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Establishment of Degradation Model and Transfer Model for P-channel Power MOSFETs Under Negative Bias Temperature Stress

Establishment of Degradation Model and Transfer Model for P-...

引用

International conference on Power Electronics and Motion control (IPEMC)

作者： Cen Chen Haodong Wang Haonan Yin Wei Zheng Guofu Zhai School of Electrical Engineering and Automation Harbin Institute of Technology China Beijing Aerospace Automatic Control Institute China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Negative Bias Temperature Instability (NBTI) is the main reason for the degradation of gate oxide in P-channel power MOSFETs. Affected by long-term negative bias gate voltage and high temperature, the threshold voltage of power MOSFETs drifts, which in turn affects the performance of the device. Based on the analysis of NBTI failure mechanism, this paper conducted accelerated degradation test on six different types of P-channel power MOSFETs. Secondly, the Iterative Reweighted Least Square (IRLS) method was applied to establish a threshold voltage drift’s degradation model, which can accurately describe actual degradation data. In addition, a transfer model was established using multiple linear regression theory to approximately describe the relationship between the fitting parameters of degradation model and power MOSFETs’ process parameters, providing a new approach for the fast reliability assessment of P-channel power MOSFETs.

关键词： Semiconductor device modeling Degradation Negative bias temperature instability MOSFET Thermal variables control Fitting Life estimation

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Real-time Construction and control on Dynamic Systems 6

Real-time Construction and Control on Dynamic Systems

引用

conference on Real-Time Photonic Measurements, data Management, and processing VI

作者： Liu, Hua Wang, Zhaoyang Sci & Technol Electroopt Control Lab Beijing Peoples R China Luoyang Inst Electroopt Equipment AVIC Luoyang Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (数字)9781510646544

ISBN: (纸本)9781510646544;9781510646537

Basic principle, modeling method, error analysis and process design of products assembly and adjustment are analyzed. In order to improve the resolution of the imaging system and reach the theoretical limit, the off-axis system optimization method is promoted from the perspective of theory to engineering. The simulation results show that the system can meet the application requirements of MTF and other related criteria. And an engineering method is explored for real-time construction and control of dynamic systems.

关键词： System Engineering Photonics Optimization

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A Novel Contingency analysis-Driven Deep Learning Framework for Evaluation of a Primary Power System Security Metric

A Novel Contingency Analysis-Driven Deep Learning Framework ...

引用

IEEE Texas Power and Energy conference (TPEC)

作者： Adam King Santiago Grijalva School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Continuous operation of power systems is critical to modern society, even during unexpected or emergency situations. One method to evaluate the security of power systems is contingency analysis, a computationally-intensive process which considers how a set of possible scenarios would affect the electrical grid. This paper presents a novel security evaluation method which enables a topology- and parameter-free estimation of a system-level security metric, the system aggregate megawatt contingency overload (SysAMWCO), by leveraging a novel deep learning-based approach. Using the results of conventional contingency analysis for a simulated power system, a deep neural network (DNN) is trained to predict the SysAMWCO value using only bus loads as input. Experimentation across different neural network parameters reveals a complex relationship between these parameters and the accuracy of the predicted SysAMWCO value. While each DNN is trained on data generated using conventional contingency analysis methods, fully-trained models achieve R-squared scores in excess of 0.98 without needing electrical grid topology or power device parameter information as input. Given these results, this framework shows promise for potential real-world applications.

关键词： Measurement Network topology Contingency management Power system control Estimation Artificial neural networks Power systems Topology Power system security Load modeling

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Unsupervised Learning for Land Cover Mapping of Casablanca Using Multispectral Imaging

Unsupervised Learning for Land Cover Mapping of Casablanca U...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International conference in

作者： Hafsa Ouchra Abdessamad Belangour Allae Erraissi Laboratory of Information Technology and Modeling LTIM Faculty of Sciences Ben M'sik Hassan II University Casablanca Morocco Polydisciplinary Faculty of Sidi Bennour Chouaib Doukkali University El Jadida Morocco

Precise and current land use data hold immense significance in facilitating efficient urban planning and appropriate environmental oversight. This paper proposes an approach to the unsupervised classification of Casablanca's land use using the Google Earth Engine (GEE) platform. The study relies on multispectral satellite imagery, in particular data from Landsat satellites, to extract meaningful land use categories without resorting to manual labeling. The operational process includes data collection, pre-processing, unsupervised clustering, and graphical display of results. By applying the k-means and Lvq clustering algorithms, the urban area is split into distinct groups, each representing a specific land use class. The resulting land use map provides valuable data on Casablanca's urban fabric, highlighting wooded areas, agricultural land, built infrastructure, water bodies, and barren land. This automated approach demonstrates GEE's potential as a powerful tool for analyzing land use, enabling informed, data-driven decisions on urban development and environmental monitoring. The methodology outlined can serve as a reference for similar research in other regions, helping to advance remote sensing and geospatial analysis techniques in urban and environmental studies. The effectiveness of these two algorithms is assessed in terms of overall accuracy and kappa coefficient. The k-means algorithm showed moderate accuracy, while the Lvq algorithm showed the least satisfactory results.

