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第三十九届中国控制会议

作者： Fang Gao Yaoxiong Wang Qing Gao Dewen Cao Feng Shuang College of Electrical Engineering Guangxi University Institute of Intelligent Machines Chinese Academy of Sciences School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University

The information of quantum pathways can be extracted in the framework of the Hamiltonianencoding and Observable-decoding *** closed quantum systems,only off-diagonal elements of the Hamiltonian in the Hilbert space is required to be encoded to obtain the desired *** open quantum systems,environment-related terms will appear in the diagonal elements of the Hamiltonian in the Liouville *** diagonal encodings have to be performed to differentiate different pathways,which will lead to self-to-self transitions and inconsistency of pathway amplitudes with Dyson *** this work,a well-designed transformation is proposed to avoid the counterintuitive transitions and the inconsistency,with or without control *** simulations show that the method are consistent with Dyson expansion.

关键词： Self-to-self transitions in open quantum systems:the origin and solutions origin

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RETRACTED ARTICLE: Continuous Activity-Aware Stress Detection Using Sensors

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Wireless Personal Communications 2021年第1期127卷 17-17页

作者： Karthick, T. Sangeetha, M. Ramprasath, M. Ananthajothi, K. Department of Data Science and Business Analytics School of Computing SRM Institue of Science and Technology Chennai India Department of Computer Science and Engineering Misrimal Navajee Munoth Jain Engineering College Chennai India

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Outlier detection algorithm based on density and distance

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Journal of Physics: Conference Series 2021年第1期1941卷

作者： Ying Qu Fei Guo School of Economics and Management Hebei University of Science and Technology Shijiazhuang Hebei 050018 China Data Science and Intelligent Computing Research Center Hebei University of Science and Technology Shijiazhuang Hebei 050018 China

In order to solve the problem that the existing outlier detection algorithm is difficult to detect the one-dimensional integer data set with uneven frequency distribution and uniform distance distribution and low accuracy, the advantages of density outlier detection and distance outlier detection can be combined. An outlier detection algorithm DAD (Density and Distance) based on density and distance was proposed. In order to improve the possibility of outlier sample distance, the algorithm can define the weight distance; introduce the global density outlier factor combined with the weight distance as the relative distance, and use the cutting edge strategy to quickly cut the outliers based on the minimum spanning tree. Then, an artificial data set was used to test the algorithm. Experimental results showed that the algorithm had a good outlier detection accuracy in dealing with data sets with uneven frequency distribution and uniform distance distribution.

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Spatial resolution for forest carbon maps

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science (New York, N.Y.) 2025年第6732期387卷 370-371页

作者： Laura Duncanson Neha Hunka Tommaso Jucker John Armston Nancy Harris Lola Fatoyinbo Christopher A Williams Jeff W Atkins Brett Raczka Shawn Serbin Michael Keller Ralph Dubayah Chad Babcock Mark A Cochrane Andrew Hudak George C Hurtt Paul M Montesano L Monika Moskal Taejin Park Sassan Saatchi Carlos A Silva Hao Tang Rodrigo Vargas Aaron Weiskittel Konrad Wessels Scott J Goetz Geographical Sciences University of Maryland College Park MD USA. School of Biological Sciences University of Bristol Bristol UK. World Resources Institute Washington DC USA. NASA Goddard Space Flight Center Greenbelt MD USA. Graduate School of Geography Clark University Worcester MA USA. US Department of Agriculture (USDA) Forest Service Southern Research Station New Ellenton SC USA. Department of Biology Virginia Commonwealth University Richmond VA USA. Computational and Information Systems Lab Data Assimilation Research Section National Center for Atmospheric Research Boulder CO USA. International Institute of Tropical Forestry USDA Forest Service Rio Piedras PR USA. Forest Resources Department University of Minnesota St. Paul MN USA. University of Maryland Center for Environmental Science Frostburg MD USA. Rocky Mountain Research Station USDA Forest Service Moscow ID USA. NASA Goddard Space Flight Center ADNET Systems Greenbelt MD USA. School of Environmental and Forest Sciences University of Washington WA USA. Bay Area Environmental Research Institute Moffett Field CA USA. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. CTrees Pasadena CA USA. Forest Biometrics Remote Sensing and Artificial Intelligence Lab (SilvaLab) School of Forest Fisheries and Geomatics Science University of Florida Gainesville FL USA. Department of Geography National University of Singapore Singapore. Department of Plant and Soil Sciences University of Delaware Newark DE USA. Center for Research on Sustainable Forests University of Maine Orono ME USA. Geography and Geoinformation Science George Mason University Fairfax VA USA. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA.

