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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Using Ontologies for the Development of the University Educational Ecosystem 22

Using Ontologies for the Development of the University Educa...

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22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

作者： Pustovalova, Natalya Avdeenko, Tatiana Pustovalova, Alexandra Novosibirsk State Technical University Dept. Automation and Computer Engineering Novosibirsk Russia Novosibirsk State Technical University Dept. Applied Mathematics and Computer Science Novosibirsk Russia

ISBN: (纸本)9781665414982

The paper considers the possibility of improving the educational ecosystem by using ontologies in the architecture of the university electronic information environment. Various types of actors of the modern educational ecosystem, interacting within the educational process with educational resources, are presented;their needs that are relevant for its development shortly are analyzed. This paper describes a general approach to using ontology as an element of the IT architecture of a university. Formalizing objects of the educational process in the form of ontology (OWL, RDF models) allows creating various applications for its intellectual support, ensuring the uniformity of architectural solutions and further evolutionary development of the university information environment based on the IEEE LOM and TinCanAPI standards. © 2021 IEEE.

关键词： Ontology

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region

Analysis of Energy Potential of Micro Wind Turbines at Three...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Nikolay Valov Katerina Gabrovska-Evstatieva Ivaylo Stoyanov Tsvetelina Kaneva Seher Kadirova Boris Evstatiev Nicolay Mihailov Dept. of Automation and Electronics University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Science University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Electrical Power Engineering University of Ruse "Angel Kanchev" Ruse Bulgaria Dept. of Computer Systems and Technologies University of Ruse "Angel Kanchev" Ruse Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations – Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m 2 in February, followed by Calarasi with 0.38 kWh/day/m 2 . The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m 2 . The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines.

关键词： Hands Temperature distribution Renewable energy sources Wind energy Wind speed Urban areas Wind turbines Rivers Wind forecasting Investment

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Shen Wang Yaofei Ma Jing Bi Jinhong Yang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

关键词： Energy consumption Federated learning Collaboration Simulated annealing Programming Genetics Servers Resource management Particle swarm optimization Optimization

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Load Shedding automation System on Smart Meter Using Fuzzy Logic Method

Load Shedding Automation System on Smart Meter Using Fuzzy L...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial automation, ICAMIMIA 2021

作者： Farid, Imam Wahyudi Priananda, Ciptian Weried Yuhansyah, Divan Andika Arifiani, Siska Hariadi, Ridho Rahman Priyadi, Ardyono Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Dept. Electrical Automation Engineering Dept. Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. Electrical Automation Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. Informatics Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665499699

The need for electrical energy for the household sector has become a basic need. However, there are still people who complain about expensive electricity bills. In addition, the use of electricity is not recommended to exceed the power capacity which can cause a short circuit. Therefore, a tool is needed, namely a Smart Meter that can monitor the use of electrical energy and can perform load shedding automatically to reduce the occurrence of short circuits. The load shedding automation system uses one of the methods of artificial intelligence technology, namely by using Mamdani's Fuzzy Logic so that it can recognize patterns, classify/identify, predict, and optimize the use of electrical energy. Load shedding is carried out based on priority loads and non-priority loads. From testing the load shedding automation system which was carried out 5x with 6x load combination trials, it resulted that in the 1st to 4th experiments, Relay_Hidup3 output was obtained. To carry out the load shedding process, the average delay time is 2,302 seconds © 2021 IEEE.

关键词： Fuzzy logic

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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PyFCS: A new Python library to create and manipulate Fuzzy Color Spaces

PyFCS: A new Python library to create and manipulate Fuzzy C...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Rafael Vazquez-Conejo María Tejada-Casado Luis Javier Herrera Razvan Ghinea Jose Manuel Soto-Hidalgo Dept. Optics University of Granada Spain Dpt. Optics University of Granada Spain Dpt. Computer Engineering Automation and Robotics University of Granada Spain

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Color, as a fundamental element in digital imagery, plays a vital role across various domains, from art and design to scientific imaging. However, its representation and manipulation are challenged by the inherent subjectivity of human color perception and the limitations of traditional color naming and categorization methods. Addressing these challenges, this paper introduces PyFCS, a novel Python library developed to create and manipulate fuzzy color spaces, offering a more nuanced and realistic approach to color modeling. PyFCS leverages fuzzy logic and the conceptual spaces theory to allow flexible and accurate representation of color categories, overcoming the constraints of crisp boundaries. Building upon the groundwork laid by the Java-based JFCS software, PyFCS harnesses Python's strengths in computation and its rich ecosystem of libraries for enhanced data processing and image analysis capabilities. The library's user-friendly and scalable design, coupled with its integration with Python's computational resources, makes it accessible to a broad range of users, from beginners to advanced researchers. The efficacy of PyFCS is demonstrated through a case study involving the creation of fuzzy color spaces from a paint catalog and its subsequent application in mural painting, exemplifying its practical utility in real-world scenarios. Furthermore, this paper details how PyFCS seamlessly integrates with other libraries to offer a comprehensive color analysis, exemplified by linguistic description of colors present in an urban mural painting. This study introduces a sophisticated color space tool and enhances scientific collaboration by providing open-source access to PyFCS on GitHub.

关键词： Fuzzy logic Accuracy Image color analysis Color Machine learning Media Libraries

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Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness

Coordinating Cooperative Perception in Urban Air Mobility fo...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Timo Häckel Luca Von Roenn Nemo Juchmann Alexander Fay Rinie Akkermans Tim Tiedemann Thomas C. Schmidt Dept. of Computer Science Hamburg University of Applied Sciences Hamburg Germany Institute of Automation Technology Helmut Schmidt University Hamburg Germany Dept. of Automotive and Aeronautical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The trend for Urban Air Mobility (UAM) is growing with prospective air taxis, parcel deliverers, and medical and industrial services. Safe and efficient UAM operation relies on timely communication and reliable data exchange. In this paper, we explore Cooperative Perception (CP) for Unmanned Aircraft Systems (UAS), considering the unique communication needs involving high dynamics and a large number of UAS. We propose a hybrid approach combining local broadcast with a central CP service, inspired by centrally managed U-space and broadcast mechanisms from automotive and aviation domains. In a simulation study, we show that our approach significantly enhances the environmental awareness for UAS compared to fully distributed approaches, with an increased communication channel load, which we also evaluate. These findings prompt a discussion on communication strategies for CP in UAM and the potential of a centralized CP service in future research.

关键词： Communication channels Market research Reliability Aircraft Vehicle dynamics Public transportation

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Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness

arXiv

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arXiv 2024年

作者： Häckel, Timo von Roenn, Luca Juchmann, Nemo Fay, Alexander Akkermans, Rinie Tiedemann, Tim Schmidt, Thomas C. The Dept. of Computer Science Hamburg University of Applied Sciences Hamburg Germany The Institute of Automation Technology Helmut Schmidt University Hamburg Germany The Dept. of Automotive and Aeronautical Engineering Hamburg University of Applied Sciences Hamburg Germany

关键词： Air mobility

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