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arXiv 2023年

作者： Guo, Di Xu, Runmin Wu, Jinyu Lin, Meijin Du, Xiaofeng Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China Department of Applied Marine Physics and Engineering Xiamen University Xiamen361102 China

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high. © 2023, CC BY.

关键词： Nuclear magnetic resonance spectroscopy

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Sensor-Integrated Transformer-RF Model for HAR

Sensor-Integrated Transformer-RF Model for HAR

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yisen Kang Zheng Wang Xiaoqi Sun Huan Wang Ruiqi Lu Dengpeng Zou Mingyuan Liao Xiaokang Shi Yanwen Wang Renfa Li School of Computer Science and Engineering Central South University Changsha Hunan China Artificial Intelligence Application Research Center China Electric Power Research Institute Beijing China Hunan University Changsha Hunan China School of Life Sciences Langfang Normal University Langfang Hebei China State Grid Hunan Electric Power Company Limited Changsha Hunan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

The precise classification of human activities through sensor data collection and analysis addresses the broad demands in healthcare, security surveillance, and smart home applications amidst the rapid development of IoT technology. However, achieving high efficiency and accuracy remains a significant challenge for HAR algorithms. This paper proposes a HAR algorithm based on Transformer and Random Forest (Transformer-RF). The algorithm extracts and integrates multimodal features in the time domain, frequency domain, and statistical metrics, constructing one-dimensional and two-dimensional feature sets through feature transformation. The Transformer component, leveraging self-attention mechanisms, captures long-range dependencies and extracts global contextual information. Concurrently, the Random Forest component randomly selects features and samples, enhancing model diversity and improving complex human activity recognization capabilities. Experimental results demonstrate that compared with state-of-the-art algorithms, the Transformer-RF model achieves superior performance on both one-dimensional and two-dimensional feature sets, with an accuracy of up to 94.17%. The primary contribution of this paper lies in the introduction of an innovative Transformer-RF human activity recognization method, which not only ensures high accuracy but also exhibits excellent generalization capability and practical application potential. This study provides new insights and technical solutions for the field of human activity recognization, offering significant theoretical and practical value.

关键词： Accuracy Smart homes Medical services Transformers Feature extraction Classification algorithms Human activity recognition Security Internet of Things Random forests

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A 1D Plug-and-Play Synthetic Data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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Design and Development of a Low-Power Wireless MEMS Lead-Free Piezoelectric Accelerometer System

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷

作者： Li, Cheng-Ying Chen, Ze-Hui Kao, Hsueh-Yu Chang, Sheng-Kai Hsiao, Po-Yu Huang, Yen-Hsiang Huang, Yu-Chieh Chu, Sheng-Yuan Wei, Chia-Ling Liu, Yun-Hui Cheng, Kuang-Wei Tsai, Cheng-Che National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan National Cheng Kung University Nano-Integrated-Circuit Engineering Program Tainan701 Taiwan Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University College of Electrical Engineering & Computer Science Department of Electrical Engineering Tainan701 Taiwan Tung Fang Design University School of Application Design Department of Digital Game and Animation Design Kaohsiung82941 Taiwan

In this article, a complete 3-D integrated sensing system intended for practical applications was presented on a printed circuit board (PCB) board, starting with lead-free piezoelectric films deposited using an RF sputtering system, an MEMS structure design/simulation, device fabrication, and a signal processing chip design, including charge- to-voltage transformation, signal amplification, 60-Hz noise rejection, and low-power RF wireless transmission chip for remote monitoring. Both the signal processing chips and the RF wireless transmitter chips were manufactured using the Taiwan Semiconductor Manufacturing Company (TSMC) CMOS process. The piezoelectric coefficient d33 was measured as 27.21 pm/V of the proposed ZnO:Li doped Mg (MLZO) films. To meet the frequency specifications of industrial applications, the ring-structure devices with a 7730-Hz resonance frequency were designed using ANSYS simulation software. There was only a 0.39% deviation compared to the simulation results with the actual experimental data. The measurement results of the proposed system showed that the acceleration detection range was approximately 137 mV/ g (p-p). The voltage-controlled oscillator (VCO) adopted in the wireless transmitter chip had a four-leaf clover structure inductor with two symmetrical axes resulting in better suppression of magnetic field radiation. Finally, this system was applied to industrial spindles, and the normal and abnormal operation information of spindles was successfully and wirelessly monitored. © 1963-2012 IEEE.

