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Technology, systems and Applications (VLSI-TSA/VLSI-DAT), International VLSI Symposium on

作者： Zhonghai Lu Mingrui Liu Department of Electrical Engineering School of EECS KTH Royal Institute of Technology Stockholm Sweden Embedded Systems master program School of EECS KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350360349

ISBN: (纸本)9798350360356

Inspired by PiN, Processing in Network-on-Chip (NoC), we propose a computational NoC as a convolution engine for accelerating convolutional neural networks in hardware. In contrast to traditional compute architectures where computation and communication are conducted serially and in separation, our computational NoC enables in-transit computation, meaning that computation is performed while packets are transferred in the network. In the paper, we present the router architecture that supports the novel in-transit computation concept, and use a running example to detail the entire convolution process in the computational NoC. Finally, we show simulated performance results in comparison with traditional NoC-based convolution engine.

关键词： Convolution Simulation Computer architecture Network-on-chip Very large scale integration Hardware Pins

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Physics-Informed Neural Network-Based Predictive Model for Virtual Synchronous Machines in Inverter-Based Microgrids

Physics-Informed Neural Network-Based Predictive Model for V...

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International Conference on systems and Control (ICSC)

作者： Abdullah Baraean Mahmoud Kassas Ahmed Bawazir Department of Electrical Engineering College of Engineering and Physics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at KFUPM Dhahran Kingdom of Saudi Arabia Interdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

Virtual synchronous machines (VSMs) have recently emerged as a practical solution for incorporating virtual mechanical inertia into power systems using power electronic converters to add frequency support to the grid to handle the increase in the rate of change of frequency (RoCoF) that may lead to stability problems. Analyzing this concept compared to frequency-droop-based control schemes provides valuable physics-based insights into the tuning and operation of VSM controllers. However, the design and control of VSMs necessitate accurate predictive models that can adapt to microgrids' dynamic and complex nature. Physics-informed neural network (PINN) is an emerging discipline that combines principles from physics and neural networks to enhance the accuracy and efficiency of predictive models. By incorporating the fundamental laws and equations that govern a system, PINNs guide the learning process of neural networks and improve the interpretability of the resulting models. This study proposes PINNs trained to predict the VSMs model while ensuring compliance with the fundamental principles of physics described by the conventional swing equation governing synchronous machines (SMs). The effectiveness of the predicted model has been verified via simulations carried out using MATLAB software, demonstrating evidence of its potential to improve microgrid stability and power quality.

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Revolutionizing electric robot charging infrastructure through federated transfer learning and data route optimization

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Cluster Computing 2025年第6期28卷 1-14页

作者： Hayawi, Kadhim Sajid, Junaid Malik, Asad Waqar Trabelsi, Zouheir College of Interdisciplinary Studies Computational Systems Zayed University Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Computer Engineering National Strategic Planning and Analysis Research Center Mississippi State University Starkville USA Department of Information System and Security United Arab Emirates University Al Ain United Arab Emirates

The growing prevalence of electric robots within industrial settings calls for innovative solutions to address the limitations in the existing charging infrastructure, ultimately improving the efficiency and sustainability of these robotic systems. However, internet connectivity, which may be required for prior scheduling and charging, is often unavailable due to security concerns, harsh conditions, operational control requirements, and regulatory compliance. Protecting sensitive data, ensuring reliability, maintaining production control, and adhering to industry-specific regulations drive the decision to isolate internal networks from the internet. In such scenarios, locating a suitable charging spot for ERs, particularly during urgent situations with critically low battery levels, becomes challenging. This paper proposes a scheduling algorithm that relies on an ER-to-ER mechanism for charging at available stations. Moreover, since the process relies on ad-hoc communication susceptible to potential malicious actions by intermediate entities, it poses a risk to the overall process. To mitigate this risk, we propose a machine learning-driven method to detect malicious connections, safeguarding the network’s stability. Moreover, to reduce network congestion resulting from routing requests, we have modified the Grey-wolf optimization algorithm specifically for industrial settings. This modification enables the efficient scheduling of charging requests at optimal stations, all while prioritizing the privacy of robots.

