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Application of TQWT based filter-bank for sleep apnea screening using ECG signals

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 893-904页

作者： Nishad, Anurag Pachori, Ram Bilas Acharya, U. Rajendra Discipline of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Biomedical Engineering School of Science and Technology SIM University Singapore599491 Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Filter banks

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Analyzing Neural Network Robustness Using Graph Curvature 3

Analyzing Neural Network Robustness Using Graph Curvature

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3rd International Conference on Assured Autonomy, ICAA 2024

作者： Tan, Shuhang Sia, Jayson Bogdan, Paul Ivanov, Radoslav Rensselaer Polytechnic Institute Computer Science Department TroyNY United States University of Southern California Dept. of Electrical and Computer Engineering Los AngelesCA United States

ISBN: (纸本)9798331521011

This paper presents a new look at the neural network (NN) robustness problem, from the point of view of graph theory analysis, specifically graph curvature. Graph curvature (e.g., Ricci curvature) has been used to analyze system dynamics and identify bottlenecks in many domains, including road traffic analysis and internet routing. We define the notion of neural Ricci curvature and use it to identify bottleneck NN edges that are heavily used to "transport data"to the NN outputs. We provide an evaluation on MNIST that illustrates that such edges indeed occur more frequently for inputs where NNs are less robust. These results will serve as the basis for an alternative method of robust training, by minimizing the number of bottleneck edges. © 2024 IEEE.

关键词： Graph neural networks

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A Deep Learning Based Denoising Approach for Synthetic Aperture Sonar Imaging 2

A Deep Learning Based Denoising Approach for Synthetic Apert...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Lu, Xicheng Sun, Guanxi Liu, Wei Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9798331515669

Synthetic Aperture Sonar (SAS) is one of the techniques used for underwater imaging, and in this paper, we use the range Doppler algorithm for SAS imaging and consider the position errors of sonar platforms in practical scenarios. These errors can seriously degrade the imaging result;however, correcting them and performing compensation with high-precision equipment could be very costly. In this paper, a deep learning approach is proposed using artificial neural networks for image denoising, specifically targeting noise induced by motion uncertainties. As demonstrated by computer simulations, it can recover the images with better quality without employing complex algorithms and hardware compensation. © 2024 IEEE.

关键词： Underwater imaging

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Energy Management in Microgrids: Sufficient Conditions for Convex Bi-Directional Converter Operation Model 16

Energy Management in Microgrids: Sufficient Conditions for C...

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16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Liang, Zipeng Chung, C.Y. Dong, Hanjiang He, Shangyang Yin, Xin Chen, Jianrun Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Research Centre for Grid Modernisation Hong Kong School of Electric Power Engineering South China University of Technology Guangzhou China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350386127

Hybrid AC/DC microgrids with bi-directional converters (BDCs) have garnered increasing attention in recent years due to their ability to combine the benefits of both AC and DC microgrids. However, the non-convex nature of the BDC constraint, which enforces non-simultaneous rectification and inversion, often leads to locally optimal solutions and significant computational challenges. To overcome this issue, we develop a convex model for hybrid AC/DC microgrids by identifying sufficient conditions to relax the non-convex BDC constraint. Additionally, we extend these conditions to scenarios involving branch flow constraints, thereby broadening the applicability of the model. Finally, we validate the derived conditions and demonstrate the advantages of the proposed convex model through case studies on hybrid AC/DC microgrids. © 2024 IEEE.

关键词： Microgrids

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Controller Design And Modelling of UAV Quadcopter 4

Controller Design And Modelling of UAV Quadcopter

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Maijama'a, Ladan Jiya, Jibril Danladi Anene, Ejike C. Miya, Hassan Sabo School of Engineering Technology Federal Polytechnic Department of Computer Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Electrical and Electronics Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Mechatronics and System Engineering Bauchi Nigeria

