Engineering Commons^™

Kai Wu, The Editorial Board Member Of Journal Of Electrochemistry And Chief Scientist Of Catl, Receives The National Science And Technology Progress Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Sorbitol-Electrolyte-Additive Based Reversible Zinc Electrochemistry, Qiong Sun, Hai-Hui Du, Tian-Jiang Sun, Dian-Tao Li, Min Cheng, Jing Liang, Hai-Xia Li, Zhan-Liang Tao

Journal of Electrochemistry

The unstable zinc (Zn)/electrolyte interfaces formed by undesired dendrites and parasitic side reactions greatly hinder the development of aqueous zinc ion batteries. Herein, the hydroxy-rich sorbitol was used as an additive to reshape the solvation structure and modulate the interface chemistry. The strong interactions among sorbitol and both water molecules and Zn electrode can reduce the free water activity, optimize the solvation shell of water and Zn²⁺ ions, and regulate the formation of local water (H₂O)-poor environment on the surface of Zn electrode, which effectively inhibit the decomposition of water molecules, and thus, achieve the thermodynamically stable …

Data-Driven Viewpoint For Developing Next-Generation Mg-Ion Solid-State Electrolytes, Fang-Ling Yang, Ryuhei Sato, Eric Jian-Feng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin-Ichi Orimo, Hao Li

Journal of Electrochemistry

Magnesium (Mg) is a promising alternative to lithium (Li) in solid-state batteries due to its abundance and high theoretical volumetric capacity. However, the sluggish Mg-ion conduction in the lattice of solid-state electrolytes (SSEs) is one of the key challenges that hamper the development of Mg-ion solid-state batteries. Though various Mg-ion SSEs have been reported in recent years, key insights are hard to be derived from a single literature report. Besides, the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs. In this Viewpoints article, we analyze the structural characteristics of …

First Announcement Of 76th Annual Meeting Of The International Society Of Electrochemistry, International Society Of Electrochemistry (Ise)

Journal of Electrochemistry

No abstract provided.

Professor Yong Yang, The Editorial Board Member Of Journal Of Electrochemistry, Is Elected As A Fellow Of The Electrochemical Society, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Integrated Multi-Omics Analysis Of Cerebrospinal Fluid In Postoperative Delirium, Bridget A. Tripp, Simon T. Dillon, Min Yuan, John M. Asara, Sarinnapha M. Vasunilashorn, Tamara G. Fong, Sharon K. Inouye, Long H. Ngo, Edward R. Marcantonio, Zhongcong Xie, Towia A. Libermann, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual …

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Distributed Energy Trading Models Utilizing Reinforcement Learning And Game-Theoretic Based Approaches In Smart Grids, Nicholas Kemp

Electrical and Computer Engineering ETDs

Community-driven energy initiatives have become crucial for effective energy management, particularly in trading and management. The rise of Distributed Energy Resources in smart grids demands a redesign of traditional Demand Response Management (DRM) models to account for prosumers' dynamic behavior in energy markets. Decentralization, including peer-to-peer (P2P) energy trading, is vital for resilience and sustainability. This thesis introduces two coalitional DRM models: one based on hedonic community formation games, and the other combining matching theory with coalition games. These models empower prosumers to autonomously select energy trading communities using partially available data. To optimize energy consumption, two additional models are …

Data Lakes: A Survey Of Concepts And Architectures, Sarah Azzabi, Zakiya Alfughi, Abdelkader Ouda

Electrical and Computer Engineering Publications

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further …

Deep Integration Of Technological Innovation And Industrial Innovation In Modern Industrial System: Inspiration From Global New Generation Lithography Systems, Jiang Yu, Feng Chen, Yue Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

