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arXiv 2024年

作者： Lu, Liyang Wang, Zhaocheng Gao, Zhen Chen, Sheng Poor, H. Vincent The Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China The Tsinghua Shenzhen International Graduate School Shenzhen518055 China Beijing Institute of Technology Beijing100081 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts measuring holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework elaborated for analyzing both noiseless and noisy recovery conditions. Specifically, we present the exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and the signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP, which we introduce. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than the existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the sophisticated results derived are universal and applicable to all these tensorized variants. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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A Surveillance System Utilizing mmWave-FMCW Radar for Restricted Control Area

A Surveillance System Utilizing mmWave-FMCW Radar for Restri...

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international Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we introduce a surveillance system utilizing mmWave-FMCW radar designed for restricted control areas. The experiment, conducted in our laboratory, defines the restricted control area parameters. Employing the TI-IWR6843AOPEVM model, operating at 60–64 GHz, and providing point cloud and Doppler information, we assumed walking paths for testing, partitioning scenarios into four distinct cases to simulate real-world scenarios. System performance evaluation, assessed through confusion matrices, demonstrates an achieved accuracy of up to 76.9%. Notably, errors are mainly caused by multipath effects due to static objects within the testing environment.

关键词： Point cloud compression Measurement Accuracy Surveillance System performance Radar tracking Doppler radar

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Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

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engineering Applications of Artificial Intelligence 2024年第PartE期133卷

作者： Zhang, Chao Wen, Qifei Li, Deyu Sangaiah, Arun Kumar Lin, Mingwei School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos1102-2801 Lebanon College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). © 2024 Elsevier Ltd

关键词： Sentiment analysis

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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FMCW Radar-Based Enhancing Moving Target Positioning Accuracy in Spectrogram Mapping

FMCW Radar-Based Enhancing Moving Target Positioning Accurac...

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international Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we propose a methodology aimed to enhancing the accuracy of moving target positioning using FMCW radar within spectrogram mapping. Utilizing a 10 GHz operating frequency, the FMCW radar effectively detects moving targets. However, the interpretation of intermediate frequency (IF) signals posed challenges due to multiple reflections and resulting ambiguous distances between objects. For solving this problem, we propose two steps of solution: employing an enveloping method and normalization of the IF signal. This process can extract maximum amplitude peaks specific to the target. Experimental validation involved detecting a drone moving away from the radar. Comparative analysis pre and post signal processing in spectrogram terms demonstrate the efficacy of our technique. Furthermore, verification of the detection range of radar was conducted using the GSM-015 GNSS speed meter. Results confirm the accuracy of the positioning capabilities of radar, emphasizing the effectiveness of the proposed approach.

关键词： Visualization Accuracy Refining Radar detection Radar Radar antennas Reflection

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Unavoidable multilevel biaxial symmetry breaking in chiral hybrid liquid crystals

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Physical Review E 2025年第4期111卷 045410-045410页

作者： Jin-Sheng Wu Marina Torres Lázaro Souvik Ghosh Haridas Mundoor Henricus H. Wensink Ivan I. Smalyukh Department of Physics and Chemical Physics Program University of Colorado Boulder Colorado 80309 USA Laboratoire de Physique des Solides–UMR 8502 Université Paris-Saclay and CNRS 91405 Orsay France International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University Higashihiroshima Japan Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program and Soft Materials Research Center University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory and University of Colorado Boulder Colorado 80309 USA

Chiral nematic or cholesteric liquid crystals (LCs) are fluid mesophases with long-ranged orientational order featuring a quasilayered periodicity imparted by a helical director configuration but lacking long-range positional order. Doping molecular cholesteric LCs with strongly anisotropic uniaxial colloidal particles adds another level of complexity because of the interplay between weak surface-anchoring boundary conditions and bulk-based elastic distortions near the particle-LC interface. Using cylindrical colloidal disks and rods with different geometric shapes and surface conditions, we demonstrate that these colloidal inclusions generically exhibit biaxial orientational probability distributions which may impart anomalously strong local biaxiality onto the hybrid cholesteric LC structure. Unlike nonchiral hybrid molecular-colloidal LCs, where biaxial order emerges only at critical colloid volume fractions exceeding some uniaxial-biaxial transition value, the orientational probability of the colloidal inclusions immersed in chiral nematic hosts is unambiguously biaxial even at infinite dilution. We demonstrate that the colloids induce local biaxial perturbations within the molecular orientational order of the LC host medium which strongly enhances the weak but native biaxial order of chiral nematic LC induced by the chiral symmetry breaking of the director field. With the help of analytical modeling and computer simulations based on the Landau–de Gennes free energy of the host LC around the colloids, we rationalize the observed multilevel biaxial order and conclude that it is not only unavoidable but also strongly enhanced compared to both achiral hybrid LCs and purely molecular cholesteric LCs.

