images to study the detectors in iris recognition scenarios better. The provided dataset (KartalOl) is available free for the research community. In the proposed framework, to better generalizability, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Also, data augmentation technics are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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