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30th European Signal Processing Conference, EUSIPCO 2022

作者： Moral, Özge Taylan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Turkey Department of Computer Engineering Izmir Katip Celebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions are generated by a text decoder, using convolution neural networks (CNN) and recurrent neural networks (RNN), respectively. Although this framework is promising for image captioning, it has limitations in utilizing the encoded visual information for generating grammatically and semantically correct captions in the RNN decoder. More specifically, the RNN decoder is ineffective in using the contextual information from the encoded data due to its limited ability in capturing long-term complex dependencies. Inspired by the advantage of gated recurrent unit (GRU), in this paper, we propose an extension of conventional RNN by introducing a multi-layer GRU that modulates the most relevant information inside the unit to enhance the semantic coherence of captions. Experimental results on the MSCOCO dataset show the superiority of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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Auxiliary Classifier based Residual RNN for Image Captioning 30

Auxiliary Classifier based Residual RNN for Image Captioning

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30th European Signal Processing Conference, EUSIPCO 2022

作者： Çayli, Özkan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Çelebi University Turkey Department of Computer Engineering Izmir Katip Çelebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent advances in deep neural networks, natural and semantic text generation has been improved in image captioning. However, maintaining the gradient flow between neurons in consecutive layers becomes challenging as the network gets deeper. In this paper, we propose to integrate an auxiliary classifier in the residual recurrent neural network, which enables the gradient flow to reach the bottom layers for enhanced caption generation. Experiments on the MSCOCO and VizWiz datasets demonstrate the advantage of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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On Reliability Assessment of a Battery Energy Storage Systems Supporting PV Plants 8

On Reliability Assessment of a Battery Energy Storage System...

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8th Southern Power electronics Conference and the 17th Brazilian Power electronics Conference, SPEC / COBEP 2023

作者： Amorim, William Caires Silva Cupertino, Allan Fagner Mendes, Victor Flores De Sousa, Renata Oliveira De Barros, Rodrigo Cássio Pereira, Heverton Augusto Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Federal University of Juz de Fora Department of Electrical Energy Juiz de Fora Brazil Federal University of Minas Gerais Department of Electrical Engineering Belo Horizonte Brazil Federal University of Tech. - Paraná Department of Electronics Curitiba Brazil Federal University of Recôncavo da Bahia Department of Electrical Engineering Cruz das Almas Brazil Federal University of Viçosa Department of Electrical Engineering Viçosa Brazil

ISBN: (纸本)9798350321128

Battery energy storage system (BESS) has been highlighted for its possibilities of performing ancillary services to the power system, such as voltage and frequency regulation, power quality, power smoothing, and peak shaving, among others. The BESS is very promising especially associated with renewable energies, such as photovoltaic (PV) and wind. Among some of these ancillary services, this work evaluates the battery lifetime and reliability. The proposed methodology includes the combined analysis of rainflow counting algorithm, Monte Carlo simulation, and nonlinear damage accumulation rule for battery reliability assessment. Monte Carlo simulation is used to approximate the battery reliability estimation of practical applications, by considering stochastic variations in the battery lifetime model. The case study is based on a 100 kW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânla-Brazll associated with a mission profile of 100 kW industrial load. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10% of PV nominal power per minute. The peak shaving service presented a decrease in the battery lifetime by approximately 11 % in relation to the deterministic battery lifetime analysis. On the other hand, the peak shaving presented a decrease in the battery lifetime by approximately 17.4 %. In addition, the pack-level analysis revealed a maximum reduction in the battery lifetime by approximately 30%, when compared to the conventional deterministic battery reliability estimation procedure. © 2023 IEEE.

关键词： Battery energy storage system mission profile Monte Carlo peak shaving power smoothing reliability

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Humanoid robot teleoperation system using a fast vision-based pose estimation and refinement method

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IEIE Transactions on Smart Processing and Computing 2021年第1期10卷 24-30页

作者： Sa, Jae-Min Choi, Kang-Sun Interdisciplinary Program on Creative Engineering KOREATECH Cheonan-si31253 Korea Republic of Department of Electrical Electronics and Communication Engineering KOREATECH Cheonan-si31253 Korea Republic of

Humanoid robot teleoperation is a complex task that requires sophisticated hardware and software interfaces. Three-dimensional human pose estimation can be effectively applied to the teleoperation of humanoid robots thanks to its intuitive and easy usability. However, when the human pose is wrongly inferred, it can cause critical and dangerous situations for robot operation. In this paper, a reliable humanoid robot teleoperation system is proposed, which involves the operation of a humanoid robot in an isolated location. The status of the humanoid robot is monitored and communicated to the operator in real time. This improves the telepresence of scenes compared to other methods. On the operator's side, data are retrieved from the humanoid robots and manipulated to control the robot by estimating the operator's pose visually. To reduce the risk of inaccurate pose estimation, pose candidates obtained from different points of view are fused while removing irrelevant poses. Then, a simple yet effective cascade filter that generates smooth motion changes is used for stable and flexible robot manipulation. Experiments confirmed that the proposed system can functions efficiently in real time and generates more accurate motion compared to conventional systems. © 2021 The Institute of electronics and Information Engineers

关键词： Anthropomorphic robots

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Optimizing Battery Consumption for Android-based Automatic Identification System (AIS) Application for Indonesian Fishermen 11

Optimizing Battery Consumption for Android-based Automatic I...

