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Energy Conversion and Economics 2022年第3期3卷 156-169页

作者： Mohan, Vivek Antonis, Anjula Mary Jisma, M. Krishnakumar, Nila Bu, Siqi Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Sustainable Energy Faculty of Electrical Engineering Applied Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Electrical Engineering The Hong Kong Polytechnic University Kowloon SAR Hong Kong

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost-effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre-tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value-at-risk (CVaR) approach;and (2) by co-optimizing the conflicting profits of the utility and prosumers using non-dominated sorting particle swarm optimization (NSPSO) to obtain a risk-adjusted Pareto-optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid-connected CIGRE low-voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare-optimized and cost-efficient. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Profitability

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A Fair Multi-Partner Profit Allocation for Islanded Micro-grid 2

A Fair Multi-Partner Profit Allocation for Islanded Micro-gr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Oladejo, Ismaheel O. Folly, Komla A. Brahma, Biswajit Ajagbe, Sunday Adeola Bandyopadhyay, Anjan Awotunde, Joseph Bamidele Department of Electrical Engineering The Polytechnic Ibadan Nigeria Department of Electrical Engineering University of Cape Town South Africa McKesson Corporation 32559 Lake Bridgeport St FremontCA94555 United States Department of Computer Engineering First Technical University Ibadan200255 Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Nigeria

A micro-grid (MG) is regarded as a localized small-scale power supply system designed for providing electrical power for a remote community, which may be connected to the grid. The paper considers six participants' sites in a remote community. The peak energy consumption patterns for these participants must be different to ensure coalition for mutual benefits and this would facilitate efficient utilization of energy resources. This community concerned may include rural communities having participants such as schools, housing estates, industrial sites, etc. The propose approach would ensure that many participants and their needs are captured and optimized profit of the participants would ensure mutual benefits. In islanded MG, most of the cost/profit distributions in energy trading problems are done based on the self-interest of the participants. Some consider a fair (acceptable or reasonable) cost/profit distribution using the cooperative game theory (CGT) based on the Nash bargaining solution (NBS). However, this method leads to a high degree of dissatisfaction. To improve the fairness and mutual benefits for all MG participants, a new flexible approach is developed. Specifically, CGT which uses a generalized NBS (GNBS) has been proposed, which relaxes certain axiom of the NBS to ensure that fairness of game theory is achieved. The approach allows the profit of the MG participants to be shared using a mechanism based on the negotiation power (i.e. weighted fairness) so that the MG would attain an improved economic outcome. Empirical evaluation of the approach indicates that overall profits using CGT is higher when compared with an independent approach. Moreover, different in negotiation power is achieved in which higher profit is allocated to any participant based on participant's mutual agreement in achieving fair profit distribution. A robust optimization algorithm called Teaching-Learning-Based-Optimization (TLBO) is developed to obtain optimal results. The heur

关键词： Profitability

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Design of a Low-Cost Vector Based Muometric Navigation System

Design of a Low-Cost Vector Based Muometric Navigation Syste...

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IEEE Southeastcon

作者： Maxx A. Lowy Muhammad Sana Ullah Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This paper presents a low-cost vector based Muometric navigation system for precision location finding applications where GPS does not work. The proposed system obtains the amplitude information from a SiPM photodetector pulse using low-cost techniques. The system then obtains the location of the device using the data from a reference detector.

关键词： Scintillators Voltage measurement Mesons Navigation Microcontrollers Detectors Vectors Transistors Photonics Global Positioning System

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Cyberattack Detection and Detector Limitations for Stochastic Processes

Cyberattack Detection and Detector Limitations for Stochasti...

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IEEE Southeastcon

作者： Onur Toker Electrical and Computer Engineering Florida Polytechnic University Lakeland FL

In this paper, we consider cyberattack detection and detector performance limitations in the context of stochastic processes. In cyberattack detection, the main problem is to make a decision based on certain observations, and depending on the methodology used for detector design, one can have varying degrees of performance. A “good” detector will catch almost all anomalies (Low type II error rate, $P_{II}$ ), and will generate almost no false alarms (Low type I error rate, $P_{1}$ ). Therefore, we define $1-(P_{I}+P_{II})/2$ as the detector performance metric. In our mathematical framework, we consider two stochastic processes; one corresponding to normal system operation and the other corresponding to cyberattack. The detector will be the “device” that will use certain number of observations and decide whether the system is operating under normal mode or not. Minute anomalies that may be hard to detect with few number of observations will be increasingly easier to detect as we gather more and more observations. In cyberattack detection applications, there are two fundamental requirements: (1) Detectors should have high performance, and (2) Attack assessment should be made as quickly as possible. These two requirements are naturally conflicting with each other, and finding a good balance between the two is essential for any kind of cyberattack detector design. In this paper, we study the best achievable cyberattack detector performance as a function of the number of observations, and derive practical bounds for “good” detector design. As an application of the developed theory, we will consider the false data injection (FDI) attack and derive bounds on the minimum number of observations to achieve “good” detector performance.