Utilizing technological innovation to lead the construction of a modern industrial system is a strategic choice for seizing the opportunities of the new round of technological revolution and industrial transformation. It is also a necessary step for winning the strategic initiative towards high-level self-reliance and self-improvement. Technological innovation is the intrinsic driving force behind industrial innovation, and industrial innovation is the value embodiment of technological innovation. The deep integration of technological innovation and industrial innovation is the key to constructing and improving a modern industrial system. Taking the global extreme ultra-violet (EUV) lithography system as an example, based on the …

Insights On Strategy And Approach For China To Construct A Modern Integrated Circuits Industrial System, Ximing Yin, Beibei Zhang, Tailun Chen, Jiang Yu, Jin Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

The integrated circuit (IC) industry is highly complex and systematic, and its key core technology breakthroughs are highly dependent on the support of systematic capabilities. The West especially the United States has accelerated the promotion of the “small-yard, high-fence” strategy, the “New Washington Consensus”, the “de-risking”, and other systematic policies to curb China’s rise. China’s IC industry chain is facing extreme risks such as rupture or blockage. Meanwhile, facing the new mission and requirements of Chinese modernization and new-quality productivity, China needs to accelerate the modernization of the IC industry with new development paradigms, new strategies, and new approaches. Based …

Optoelectronic And Morphological Surface Resistance Evaluation Of Laser-Induced Graphene Counter Electrodes For Potential Applications In Cesium Lead Halide Perovskite Solar Cells, Ziad Khalifa, Sameh Osama Abdellatif, Rami Ghannam

Chemical Engineering

This study investigates the physicochemical, optical, and electrical characterization of laser-induced graphene (LIG) samples for integration as counter electrodes in cesium lead halide perovskite solar cells. The impact of laser processing parameters on electrode performance is explored, including laser power, laser speed, and beam defocus. Density functional theory (DFT) computational modeling is employed for atomistic investigation and work function estimation, demonstrating the density of states (DOS), quantum capacitance of the graphene sheets, and energy work function. NiO is utilized as a hole transport layer, and the energy work function of LIG is tuned accordingly. SEM measurements estimate thin film porosity, …

Development And Evaluation Of An Expedited System For Creation Of Single Walled Carbon Nanotube Platforms, Ivon Acosta Ramirez, Omer Sadak, Wali Sohail, Xi Huang, Yongfeng Lu, Nicole M. Iverson

Department of Electrical and Computer Engineering: Faculty Publications

Single-walled carbon nanotubes (SWNT) have a strong and stable near-infrared (nIR) fluorescence that can be used to selectively detect target analytes, even at the single molecule level, through changes in either their fluorescence intensity or emission peak wavelength. SWNTs have been employed as NIR optical sensors for detecting a variety of analytes. However, high costs, long fabrication times, and poor distributions limit the current methods for immobilizing SWNT sensors on solid substrates. Recently, our group reported a protocol for SWNT immobilization with high fluorescence yield, longevity, fluorescence distribution, and sensor response, unfortunately this process takes 5 days to complete. Herein …

Exploration Of Semiconductor Gain Medium, Resonator, Pump, And Frequency Stabilization For Laser Guide Star Applications, Mingyang Zhang

Optical Science and Engineering ETDs

Laser Guide Star (LGS) systems are essential for adaptive optics in ground-based astronomical observation. This dissertation demonstrates the feasibility of semiconductor-based LGS systems using the membrane external-cavity surface-emitting laser (MECSEL) platform, employing multiple quantum wells. Various laser cavity configurations were analyzed through simulations and experiments. The in-well pumping method was explored to reduce the quantum defect and address thermal limitations. Multi-pass pumping schemes were designed with Zemax modeling and demonstrated experimentally. To simplify multi-pass pumping, the hybrid-MECSEL (H-MECSEL) design was introduced. COMSOL modeling studied thermal management and thermal lensing effect.