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Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks

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Computers and Geosciences 2025年 204卷

作者： Liu, Yecheng Li, Diquan Li, Jin Hu, Yanfang Liu, Zijie Zhang, Xian School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor Ministry of Education Central South University Changsha 410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha 410081 China Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha 410205 China

Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference. © 2025 Elsevier Ltd

关键词： Controlled-source electromagnetic method Deep learning Noise Signal detection

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Progressive defense against adversarial attacks for deep learning as a service in internet of things

arXiv

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arXiv 2020年

作者： Wang, Ling Zhang, Cheng Luo, Zejian Liu, Chenguang Liu, Jie Zheng, Xi Vasilakos, Athanasios Department of Computer Science and Technology Harbin Insititute of Technology China University of Texas Austin United States Department of Computer Science and Technology Harbin Insititute of Technology ShenZhen China Department of Computing Macquaire University Australia School of Electrical and Data Engineering University of Technology Sydney Australia

Nowadays, Deep Learning as a service can be deployed in Internet of Things (IoT) to provide smart services and sensor data processing. However, recent research has revealed that some Deep Neural Networks (DNN) can be easily misled by adding relatively small but adversarial perturbations to the input (e.g., pixel mutation in input images). One challenge in defending DNN against these attacks is to efficiently identifying and filtering out the adversarial pixels. The state-of-the-art defense strategies with good robustness often require additional model training for specific attacks. To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type. We evaluated our progressive defense strategy against various attack methods on two well-known datasets. The result shows it outperforms the state-of-the-art while reducing the cost of model training by 50% on average. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

arXiv

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arXiv 2020年

作者： Pan, Xiang Chen, Minghua Zhao, Tianyu Low, Steven H. Department of Information Engineering The Chinese University of Hong Kong Hong Kong School of Data Science City University of Hong Kong Hong Kong Department of Computing and Mathematical Sciences Department of Electrical Engineering California Institute of Technology United States

To cope with increasing uncertainty from renewable generation and flexible load, grid operators need to solve alternative current optimal power flow (AC-OPF) problems more frequently for efficient and reliable operation. In this paper, we develop a Deep Neural Network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional iterative solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized a prediction-and-reconstruction procedure in our previous studies, DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining ones by solving the power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to be predicted by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process towards guaranteeing the inequality constraints. We also drive a condition for tuning the DNN size according to the desired approximation accuracy, which measures its generalization capability. It provides theoretical justification for using DNN to solve AC-OPF problems. Simulation results for IEEE 30/118/300-bus and a synthetic 2000-bus test cases demonstrate the effectiveness of the penalty approach. They also show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art iterative solver, at the expense of Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

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AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Feature Location Benchmark for Decomposing and Reusing Android Apps

arXiv

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arXiv 2020年

作者： Tang, Yutian Zhou, Hao Xu, Zhou Luo, Xiapu Cai, Yan Zhang, Tao Department of Computing Hong Kong Polytechnic University Hong Kong School of Big Data and Software Engineering Chongqing University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Faculty of Information Technology Macau University of Science and Technology China

Software reuse enables developers to reuse architecture, programs and other software artifacts. Realizing a systematical reuse in software brings a large amount of benefits for stakeholders, including lower maintenance efforts, lower development costs, and time to market. Unfortunately, currently implementing a framework for large-scale software reuse in Android apps is still a huge problem, regarding the complexity of the task and lacking of practical technical support from either tools or domain experts. Therefore, proposing a feature location benchmark for apps will help developers either optimize their feature location techniques or reuse the assets created in the benchmark for reusing. In this paper, we release a feature location benchmark, which can be used for those developers, who intend to compose software product lines (SPL) and release reuse in apps. The benchmark not only contributes to the research community for reuse research, but also helps participants in industry for optimizing their architecture and enhancing modularity. In addition, we also develop an Android Studio plugin named caIDE for developers to view and operate on the benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer software reusability

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Retraction notice to “Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference” [Optik 270 (2022) 169956]

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Optik 2023年 288卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid K. Sathishkumar Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

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