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Dissipative State Feedback of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition Concept

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SSRN

SSRN 2024年

作者： Feng, Qian Zheng, Wei Xing Wang, Xiao Yu Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Stabilizing autonomous linear time delay systems, particularly when addressing an unlimited number of pointwise and distributed delays (DDs) under dissipative constraints, poses a significant challenge. Existing solutions are often hindered by theoretical limitations, numerical obstacles, or an inability to address the complexities of the delay integral kernels. In this paper, we propose a unified framework to tackle the above problem by employing the concept of the Kronecker-Seuret decomposition (KSD) for matrix-valued functions, which we recently have developed for the analysis of complex delay structures in coordination with the Krasovski\u{\i} functional approach. Our strategy can simultaneously address two distinct control problems, where the matrix kernels of DDs can contain an unlimited number of square-integrable functions. We show in detail how the KSD can factorize and approximate different kernel functions simultaneously without introducing conservatism. Furthermore, the use of KSD also enables us to construct complete-type functionals, whose integral kernels can include any number of weakly differentiable and linearly independent functions, underpinned by the utilization of novel integral inequalities derived from the least-squares principle. The solution to each synthesis problem comprises two theorems accompanied by an iterative algorithm, which can be utilized as a single package to compute controller gains, thus eliminating the need for nonlinear solvers. We present the testing results of two challenging examples, which could not be addressed by existing methods, to demonstrate the effectiveness of our methodology. Additionally, the paper reviews recent advancements in the research of time-delay systems, providing a valuable reference for both emerging and established researchers. © 2024, The Authors. All rights reserved.

关键词： State feedback

Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

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arXiv 2022年

作者： Yang, Chengran Xu, Bowen Khan, Junaed Younus Uddin, Gias Han, Donggyun Yang, Zhou Lo, David School of Computing and Information System Singapore Management University Singapore Department of Electrical and Computer Engineering University of Calgary Alberta Canada

APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the great success achieved by pre-trained models on many software engineering tasks, this study fine-tunes six pre-trained models for the aspect-based API review classification task and compares them with the current SOTA solution on an API review benchmark collected by Uddin et al. The investigated models include four models (BERT, RoBERTa, ALBERT and XLNet) that are pre-trained on natural languages, BERTOverflow that is pre-trained on text corpus extracted from posts on Stack Overflow, and CosSensBERT that is designed for handling imbalanced data. The results show that all the six fine-tuned models outperform the traditional machine learning-based tool. More specifically, the improvement on the F1-score ranges from 21.0% to 30.2%. We also find that BERTOverflow, a model pre-trained on the corpus from Stack Overflow, does not show better performance than BERT. The result also suggests that CosSensBERT also does not exhibit better performance than BERT in terms of F1, but it is still worthy of being considered as it achieves better performance on MCC and AUC. © 2022, CC BY.

关键词： Classification (of information)

CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

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arXiv 2023年

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Islamabad44000 Pakistan The Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of The School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging Non-Volatile Memories (NVMs), with their better scalability, non-volatility and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This paper proposes CRAFT, i.e., Criticality-Aware Fault-Tolerance Enhancement Techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of non-critical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs against s

关键词： Errors

system for coronavirus classification based on a new textural descriptor

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System for coronavirus classification based on a new textura...

International Conference on Sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Fathia Boubakri Norhene Gargouri Alima Damak Masmoudi Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering Computer Imaging and Electronic System Group Sfax Engineering School University of Sfax Sfax Tunisia

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve $(A_{z})$ of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists.

关键词： COVID-19 Radiography Measurement Reactive power Pulmonary diseases Lung Feature extraction

Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

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Improved Res2Net Based on Multi-scale Attention Mechanism fo...

12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units

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arXiv 2024年

作者： Manalu, Gabriel D.M. Christian, Mulomba Mukendi You, Songhee Choi, Hyebong Department of Computer Science and Electrical Engineering Handong Global University Korea Republic of Department of Advanced Convergence Handong Global University Korea Republic of School of Spatial Environment System Engineering Handong Global University Korea Republic of School of Global Entrepreneurship and ICT Handong Global University Korea Republic of

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Intensive care units

The quantized chemical reaction resonantly driven by multiple MIR-photons:From nature to the artificial

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Nano Research 2021年第12期14卷 4367-4369页

作者： Feng Zhang Bo Song Lei Jiang Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing100190China School of Future Technology University of Chinese Academy of SciencesBeijing100049China

Biochemical reactions in vivo occur at the temperature usually lower than that in vitro,however the underlying mechanism still remains a *** by our recent studies of adenosine triphosphate(ATP)releasing photons to resonantly drive DNA replication in a quantum way,we propose a quantized chemical reaction driven by multiple mid-infrared(MIR)*** space confinement effect of enzymes on a reactant molecule increases the lifetime of excitation state of its bond vibration,providing a chance for the bond to resonantly absorb multiple *** the energy of each MIR photon is significantly lower than that of chemical bond,the resonant absorption of multiple photons can break the appointed bond of confined *** from the traditional thermochemistry and photochemistry,the quantized chemical reactions could have a high energy efficiency and ultrahigh *** addition,we also suggest a quantum driving source for our quantum-confined superfluid reactions proposed *** quantized chemical reaction resonantly driven by multiple MIR photons holds great promise to develop novel approaches for the chemical engineering in future.

关键词： quantized chemical reaction quantized bio-synthesis quantized chemical engineering multiple mid-infrared(MIR)/far-infrared(FIR)-photon driving high efficiency and ultrahigh selectivity

An Evolutionary Task Scheduling Algorithm Using Fuzzy Fitness Evaluation Method for Communication Satellite Network

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arXiv 2024年

作者： Jiang, Xuemei Guo, Yangyang Zhang, Yue Song, Yanjie Pedrycz, Witold Xing, Lining School of Electronic Engineering Xidian University Xi’an China School of Systems Science Beijing Jiaotong University Beijing China School of Reliability and Systems Engineering Beihang University Beijing China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

Model-Driven Inverse Lithography Via Hypergraph Convolutional Neural Networks and its Experimental Demonstration

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SSRN

SSRN 2024年

作者： Zhang, Shengen Ma, Xu Huang, Chaojun Wang, Fuli Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Inverse lithography technology (ILT) is a pivotal approach to correct the optical proximity effect in a lithography process by optimizing the mask pattern at the pixel level. However, traditional ILT algorithms are computationally intensive due to the huge data volume throughput. This paper proposes a novel ILT method based on model-driven hypergraph convolutional network (MHGCN) to improve the computational efficiency and lithography image fidelity simultaneously. In the MHGCN framework, a dense concentric circular sampling (DCCS) template is first used to extract the feature matrices of the target layout, which are then imported to a hyperedge graph convolutional network (encoder) to predict the ILT result. Then, a lithography-model-based decoder is used to calculate the wafer image of the ILT mask. A low-cost unsupervised training method of the network is developed through the collaboration between the encoder and decoder. To demonstrate the proposed method, a digital-micromirror-device (DMD) maskless lithography testbed is established, and the imaging model of the testbed is calibrated based on a set of training layout patterns. The superiority of the proposed method in both of optimization capacity and computational efficiency are proved by the simulation and experimental results. © 2024, The Authors. All rights reserved.

关键词： Image enhancement