关键词： Wireless sensor networks

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Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means

Behavioral Time Management Analysis: Clustering Productivity...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： D David Winster Praveenraj Mohammed I. Habelalmateen Akrati Shrivastava Amarpreet Kaur A.S. Valarmathy Chintamani Prasad Patnaik School of Business and Management CHRIST University Bangalore India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of MBA Aditya Institute of Technology and Management Tekkali Andhra Pradesh India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time

关键词： Productivity Time-frequency analysis Employment Machine learning Multitasking Market research Telecommunication computing Personnel Usability Stress

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ComR:a combined OWL reasoner for ontology classification

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Frontiers of computer science 2019年第1期13卷 139-156页

作者： Changlong WANG Zhiyong FENG Xiaowang ZHANG Xin WANG Guozheng RAO Daoxun FU School of Computer Science and Technology Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin 300072China School of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

Ontology classification,the problem of computing the subsumption hierarchies for classes (atomic concepts),is a core reasoning service provided by Web Ontology Language (OWL)*** general-purpose OWL 2 reasoners employ sophisticated optimizations for classification,they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient;however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL *** this paper,we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive *** optimize the workload,we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure *** the ontology classification,the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 *** proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology *** the wellknown ontology NCI,the classification time is reduced by 96.9%(resp.83.7%)compared against the standard reasoner Pellet (*** modular reasoner MORe).

关键词： OWL ontology classification reasoner

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Full-Coverage Mapping of Daily High-Resolution XCO2 across China from 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Yi Yan, Jining Zhong, Liheng Bao, De Sun, Leigang Li, Guangyu China University of Geosciences School of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Ant Group World Financial Center Beijing100020 China Hebei Academy of Sciences Institute of Geographical Sciences Shijiazhuang050011 China Hebei Technology Innovation Center for Geographic Information Application Shijiazhuang050011 China Environmental Development Center of the Ministry of Ecology and Environment China

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1° spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1° resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R2 of 0.86 and an RMSE of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. © 1980-2012 IEEE.

关键词： Atmospheric movements

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Kerr effect in ultra-compact hybrid plasmonic metal-insulator-metal nano-focusing structure

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Chinese Optics Letters 2022年第3期20卷 106-110页

作者： Kejian Zhu Pengfei Sun F|engfei Xu Xingpeng Liu Tangyou Sun Haiou Li Zhiping Zhou State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin 541004China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China

Nano-focusing structures based on hybrid plasmonic waveguides are likely to play a key role in strong nonlinear optical *** the insertion loss is considerable,a significant nonlinear phase shift may be achieved by decreasing the nano-focusing device footprint and careful parameter ***,we study the Kerr effect in hybrid plasmonic waveguides by analyzing the mode effective area,energy velocity,and insertion ***,by utilizing plasmonics to manipulate the effective index and mode similarity,the TM mode is reflected and absorbed,while the TE mode passes through with relatively low propagation *** providing a deep understanding of hybrid plasmonic waveguides for nonlinear applications,we indicate pathways for their future optimization.

关键词： silicon photonics plasmonics nonlinear optics nano-focusing

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Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems

arXiv

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arXiv 2022年

作者： Chen, Chong Gao, Ying Shi, Leyu Huang, Siquan The School of Computer Science & Engineering South China University of Technology Guangzhou510006 China The Guangdong Prov. Key Lab. of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510006 China

Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from data poisoning attacks. Poisoned data can be fabricated by falsifying medical data, which urges a security defense to IoMT systems. Due to the lack of specific labels, the filtering of malicious data is a unique unsupervised scenario. One of the main challenges is finding robust data filtering methods for various poisoning attacks. This paper introduces a Federated Data Sanitization Defense, a novel approach to protect the system from data poisoning attacks. To solve this unsupervised problem, we first use federated learning to project all the data to the subspace domain, allowing unified feature mapping to be established since the data is stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The clustering is based on the consistent association of data and its semantics. After we get the clustering of the private data, we do the data sanitization with a simple yet efficient strategy. In the end, each device of distributed ImOT is enabled to filter malicious data according to federated data sanitization. Extensive experiments are conducted to evaluate the efficacy of the proposed defense method against data poisoning attacks. Further, we consider our approach in the different poisoning ratios and achieve a high Accuracy and a low attack success rate. © 2022, CC BY-NC-ND.

关键词： Semantics

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management

Analyzing the Potential Benefits of Data Mining for Customer...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Aravind A R Shilpa M. Katikar Kalyan Acharjya G. Ezhilarasan Surendra Yadav Abhinav Mishra Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty.

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