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International Conference on Systems and control (ICSC)

作者： Ali Omar M. Alsharif A. Jmal O. Naifar Ausama H. Ahmed Hamdi Gassara A. Ben Makhlouf Electrical Engineering Department Control and energy management laboratory (CEM lab) Sfax University Engineering National School Sfax Tunisia Department of Electrical & Computer Engineering Faculty of Engineering Elmergib University Alkhums Libya Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Tunisia Department of Mathematics Faculty of Sciences of Sfax Sfax Tunisia

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class.

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Optimizing network efficiency of dataflow architectures through dynamic packet merging

Optimizing network efficiency of dataflow architectures thro...

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International Green Computing Conference

作者： Yujing Feng Han Li Xu Tan Xiaochun Ye Dongrui Fan Zhimin Tang School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538674673

Dataflow processor has shown its unique advantages in executing high performance computing applications with its communication-exposed microarchitecture. In dataflow processors, large amounts of data are directly transferred between instructions through a network-on-chip. The efficiency of data transfer is an imperative performance metric that needs to be optimized in most dataflow processors. Based on the specific features of the dataflow network, we propose a mechanism for dynamically merging the packets in the routers. By testing workloads with varying characteristics, the experiment results demonstrate that the average latency of data transfer is reduced by 11.8%, the performance of dataflow accelerator is improved by 14.0%.

关键词： Merging Routing Data transfer Bandwidth Data models Arrays

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scIAC: clustering scATAC-seq data based on Student’s t-distribution similarity imputation and denoising autoencoder

scIAC: clustering scATAC-seq data based on Student’s t-dist...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jin Ye Xun Sun Xiaoshu Zhu Qingfeng Chen Yi Pan School of computer electronic and information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of computer electronic and information Guangxi University Nanning China School of Computer Science and Engineering Yulin Normal University Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

关键词： Sequential analysis Runtime Clustering methods Noise reduction Neural networks Feature extraction Data models

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Sampled-data H∞ filter for delayed neural network subject to sensor saturation

Sampled-data H∞ filter for delayed neural network subject t...

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第36届中国控制会议

作者： Liuwen Li Wenlin Zou Shumin Fei Jie Cao Pan Wang Key Laboratory of Measurement and Control of CSE (Ministry of Education) School of AutomationSoutheast University School of Information Engineering Nanjing Xiaozhuang University School of Computer Science and Engineering Nanjing University of Science and Technology

This paper is concerned with the problem of sampled-data H∞filtering for delayed neural network subject to sensor saturation. Rather than the continuous measurements, the neural network measurements are sampled and then transmitted to the filter simultaneously considering sensor saturation constraints. By using the input delay approach,the sampling period is converted into a bounded time-vary delay in the filter error system. Sensor saturation function is decomposed into a linear part and a dead-zone nonlinear remainder term. By utilizing Lyapunov-functional approach,sufficient conditions have been obtained to ensure that the filter error dynamics is exponentially stable. Finally, an example is provided to illustrate the superiority and usefulness of the proposed filter approach.

关键词： sampled-data systems sensor saturation H_∞ filter delayed neural network

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Mobile Crowdsourcing Based on 5g and 6g: A Survey

SSRN

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SSRN 2024年

作者： Wang, Yingjie Yingxin, Li Weilong, Wang Peiyong, Duan Maradapu Vera Venkata Sai, Akshita Cai, Zhipeng School of Computer and Control Engineering Yantai University Yantai264005 China Department of Computer Science Georgia State University AtlantaGA30303 United States

