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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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Cold Ironing Techno-Economical Study for a Port with Photovoltaic Plants

SSRN

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SSRN 2024年

作者： Tsekouras, George J. Lamprinidi, Eleni P. Orfanos-Pepainas, Stamatios Kanellos, Fotios D. Prousalidis, John M. Vlachos, Andreas Tsirekis, Constantinos D. School of Electrical & Computer Engineering National Technical University of Athens Greece Department of Electrical and Electronics Engineering Faculty of Engineering University of West Attica Greece School of Electrical & Computer Engineering Technical University of Crete Greece School of Naval Architecture & Marine Engineering National Technical University of Athens Greece Department of Long Term Energy Planning Renewable Energy Sources & Environmental Policy Regulatory Authority of Energy Greece Strategy & System Planning Department Hellenic Independent Power Transmission Operator Greece

According to European regulation 2023/1804/EU, all member states shall ensure the availability of cold ironing (CI) in large ports for at least 90% of the total number of port calls of seagoing ships above 5,000 gross tonnes, by the end of 2029. This is a measure expected to improve air quality, especially in the vicinity of the port’s urban environment. Preliminary studies show that there are financial viability issues related to CI installations, under the current electricity pricing policies and the high investment cost for CI infrastructure. In this paper, the financial viability of the penetration of photovoltaic (PV) units optimized from spatial and power produced point of view is investigated. The proposed method is applied to various ship types, indicatively for the Thessaloniki Port, taking into consideration the connection with the Medium Voltage (MV) electricity distribution grid, as well as the construction, operation, and maintenance costs, under modified economic criterion of benefit cost ratio (BCR). © 2024, The Authors. All rights reserved.

关键词： Air quality

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Digital Over-the-Air Federated Learning in Multi-Antenna systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps

arXiv

引用

arXiv 2022年

作者： Kim, HyunJin Shin, Jungwoo Del Barrio, Alberto A. School of Electronics and Electrical Engineering Dankook University 152 Jukjeon-ro Suji-gu Gyeonggi-do Yongin-si Korea Republic of Department of Computer Architecture and System Engineering Complutense University of Madrid Madrid Spain

This paper proposes a training method having multiple cyclic training for achieving enhanced performance in low-bit quantized convolutional neural networks (CNNs). Quantization is a popular method for obtaining lightweight CNNs, where the initialization with a pretrained model is widely used to overcome degraded performance in low-resolution quantization. However, large quantization errors between real values and their low-bit quantized ones cause difficulties in achieving acceptable performance for complex networks and large datasets. The proposed training method softly delivers the knowledge of pretrained models to low-bit quantized models in multiple quantization steps. In each quantization step, the trained weights of a model are used to initialize the weights of the next model with the quantization bit depth reduced by one. With small change of the quantization bit depth, the performance gap can be bridged, thus providing better weight initialization. In cyclic training, after training a low-bit quantized model, its trained weights are used in the initialization of its accurate model to be trained. By using better training ability of the accurate model in an iterative manner, the proposed method can produce enhanced trained weights for the low-bit quantized model in each cycle. Notably, the training method can advance Top-1 and Top-5 accuracies of the binarized ResNet-18 on the ImageNet dataset by 5.80% and 6.85%, respectively. © 2022, CC BY.

关键词： Large dataset

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients

Design and Implementation of a QoL Improvement System for Vi...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Roy Abi Zeid Daou Jad Beaino Johnny Hawat Ramy Ibrahim Ali Hayek Josef Boercsoek Jose Javier Serrano Olmedo Mechatronics department Faculty of Engineering Université La Sagesse Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department Lippstadt 2 Hardware Engineering University of Applied Science Hamm-Lippstadt Hamm Department of Computer Architecture and System Programming Faculty of Electrical Engineering/Computer Science University of Kassel Germany Lab of Bioinstrumentation and Nanomedicine Centro de Tecnología Biomédica Universidad Politécnica de Madrid Spain

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.

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Improvement approaches in the field of reliability forecasting for components/systems in automobile applications with respect of multivariate stress factors 29

Improvement approaches in the field of reliability forecasti...

