Heng-Ming Tai

Bio: Heng-Ming Tai is an academic researcher from University of Tulsa. The author has contributed to research in topics: Junction temperature & Inverter. The author has an hindex of 25, co-authored 170 publications receiving 2024 citations. Previous affiliations of Heng-Ming Tai include Chongqing University & Texas Tech University.

Autonomous local path planning for a mobile robot using a genetic algorithm

Abstract: This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

DC Link Active Power Filter for Three-Phase Diode Rectifier

Abstract: In this paper, a dc link active power filter (APF) for three-phase diode rectifier is proposed. The proposed dc link APF, which is composed of two series-connected bidirectional boost converters, intends to eliminate the input current harmonics. It is paralleled at the dc link of the diode rectifier and is coupled to the ac input with three line-frequency switches. Compared with the full power processed power factor correction (PFC) solution, the dc link APF is partially power processed in that it only compensates for the harmonic current component at the dc link. Thus, it features with lower power processing. Moreover, it exhibits better total harmonic distortion of the ac line current when compared with the traditional ac side shunt APF. Voltage and current loop models are developed for average current control, and the selection of the current loop bandwidth is presented. Switching stresses of the ac APF, the dc link APF, and the six-switch PFC are also calculated and analyzed. Experimental and simulation results demonstrate the effectiveness of this dc link APF.

Double-Frequency Buck Converter

Abstract: Improving the efficiency and dynamics of power converters is a concerned tradeoff in power electronics. The increase of switching frequency can improve the dynamics of power converters, but the efficiency may be degraded. A double-frequency (DF) buck converter is proposed to address this concern. This converter is comprised of two buck cells: one works at high frequency, and another works at low frequency. It operates in a way that current in the high-frequency switch is diverted through the low-frequency switch. Thus, the converter can operate at very high frequency without adding extra control circuits. Moreover, the switching loss of the converter remains small. The proposed converter exhibits improved steady state and transient responses with low switching loss. An ac small-signal model of the DF buck converter is also given to show that the dynamics of output voltage depends only on the high-frequency buck cell parameters, and is independent of the low-frequency buck cell parameters. Simulation and experimental results demonstrate that the proposed converter greatly improves the efficiency and exhibits nearly the same dynamics as the conventional high-frequency buck converter. Furthermore, the proposed topology can be extended to other dc-dc converters by the DF switch-inductor three-terminal network structure.

Performance Analysis of a Block-Neighborhood-Based Self-Recovery Fragile Watermarking Scheme

Abstract: In this paper, we present the performance analysis of a self-recovery fragile watermarking scheme using block-neighbor- hood tamper characterization. This method uses a pseudorandom sequence to generate the nonlinear block-mapping and employs an optimized neighborhood characterization method to detect the tampering. Performance of the proposed method and its resistance to malicious attacks are analyzed. We also investigate three optimization strategies that will further improve the quality of tamper localization and recovery. Simulation results demonstrate that the proposed method allows image recovery with an acceptable visual quality (peak signal-to-noise ratio (PSNR) as 25 dB) up to 60% tampering.

An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting

Abstract: Electricity price is an important indicator of the market operation. Accurate prediction of electricity price will facilitate the maximization of economic benefits and reduction of risks to the power market. At the same time, because of the excellent performance of deep learning models, using long-short term memory neural network (LSTM) and other deep learning models to predict time series has gradually become a research hotspot. In this paper, an optimized heterogeneous structure LSTM model is proposed to solve the problems of the single network structure and hyperparameter selection existing in the current research on LSTM. The heterogeneous structure LSTM is constructed based on the decomposed and reconstructed electricity price data, and sequence model-based optimization (SMBO) is used to optimize its hyperparameters. In order to verify the proposed model, online hourly forecasting and day-ahead hourly forecasting are tested on the electricity markets of Pennsylvania-New Jersey-Maryland (PJM). The experimental results show that the performance of the proposed model is much better than that of the general LSTM model and traditional models in accuracy and stability, providing a new idea for the use of LSTM for time series prediction.