With the rapid development of Internet of Things (IoT), 5G and 6G networks, and advancements in hardware and software devices has enabled mobile devices to be equipped with rich sensors (smart phones, smart bracelets, etc.). This led to devices with powerful sensing capabilities that can easily connect and communicate with other devices. As a result, ordinary users could efficiently complete various sensing data tasks and get paid accordingly, promoting the rapid development of Mobile Crowdsourcing (MCS) technology and effectively facilitating people's daily lives. However, the current MCS is still a long way from being complete and still faces several challenges. Therefore, many researchers have conducted extensive research on MCS in recent years. In this survey, we aim to provide a comprehensive summary of the recent research progress on MCS. With respect to the main research directions and real-world applications of MCS, we review the research in three main areas (task allocation, privacy protection and incentive mechanism) and real-world application scenarios of MCS. Although there is a lot of existing research on MCS, there are still some crucial problems to be solved. Therefore, we conclude with some lessons learned and suggest future research directions by analyzing the studies covered in this paper. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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2DSM vs FFDM: A computeraided diagnosis based comparative study for the early detection of breast cancer

2DSM vs FFDM: A computeraided diagnosis based comparative st...

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Expert Systems

作者： Raghavendra, U. Gudigar, Anjan Ciaccio, Edward J. Ng, Kwan Hoong Chan, Wai Yee Rahmat, Kartini Acharya, U. Rajendra Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal576104 India Department of Medicine Columbia University Medical Center New York United States Department of Biomedical Imaging University of Malaya Kuala Lumpur Malaysia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Clementi599491 Singapore School of Medicine Malaysia

Purpose: Accurate and early detection of breast cancer using effective imaging modalities is an active area of research in medical image analysis. computeraided diagnosis (CAD) of breast cancer using digital mammograms may help in early diagnosis and can assist in maintaining patient health. The breast imaging reporting and data system (BIRADS) is widely used for risk assessment and classification grading in breast cancer screening. It contains seven different grading systems for breast cancer risk assessment. These range from grade 0 (incomplete) to grade 6 (proven malignancy). All other intermediate stages state the progression of risk. Methods: The current research results have shown that two-dimensional synthesized mammogram (2DSM) imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. To the best of our knowledge, there is no study which has yet compared the BIRADS discrimination power of 2DSM and FFDM imaging modalities. In this paper we present a novel CAD-based comparative study, using texton and gist for the characterization of breast cancer with 2DSM and FFDM imagery. Results: The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold crossvalidation. Hence, our proposed model showed that FFDM images are more effective than the 2DSM imaging modality in discriminating BIRADS grades. Conclusion: The obtained results confirmed that our method performed well in the early detection of breast cancer. Consequently, it can be used as a distinct system in rural hospitals. © 2021, John Wiley and Sons Inc. All rights reserved.

关键词： Medical imaging computer aided diagnosis Diseases Grading Hospitals Mammography Risk assessment Tomography X ray screens Breast cancer risk assessments Breast imaging reporting and data systems Digital breast tomosynthesis Early detection of breast cancer Full field digital mammography gist Probabilistic neural networks texton

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Predicting Telecommunication User Favorite Package by Using Deep Neural Network

Predicting Telecommunication User Favorite Package by Using ...

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2021 International Conference on Networking, Communications and Information Technology, NetCIT 2021

作者： Li, Tingshun Yang, Huiyu Wang, Dadi Liu, Zesan North China Electric Power University School of Control and Computer Engineering Beijing102206 China China Power Information Technology Co. Ltd Beijing China R D State Grid Information Telecommunication Group Co. Beijing102211 China

ISBN: (纸本)9781665400701

With the popularity of mobile phone, telecom companies have launched more and more package services. It is very difficult to choose telecom user package suitable for him or her. This paper provides a method to build a model based on deep neural network (DNN) for multi-classification, which has high accuracy to help user to pick out a favorite one from dozens of packages. The model is trained out from telecom user big data. First, the telecom big data are preprocessed to be completed integrity, normalized, balanced., and then divided into training data set, validating data set and testing data set. Second, feature engineering needs to be done in order to improve prediction model. Two types of feature engineering are presented in this work to compare which is better. Manual feature engineering is a way to build new features from expertise, while auto feature engineering is from third-party library. Third, an experimental process is design out to obtain prediction models, and use them to predict the testing data set. Finally, some conclusions are obtained by analyzing the experimental results. So, this paper proposes a multi-classification model to recommend a suitable package to a telecom user, which is trained out from telecom big data with high accuracy, more practical. Moreover, this work show that feature engineering and data preprocessing are helpful to obtain better machine learning model. © 2021 IEEE.

