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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alyoucef, Afnan Qaisar, Saeed Mian Hafsi, Meriem Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne69100 France

ISBN: (纸本)9781665472753

The generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent 'battery management systems' (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the 'Lithiumion' (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The 'National Aeronautics and Space Administration' (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach. © 2023 IEEE.

关键词： Battery management systems

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Stochastic Unit Commitment: Model Reduction via Learning

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Current Sustainable/Renewable Energy Reports 2023年第2期10卷 36-44页

作者： Liu, Xuan Conejo, Antonio J. Constante Flores, Gonzalo E. Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States Department of Integrated Systems Engineering The Ohio State University ColumbusOH43210 United States Davidson School of Chemical Engineering Purdue University West LafayetteIN47907 United States

Purpose of Review: As weather-dependent renewable generation increases its share in the generation mix of most electric energy systems, a stochastic unit commitment becomes the natural day-ahead scheduling tool. However, such a tool is generally computationally intractable if a detailed uncertainty description is considered. Taking this into account, we proposed a learning method to make the stochastic unit commitment problem tractable. Recent Findings: Recent advances in statistical learning and machine learning to address optimization problems can be advantageously applied to the rather intractable stochastic unit commitment problem. Considering these advances, we explore simple learning techniques to drastically reduce the size of a stochastic unit commitment problem without significantly altering its optimal solution. Summary: The considered stochastic unit commitment problem is formulated as a two-stage stochastic programming problem. The first stage represents commitment decisions, while the second one represents the operation conditions under different scenarios. Taking into account historical solved instances (or proxies for them), we reduce the size (measured by numbers of constraints and variables) of the stochastic unit commitment problem by (i) fixing unchanged binary variables and by (ii) eliminating inactive inequality constraints. Our numerical results show that the reduced problem generally requires significantly less time to solve while obtaining high-quality solutions, which are very close to or indistinguishable from the one obtained by solving the original problem. We use an Illinois 200-bus system to illustrate and characterize the performance of the proposed problem-reduction method. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Stochastic programming

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Distributed Gesture Controlled systems for Human-Machine Interface

Distributed Gesture Controlled Systems for Human-Machine Int...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Johnson, Hans Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

This paper presents the design flow of an IoT human-machine touchless interface. The device uses embedded computing in conjunction with the Leap Motion Controller to provide an accurate and intuitive touchless interface. Its main function is to augment current touchscreen devices in public spaces through a combination of computer vision technology, event-driven programming, and machine learning. Especially following the COVID-19 pandemic, this technology is important for hygiene and sanitation purposes for public devices such as airport, food, and ATM kiosks where hundreds or even thousands of people may touch these devices in a single day. A prototype of the touchless interface was designed with a Leap Motion Controller housed on a Windows PC exchanging information with a Raspberry Pi microcontroller via internet connection. © 2022 IEEE.

关键词： Internet of things

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Autonomous Robotic Arm for Object Sorting and Motion Compensation using Kalman Filter

Autonomous Robotic Arm for Object Sorting and Motion Compens...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Ndambani, Melanie Acelor Fang, Tianyang Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Robotic arm has played a key role in the industry for more than a half-century. Although robotic arms have superior accuracy and efficiency, they are not always cost-effective, especially for small-scale applications. This paper proposes an autonomous robotic arm system that can identify, sort, and interact with different objects, using IoT devices. For precision robotic arm movement, we have developed a Kalman filter-based motion compensation system. The embedded computing system is used for real-time object recognition and motion compensation. Experimental results show that the proposed high precision autonomous robotic arm system can recognize objects with 95% accuracy, and locate and sort the moving objects with a mean error of 1.1mm. © 2022 IEEE.

关键词： Kalman filters

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WIP: An Engaging Undergraduate Intro to Model Checking in Software engineering Using TLA+ 54

WIP: An Engaging Undergraduate Intro to Model Checking in So...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Loyola University Software and Systems Laboratory Department of Computer Science Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

ISBN: (纸本)9798350351507

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive 'failuresy' through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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A robust and consistent stack generalized ensemble-learning framework for image segmentation

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Journal of engineering and Applied Science 2023年第1期70卷 74页

作者： Faska, Zahra Khrissi, Lahbib Haddouch, Khalid El Akkad, Nabil Laboratory of Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco LIIAN Department of Computer Science FSDM of Fez Sidi Mohamed Ben Abdellah University Fez Morocco

In the present study, we aim to propose an effective and robust ensemble-learning approach with stacked generalization for image segmentation. Initially, the input images are processed for feature extraction and edge detection using the Gabor filter and the Canny algorithms, respectively;our main goal is to determine the most feature descriptions. Subsequently, we applied the stacking generalization technique, which is generally built with two main learning levels. The first level is composed of two algorithms that give good results in the literature, namely: LightGBM (Light Gradient Boosting Machine) and SVM (support vector machine). The second level is the meta-model in which we use a predictor model that takes the base-level predictions to improve the accuracy of the final prediction. In the stacked generalization process, we use the Extreme Gradient Boosting (XGBoost);it takes as input the sub-models’ outputs to better classify each pixel of the image to give the final prediction. Today, several research works exist in the literature using different machine learning algorithms;in fact, instead of trying to find a single efficient and optimal learner, ensemble-based techniques take the advantage of each basic model;they integrate their outputs to obtain a more consistent and reliable learner. The result obtained from the models of individuals and our proposed approach is compared using a set of evaluation measures for image quality such as IoU, DSC, CC, SSIM, SAM, and UQI. The evaluation and a comparison of the results obtained showed more consistent predictions for the proposed model. Thus, we have made a comparison with some recent deep learning-based unsupervised segmentation methods. The evaluation and a comparison of the results obtained showed more coherent predictions for our stacked generalization in terms of precision, robustness, and consistency. © 2023, The Author(s).