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Text-dependent Speaker Verification Challenge 2024: Exploring Shared and User-defined Passphrases

Text-dependent Speaker Verification Challenge 2024: Explorin...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zeinali, Hossein Lee, Kong Aik Alam, Jahangir Burget, Lukáš Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computer Graphics and Multimedia Brno University of Technology Brno Czech Republic Computer Research Institute of Montreal Montreal Canada

ISBN: (纸本)9798350368741

In contrast to text-independent speaker verification, which has received significant attention from researchers and has many competitions dedicated to it, text-dependent speaker verification (TdSV) has been less explored recently. The TdSV Challenge 2024 was organized to analyze and explore novel methods for this type of speaker verification and aims to motivate participants to develop new approaches to TdSV, conduct comprehensive analyses, and investigate advanced techniques such as self-supervised learning. This challenge builds on the achievements of the short-duration speaker verification (SdSV) Challenges held in 2020 and 2021 and focuses specifically on TdSV in two distinct scenarios. The first scenario involves conventional TdSV, while the second focuses on speaker enrollment using user-defined passphrases. This paper provides a detailed description of both tasks, introduces the evaluation rules, and presents a comprehensive analysis of the results obtained from this challenge. © 2025 IEEE.

关键词： DeepMine dataset TdSV Challenge Text-dependent Speaker Verification User-defined passphrase

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Online Retail Customer Segmentation using RFM Quantiles and Clustering Technique 4

Online Retail Customer Segmentation using RFM Quantiles and ...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Majilya, Mouli Maji, Giridhar Ghosh, Partha Sen, Soumya A.K.Choudhury School of Information Technology University of Calcutta Kolkata India Asansol Polytechnic Department of Electrical Engineering Asansol India Academy of Technology Department of Computer Science and Engineering Adisaptagram India

ISBN: (纸本)9798350389128

In the fiercely competitive landscape of modern business, making effective decisions is imperative for generating substantial revenue. With organizations employing diverse strategies to stay ahead, leveraging data for informed decision-making is crucial. Understanding individual preferences and purchasing behaviors can be challenging, but machine learning techniques offer a solution by organizing and analyzing vast datasets. Customer segmentation, particularly through K-means clustering, is instrumental in identifying target groups with similar attributes and interests. The present study focuses on customer clustering method-ologies to give businesses a competitive edge. By collecting, analyzing, and visualizing customer data, we aim to develop a data science model that utilizes K-means clustering and the RFM model (Recency, Frequency, Monetary) for existing customers. Utilizing the UK's e-commerce dataset, which delves into customer purchasing behaviors, we aim to group customers into clusters such as high-value customers, mid-range, and those at risk of churn based on RFM scores. © 2024 IEEE.

关键词： Sales

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Impact of network architecture and training strategy for photon counting CT data correction 15

Impact of network architecture and training strategy for pho...

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15th SPIE Conference on Developments in X-Ray Tomography

作者： Morovati, Bahareh Li, Mengzhou Han, Shuo Xu, Yongshun Zhou, Li Wang, Ge Yu, Hengyong Dept. of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States Dept. of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

ISBN: (纸本)9781510679641

In recent years, X-ray photon-counting detectors (PCDs) have become increasingly popular due to their ability to discriminate energy and low noise levels. However, technical issues (e.g., charge splitting and pulse pileup effects) can affect the data quality by distorting the energy spectrum. To address those issues, based on a deep neural network-based approach using a Wasserstein generative adversarial network (WGAN) framework for PCD data correction, we evaluate the effectiveness of pre-trained and training-from-scratch convolutional neural networks (CNNs) as perceptual loss functions to address charge splitting and pulse pileup correction challenges in photon counting computed tomography (CT) data. Different CNN architectures, including VGG11, VGG13, VGG16, VGG19, ResNet50, and Xception, are evaluated. Compared with the method using a pre-trained network, our findings indicate that training the CNNs from scratch on our dataset produces better results. It significantly affects the performance for the choice of CNN architecture as a perceptual loss in the WGAN framework. Furthermore, because recent explosive interest on transformers has suggested their potential to be useful for computer vision tasks, we also evaluate transformers to maximize the attribute-related information contained in the image feature by texture features extraction. Our study emphasizes the importance of selecting appropriate network architecture and training strategy when implementing the WGAN framework for photon counting CT data correction. © 2024 SPIE.