ISBN: (纸本)9798350387988

The dynamics of a quadcopter unmanned aerial vehicle (UAV) and proportional integral derivative (PID) control based on disturbance rejection are mathematically modelled in this work. The model presented is a nonlinear in nature and modelled with respect to the two fundamental frames of reference and physical mathematical laws. The objective is to create the most accurate replica of the vehicle. The created model is utilized to develop a PID controller method that will precisely and steadily operate the quadrotor system. The development of a controller is to obtain stability in the quadcopter system. First, the nonlinear and couple model quadcopter were established, and control described the principles of the ability of decoupling rejection control then the model was decoupled. The system contains four input forces, which are basically the thrust provided by each propeller coupled to each rotor with a fixed angle. Pitch angle must change in order to maintain forward (backward) motion, which is accomplished by simultaneously increasing (decreasing) front (rear) rotor speed and decreasing (increasing) rear (front) rotor speed. The same method is used to change the roll angle for left and right motion. The motors in the front and rear spin counter-clockwise, while the motors in other motor positions rotate clockwise. This causes the yaw command to be generated by either raising or reducing the speed of the counter-clockwise motors while reducing or increasing the speed of the clockwise motors. The achieved goal here is the derivation of a model that properly reflects the physical properties enabling realistic simulations and is simple enough to be applied to controller design in the future. The system and controller's mathematical models were converted to matching Simulink models in order to make system simulations and investigations easier. The results show the accuracy and significant disturbance rejection of the nonlinear model. © 2024 IEEE.

关键词： Three term control systems

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Personalized Data-Driven State Models of the Circadian Dynamics in a Biometric Signal 46

Personalized Data-Driven State Models of the Circadian Dynam...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ike, Chukwuemeka O. Wen, Yunshi Wen, John T. Oishi, Meeko M.K. Brown, Lee K. Julius, A. Agung Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States Engineering Research Center TroyNY United States University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States University of New Mexico Department of Internal Medicine AlbuquerqueNM United States

ISBN: (纸本)9798350371499

Circadian rhythms are endogenous 24-hour oscillations that are vital for maintaining our overall well-being. They are driven at a high level by a core circadian clock located in the brain, making their dynamics difficult to track. Various modeling approaches exist to predict the dynamics, but as the models are typically designed on population-level data, their performance is diminished on the individual level. This paper proposes a method for learning personalized latent state models, i.e., dynamical models that explicitly use latent state variables, that relate circadian input(s) to observable biometric signals. Our models combine an autoencoder with a recurrent neural network and use the pair to model the salient dynamics present in the data. We validate our method using experimental data, where the circadian input is light and the biometric data are actigraphy signals. We demonstrate that our method results in models with low-dimensional latent state that can accurately reconstruct and predict the observable biometric signals. Further, we show that the oscillation of the learned latent state agrees with the subjects' circadian clock oscillation as estimated with melatonin *** relevance - This proposes a technique for personal-ized modeling of the circadian system with potential applications in feedback control and individualized circadian studies. © 2024 IEEE.

关键词： Feedback control

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RHC-Based Systematic Design of a Wide-Angle Periodic Coded THz Reflectarray 11

RHC-Based Systematic Design of a Wide-Angle Periodic Coded T...

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11th International Symposium on Telecommunication, IST 2024

作者： Yousefi, Pezhman Khajeh, Ali Yahaghi, Alireza Hosseini, Keyhan Polytechnic University of Bari Department of Electrical and Information Engineering Via Orabona 4 Bari70125 Italy Shiraz University School of Electrical and Computer Engineering Shiraz71946 Iran University of Kurdistan Faculty of Engineering Department of Electronics and Communication Engineering Kurdistan Sanandaj66177 Iran

ISBN: (纸本)9798350356250

A reflectarray designed by Random Hill Climbing (RHC) optimization process is demonstrated and simulated. On the basis of top layer effect on reflection phase and exploiting the relation between phase distribution and the direction of the reflected beam, six pixelated subcells are optimized for two incident angle waves simultaneously. The far field plot shows that for beams of incident angles 19.471° and 41.81° and in the precincts of them the reflectarray deflects the incident waves into reflections close to normal to the reflectarray surface. This shows a proximate independency of incident wave angle for the reflectarray in a certain angular range. The proposed cells provide 342° reflection phase range. Also, the reflection loss of the cell for the incident angles 19.471° and 41.81° are -1.9 dB and -1.4 dB, respectively. The pattern gain and fractional bandwidth for a finite reflectarray of size 300 λ x 150 λ are reported to be 19.44 dB and 25% for the incident angle 19.471°, and 19.17 dB and 30% for the incident angle 41.81°. © 2024 IEEE.

