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Quantum sensing of supersensitivity for the Ohmic quantum reservoir

Physical Review A 2022年第3期106卷 032602-032602页

作者： Qing-Shou Tan Wei Wu Lan Xu Jing Liu Le-Man Kuang Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang 414000 China Key Laboratory of Theoretical Physics of Gansu Province and Lanzhou Center for Theoretical Physics Lanzhou University Lanzhou 730000 China School of Physics and Chemistry Hunan First Normal University Changsha 410205 China MOE Key Laboratory of Fundamental Physical Quantities Measurement National Precise Gravity Measurement Facility School of Physics Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Synergetic Innovation Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou 450002 China

We propose an approach to implement a supersensitive estimation of the key parameters of the Ohmic-family spectral density with coherent spin states as the quantum sensor. This method can dramatically improve the estimation precision of the reservoir coupling strength as well as the cutoff frequency of the spectral density, by using both the number of spins N and encoding time t as effective resources. The quantum Fisher information indicates that the estimation sensitivity of the spectral density can surpass the shot-noise limit for all the sub-Ohmic, Ohmic, and super-Ohmic reservoirs. In particular, for super-Ohmic reservoirs, the precision can achieve a scaling ∝1/(Nt). We also present the measurement scheme which can saturate the quantum Cramér-Rao bound.

关键词： Quantum metrology Quantum sensing Dynamical systems Hamiltonian systems Information theory

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Optimal Reordering Trace Files for Improving Software Testing Suitcase

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Computers, Materials & Continua 2021年第4期67卷 1225-1239页

作者： Yingfu Cai Sultan Noman Qasem Harish Garg Hamïd Parvïn Kim-Hung Pho Zulkefli Mansor School of Measurement and Communication Harbin University of Science&TechnologyHarbinChina Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Department of Computer Science Faculty of Applied ScienceTaiz UniversityTaizYemen School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatiala147004India Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsan MalaysiaUKM BangiSelangor43600Malaysia

An invariant can be described as an essential relationship between program *** invariants are very useful in software checking and *** tools that are used to detect invariants are invariant *** are two types of invariant detectors:dynamic invariant detectors and static invariant *** software is an available computer program that implements a special case of a dynamic invariant detection *** proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical *** method has some *** of its biggest drawbacks is its overwhelming time *** is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace *** is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this *** findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

关键词： Dynamic invariant detection software testing genetic algorithm

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Gas turbine rotor system fault diagnosis method based on improved deep convolutional generative adversarial networks

Research Square

引用

Research Square 2021年

作者： Liu, Shucong Wang, Hongjun Han, Fengxia Zhang, Xiang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100124 China Key Laboratory of Modern Measurement and Control Technology of Ministry of Education Beijing Information Science and Technology University Beijing100192 China

In gas turbine rotor system fault diagnosis intelligent method based on data-driven is an important means to monitor the health status of gas turbine, it is necessary to obtain sufficient effective fault data to train the intelligent diagnosis model. In the actual operation of gas turbine, the collected gas turbine fault data is limited, and the small and imbalanced fault samples seriously affect the accuracy of fault diagnosis method. Aiming at the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signal is proposed, an structural optimization on generator of Deep Convolutional Generative Adversarial Network (DCGAN) and gradient penalty improvement on the loss function are introduced to generate effective fault data and improve the classification accuracy. The experiment results of gas turbine test bench demonstrated that the proposed method generated effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improved the fault classification and diagnosis performance of gas turbine rotor in the case of small samples, The proposed method can be used as a solution to the problems of small unbalanced fault samples, and provides an effective method for gas turbine fault diagnosis. © 2021, CC BY.

关键词： Failure analysis

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A Combined Alignment Method for Strapdown Inertial Navigation System on Stationary Base

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Journal of Harbin Institute of technology(New Series) 2020年第6期27卷 57-71页

作者： Shuai Chen Xinzhi Liu Qian Sun Ya Zhang School of Information and Control Engineering Liaoning Shihua UniversityFushun 113001LiaoningChina Department of Applied Mathematics University of WaterlooWaterloo N2L3G1Canada College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Electrical Engineering&Automation Harbin Institute of TechnologyHarbin 150001China

Owing to the weak observability of the azimuth misalignment angle,alignment accuracy and time are always the contradictory issues in the initial alignment process of Strapdown Inertial Navigation System(SINS),which requires a compromise between *** this paper,a combined alignment mechanism is proposed to construct an observable and controllable system model,which can effectively achieve higher azimuth alignment accuracy during the fixed time ***,the Reduced Order Kalman Filter(ROKF)alignment algorithm was utilized to calculate the misalignment angles in parallel with the classical gyrocompass alignment ***,the misalignment angles calculated by the gyrocompass alignment method were used to formulate the augmented measurement model with zero velocity ***,the zero velocity model of the ROKF method was switched into the augmented measurement model when the azimuth misalignment angle of the gyrocompass alignment method was close to steady *** combined alignment method was analyzed reasonably by the observability and the mathematical *** comparison results of the numerical simulation and the experimental data test showed that the combined method had good performance in terms of estimation accuracy and consistency of the alignment results.