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Model analysis and Application of Quantum Computers in Credit Scoring Card Combination Optimization

Model Analysis and Application of Quantum Computers in Credi...

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data Driven control and Learning Systems (DDCLS)

作者： Xiaohan Zhou Ruikun Zhang School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the context of business such as bank credit cards or related loans, banks score customers through credit scoring cards. Loan interest income and bad debt losses determined by the combination of thresholds of different credit scoring cards are key factors that determine the bank's final income. This paper mainly studies the combination optimization of credit scoring cards by establishing an optimization model and transforming it into a Quadratic Unconstrained Binary Optimization (QUBO) problem. First, the optimization problem of a single credit scoring card is studied, the data is preprocessed and zero-one integer programming model is established, then convert the objective function into a Hamiltonian to obtain an optimization model in the form of QUBO, and obtain the maximum bank income and the threshold of a single credit scoring card through the Simulated Annealing. Then, the credit scoring card combination optimization problem is studied. On the basis of the above research, variable substitution is used to reduce function degree, and a penalty function is introduced to obtain an unconstrained QUBO model for the combination problem. The D-Wave quantum annealing algorithm is used to obtain the optimal combination solution.

关键词： Learning systems Analytical models Annealing Optimization models Computational modeling process control Simulated annealing

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Comparative analysis of data Driven Prediction modeling Strategies for Aquaculture Healthcare 4

Comparative Analysis of Data Driven Prediction Modeling Stra...

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4th International conference on Innovative Computing (ICIC)

作者： Amin, Shahzad Cuomo, Francesca Kamal, Maryam Sapienza Univ Rome Dept Comp Control & Management Engn DIAG Rome Italy Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Rome Italy

ISBN: (纸本)9781665400916

Aquaculture is a significantly important part of our environment. It is the process of rearing, breeding and harvesting of aquatic and marine life in controlled aquatic environments such as seas, oceans and rivers. If the health of aquatic environment is not monitored satisfactorily, conditions like algae bloom, depletion of oxygen and toxins secretions might prevail. This not only contaminates the aquatic environment but is also a threat to entire ecosystem. In this study, we have aimed to predict levels of dissolved oxygen in diverse aquaculture environments. We have established a comprehensive comparative analysis of three well-known and up-to-date machine learning algorithms to predict aquaculture health based on dissolved oxygen levels of the water. We have considered factors such as amount of nitrate, temperature, oxygen saturation and PH level to predict dissolved oxygen (DO). Our results show the high achievement of prediction accuracy of 87.88% by Facebook prophet (FBprophet) as compared to 86.94% by Support Vector Regression (SVR) and 81.06% by Extreme Gradient Boosting (XGBoost).

关键词： Machine Learning Prediction modeling data Science Aquaculture Marine Life Statistical analysis

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LSTM-Based UAV Swarm Trajectory Prediction and Intent Recognition

LSTM-Based UAV Swarm Trajectory Prediction and Intent Recogn...

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Advanced Algorithms and control Engineering (ICAACE), International conference on

作者： Dianqiu Ma Xianyi Fu Xueqin Huang Qinglin Li Xianqiang Zhu National Key Laboratory of Information Systems Engineering National National University of Defense Technology Changsha China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

This study focuses on the problem of trajectory prediction and intention recognition of UAV clusters in military defence, and explores a new method for UAV cluster trajectory prediction by constructing UAV cluster dataset autonomously and applying Long Short-Term Memory (LSTM) network for experimental validation. Traditional studies have mostly focused on the trajectory analysis of individual UAVs, while the dynamic behavioural patterns of UAV clusters are less studied. To this end, we generate a UAV cluster dataset covering multiple flight modes, and model and predict their trajectories using the LSTM model. The experimental results show that LSTM can effectively capture the temporal characteristics of UAV clusters, predict their future trajectories and identify potential intrusion intentions, providing early warning support for air defence systems. This study provides a new technical idea for UAV cluster intent recognition and promotes the research process of intelligent development of air defence system.

关键词： Training Accuracy Intent recognition Atmospheric modeling Predictive models Autonomous aerial vehicles data models Trajectory Time factors Long short term memory

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Modelling an IT solution to anonymise selected data processed in digital documents 17

Modelling an IT solution to anonymise selected data processe...