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Structural health monitoring: Frequency domain analysis of beam with breathing crack 2

Structural health monitoring: Frequency domain analysis of b...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Gunawan, F.E. Kanto, Y. Kamil, I. Sutikno Tran, H.N. Industrial Engineering Department BINUS Graduate Program-Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Mechanical Systems Engineering College of Engineering Ibaraki University Ibaraki Japan Department of Industrial Faculty of Engineering Andalas University Padang Indonesia Department of Mechanical Engineering Department Faculty of Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Automotive Engineering Faculty of Transportation Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

Structural Health Monitoring (SHM) system can be considered as an Internet-of-Things (IoT) system implemented to engineering structure to provide real-time information on the integrity of the structure. In doing so, the system acquires data from various sensors distributed across the structure and infers damage sensitive quantities. In the research, we proposed a damage index derived from the governing dynamics of the structure in the frequency domain. The proposal was empirically verified by applying the index to analyze the case of a cracked cantilever beam subjected to a harmonic load. The value of the damage index was computed at some points surrounding the crack to understand the effect of the distance of the monitoring point to the crack to the index sensitivity. The results suggest that the index is sensitive to the crack particularly when the observation point is near the crack. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Advancing Precision Aquaculture Through Big Data Analytics and Machine Learning in Canadian Fish Farming

Advancing Precision Aquaculture Through Big Data Analytics a...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Bravo, Francisco Amorim, Joana Amirkandeh, Melika Besharati Bodorik, Peter Cerqueira, Vitor Gomes, Nuno R.C. Korus, Jennie Oliveira, Mariana Parent, Marianne Pimentel, João Reilly, Derek Sclodnick, Tyler Grant, Jon Filgueira, Ramón Whidden, Christopher Universidad Adolfo Ibañez Facultad de Ingenieria y Ciencias Santiago Chile Dalhousie University Faculty of Computer Science Halifax Canada University of Porto Faculty of Engineering Portugal Barcelona Spain Innovasea Marine Systems Bedford Canada Charlottetown Canada Dalhousie University Department of Oceanography Halifax Canada Marine Affairs Program Dalhousie University Halifax Canada

ISBN: (纸本)9798331540081

The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

关键词： Aquaculture

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Design of an Advanced System-on-Chip Architecture for Chaotic Image Encryption

Design of an Advanced System-on-Chip Architecture for Chaoti...

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Symposium on Integrated Circuits and systems Design (SBCCI)

作者： Arthur M. Lima Lucas G. Nardo Erivelton Nepomuceno Janier Arias-Garcia Jones Yudi Graduate Program in Mechatronic Systems University of Brasília Brasília Brazil Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Electronic Engineering Center for Ocean Energy Research Maynooth University Maynooth reland Department of Electronic Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Mechanical Engineering University of Brasília Brasília Brazil

With the rise of interconnected systems, security has become a crucial concern. As a result, there has been a growing interest in developing low-cost embedded cryptographic algorithms that are lightweight and can be integrated into System-on-Chip (SoC) devices. Digital chaotic systems have emerged as a promising approach for building secure communication systems, where various cryptosystems utilize chaotic dynamics to encrypt images into noise-like representations. Recent studies have demonstrated that finite-precision error can be used to derive chaos, which can be systematically applied to encrypt images. However, there is a lack of research on how SoC-based design constraints affect the overall performance of image encryption systems, especially for applications that are sensitive to latency. This study aims to address this gap by presenting an efficient architecture that explores a hardware/software co-design for image encryption based on finite-precision error, specifically designed for resource-limited devices and latency-sensitive applications. Our platform performs the capture and encryption of images with a size of 320 × 240. Using a benchmark image, results show that the developed cryptosystem architecture can encrypt images efficiently while offering low hardware occupation.

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Preference Location of Assignment engineering Professionals in the Middle East: A Conjoint Analysis Approach 22

Preference Location of Assignment Engineering Professionals ...

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4th International Conference on Management Science and Industrial engineering, MSIE 2022

作者： Pineda, Jan Russell P. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn Ayuwati, Irene Dyah School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines Entrepreneurship Department Binus Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok10220 Thailand Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Information Technology Institut Teknologi Telkom Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

This study examines the preference of location of assignment of engineering professionals working in the Middle East. A survey method that allows for the use of structured questionnaire was adopted. The study was conducted to engineering professionals with a total of 55 participants. Data gathered was coded and analyzed using Conjoint Analysis. Results revealed that location of assignment in design offices or cities is still preferred over remote area assignments with attractive salary and other additional compensation packages. The research results recommend employers to also consider non-monetary aspects in order to retain valued employees in their companies. © 2022 ACM.

