15 hasil untuk "Shih-Lin Lin"
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Artikel Jurnal
Application of empirical mode decomposition to improve deep learning for US GDP data forecasting
The application of deep learning methods to construct deep neural networks for the prediction of future econometric trends and econometric data has come to receive a lot of research attention. However, it has been found that the long short-term memory (LSTM) model is unstable and overly complex. It…
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Artikel Jurnal
Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks
This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing…
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Artikel Jurnal
Research on tire crack detection using image deep learning method
Abstract Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection…
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Artikel Jurnal Teks lengkap
Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes…
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Artikel Jurnal Teks lengkap
Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image…
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Artikel Jurnal Teks lengkap
Energy regeneration: A study on dynamic capacitance adjustment technology in piezoelectric shock absorbers for electric vehicles under varied road conditions.
This study investigates the performance of dynamic capacitance regulation technology in electric vehicle piezoelectric shock absorbers for energy recovery under varying road conditions. By simulating a quarter-vehicle suspension system, this paper comprehensively analyzes the energy recovery…
Juga tersedia di: PLOS
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Artikel Jurnal
Nonlinear dynamics and pyroelectric energy recovery in automotive ABS braking systems
This study aims to investigate the braking process in automotive Anti-lock Braking Systems, with a particular focus on the relationship between brake disk temperature variation and pyroelectric energy recovery. We developed a detailed numerical simulation model that considers the wheel dynamics…
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Artikel Jurnal
Advancements in noise reduction for wheel speed sensing using enhanced LSTM models
Abstract This research presents an Enhanced Long Short-Term Memory (LSTM) deep learning model for robust noise reduction in automotive wheel speed sensors. While wheel speed sensors are pivotal to vehicle stability, high-intensity or non-stationary noise often degrades their performance…
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Artikel Jurnal Teks lengkap
The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment
This paper proposes a new method called independent component analysis–variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in…
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Artikel Jurnal Teks lengkap
Intelligent Connected Vehicles
The development of intelligent connected vehicles (ICVs) continues to be shaped by simultaneous advances in sensing, communication, autonomy, and data-driven system design [...]
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Artikel Jurnal
Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections
The rapid advancement of autonomous driving and autonomous vehicle technology has intensified the need for robust environment perception. In this work, we leverage automotive LiDAR point cloud data, which are inherently sparse, noisy, and unstructured, posing challenges to conventional…
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Artikel Jurnal
Technique on Vehicle Damage Assessment After Collisions Using Optical Radar Technology and Iterative Closest Point Algorithm
With the rapid advancement of technology, there is an increasing need for quick and accurate analysis of vehicle accident severity, determination of accident liability, assessment of vehicle damage extent, and efficient calculation of insurance compensation. This study proposes an innovative…
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Artikel Jurnal
Improving Deep Learning for Forecasting Accuracy in Financial Data
Financial forecasting is based on the use of past and present financial information to make the best prediction of the future financial situation, to avoid high-risk situations, and to increase benefits. Such forecasts are of interest to anyone who wants to know the state of possible finances in…
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Artikel Jurnal Teks lengkap
Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and…