Discrete-Time Speech Signal Processing: Principles and Practice

Abstract: (NOTE: Each chapter begins with an introduction and concludes with a Summary, Exercises and Bibliography.) 1. Introduction. Discrete-Time Speech Signal Processing. The Speech Communication Pathway. Analysis/Synthesis Based on Speech Production and Perception. Applications. Outline of Book. 2. A Discrete-Time Signal Processing Framework. Discrete-Time Signals. Discrete-Time Systems. Discrete-Time Fourier Transform. Uncertainty Principle. z-Transform. LTI Systems in the Frequency Domain. Properties of LTI Systems. Time-Varying Systems. Discrete-Fourier Transform. Conversion of Continuous Signals and Systems to Discrete Time. 3. Production and Classification of Speech Sounds. Anatomy and Physiology of Speech Production. Spectrographic Analysis of Speech. Categorization of Speech Sounds. Prosody: The Melody of Speech. Speech Perception. 4. Acoustics of Speech Production. Physics of Sound. Uniform Tube Model. A Discrete-Time Model Based on Tube Concatenation. Vocal Fold/Vocal Tract Interaction. 5. Analysis and Synthesis of Pole-Zero Speech Models. Time-Dependent Processing. All-Pole Modeling of Deterministic Signals. Linear Prediction Analysis of Stochastic Speech Sounds. Criterion of "Goodness". Synthesis Based on All-Pole Modeling. Pole-Zero Estimation. Decomposition of the Glottal Flow Derivative. Appendix 5.A: Properties of Stochastic Processes. Random Processes. Ensemble Averages. Stationary Random Process. Time Averages. Power Density Spectrum. Appendix 5.B: Derivation of the Lattice Filter in Linear Prediction Analysis. 6. Homomorphic Signal Processing. Concept. Homomorphic Systems for Convolution. Complex Cepstrum of Speech-Like Sequences. Spectral Root Homomorphic Filtering. Short-Time Homomorphic Analysis of Periodic Sequences. Short-Time Speech Analysis. Analysis/Synthesis Structures. Contrasting Linear Prediction and Homomorphic Filtering. 7. Short-Time Fourier Transform Analysis and Synthesis. Short-Time Analysis. Short-Time Synthesis. Short-Time Fourier Transform Magnitude. Signal Estimation from the Modified STFT or STFTM. Time-Scale Modification and Enhancement of Speech. Appendix 7.A: FBS Method with Multiplicative Modification. 8. Filter-Bank Analysis/Synthesis. Revisiting the FBS Method. Phase Vocoder. Phase Coherence in the Phase Vocoder. Constant-Q Analysis/Synthesis. Auditory Modeling. 9. Sinusoidal Analysis/Synthesis. Sinusoidal Speech Model. Estimation of Sinewave Parameters. Synthesis. Source/Filter Phase Model. Additive Deterministic-Stochastic Model. Appendix 9.A: Derivation of the Sinewave Model. Appendix 9.B: Derivation of Optimal Cubic Phase Parameters. 10. Frequency-Domain Pitch Estimation. A Correlation-Based Pitch Estimator. Pitch Estimation Based on a "Comb Filter<170. Pitch Estimation Based on a Harmonic Sinewave Model. Glottal Pulse Onset Estimation. Multi-Band Pitch and Voicing Estimation. 11. Nonlinear Measurement and Modeling Techniques. The STFT and Wavelet Transform Revisited. Bilinear Time-Frequency Distributions. Aeroacoustic Flow in the Vocal Tract. Instantaneous Teager Energy Operator. 12. Speech Coding. Statistical Models of Speech. Scaler Quantization. Vector Quantization (VQ). Frequency-Domain Coding. Model-Based Coding. LPC Residual Coding. 13. Speech Enhancement. Introduction. Preliminaries. Wiener Filtering. Model-Based Processing. Enhancement Based on Auditory Masking. Appendix 13.A: Stochastic-Theoretic parameter Estimation. 14. Speaker Recognition. Introduction. Spectral Features for Speaker Recognition. Speaker Recognition Algorithms. Non-Spectral Features in Speaker Recognition. Signal Enhancement for the Mismatched Condition. Speaker Recognition from Coded Speech. Appendix 14.A: Expectation-Maximization (EM) Estimation. Glossary.Speech Signal Processing.Units.Databases.Index.About the Author.

Three-Phase PLLs: A Review of Recent Advances

Abstract: A phase-locked loop (PLL) is a nonlinear negative-feedback control system that synchronizes its output in frequency as well as in phase with its input PLLs are now widely used for the synchronization of power-electronics-based converters and also for monitoring and control purposes in different engineering fields In recent years, there have been many attempts to design more advanced PLLs for three-phase applications The aim of this paper is to provide overviews of these attempts, which can be very useful for engineers and academic researchers

Self-driving cars: A survey

Abstract: We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.