关键词： Location

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Koopman-LQR Controller for Quadrotor UAVs from Data

Koopman-LQR Controller for Quadrotor UAVs from Data

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Smart Mobility (SM), IEEE International Conference on

作者： Zeyad M. Manaa Ayman M. Abdallah Mohammad A. Abido Syed S. Azhar Ali IRC-ASE and the Department of Aerospace Engineering King Fahd University for Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence and the Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350390131

ISBN: (纸本)9798350390148

Quadrotor systems are common and beneficial in many fields, but their intricate behavior often makes it challenging to design effective and optimal control strategies. Some traditional approaches to nonlinear control often rely on local linearizations or complex nonlinear models, which can be inaccurate or computationally expensive. We present an approach based on data to identify the dynamics of a given quadrotor system using Koopman operator theory which offers a linear, but infinite dimensional representation of nonlinear dynamics. This facilitates the use of globally linear models to get an approximation for the nonlinear systems, which can be analyzed and controlled using standard linear optimal control techniques. We leverage the method of extended dynamic mode decomposition (EDMD) to identify the Koopman operator from data. We demonstrate that the identified model can be stabilized and controlled by designing a controller using the linear quadratic regulator (LQR).

关键词： Regulators Computational modeling Scalability Optimal control Predictive models Trajectory Nonlinear dynamical systems Topology Standards Quadrotors

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Dynamic Performance Assessment Through Simulated Model of Photovoltaic Water Pumping System for Off-Grid Communities

Dynamic Performance Assessment Through Simulated Model of Ph...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Sohaib Zafar Muhammad Khalid Department of Electrical Engineering Punjab Tianjin University of Technology Lahore Pakistan Interdisciplinary Research Center for Renewable Energy and Power Systems KFUPM Dharan Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals (KFUPM) Dharan Saudi Arabia

The provision of the clean drinking water in remote and energy constrained areas have been a persistent problem in the developing world. Developing countries in general have financial constraints to expand electric grid, thus impeding the energy servicing for essential provisions. To this end, Solar photovoltaic-powered water pumping systems (SPVWPS) have long been explored as a highly potent solution for provision of water in off-grid areas. In this context, many modeling exercises have been conducted to develop customized and optimal solutions for specific climatic conditions of different regions in the world. Most of these models simulate or validate critical aspects of the systems, such as pumped volumetric flow using climatic data as model input. These models are centered around different aspects of the energy conversion chain and models of solar photovoltaic arrays. It is rare to see models involving the volumetric flow rate by users as an input parameter. This study incorporates volumetric flow rate of the water usage as an input parameter. Such a model has not been developed and simulated for particular climatic conditions of Pakistan. This study used climatic data (Ambient Temperature and Irradiance) and water usage volumetric flow rate as input parameters. The water usage flow rate helps incorporate user behavior in the model, an under explored research gap in such SPVWPS models. The model was simulated on MATLAB Simulink, using the climatic data for Lahore, Pakistan. The representative reliability of the system design were assessed. The model serves as a simple, yet effective tool to gauge the reliability of SPVWPS designed on a static lump sum water demand basis in a dynamic setting. This reduces the chances of the over or under sizing the system and gives a preliminary estimate of the need and feasibility to incorporate cutting-edge technologies such as AI in such systems.

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Determining Marketing Strategy for Coffee Shops with Conjoint Analysis

Determining Marketing Strategy for Coffee Shops with Conjoin...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yogi Tri Prasetyo Krisna Chandra Susanto Sheree Mae A. Asiddao Omar Paolo Benito Jui-Hao Liao Michael Nayat Young Satria Fadil Persada Reny Nadlifatin International Bachelor Program in Engineering Yuan Ze University Chung-Li Taiwan Department of Industrial Engineering and Management Yuan Ze University Chung-Li Taiwan School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Coffee has become an important part of any individual nowadays. The purpose of this study is to determine the marketing strategy for the coffee shops. This study uses several coffee attributes such as coffee type, sugar level, temperature, coffee beans, and frappe were analyzed by utilizing a conjoint approach. A questionnaire was utilized for this research which collected 500 respondents. Conjoint analysis showed that sugar level was the most important coffee attribute (43.013%), followed by temperature (37.75%), coffee type (14.701%), coffee beans (2.601%), and frappe (1.936%). Interestingly, cold cappuccino with Arabica coffee bean, less sugar, and with frappe was found to be the best combination for marketing strategy. This study can be utilized for developing the marketing strategy of coffee shops. By Understanding the relationship between the factors, coffee shops can find out which factors influence customers the most.

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