关键词： Reflection

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Hybrid Method for Photovoltaic System Forecasting Based on Wavelet Transform and LSTM 2

Hybrid Method for Photovoltaic System Forecasting Based on W...

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2nd IEEE International Conference on electrical engineering, computer and Information Technology, ICEECIT 2024

作者： Larasati, Devita Ayu Widjonarko Lin, Tzu-Chiao Chen, Chao-Rong Bachroin, Nabil Prakosa, Setya Widyawan Mujiono Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering University of Jember Jember Indonesia Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Information Technology State Polytechnic of Jember Jember Indonesia

ISBN: (纸本)9798331504373

Photovoltaic (PV) forecasting is done to help in managing planning and knowing PV energy production. However, PV systems are always associated with fluctuating weather parameters, such as solar irradiance, temperature, wind speed, and humidity. In order, to forecast uncertain data, this object over here study proposed a combination method by using a concept of wavelet transform (WT) and the ability of long-short-term memory (LSTM). The WT is used to decompose the output power with the feature extraction process. Then it is used as input to the LSTM to forecast the futuristic value of output power. The forecasting results are shown in a curve and compared with actual data at several time horizons of 1 day, 15 days, 30 days, and 60 days. In addition, the results are also compared with several other ML methods such as Ridge Regression (RR), Linear Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (ENET). Each of these treatments shows that the forecasting results using the proposed method are superior. This is also supported by evidence on the percent error values of Root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). © 2024 IEEE.

关键词： Mean square error

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Feasibility and Potential Assessment of Wind Resource a Case Study in North Shewa Zone, Amhara, Ethiopia

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Distributed Generation and Alternative Energy Journal 2023年第6期38卷 1863-1884页

作者： Feleke, Solomon Anteneh, Degarege Khan, Baseem Álvarez, Roberto Marcelo Electrical and computer engineering Debre Berhan University Debre Berhan Ethiopia Electrical and computer engineering Hawassa University Hawassa Ethiopia Higher Polytechnic School Universidad Europea del Atlántico C/Isabel Torres 21 Santander39011 Spain Department of Project Management Universidad Internacional Iberoamericana Campeche24560 Mexico

The assessment of wind resource potential and feasibility is critical for generating and forecasting power generation, as wellas resource identification. In Ethiopia, the majority of the country lacks a wind atlas, making it difficult to determine the availability of sources. Seven different areas (Debre Berhan, Alem Ketema, Mehal Meda, Eneware, Gundo Meskel, Majete and Shewa Robit) were investigated. For the work data was collected from various sources and analyzed using MATLAB software. The basic sources of data that were obtained nationally were from the NMA, which is the National Metrological Agency found in Addis Ababa, and were obtained centrally from each local delegate’s registered report from a height of 2 meters and 10 meters in each listed districts above. According to the results analysis, the average wind speed at most sites is reasonable and 4 m/s at a height of 10 meters, and some of the case study sites have an average wind speed of less than 3 m/s. The extrapolation prediction method produces more realistic results at 30 and 50 meters;for example, when 10 meter is extrapolated to 30 and 50 meters, the wind power densities are 75.2 w/m2, 300.9 w/m2, and 680.5 w/m2, respectively. Similarly, the average yearly energy density for 10 meter, 30 meter, and 50 meter is 2110.8, 4122.6, and 8219.9 Kwh/m2/year, respectively. As per the international standard for wind power and wind speed classification, Eneware and Mehal Meda are categorized under class 7, whereas Debre Berhan is categorized under class 3, while the remaining sites such as Shewarobit, Gunde Meskel, Alem Ketema, and Majete are classified under class 1 for the majority of the year. © 2023 River Publishers.

关键词： Wind power

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Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

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Journal of Energy Chemistry 2024年第4期91卷 512-521页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chunsheng Hu School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Advanced Interdisciplinary Studies Ningxia UniversityYinchuan 750000NingxiaChina

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured *** data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low *** response,this study integrates domain knowledge into deep learning to enhance the RUL prediction *** demonstrate accurate RUL prediction using only a single charging ***,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging *** parameters inform a deep neural network(DNN)to predict RUL with high accuracy and *** trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged *** data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,*** its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and *** inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

关键词： Lithium-ion batteries Remaining useful life Physics-informed machine learning

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