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11th International Conference on electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Arleiny, Arleiny Arfianto, Afif Zuhri Supriyanto, Eko Maulidi, A.K.H. Suwandi, Bondan Kurniawan Bokau, Joe Ronald Program Studi Nautika Politeknik Pelayaran Surabaya Surabaya Indonesia Politeknik Perkapalan Negeri Surabaya Dept. of Marine Electrical Engineering Surabaya Indonesia Politeknik Negeri Semarang Dept. of Electrical Engineering Semarang Indonesia Politeknik Negeri Madura Safety Engineering Technology Sampang Indonesia Indonesia & Research Center for Electronics National Research and Innovation Agency Jakarta Indonesia Prodi Nautika Politeknik Pelayaran Barombong Makassar Indonesia

ISBN: (纸本)9798350355314

Fishing is integral to Indonesia's economy and the livelihood of many citizens. Indonesian fishermen often spend long periods at sea with limited access to power, making energy efficiency crucial for mobile applications, especially those used for navigation and safety. AIS is vital for maritime navigation, providing real-time information on vessel locations, weather, and emergencies. However, high battery consumption can cause AIS apps to fail when needed most. This study aims to optimize battery consumption for a mobile app used by Indonesian fishermen by implementing techniques like batch data transmission, efficient background task management, and optimized location services. The effectiveness of these techniques was evaluated based on key performance indicators such as battery usage, latency, data transmission, and task completion rates. The results show a significant reduction in average battery usage from 3000 mAh to 2000 mAh and improved application performance with latency decreasing from 150 ms to 120 ms. Data transmission reduced by 10% and task completion rates increased from 85% to 95%. Further analysis revealed that Wi-Fi networks are more energy-efficient than cellular networks, with average battery usage of 1800 mAh compared to 2200 mAh. Enhancing the energy efficiency of mobile applications is crucial for users in remote areas with limited power resources. The implemented techniques improved the reliability and efficiency of the AIS application, thereby enhancing the safety and operational effectiveness of Indonesian fishermen at sea. © 2024 IEEE.

关键词： Fisheries

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electrical Characteristic and Power Fluctuations of GAA Si NS CFETs by Simultaneously Considering Six Process Variation Factors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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NFT-Based Hydroponic Automated Control Using Adaptive Network-Based Fuzzy Inference System 2

NFT-Based Hydroponic Automated Control Using Adaptive Networ...

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2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022

作者： Surantha, Nico Vincentdo, Vito Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Tokyo City University Faculty of Scienc and Engineering Department of Electrical Electronics and Communication Engineering Japan

ISBN: (纸本)9781665459440

The rapid population growth and industrial development in developing countries harm the agricultural sector because many agricultural lands are converted into residential or industrial areas. Applying modern agriculture technologies such as hydroponic could help to overcome the problem. However, hydroponics requires special attention in adjusting the pH and nutrient levels to maximize plant growth, so an automated system is needed to manage the process. In this research, a smart hydroponic system is proposed by applying Adaptive Networkbased Fuzzy Inference System (ANFIS) and Internet-of-Things. The IoT system consists of sensor, actuator, and data processing layer is designed to monitor and control the condition of pH and nutrition of the observed plants. Then, the ANFIS algorithm is designed to control the level of pH and nutrition. The experiment results show that the system can automatically adjust the pH and nutrient levels to the expected range for growing plants, and the fuzzy controller made using ANFIS are more accurate and stable than the fuzzy controller made using Sugeno. This study shows that ANFIS has excellent performance when controlling multiple actuators, as long as the data set has great granularity and well defined. © 2022 IEEE.

关键词： Hydroponics

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Fuzzy Classifier Based Colorimetric Quantification Using a Smartphone

Fuzzy Classifier Based Colorimetric Quantification Using a S...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Mercan, Öykü Berfin Kılıç, Volkan Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Izmir Turkey

ISBN: (纸本)9783030511555

The recent advances in smartphone technology have led to the emergence of simple, low-cost and portable platforms that aim reliable qualitative and quantitative analysis of colored solutions. In this paper, we present a new approach based on fuzzy classification for quantitative detection of peroxide relying on color change due to concentration level. The proposed fuzzy classification algorithm was integrated with a smartphone via a cloud system. Our custom-designed Android application was developed with a user-friendly interface to allow the users to capture the image of peroxide strips and crop the region of interest on the image for quantification. Average RGB (Red-Green-Blue) values in the cropped region were calculated to transfer these values to our proposed fuzzy logic classifier via a cloud system to quantify the peroxide content. The proposed fuzzy classifier evaluates the concentration value based on RGB values and sends back to the Android application via the cloud system. Experimental investigations show the advantage of the proposed system for the quantification of peroxide. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smartphones

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A Federated Dictionary Learning Method for Process Monitoring With Industrial Applications

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1017-1028页

作者： Huang, Keke Liu, Xinyi Li, Fanbiao Yang, Chunhua Kaynak, Okyay Huang, Tingwen Central South University School of Automation Changsha410083 China Peng Cheng Laboratory Shenzhen518055 China Bogazici University Department of Electrical and Electronics Engineering Istanbul34342 Turkey University of Science and Technology Beijing Beijing100083 China Texas A&m University at Qatar Science Program Doha23874 Qatar

Edge computing is an indispensable technology in the Industry 4.0 era. The data are stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by the K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the continuous stirred tank heater benchmark process, and the industrial aluminum electrolysis process. Process monitoring can help industrial enterprises monitor the operating status of the industrial process in real time based on the collected process data. However, industrial data are stored at the edge and enterprises pay more attention to the privacy of industrial data. The prohibition of external transmission of raw data leads to challenges for traditional centralized process monitoring methods. The federated dictionary learning method proposed in this article can have the good process monitoring performance under the premise of protecting data privacy, which is superior to some state-of-the-art methods. For example, in the aluminum electrolysis industrial process, the false alarm rate is only 1.91%, and the false discovery rate is 86.23%. The method has high data security and low mod

关键词： Process monitoring

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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