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IEEE International Conference on Systems, Man and Cybernetics

作者： Yongze Lin Junfei Qiao Jing Bi Haitao Yuan Jiahui Zhai MengChu Zhou Beijing Laboratory of Smart Environmental Protection Beijing University of Technology Beijing China School of Information Science and Technology Beijing University of Technology Beijing China College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction is based on historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from time series data and capture complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called PSGT for short, which integrates Patch Savitsky-Golay filtering and Transformer. First, this work adopts a Patching method to embed sub-time series data and obtains the trends and semantic information of the time series. Second, it uses the Savitsky-Golay filtering to effectively remove the noise data in the patch and improve the prediction accuracy. Third, it uses a Transformer mechanism to address the nonlinear problem of water quality time series and improve long-term prediction capability. Two real-world datasets are utilized to evaluate the proposed PSGT, and experiments prove that PSGT performs better than other benchmark models by at least 6%.

关键词： Filtering Time series analysis Noise Semantics Water quality Telecommunication traffic Predictive models Transformers Market research Hazards

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

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Journal of Earth Science 2023年第5期34卷 1433-1446页

作者： Qinjun Qiu Zhen Huang Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China Wuhan Geomatics Institute Wuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China Geological Environmental Center of Hubei Province Wuhan 430034China Faculty of Earth Resources China University of GeosciencesWuhan 430074China State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhan 430078China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

关键词： geological hazard report spatiotemporal information geological hazard ontology natural language processing gazetteers onlology machine learning

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Solar-powered shape-changing origami microfliers

arXiv

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arXiv 2023年

作者： Johnson, Kyle Arroyos, Vicente Ferran, Amélie Elberier, Tilboon Villanueva, Raul Yin, Dennis Aliseda, Alberto Fuller, Sawyer Iyer, Vikram Gollakota, Shyamnath Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Mechanical Engineering University of Washington United States LEGI Laboratory Université Grenoble Alpes France Department of Electrical and Computer Engineering University of Washington United States

Using wind to disperse microfliers that fall like seeds and leaves can help automate large-scale sensor deployments. Here, we present battery-free microfliers that can change shape in mid-air to vary their dispersal distance. We design origami microfliers using bi-stable leaf-out structures and uncover an important property: a simple change in the shape of these origami structures causes two dramatically different falling behaviors. When unfolded and flat, the microfliers exhibit a tumbling behavior that increases lateral displacement in the wind. When folded inward, their orientation is stabilized, resulting in a downward descent that is less influenced by wind. To electronically transition between these two shapes, we designed a low-power electromagnetic actuator that produces peak forces of up to 200 millinewtons within 25 milliseconds while powered by solar cells. We fabricated a circuit directly on the folded origami structure that includes a programmable microcontroller, Bluetooth radio, solar power harvesting circuit, a pressure sensor to estimate altitude and a temperature sensor. Outdoor evaluations show that our 414 milligram origami microfliers are able to electronically change their shape mid-air, travel up to 98 meters in a light breeze, and wirelessly transmit data via Bluetooth up to 60 meters away, using only power collected from the sun. © 2023, CC BY-NC-ND.

关键词： Energy harvesting

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Adaptive Frequency Hopping for 5G New Radio mMTC Security

Adaptive Frequency Hopping for 5G New Radio mMTC Security

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IEEE International Conference on Industrial Technology (ICIT)

作者： Wai Ming Chan Hyuck M. Kwon Rémi A. Chou David J. Love Sonia Fahmy Syed Rafiul Hussain Sang Wu Kim Chris Vander Valk Christopher G. Brinton Vuk Marojevic Khanh D. Pham Taejoon Kim Department of Electrical Engineering and Computer Science The University of Kansas Lawrence KS USA Department of Electrical and Computer Engineering Wichita State University Wichita KS USA School of Computing Wichita State University Wichita KS USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Computer Science Purdue University West Lafayette IN USA Department of Computer Science and Engineering The Pennsylvania State University State College PA USA Department of Electrical and Computer Engineering Iowa State University Ames IA USA Raytheon BBN Cambridge MA USA Department of ECE Mississippi State University Starkville MS USA Air Force Research Laboratory Albuquerque NM USA

3GPP standards (e.g., LTE and 5G New Radio (NR)) have revolutionized commercial broadband access. Unfortunately, the openness of $\mathbf{3GPP}$ specifications and high-level of adaptability make fifth-generation (5G) networks increasingly sensitive to unsophisticated attacks in the network. While 5G NR has introduced frequency hopping (FH) for massive machine-type communications $(\mathbf{mMTC})$ , there has not been a unified approach to designing the FH patterns in practical 5G systems. In this paper, we investigate the multi-user FH design problem at the physical resource grid of the 5G communication networks. We present various criteria for FH pattern design and propose two algorithms that are implementable with low complexity. The first scheme utilizes known interference patterns to design a set of user equipment (UE) FH patterns that are immune to interfering attacks. The second scheme leverages interference statistics and minimizes the expected number of hits by the interferer. Through numerical simulations and analytical modeling, we demonstrate the efficacy of the proposed schemes, which outperform the uniform FH pattern and achieve near-optimal performance.