关键词： Support vector machines

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PowerShell Malware Analysis Using a Novel Malware Rating System

PowerShell Malware Analysis Using a Novel Malware Rating Sys...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Arnold, David David, Charlotte Saniie, Jafar Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States

ISBN: (纸本)9781665480093

Recent high-profile cyberattacks highlight an increased use of social engineering attacks and ransomware by hackers worldwide. These attacks target human operators directly, bypassing many of the cyber-safeguards developed through years of malware analysis. In response to these challenges, many organizations have turned to white-hat hackers and penetration testing to identify potential weaknesses and reinforce cyber-safety protocols. To assist in the threat evaluation process, malware rating systems are often used to highlight the danger and potential damage malware may cause. Current malware rating systems focus on assigning a danger score for malware based on its ability to move throughout the network, damage system resources, and evade detection. Due to the increased reliance on social engineering, a new malware rating system is proposed that incorporates malware deceitfulness as a means to trick human operators. The novel rating system will score malware based on its Stealth, Ease of Creation, Deceitfulness, Versatility, Instantaneousness, and Persistence. This system provides operators with insight into each key characteristics as opposed to a single value. To showcase the malware evaluation process, different PowerShell Reverse Bind Shell malwares are rated based on the proposed criteria. © 2022 IEEE.

关键词： Malware

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FAIR-FATE: Fair Federated Learning with Momentum 23rd

FAIR-FATE: Fair Federated Learning with Momentum

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23rd International Conference on Computational Science, ICCS 2023

作者： Salazar, Teresa Fernandes, Miguel Araújo, Helder Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering of the University of Coimbra University of Coimbra Coimbra Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering of the University of Coimbra University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031359941

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data heterogeneity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Momentum

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Comprehensive Modeling and Investigation of Intrinsic Variation Source Fluctuation in 2D-Layered Thin Film Transistors

Comprehensive Modeling and Investigation of Intrinsic Variat...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Du, Tao Zhang, Wei Fong, Xuanyao Hu, Guangxi Zhou, Peng Lu, Ye Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai 200403 China National University of Singapore (NUS) Department of Electrical and Computer Engineering Singapore 117583 Singapore

ISBN: (纸本)9798331504779

In this work, a Voronoi-TCAD co-simulation framework is developed to investigate the impact of intrinsic and extrinsic variation sources on the performance and variability of 2D-layered TFTs. It is found that : i) High trap density exacerbates device variability by amplifying localized energy barriers and enhancing barrier interference;ii) The device variability reaches peak value for intermediate numbers of grains, while increased numbers of grains reduce overall variability due to statistical averaging effects, highlighting the importance of considering both the quantity of grain boundaries and their spatial distribution as key variability sources;iii) interlayer interactions in multilayer structures further exaggerate the device variability, particularly for the on-current. This demands precise control of grain alignment and distribution. These findings emphasize the importance of grain engineering strategies, such as optimizing grain size, reducing defect density, and improving interlayer uniformity, to suppress variability and enhance device performance in large-scale 2D-layered TFT applications. © 2025 IEEE.

关键词： grain boundary (GB) Mos2 polycrystalline variability Voronoi

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Image Processing for Detecting Botnet Attacks: A Novel Approach for Flexibility and Scalability

Image Processing for Detecting Botnet Attacks: A Novel Appro...

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2022 IEEE International Conference and Expo on Real Time Communications at IIT, RTC 2022

作者： Agniel, Aurelien Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665475037

ISBN: (纸本)9781665475037

Continued adoption of the Internet of Things (IoT) redefines the paradigm of network architectures. Historically, network architectures relied on centralized resources and data centers. The introduction of the IoT challenges this notion by placing computing resources and observation at the edge of the network. As a result, decentralized approaches for information processing and gathering can be adopted and explored. However, this shift greatly expands the network footprint and shifts traffic away from the center of the network, where observation and cybersecurity monitoring tools are frequently located. Further, IoT devices are often computationally constrained, limiting their readiness to deal with cyber-threats. These security vulnerabilities make the IoT an easy target for hacking groups and lead to the proliferation of zombie networks of compromised devices. Frequently, zombie networks, otherwise known as botnets, are coordinated to attack targets and overload network resources through a Distributed Denial of Service (DDoS) attack. In order to crack down on these botnets, it is essential to develop new methods for quickly and efficiently detecting botnet activity. This study proposes a novel botnet detection technique that first pre-processes network data through computer vision and image processing. The processed dataset is then sent to a neural network for final classification. Two neural networks will be explored, a sequential model and an auto-encoder model. The application of image processing has two advantages over current methods. First, the image processing is simple enough to be completed at the edge of the network by the IoT devices. Second, preprocessing the data allows us to use a shallower network, decreasing detection time further. We will utilize the N-BaIoT dataset and compare our findings to their results. © 2022 IEEE.

关键词： Cybersecurity

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