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Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

IEEE Robotics and Automation Letters 2024年第4期9卷 3878-3885页

作者： Tsai, Tsung-Han Yan, Bing Luh, Peter B. Yang, Haw-Ching Bragin, Mikhail A. Cheng, Fan-Tien National Cheng Kung University Institute of Manufacturing Information and Systems Tainan701401 Taiwan Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei106329 Taiwan National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung824005 Taiwan University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this letter is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future. © 2016 IEEE.

关键词： Integer programming

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Backup subscription scheme for differential protection using IEC61850-9-2 sampled values

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Energy Informatics 2024年第1期7卷 110页

作者： Katoulaei, Mohammad Khalili Rahmani, Aamir Høidalen, Hans Kristian Oleinikova, Irina Mork, Bruce Department of energy systems Norwegian University of Science and Technology Trondheim Norway Department of electrical power system Michigan Technical University Michigan United States Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI49931 United States

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. © The Author(s) 2024.

关键词： Hardware-in-the-loop simulation

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Flexible temperature sensor with high sensitivity ranging from liquid nitrogen temperature to 1200℃

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International Journal of Extreme Manufacturing 2023年第1期5卷 355-366页

作者： Zhaojun Liu Bian Tian Zhuangde Jiang Shuimin Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Peng Shi Qijing Lin State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore

Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic ***,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big *** work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin *** has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature *** temperature sensor has huge application potential in biomedicine,aerospace and other fields.

关键词： flexible sensor temperature thermocouple

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FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Reader 6

FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Read...

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6th Australian Microwave Symposium, AMS 2025

作者： Lasantha, Likitha Aliasgari, Javad Khan, Shahed I. Ray, Biplob Karmakar, Nemai C. Monash University Department of Electrical and Computer Systems Engineering Clayton Australia Central Queensland University School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350379969

Ultra-wideband (UWB) technology has gained traction for applications demanding high bandwidth, precise target localisation, and low power consumption. This paper presents an FPGA-based UWB pulse generator tailored for an Impulse-radio (IR) UWB chipless RFID reader. Using a low-cost FPGA platform, the design generates a square wave signal with a pulse repetition frequency (PRF) of 1-50 MHz. This base signal undergoes pulse sharpening to produce picosecond-scale pulses, achieving the bandwidth exceeding 500 MHz necessary for UWB communication. Such a high-bandwidth signal ensures better range resolution, meeting the requirements of applications of UWB chipless RFID readers. By implementing pulse generation and PRF control on an FPGA, our system combines low cost, flexibility, and scalability, making it suitable for diverse UWB applications. © 2025 IEEE.

关键词： Ultra-wideband (UWB)

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Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control systems:A Case Study in Level Control Process

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 762-780页

作者： Majid Ghorbani Mahsan Tavakoli-Kakhki Aleksei Tepljakov Eduard Petlenkov Department of Computer Systems Tallinn University of TechnologyTallinn 12618Estonia Faculty of Electrical Engineering K.N.Toosi University of TechnologyTehran ***Iran IEEE

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this *** uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world ***,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)*** the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time *** three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise ***,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

关键词： Interval uncertainty FOPID controller fractional-order systems robust stability analysis smith predictor

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A review on simulation models of cascading failures in power systems

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iEnergy 2023年第4期2卷 284-296页

作者： Zhenping Guo Kai Sun Xiaowen Su Srdjan Simunovic Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTNUSA Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTXUSA Computational Sciences&Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Among various power system disturbances,cascading failures are considered the most serious and extreme threats to grid operations,potentially leading to significant stability issues or even widespread power *** power systems’behaviors during cascading failures is of great importance to comprehend how failures originate and propagate,as well as to develop effective preventive and mitigative control *** intricate mechanism of cascading failures,characterized by multi-timescale dynamics,presents exceptional challenges for their *** paper provides a comprehensive review of simulation models for cascading failures,providing a systematic categorization and a comparison of these *** challenges and potential research directions for the future are also discussed.

关键词： Cascading failures power systems security and reliability simulation models

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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An Optimization-Based HVAC Load and PV Disaggregation Methodology With Contextual Supervision

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IEEE Transactions on Smart Grid 2025年第4期16卷 3457-3460页

作者： Kim, Hyeoniin Yu, Min Gyung Wu, Di Lu, Ning Systems Center Electrical and Computer Engineering Department RaleighNC27606 United States Pacific Northwest National Laboratory RichlandWA99354 United States

This letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV. © 2010-2012 IEEE.

关键词： HVAC Dictionaries Load modeling Estimation Accuracy Vectors Training Optimization Market research Data mining

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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computer Modeling in engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Joint Device and Training Scheduling for Wireless Federated Learning

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IEEE Internet of Things Journal 2025年第12期12卷 18806-18819页

作者： Chen, Xiaobing Zhou, Xiangwei Zhang, Hongchao Sun, Mingxuan Zhao, Taibiao Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States Louisiana State University Department of Mathematics Baton RougeLA70803 United States Louisiana State University Division of Computer Science and Engineering Baton RougeLA70803 United States

The advent of ubiquitous computing devices in the Internet of Things (IoT) has resulted in an explosion of data. Traditional centralized machine learning models face challenges including limited bandwidth in wireless environments and privacy concerns due to their data aggregation approach. Federated learning addresses these challenges via decentralizing model training across numerous devices, leveraging model updates to enhance privacy and reduce communication overhead. To improve its cost efficiency, current research focuses on minimizing either time or energy costs but rarely both, and does not jointly optimize the parameters of device and training scheduling in the presence of system and data heterogeneity inherent in IoT networks. In our paper, we first introduce a multi-group transmission scheme and propose a comprehensive device scheduling framework, Group Scheduling on Orthogonal Frequency-division Multiple Access (GS-OFDMA), to address time bottlenecks. Then we formulate a joint optimization problem for device and training scheduling that minimizes the total cost of training while ensuring model convergence. To tackle the resulting mixed integer nonlinear programming problem, we develop an iterative algorithm. Experimental results show that our approach significantly reduces the total cost by at least 35% across various real-world datasets and data distributions in comparison with random participant selection. The proposed GS-OFDMA protocol also exhibits higher time efficiency over other device scheduling schemes. © 2014 IEEE.

关键词： Federated learning

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