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IEEE Texas Power and Energy Conference (TPEC)

作者： Arash Moradzadeh Mostafa Mouhammadpourfard Yang Weng Suhas Pol S. M. Muyeen Department of Electrical Engineering Qatar University Doha Qatar National Wind Institute Texas Tech University Lubbock TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Renewable Energy Program Texas Tech University Lubbock TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets.

关键词： Deep learning Accuracy Electricity Noise reduction Bidirectional long short term memory Predictive models Data models Stakeholders Forecasting Testing

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A Design for Improvement of Visual SLAM in Dynamic Environments Using Feature-Point Removal of Moving Persons

A Design for Improvement of Visual SLAM in Dynamic Environme...

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International Automatic Control Conference

作者： Kai-Tai Song Ching-Hao Meng Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of Robotics College of Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper presents a design to improve the robustness of visual SLAM(vSLAM). A processing step of feature-removal is added to the tracking thread of the conventional ORB-SLAM2 algorithm to improve the localization accuracy of a mobile robot in an environment with moving persons. Instance segmentation and motion tracking are intergrated to identify motion state of people in an image. ORB feature points belonging to moving persons are removed for further processing of the vSLAM pipeline. The advantage of this method is that the vSLAM can remove feature points of moving people, while retain those belonging to static people in the environment, which improves the accuracy of robot pose estimation. The improved ORB-SLAM2 algorithm has been implemented in a NVIDIA Xavier embedded system, which is integrated to a mobile robot. In practical robot navigation experiments, the average positioning error of the proposed method is within 4cm for 22.4m travel distance. Compared with conventional ORB-SLAM2, the average accuracy of our vSLAM method improves 97% in a dynamic environment with moving people.

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Location Analysis Probing Rockets in Ground with Telemetry Data

Location Analysis Probing Rockets in Ground with Telemetry D...

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Nilton Rodrigues Cantanhede Ewaldo Eder Carvalho Santana Paulo Fernandes da Silva Júnior Raimundo Carlos Silvério Freire Tiago Pereira Santana Lúcio Flávio de Albuquerque Campos Gabrielle Muniz Fernandes Maria Tereza Cunha de Albuquerque Sophia Viana Macedo Graduate Program in Electrical Engineering Federal University of Maranhão São Luís Brazil Graduate Program in Computer and System Engineering State University of Maranhão São Luís Brazil Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Graduate Program in Computational Engineering and Systems State University of Maranhão São Luís Brazil Undergraduate Program in Computational Engineering State University of Maranhão São Luís Brazil Graduate Program in Electrical Engineering Federal University of Paraiba João Pessoa Brazil

ISBN: (数字)9798350350869

ISBN: (纸本)9798350350876

Rocket launch centers are crucial in technological development across various fields, including meteorology, long-distance real-time communications, geoprocessing, and satellite operations. However, one of the challenges in tracking probing rockets is accurately identifying their position from the moment of launch until the initial seconds, due to ground noise that interferes with traditional radar location systems. In this study is proposed an innovative method to precisely determine the distance between the rocket and the ground telemetry station. We utilize onboard telemetry signals processed by the Acquisition and Processing Data Trajectory (APDtraj) system, developed by Delsis Aerospace for the Launch Center. This system enables the identification of the rocket's position both on the ground and during the early stages of flight, using telemetry data processed by APDtraj. This monitoring equipment alone calculated the distance between the Telemetry Station and the rocket just moments before launch lift-off with an error of only 16.844 meters from the actual position of origin. Based on our studies, we were able to measure the radial distance and location between the rocket and the telemetry station just four seconds after launch, even without reliable radar information, which is often disrupted by ground noise. This technological advancement not only enhances the efficiency of rocket launch operations but also expands overall tracking capabilities.

关键词： Rockets Transducers Satellites Radar measurements Spaceborne radar Noise Radar tracking Real-time systems Trajectory Telemetry

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Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications 8

Quality Improvements in the Electrical Design of Typical DC-...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Recto, King Harold A. Escoto, Miguel T. Hernandez, Noel B. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Centro Escolar University Post-doctoral Program in Quality Management of the Graduate School Manila Philippines

ISBN: (纸本)9781665491068

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements. © 2022 IEEE.

关键词： Six sigma

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An Empirical Generation Technique on Background Music Using Gated Recurrent Neural Networks

An Empirical Generation Technique on Background Music Using ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Wei-Ho Tsai International Graduate Program of Electrical Engineering and Computer Science (IEECS) National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, this BGM is necessary to enhance the intended message expressed to the other audience. This work aimed to provide the model network of GRU which is based on RNN to generate multi-label genres of music by using the open source of GTZAN to evaluate the new BGM. Our GRU networks can solve the vanishing gradient problem by utilizing both the reset gate and the update gate on the network. In the results, we achieved a new BGM that synchronized with the human mood which made more variety of sounds.