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A Machine Learning Approach to Predicting Dry Eye-Related Signs, Symptoms and Diagnoses from Meibography Images

SSRN

引用

SSRN 2024年

作者： Graham, Andrew D. Kothapalli, Tejasvi Wang, Jiayun Ding, Jennifer Tse, Vivien Asbell, Penny A. Yu, Stella X. Lin, Meng C. Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Vision Science Group University of California Berkeley United States Clinical Research Center School of Optometry University of California Berkeley United States Department of Bioengineering University of Memphis United States International Computer Science Institute Berkeley United States

Purpose: To use artificial intelligence to identify relationships between morphological characteristics of the Meibomian glands (MGs), subject factors, clinical outcomes, and subjective symptoms of dry eye. Methods: A total of 562 infrared meibography images were collected from 363 subjects (170 contact lens wearers, 193 non-wearers). Subjects were 67.2% female and were 54.8% Caucasian. Subjects were 18 years of age or older. A deep learning model was trained to take meibography as input, segment the individual MG in the images, and learn their detailed morphological features. Morphological characteristics were then combined with clinical and symptom data in prediction models of MG function, tear film stability, ocular surface health, and subjective discomfort and dryness. The models were analyzed to identify the most heavily weighted features used by the algorithm for predictions. Results: MG morphological characteristics were heavily weighted predictors for eyelid notching and vascularization, MG expressate quality and quantity, tear film stability, corneal staining, and comfort and dryness ratings, with accuracies ranging from 65% to 99%. Number of visible MG, along with other clinical parameters, were able to predict MG dysfunction, aqueous deficiency and blepharitis with accuracies ranging from 74% to 85%. Conclusions: Machine learning-derived MG morphological characteristics were found to be important in predicting multiple signs, symptoms, and diagnoses related to MG dysfunction and dry eye. This deep learning method illustrates the rich clinical information that detailed morphological analysis of the MGs can provide, and shows promise in advancing our understanding of the role of MG morphology in ocular surface health. © 2024, The Authors. All rights reserved.

关键词： Morphology

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A method for Selecting the Control Inputs for Zero Voltage Switching and Efficiency Optimization of Bidirectional Wireless Power Transfer System

A method for Selecting the Control Inputs for Zero Voltage S...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Onai, Kuseso Ojo, Olorunfemi Tennessee Technological University Laboratory for Electric Machine and Power Electronics Department of Electrical and Computer Engineering CookevilleTN38505 United States

ISBN: (纸本)9781665436236

The bidirectional wireless power transfer system (BD-WPT) system allows power flow in both directions between a source and a load. High frequency is required to increase the power transfer efficiency and reduce the size of the magnetic components. However, high frequency operation leads to an increase in switching losses of the converter. To reduce these losses, zero voltage switching (ZVS) operation of the converter is required. This paper presents a method of optimizing the coil-to-coil efficiency of the BD-WPT system whilst ensuring that ZVS is achieved. To achieve this goal, harmonic decomposition of the input voltages is used for the ZVS, which gives a better accuracy when compared to the fundamental harmonic approximation method and avoids the complexity of time domain analysis. Lagrange optimization was employed to optimize the efficiency of the coil for a given power requirements. It was shown that for optimum efficiency operation of the converter, the current in the primary and secondary coils should be equal. © 2021 IEEE.

关键词： Zero voltage switching

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Exploration of the Non-Commensurate Performance Objectives of Bi-Directional Vehicle to Grid Resonant Converter Based Battery Charger

Exploration of the Non-Commensurate Performance Objectives o...

引用

2021 IEEE Southern Power Electronics Conference, SPEC 2021

ISBN: (纸本)9781665436236

In bidirectional Wireless power transfer (BDWPT) for electric vehicle battery chargers, aside improving efficiency, the effects of stray magnetic field on users and the general public is also of concern. Hence, the stray magnetic field and power loss are key performance indicators to be optimized. The optimization, reported in literature, is obtained with Pareto front which is derived by sweeping the physical parameters of the coil. This optimization is realized at the design stage. This work explores the optimization of these performance indicators from the control point of view and can be achieved on-line. The method discussed here involves the control of the duty cycle and displacement angles of the inverter and rectifier voltages to meet the objective of reducing the stray field and power losses. To this end, the classical Lagrange optimization procedure is employed to simultaneously minimize the stray magnetic field and power loss for a given output power condition. © 2021 IEEE.

关键词： Charging (batteries)

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Secure MIMO Communication Relying on Movable Antennas

arXiv

引用

arXiv 2024年

作者： Tang, Jun Pan, Cunhua Zhang, Yang Ren, Hong Wang, Kezhi The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China National Mobile Communications Research Laboratory Southeast University Nanjing China The Department of Computer Science Brunel University London UxbridgeUB8 3PH United Kingdom

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system's physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs' positions under the constraints of the maximum transmit power and the minimum distance between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form by utilizing the minimum mean square error (MMSE) method, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs' positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA's position while keeping others fixed. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MA-aided system over conventional fixed position antenna (FPA)-based system in enhancing system's security. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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LEVERAGING ACTIVE SUBSPACES TO CAPTURE EPISTEMIC MODEL UNCERTAINTY IN DEEP GENERATIVE MODELS FOR MOLECULAR DESIGN

arXiv

引用

arXiv 2024年

作者： Abeer, A.N.M. Nafiz Jantre, Sanket Urban, Nathan M. Yoon, Byung-Jun Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frameworks, generative molecular design models have seen fewer efforts on uncertainty quantification (UQ) due to computational challenges in Bayesian inference posed by their large number of parameters. In this work, we focus on the junction-tree variational autoencoder (JT-VAE), a popular model for generative molecular design, and address this issue by leveraging the low dimensional active subspace (AS) to capture the uncertainty in the model parameters. Specifically, we approximate the posterior distribution over the active subspace parameters to estimate the epistemic model uncertainty in an extremely high dimensional parameter space. The proposed UQ scheme does not require any alteration of the model architecture, making it readily applicable to any pre-trained model. Our experiments demonstrate the efficacy of the AS-based UQ and its potential impact on molecular optimization by exploring the model diversity under epistemic uncertainty. © 2024, CC BY.

关键词： Molecular docking

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