The H-MECSEL achieved approximately 30 W of output power at …

Embedding Direction Of Arrival And Antenna Beamforming Algorithms On Automated Software Defined Radio Platforms, Adrian J. Lewis

Electrical and Computer Engineering ETDs

Antenna arrays are an essential technology in the modern era in both communications and radar applications. The algorithms used for antenna arrays are beginning to be embedded in smart device applications to enable greater communication abilities. This thesis explores the idea of embedding direction of arrival and beamforming algorithms using a software defined radio platform. Specifically, the Multiple Signal Classification (MuSiC) algorithm and Minimum Variance Distortionless Response (MVDR) algorithm are combined. The combination of similar algorithms has the potential to be used in many internet of things applications to enable more dynamic communication ability.

Adaptive Screen Capture Video Analysis, Ugesh Egala

Electrical and Computer Engineering ETDs

This thesis presents a system for analyzing student activities during class sessions to gain insights into the learning process. A dataset consisting of 14 screen recordings, with 2 videos labeled across two stages, was used for training, validation, and testing. The methodology employs adaptive sampling, initially at 10 frames per minute to identify active regions, followed by detailed analysis at 1 frame per second using OCR to detect typing activities through character changes. The results demonstrate over a 50% reduction in computational load while maintaining high accuracy in detecting student engagement. A total of 26 hours of screen capture videos …

Bio-Inspired Robotic Framework For Spatiotemporal Analyses In Environmental Monitoring Applications, Maliha Kabir

Electrical Engineering Theses

Frequent fluctuations in the environment's temperature and climate significantly impact our crop fields. Floods and droughts occur in different places in different seasons, which is very unusual and affects the crops' health and productivity. During these calamities, they are also not able to monitor their crop fields. However, if it is possible by providing farmers with information about their fields' conditions like temperature, pressure, and soil moisture, we can help them assess environmental conditions for specific areas. This data can also aid farmers in determining the appropriate amounts of fertilizers and pesticides to apply to their crops and which crops …

Experimentally Verified Effective Doping Model For Lactate And Troponin Ofet Biosensors Using Machine Learning Algorithm, Sameh O. Abdellatif, Hana Masalam, Salma Ahmed

Electrical Engineering

No abstract provided.

Experimental Validation Of An Analytical Transient Model For Saturated Boosting Gain In Dc–Dc Converters With Variable Duty Cycle, Goliana Samir, Simon Ezzat, Wagdy Anis, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.

Low-Power Dc-Dc Converters For Smart And Environmentally-Friendly Electric Vehicles: Design, Simulation, And Fabrication On A Glass Substrate, Michelle Makar, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.

From Cnns To Transformers In Multimodal Human Action Recognition: A Survey, Muhammad Bilal Shaikh, Douglas Chai, Syed Muhammad Shamsul Islam, Naveed Akhtar

Research outputs 2022 to 2026

Due to its widespread applications, human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance as compared to relying on a single data modality. During the adoption of deep learning for visual modelling in the past decade, action recognition approaches have mainly relied on Convolutional Neural Networks (CNNs). However, the recent rise of Transformers in visual modelling is now also causing a paradigm shift for the action recognition task. This survey captures this transition while focusing on Multimodal Human Action Recognition …

On The Surface Topography Of Local Overheating And Plasma Formation Due To Initial Condition Perturbations Sourcing The Electrothermal Instability On High-Current-Density Conductors, Maren Whiting Hatch

Electrical and Computer Engineering ETDs

The electrothermal instability (ETI) is a Joule heating-driven instability that instigates runaway heating on conductors driven to high current density, altering the 3D evolution of the expansion and phase state. Most metals include complex distributions of imperfections (voids, resistive inclusions) which seed ETI. To simplify comparison with modeling and theory, experiments examined growth of ETI from various alloys of stainless steel as well as relatively void/inclusion free, 99.999% pure, diamond-turned, 1 mm-diameter aluminum rods. Aluminum surfaces included a variety of deliberately machined and well-characterized perturbations, including 10-micron-scale quasi-hemispherical voids, or “engineered” defects (ED), and sinusoidal patterns of varying wavelength and …