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29th European Safety and Reliability Conference, ESREL 2019

作者： Krini, Abderrahim Börcsök, Josef Engineering of Remanufacturing and Quality Robert Bosch GmbH Schwäbisch Gmünd Germany Department of Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (纸本)9789811127243

To remanufacture automobile systems, it is important to understand the future failure rate of the serially produced systems. In addition, remanufacturing departments need the information about the number of cores to remanufacture, the condition of these cores, as well as the number of replacement systems the market needs. Based on the existing univariate, bivariate and multivariate models a new improved forecasting model is developed, which considers practical factors like export of the vehicle to other countries, damage of the entire vehicle, reporting delay between production and in-market time or between vehicle failure and failure reporting time. This paper presents all necessary tools to achieve the improvement and its implementation in MATLAB. Copyright © 2019 European Safety and Reliability Association.

关键词： Commerce

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Forecasting reliability of components/systems in automobile applications with respect to three variables of stress in field based on neural network 29

Forecasting reliability of components/systems in automobile ...

引用

29th European Safety and Reliability Conference, ESREL 2019

ISBN: (纸本)9789811127243

Nowadays the information of future rates of systems produced in series appears to be crucial to the production plan, especially for remanufacturing departments. Several questions need to be answered: the number of cores to remanufacture the condition of those cores as well as the number of replacement system the market requires. Based on a bivariate prognosis model, a multivariate model is developed to take more variables of stress in field into account. What's more, the fitting process has been optimized by using neural networks. This paper presents all necessary tools to perform a multivariate lifetime prognosis as well as the implementation in MATLAB. © 2019 European Safety and Reliability Association. Published by Research Publishing, Singapore.

关键词： Production control

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Reliability Forecasting for Components/systems with respect to 1-, 2- And 3- Dimensions of Stress Factors 30

Reliability Forecasting for Components/Systems with respect ...

引用

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020

作者： Krini, Abderrahim B rcs k, Josef Engineering of Remanufacturing and Quality Robert Bosch GmbH Schw bisch Gm nd Germany Department of Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (纸本)9789811485930

Remanufacturing, a popular term in the automotive industry, helps to reduce the need for raw materials, increase profitability, is an effective solution to the challenges of postal items and also benefits the environment. For the remanufacturing department, the reliability and failure rate of serial products is particularly important, and therefore the accurate prediction of failure rates is also important. It must be ensured that the product meets its reliability requirements. Existing one, two and three dimensional reliability prediction models predict field failures as a function of time, based on the stress experienced by the components/systems in the field until failure. The existing models have been extended to include the parameters that allow us to predict the reliability of refurbished systems. Improvements such as registration delays, reporting delays and the corrected candidate calculation, which deals with whether the product is still within the warranty period after the observation period, are present in the models and are applied to the systems. This reduces the incompleteness of warranty data due to the influence of external factors with MATLAB. The reliability of systems can be predicted with the lifetime prediction and further conclusions can be drawn for future development. In the verification process, the predicted field failures of the models are compared with the real field failures. ©ESREL2020-PSAM15 Organizers.

关键词： Failure rate

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Improvement and verification methods for components/systems in automobile applications in the field of reliability forecasting with respect to one and two dimensions of stress factors 30

Improvement and verification methods for components/systems ...

引用

30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020

ISBN: (纸本)9789811485930

The details of future failures of systems that are serially produced are important for the remanufacturing departments to frame an optimal strategy for the post series supply. The existing one-dimensional and two-dimensional reliability prediction models predict field failures as a function of time based on the stress which the components/systems experience in the field until failure. In day-to-day situations the failure forecasts depend on many external factors like registration delay, which is the delay between the production of a system and its registration in market, reporting delay, which is the delay between the field failure and its reporting time, the export of vehicles to other countries or permanent damage caused to the vehicles. The improvement procedures are integrated into the existing reliability forecast models to reduce the incompleteness of the warranty data due to the influence of external factors using MATLAB. The verification process is to compare the forecasted field failures by the existing models with the actual field failures. The prognosis is carried out based on different failures data sets. With the help of sales data from Original Equipment Manufacturers, the forecasted failures Year to Date are verified. A validation of the results is carried out to check the impact of the influencing factors. © ESREL2020-PSAM15 *** by Research Publishing, Singapore.

关键词： Forecasting

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