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Cold ironing techno-economical study for a port with photovoltaic plants

e-Prime - Advances in electrical engineering, Electronics and Energy 2024年 8卷

作者： Lamprinidi, Eleni P. Tsekouras, George J. 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 5000 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

关键词： Cold ironing Feasibility study Photovoltaic plant Shore connection

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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A Novel Switching Asynchronous Sampled-Data Scheme: Implementations in Interconnected Feedback systems

A Novel Switching Asynchronous Sampled-Data Scheme: Implemen...

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American Control Conference (ACC)

作者： Xiaoyu Wang Feng Xiao Qian Feng State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Sampled-data control is continually under research exploration and development to optimally coordinate the limited communication resources in networked control systems. In this paper, a novel switching asynchronous sampleddata framework with double-checking consisting of two distinct sampling schemes is proposed. An established method involves designing the switching sampling scheme with event-triggered and time-triggered mechanisms. We also introduce the concept of switching event-triggered control (SETC), by which a positive minimum sampling interval can be guaranteed effectively. By an integral method and Barbalat's Lemma, sufficient conditions that ensure the stability of interconnected linear systems are derived under the SETC. Numerical examples are presented to demonstrate the effectiveness of the proposed methodology.

关键词： Linear systems Sufficient conditions Networked control systems Delay effects Switches Trajectory Numerical stability

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ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE system CONSIDERING UNCERTAIN FREQUENCY CHANGE

ROBUST OPTIMAL SCHEDULING FOR DISTRIBUTED ENERGY STORAGE SYS...

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CIRED 2022 Shanghai Workshop

作者： Cao, Yongji Zhang, Hengxu Zhang, Xiaoning Li, Changgang Qin, Hao Gao, Zhimin Centre for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781839538391

With the rapid development of distributed renewable energies, the number and severity of the active power disturbances that affect frequency stability have increased. The distributed energy storage system (DESS) characterized by high flexibility and dispatchability can provide distribution network with frequency regulation services after disturbances. This paper proposes a robust optimal scheduling approach for DESSs considering uncertain frequency changes. The concept of the probabilistic transient frequency deviation security margin (PTFDSM) is presented to quantify the power deficit that the system can tolerate. And an extended system frequency response model is employed to obtain the PTFDSM. Then, a robust optimization model is established to minimize the network loss and maintain the operating point of DESSs within the PTFDSM for frequency stability. The AC power flow constraints are introduced into the optimization model. And the second-order cone relaxation method is used to deal with the nonlinear constraints through relaxation variables. Based on the column-and-constraint generation method, the relaxed second-order cone optimization model is solved, and scheduled DESS power is attained. A case study is carried out to verify the effectiveness of the proposed approach. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Energy storage

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Dissipative State Feedback of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition Concept

SSRN

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SSRN 2024年

作者： Feng, Qian Zheng, Wei Xing Wang, Xiao Yu Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Stabilizing autonomous linear time delay systems, particularly when addressing an unlimited number of pointwise and distributed delays (DDs) under dissipative constraints, poses a significant challenge. Existing solutions are often hindered by theoretical limitations, numerical obstacles, or an inability to address the complexities of the delay integral kernels. In this paper, we propose a unified framework to tackle the above problem by employing the concept of the Kronecker-Seuret decomposition (KSD) for matrix-valued functions, which we recently have developed for the analysis of complex delay structures in coordination with the Krasovski\u{\i} functional approach. Our strategy can simultaneously address two distinct control problems, where the matrix kernels of DDs can contain an unlimited number of square-integrable functions. We show in detail how the KSD can factorize and approximate different kernel functions simultaneously without introducing conservatism. Furthermore, the use of KSD also enables us to construct complete-type functionals, whose integral kernels can include any number of weakly differentiable and linearly independent functions, underpinned by the utilization of novel integral inequalities derived from the least-squares principle. The solution to each synthesis problem comprises two theorems accompanied by an iterative algorithm, which can be utilized as a single package to compute controller gains, thus eliminating the need for nonlinear solvers. We present the testing results of two challenging examples, which could not be addressed by existing methods, to demonstrate the effectiveness of our methodology. Additionally, the paper reviews recent advancements in the research of time-delay systems, providing a valuable reference for both emerging and established researchers. © 2024, The Authors. All rights reserved.

关键词： State feedback

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Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

arXiv

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arXiv 2022年

作者： Yang, Chengran Xu, Bowen Khan, Junaed Younus Uddin, Gias Han, Donggyun Yang, Zhou Lo, David School of Computing and Information System Singapore Management University Singapore Department of Electrical and Computer Engineering University of Calgary Alberta Canada

APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the great success achieved by pre-trained models on many software engineering tasks, this study fine-tunes six pre-trained models for the aspect-based API review classification task and compares them with the current SOTA solution on an API review benchmark collected by Uddin et al. The investigated models include four models (BERT, RoBERTa, ALBERT and XLNet) that are pre-trained on natural languages, BERTOverflow that is pre-trained on text corpus extracted from posts on Stack Overflow, and CosSensBERT that is designed for handling imbalanced data. The results show that all the six fine-tuned models outperform the traditional machine learning-based tool. More specifically, the improvement on the F1-score ranges from 21.0% to 30.2%. We also find that BERTOverflow, a model pre-trained on the corpus from Stack Overflow, does not show better performance than BERT. The result also suggests that CosSensBERT also does not exhibit better performance than BERT in terms of F1, but it is still worthy of being considered as it achieves better performance on MCC and AUC. © 2022, CC BY.

关键词： Classification (of information)

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CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

arXiv

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arXiv 2023年

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Islamabad44000 Pakistan The Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of The School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging Non-Volatile Memories (NVMs), with their better scalability, non-volatility and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This paper proposes CRAFT, i.e., Criticality-Aware Fault-Tolerance Enhancement Techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of non-critical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs against s

关键词： Errors

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system for coronavirus classification based on a new textural descriptor

System for coronavirus classification based on a new textura...

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International Conference on Sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Fathia Boubakri Norhene Gargouri Alima Damak Masmoudi Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering Computer Imaging and Electronic System Group Sfax Engineering School University of Sfax Sfax Tunisia

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve $(A_{z})$ of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists.

关键词： COVID-19 Radiography Measurement Reactive power Pulmonary diseases Lung Feature extraction

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Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units

arXiv

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arXiv 2024年

作者： Manalu, Gabriel D.M. Christian, Mulomba Mukendi You, Songhee Choi, Hyebong Department of Computer Science and Electrical Engineering Handong Global University Korea Republic of Department of Advanced Convergence Handong Global University Korea Republic of School of Spatial Environment System Engineering Handong Global University Korea Republic of School of Global Entrepreneurship and ICT Handong Global University Korea Republic of

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Intensive care units

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