关键词： Photons

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On a Sparse Shortcut Topology of Artificial Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 595-608页

作者： Fan, Feng-Lei Wang, Dayang Guo, Hengtao Zhu, Qikui Yan, Pingkun Wang, Ge Yu, Hengyong Rensselaer Polytechnic Institute Department of Biomedical Engineering TroyNY12180 United States University of Massachusetts Department of Electrical and Computer Engineering LowellMA01854 United States

In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful? Why do shortcuts generalize well? In this article, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory, we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures, including ResNet and DenseNet, on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps understand the role of shortcuts and suggests further opportunities to innovate neural architectures. © 2020 IEEE.

关键词： Topology

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Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques 6

Automatic Detection of Myotonia using a Sensory Glove with R...

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6th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Cesarini, Valerio Costantini, Giovanni Amato, Federica Errico, Vito Pietrosanti, Luca Calado, Alexandre Luis Massa, Roberto Frezza, Erica Irrera, Fernanda Manoni, Alessandro Saggio, Giovanni University of Rome Tor Vergata Department of Electronic Engineering Rome Italy Polytechnic University of Turin Department of Control and Computer Engineering Turin Italy University of Rome Tor Vergata Department of Systems Medicine Rome Italy University of Rome la Sapienza Department of Information Engineering Electronics and Telecommunications Rome Italy

ISBN: (纸本)9798350396577

This paper deals with the automatic detection of Myotonia from a task based on the sudden opening of the hand. Data have been gathered from 44 subjects, divided into 17 controls and 27 myotonic patients, by measuring a 2-point articulation of each finger thanks to a calibrated sensory glove equipped with a Resistive Flex Sensor (RFS). RFS gloves are proven to be reliable in the analysis of motion for myotonic patients, which is a relevant task for the monitoring of the disease and subsequent treatment. With the focus on a healthy VS pathological comparison, customized features were extracted, and several classifications entailing motion data from single fingers, single articulations and aggregations were prepared. The pipeline employed a Correlation-based feature selector followed by a SVM classifier. Results prove that it's possible to detect Myotonia, with aggregated data from four fingers and upper/lower articulations providing the most promising accuracies (91.1%). © 2023 IEEE.

关键词： Machine learning

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From Making Spaces to Making Change: The Future of Making in ECE 53

From Making Spaces to Making Change: The Future of Making in...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Jordan, Shawn Cheville, Alan Smith, Casey The Polytechnic School Ira A. Fulton Schools of Engineering Arizona State University MesaAZ United States Bucknell University Department of Electrical & Computer Engineering LewisburgPA United States University of Illinois Urbana-Champaign Department of Electrical & Computer Engineering UrbanaIL United States

ISBN: (纸本)9798350336429

In this paper, we discuss the results and key findings of a daylong 'Making and Makerspaces in electrical and computer engineering' (ECE) workshop hosted in conjunction with the ECE Department Heads Association Annual Conference in 2022. This workshop brought together thought leaders in making and makerspaces from across ECE, engineering education, and the broader maker community to discuss the current state of (change)making in ECE, making with purpose, and anti-racist engineering spaces in addition to live maker activities at lunch and an unconference to discuss community, partnerships, and ECE in mechanical engineering makerspaces. Broadly, a key finding from the conference was the need to shift the conversation from talking about making spaces (which tend to focus more on the equipment and facilities management) to ecosystems of making change, including culture, identity, community partnerships, shifting from labs to makerspaces, and shifting from spaces to makerspaces. Another key finding was an ecosystem view of possible touchpoints for making in an engineering program, including making in pre-college, making for recruitment, making in undergraduate design classes, making in extracurricular settings, making for fun, and making at home - all leading toward growing a thriving, diverse engineering workforce of the future. The themes that emerged from the workshop discussions were summarized and presented to department heads about how makerspaces connect to broader efforts in the ECE community such as how to become more equitable and inclusive, how to rebrand programs to attract new students, and promote best practices in educating 21st century learners. © 2023 IEEE.

关键词： DEI design ECE makerspace making

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