关键词： inertial navigation instrumentation and measurement gyroscopes accelerometers Kalman filter measurement errors

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Blockchain for Energy Market: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Jiang, Tianqi Luo, Haoxiang Yang, Kun Sun, Gang Yu, Hongfang Huang, Qi Vasilakos, Athanasios V. Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Guangyuan Power Supply Company State Grid Sichuan Electric Power Company Guangyuan628033 China Peng Cheng Laboratory Shenzhen518066 China Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China Center for AI Research University of Agder Grimstad4879 Norway

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. Additionally, the energy market can address future production control and environmental concerns such as energy shortages and environmental deterioration. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. These factors have hindered the smooth operation of the energy market. Researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the

关键词： Deterioration

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Research on Nonlinear Frequency Compression Method of Hearing Aid with Adaptive Compression Ratio

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Computer Modeling in engineering & Sciences 2019年第11期121卷 551-567页

作者： Xia Wang Hongming Shen Huawei Tao Ruiyu Liang Xing Deng Haijian Shao Li Zhao Cairong Zou School of Information Science and Technology Nantong UniversityNantong226019China School of Information Science and Engineering Henan University of TechnologyZhengzhou450001China School of Communication Engineering Nanjing Institute of TechnologyNanjing211167China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Information Science and Engineering Southeast UniversityNanjing210096China

To make full use of the residual audible frequency band of hearing-loss patients and improve the intelligibility of speech,an adaptive nonlinear frequency compression(NFC)algorithm is proposed,which amplifies signals below the cutoff frequency while compresses signals above the cutoff ***,high-frequency signals are decomposed to critical band signals according to the BARK ***,the global compression ratio is determined according to the patient's cutoff frequency and maximum audible ***,the sub-band compression ratio is adaptively determined based on the global compression ratio and normalized average energy of subband ***,the high frequency signals are transposed to low frequency bands by compression mapping,and the phases are adjusted to the same as the original low frequency *** results of speech intelligibility with nine subjects demonstrate that compared to conventional amplitude amplification and nonlinear frequency compression algorithms the proposed algorithm significantly improves the intelligibility of initials and sentences,while not affects the intelligibility of finals and tones significantly.

关键词： Frequency compression sensorineural hearing loss hearing aids

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TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum

arXiv

引用

arXiv 2022年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Shenzhen518066 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Utron Technology Company Ltd. Hangzhou310056 China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Due to the decentralized and public nature of the Blockchain ecosystem, the malicious activities on the Ethereum platform impose immeasurable losses for the users. Existing phishing scam detection methods mostly rely only on the analysis of original transaction networks, which is difficult to dig deeply into the transaction patterns hidden in the network structure of transaction interaction. In this paper, we propose a Transaction SubGraph Network (TSGN) based phishing accounts identification framework for Ethereum. We first extract transaction subgraphs for target accounts and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. In order to make our model incorporate more important information about real transactions, we encode the transaction attributes into the modeling process of TSGNs, yielding two variants of TSGN, i.e., Directed-TSGN and Temporal-TSGN, which can be applied to the different attributed networks. Especially, by introducing TSGN into multi-edge transaction networks, the Multiple-TSGN model proposed is able to preserve the temporal transaction flow information and capture the significant topological pattern of phishing scams, while reducing the time complexity of modeling large-scale networks. Extensive experimental results show that TSGN models can provide more potential information to improve the performance of phishing detection by incorporating graph representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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A two-mode offspring generation selection mechanism with co-evolution for sparse large-scale multiobjective optimization

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Information Sciences 2025年 718卷

作者： Xiaodong Huang Jian Wang Gaige Wang Yong Zhang Dunwei Gong Yaochu Jin Nikhil R. Pal College of Control Science and Engineering China University of Petroleum (East China) Qingdao 266580 China College of Science China University of Petroleum (East China) Qingdao 266580 China School of Computer Science and Technology Ocean University of China Qingdao Shandong 266580 China School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu 221000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong 266061 China College of Engineering Westlake University Hangzhou Zhejiang 310024 China Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Sparse large-scale multiobjective optimization problems (LSMOPs) have a wide range of practical applications. In recent years, numerous multiobjective evolutionary algorithms (MOEAs) have been developed to address the complexities of these problems. However, many existing MOEAs designed to solve sparse LSMOPs typically rely on fixed, experience-based vectors to guide offspring generation, which often makes it challenging to determine the optimal guiding vectors for different population states, leading to premature convergence and loss of population diversity. To some extent, this leads to a subjective selection of the vector used for offspring generation. To address this issue, we propose a two-mode offspring generation selection mechanism (TOGSM) that incorporates diversified sparse knowledge into the offspring generation process. The switching between these two modes is based on a designed offspring performance indicator. We also divide the population into two subpopulations by employing techniques of Pareto dominance relationship and fitness values. In each generation, the loser subpopulation generates offspring solutions during the reproduction process, under the guidance of the winner subpopulation. Experimental results confirm that TOGSM incorporating two-mode mechanism and co-evolution strategy can generate higher quality Pareto optimal solutions with faster convergence speed than the state-of-the-art (SOTA) comparative algorithms.

关键词： Multiobjective optimization

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Gsgm: A Group-Sequence-Aware Graph Model for Personalized News Recommender System

SSRN

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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. Experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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