关键词： Deep neural networks

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Modeling of the Steam Pressure in Separator of a Once-through Unit Based on the ANN

Modeling of the Steam Pressure in Separator of a Once-throug...

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2018 International Conference on Manufacturing Technology, Materials and Chemical engineering, MTMCE 2018

作者： Li, Yingqi Liu, Changliang School of Control and Computer Engineering North China Electric Power University Baoding Hebei071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

It is challenging and interesting to establish a precise steam pressure in separator dynamic model of a once-through power plant unit in order to meet large scale load demands from the power grid and the needs of the engineering research. Steam pressure reveals the energy balance of boiler and turbine. This study proposes to establish such a steam pressure in separator mathematical model of a once-through boiler-turbine unit under dry operating conditions. Then the BP neural network model and the RBF neural network model were used to predict the steam pressure in separator, combined with running data. Moreover, in order to improve the performance of the network, the exhaustive method is used to optimize the parameters of the RBF network. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different sets of parameters under different load ranges. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Therefore, the model can be feasible and applicable for simulation analysis and testing control algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Separators

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Detection of False Data Injection Attacks Based on Spatio-Temporal Characterization in Smart Grids

Detection of False Data Injection Attacks Based on Spatio-Te...

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Chinese control and Decision Conference, CCDC

作者： Shouxuan Jiao Qiang Zhao Yinghua Han Jinkuan Wang Hantong Feng College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

With the increasing popularization of information and communication technologies, the development of smart grids has also emerged. However, the high reliance on information technology puts the grid at risk of malicious attacks. False Data Injection Attack (FDIA)have become a serious threat to the smart grid. While most of the current methods for detecting FDIA focus on determining whether the grid has been subjected to such attacks, they often overlook the importance of considering the topology and temporal correlations within the grid data and are unable to localize their attacks. To address this limitation, we have proposed a novel multi-label attack localization detection method based on spatio-temporal features. Our method considers both time and space and enables the localisation of attacks. Spatial feature extraction is conducted using a Convolutional Neural Network (CNN), which is subsequently subjected to feature filtering through an Attention Mechanism. The filtered features are then channeled into both Long-Term and Short-Term Memory network (LSTM) via a channel fusion process. This intricate process allows for the extraction of spatio-temporal features, ultimately facilitating the accurate localization of attacks. To evaluate the efficacy of our approach, we conducted extensive simulation experiments using the IEEE 14bus system and the IEEE 118-bus system. The results showcase remarkable performance with an overall accuracy of 99.6% for the former and 98.7% for the latter, underscoring the effectiveness of our proposed model.

关键词： Location awareness Correlation Attention mechanisms Accuracy Feature extraction Smart grids Topology

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Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Using Joint Independent Component Analysis

Feature-Based Approach to Fuse fMRI and DTI in Epilepsy Usin...

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Iranian Conference of Biomedical engineering

作者： Amir Hosein Riazi Hamid Soltanian-Zadeh Gholam-Ali Hossein-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran & Image Analysis Laboratory Henry Ford Health System Detroit Michigan

ISBN: (纸本)9781467331289

Functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) provide complementary information. Signal processing and statistical models may be used to fuse neuroimaging data across different imaging modalities. In this paper, we present a data driven method for fusing resting state fMRI and diffusion tensor imaging (DTI) data at feature level. The features are amplitude of low frequency fluctuations (ALFF) and fractional anisotropy (FA) extracted from fMRI and DTI datasets of epilepsy and healthy controls, respectively. We discuss main issues associated with group independent component analysis (ICA) as a fusion method. We address our proposed approach for combining two modalities across subjects and back reconstruction of independent components for each group and each subject. Our results indicate that connectivity of regions in default mode network depends on integrity of white matter that connects the two hemispheres (corpus callosum). The proposed signal processing and statistical methods facilitate evaluation of brain connectivity using different modalities. Separate analysis of data modalities does not reveal results of joint analysis.

关键词： FMRI DTI Joint ICA Group Analysis

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