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17th conference on Computer Science and Intelligence Systems (FedCSIS)

作者： Probierz, Barbara Jach, Tomasz Kozak, Jan Pacud, Radoslaw Turek, Tomasz Univ Econ Katowice Dept Machine Learning 1 Maja PL-40287 Katowice Poland Univ Econ Katowice Fac Finance 1 Maja PL-40287 Katowice Poland Czestochowa Tech Univ Fac Management Al Armii Krajowej 19 B PL-42201 Czestochowa Poland

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

Allowing access to real legal documents is an important element both for the development of science and the judiciary. On the other hand, protecting information about citizens or organizations, that appear in these documents, is crucial and required by law. Therefore, before the documents are distributed, the data anonymisation process should be carried out. Unfortunately, there is no perfect tool that can automatically anonymise documents in such a way, that the main concept of the document is preserved;especially in the case of documents written in inflectional language. The aim of this article is to show how important (and at the same time how difficult) is the task to identify personal or corporate data of a client, as well as other related personal data in documents that are subject to legal protection. We conducted research aimed at assessing the usefulness of IT techniques as well as decision rules and patterns in the anonymisation of legal documents. A set of real legal documents written in Polish was used for the research in which we identified selected types of data that need to be anonymised. Eventually, the obtained results were assessed by field experts. Additionally, in order to verify the effectiveness of the proposed solution, we conducted research on a set of 50,000 false identities with names, company names, addresses and other confidential information. The collection was created using Fake Name Generator(1). The obtained results from both experiments confirmed that the solutions we proposed is accurate even in the case of real legal documents.

关键词： Computer science Law Computational modeling Distributed databases Companies data models Task analysis

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Adaptive Split Federated Learning for IoT: Personalized Clustering and Efficient Communication

Adaptive Split Federated Learning for IoT: Personalized Clus...

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Advanced Algorithms and control Engineering (ICAACE), International conference on

作者： Lijie Chen Lulu Wang Lei Zhang Software Engineering Institute East China Normal University ShangHai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The growing number of Internet of Things (IoT) devices has led to massive datasets spread across different locations. While Federated Learning (FL) enables decentralized training without transmitting raw data, it often requires resource-limited IoT devices to train full models locally, causing high energy and computation costs. Split Federated Learning (SFL) addresses some of these issues by splitting the model between IoT devices and a central server. However, standard SFL does not fully accommodate the varied challenges of IoT environments, including uneven data distributions, devices joining or leaving over time, and frequent communication demands. To tackle these concerns, we introduce an enhanced SFL framework. Our approach groups devices based on partial feature representations from the early layers of the model, leading to a personalized training process that boosts performance under uneven data conditions. We further use hierarchical clustering to manage groups as devices join or leave, maintaining both stability and scalability. To reduce communication overhead, we compress the partial feature data and implement model migration, allowing devices that move between clusters to adapt with fewer training rounds. Through extensive experiments, we show that our method consistently surpasses existing approaches under various data imbalances, device participation rates, and communication constraints. These results confirm that our framework is a robust, efficient, and adaptive solution for IoT-based SFL, offering superior accuracy, reliability, and resource management in real-world applications.

关键词： Training Adaptation models Costs Federated learning Scalability Computational modeling data models Stability analysis Internet of Things Standards

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An Improved ARIMA Based Anomaly Detection Method for Time Series data

An Improved ARIMA Based Anomaly Detection Method for Time Se...

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IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Guangwei Sun Chaofan Yin Tian Xia Yunpeng Lu Jiawei Mao Research Center for Tobacco Cultivation and Modulation Hubei Provincial Tobacco Research Institute Wuhan China Department of Information Engineering Wuhan Vocational College of Science and Technology Wuhan China Inspection and Testing Center China Electric Power Research Institute Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Currently, IoT data processing standards in China are somewhat vague, and monitoring data is typically used in subsequent stages for analysis, prediction modeling, and applications. However, monitoring and control data are often affected by various factors that can introduce anomalies, which in turn impacts intelligent control systems. To improve the quality of monitoring data, this paper addresses the limitations of traditional anomaly detection methods, which often require high similarity in data and face challenges with highly diverse monitoring data. Additionally, traditional ARIMA models are limited by difficulties in parameter selection, threshold determination, and high false-positive rates. Thus, we propose an improved ARIMA-based anomaly detection method for time-series data. This approach encompasses the complete process of monitoring data fitting and prediction, anomaly detection, and anomaly validation. The method has three main features:*** Parameter Selection: For varied batches of monitoring data, this method combines ACF and PACF plots with AIC and BIC heatmaps to determine optimal ARIMA parameters for each data *** of a Sliding Window: A sliding window is used to analyze residual data characteristics within the window, enabling precise anomaly detection for individual data *** Validation: A K-means clustering algorithm is incorporated to validate initial anomaly detection *** method was verified in a real-world cigar leaf curing barn, and experimental results demonstrate that it effectively detects anomalies in the IoT monitoring system data, contributing to improved IoT data quality.

关键词： Temperature sensors Analytical models Accuracy Time series analysis Curing Feature extraction data models Time complexity Monitoring Anomaly detection

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