关键词： Cholesteric liquid crystals

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Machine Learning Techniques for Improving Multiclass Anomaly Detection on Conveyor Belts

Machine Learning Techniques for Improving Multiclass Anomaly...

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IEEE Instrumentation and Measurement Technology Conference

作者： Saulo N. Matos Otávio F. Coletti Rafael Zimmer Fernando U. Filho Ricardo C.C. L. de Carvalho Victor R. da Silva Jorge L. Franco Thomas V. B. Pinto Luiz G. D. de Barros Caetano M. Ranieri Bruno E. Lopes Diego F. Silva Jó Ueyama Gustavo Pessin Institute of Mathematics and Computer Science University of Sã o Paulo Brazil São Carlos School of Engineering University of São Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Graduate Program in Electrical Engineering Universidade Federal of Minas Gerais Belo Horizonte MG Brazil Vale SA Vitória Brazil

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Industrial conveyor belt systems are an efficient means of transport due to their adaptability and extension. Nonetheless, such systems are prone to various failures, including but not limited to: idler anomalies; belt tears; and pin misalignment which can cause significant disruptions in the production process. Preemptive maintenance and health monitoring of these conveyor belts is a common practice for avoiding these failures, but a challenging task due to the rarity of comprehensive anomaly detection datasets in the area, with current works aimed at evaluating the belt's immediate condition at fixed points. This study addresses this research gap by comparing multiple machine learning techniques, such as a proposed Hybrid Neural Network (HNN) tailored for classification of multiple anomaly classes, as well as machine learning approaches for time series based on feature extraction, Catch22, Minirocket Arsenal, and Time Series Forest. Our proposed HNN architecture performed better in classifying and distinguishing between different types of anomalies, with an accuracy of 95.55%. The obtained results suggest a promising approach for the area of predictive maintenance for industrial conveyor systems, as well as gives insights on possible improvements on the model and future research.

关键词： Machine learning algorithms Accuracy Time series analysis Machine learning Belts Transformers Feature extraction

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Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

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Processes 2023年第8期11卷

作者： Sajid, Injila Gautam, Ayushi Sarwar, Adil Tariq, Mohd Liu, Hwa-Dong Ahmad, Shafiq Lin, Chang-Hua Sayed, Abdelaty Edrees Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions. © 2023 by the authors.

关键词： Efficiency

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A Multi-Step Multivariate Fuzzy-based Time Series Forecasting on Internet of Things Data

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IEEE Internet of Things Journal 2025年

作者： Bitencourt, Hugo Vinicius Lucas, Patri-Cia de Oliveira Orang, Omid Silva, Petronio C. L. Guimaraes, Frederico Gadelha Universidade Federal de Minas Gerais -UFMG Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Salinas Brazil Universidade Federal de Minas Gerais -UFMG Computer Science MG Belo Horizonte31270-901 Brazil Federal Institute of Northern Minas Gerais IFNMG MG Januaria Brazil Universidade Federal de Minas Gerais -UFMG Department of Computer Science MG Belo Horizonte Brazil

Multi-step ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision-making. Thus, this study introduces a novel multiple-input single-output (MISO) forecasting method called Multi-Step Embedding-based Fuzzy Time Series (MS-EFTS), designed to predict high-dimensional non-stationary time series data. As a first-order approach, it employs a direct strategy that integrates an embedding transformation with a weighted multivariate FTS (WMVFTS) model. This combination allows for effective predictions over long-term horizons within low-dimensional, learned continuous representations. The effectiveness of the proposed MS-EFTS is assessed using three high-dimensional IoT time series in this investigation. The obtained results showcase the superior performance of the proposed method compared to some deep learning forecasting methods, including LSTM, BiLSTM, TCN, and CNN-LSTM, in terms of accuracy, parsimony, and efficiency. © 2014 IEEE.

关键词： Smart homes

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