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electrical stimulation thresholds differ between V1 and V2

Electrical stimulation thresholds differ between V1 and V2

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sabrina Jade Meikle Tim John Allison-Walker Maureen Ann Hagan Nicholas Seow Chiang Price Yan Tat Wong Department of Physiology and Biomedicine Discovery Institute - Neuroscience Program Monash University Clayton VIC Australia Department of Electrical and Computer Systems Engineering Monash University Department of Engineering RMIT University

Cortical visual prostheses are designed to treat blindness by restoring visual perceptions through artificial electrical stimulation of the primary visual cortex (V1). Intracortical microelectrodes produce the smallest visual percepts and thus higher resolution vision - like a higher density of pixels on a monitor. However, intracortical microelectrodes must maintain a minimum spacing to preserve tissue integrity. One solution to increase the density of percepts is to implant and stimulate multiple visual areas, such as V1 and V2, although the properties of microstimulation in V2 remain largely unexplored. We provide a direct comparison of V1 and V2 microstimulation in two common marmoset monkeys. We find similarities in response trends between V1 and V2 but differences in threshold, neural activity duration, and spread of activity at the threshold current. This has implications for using multi-area stimulation to increase the resolution of cortical visual prostheses.

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Shao-Yu Chien Ching-Chang Wong Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City 25137 Taiwan Electrical and Computer Engineering Department Tamkang University New Taipei City 25137 Taiwan

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method.

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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IEEE International Symposium on Biomedical Imaging

作者： J. Mason Harding Haatef Pourmotabbed Yamin Li Kimberly Rogge-Obando Kate Wang Sarah E. Goodale Shiyu Wang Camden Bibro Bergen Allee Caroline Martin Victoria L. Morgan Dario J. Englot Catie Chang Department of Electrical and Computer Engineering Vanderbilt University Department of Biomedical Engineering Vanderbilt University Department of Computer Science Vanderbilt University Neuroscience Graduate Program Vanderbilt University Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center Department of Neurological Surgery Vanderbilt University Medical Center Department of Radiology Vanderbilt University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.

关键词： Neuroimaging Brain Fluctuations Epilepsy Functional magnetic resonance imaging Electroencephalography Pattern recognition Hemodynamics Spatial resolution Signal resolution

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Robust Nonlinear $\mathcal{W}_{\infty}$ Optimal Control for Input Nonaffine Systems

Robust Nonlinear $\mathcal{W}_{\infty}$ Optimal Control for ...

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European Control Conference (ECC)

作者： Daniel N. Cardoso Marco H. Terra Guilherme V. Raffo Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte MG Brazil Department of Electrical and Computer Engineering University of São Paulo São Carlos SP Brazil Department of Electronics Engineering Federal University of Minas Gerais (UFMG) Belo Horizonte MG Brazil

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose a novel robust nonlinear $\mathcal{W}_{\infty}$ optimal control method for dynamical systems with nonaffine control inputs. The nonlinear $\mathcal{W}_{\infty}$ control formulation extends the classic nonlinear $\mathcal{H}_{\infty}$ one, considering a weighted Sobolev norm of the cost variable. This approach assumes that the cost variable belongs to the weighted Sobolev space $\mathcal{W}_{m,p,\mathbf{\Gamma}}$ , ensuring continuity and differentiability up to degree $m$ in a certain domain $\Omega$ . Consequently, in addition to the well-known features provided by the $\mathcal{H}_{\infty}$ approach in terms of disturbance attenuation, the closed-loop system benefits from the enhanced transient performance. Here, the robust nonlinear $\mathcal{W}_{\infty}$ optimal control problem is formulated via dynamic program.ing for increased-order systems, and a particular solution is proposed to the resulting Hamilton-Jacobi equation, along with the corresponding stability analysis. To validate the proposed method and its versatility, we provide numerical results for the control of a quadrotor. Additionally, leveraging the inherent $\mathcal{L}_{2}$ -gain properties of our approach, we demonstrate that the resulting controller can achieve trajectory tracking with guaranteed asymptotic stability for the whole closed-loop system.

关键词： Asymptotic stability Visualization Costs Trajectory tracking Optimal control Stability analysis Dynamic program.ing

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Low Power Consumption of Non-Volatile Memory Device by Tunneling Process engineering

SSRN

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SSRN 2024年

作者： Wang, Fucheng Chu, Mengmeng Chen, Jingwen Pan, Zhong Kim, Yongsang Song, Jang Kun Yi, Junsin Khokhar, Muhammad Quddamah Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Interdisciplinary Program in Photovoltaic System Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Compared with Si3N4 and Al2O3, SiO2 grown using thermal oxidation process as tunneling layer has the advantages of high bandgap and well interface contact with the surface of silicon wafer, which can be a great solution to the leakage current problem of metal-insulator-semiconductor (MIS) devices. This study investigates the effect of improving the SiO2 tunnel layer on the operating voltage of MIS devices with a SiO2/HfAlOx/Al2O3 structure. The result shows the operating voltage changes as the tunneling layer thickness decreases, with a minimum of only 12 V for a 1.5 nm tunneling layer thickness. In addition, we found that pinholes are generated on the film surface when annealing a 1.5 nm SiO2 tunnel layer at 850 °C N2, in which case the operating voltage of the device is reduced to only 10 V, though it was also accompanied by the deterioration of the retention characteristics. © 2024, The Authors. All rights reserved.

关键词： Silicon wafers

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