Sung Hyun You

Unbiased Finite-Memory Digital Phase-Locked Loop

Digital phase-locked loops (DPLLs) have been commonly used to estimate phase information. However, they exhibit poor performance or, occasionally, a divergence phenomenon, if noise information is incorrect or if there are quantization effects. To overcome the weaknesses of existing DPLLs, we propose a new DPLL with a finite-memory structure called the unbiased finite-memory DPLL (UFMDPLL). The UFMDPLL is independent of noise covariance information, and it shows intrinsic robustness properties against incorrect noise information and quantization effects due to the finite-memory structure. Through numerical simulations, we show that the proposed DPLL is more robust against incorrect noise information and quantization effects than the conventional DPLLs are.

A New Approach on Design of a Digital Phase-Locked Loop

In this letter, we propose a new approach to the design of a digital phase-locked loop (DPLL) with a finite impulse response (FIR) structure, deadbeat property, and H∞ performance. This DPLL is called the deadbeat H∞ FIR DPLL (DHFDPLL). The proposed DHFDPLL ensures the H∞ performance against incorrect information on noise and has intrinsic robustness against quantization effects because of the FIR structure. Demonstrative simulations are provided to show that the DHFDPLL exhibits excellent robustness against effects of incorrect noise and quantization compared with the existing DPLLs.

Optimal Memory Size Formula for Moving-Average Digital Phase-Locked Loop

This letter proposes a new moving-average form of digital phase-locked loop (DPLL) that uses the average value of measurements on a memory horizon and the correction term to estimate phase information. This ensures the desired unbiasedness property for the phase information. A new formula for the optimal memory size of the proposed DPLL with minimization of the expected squared phase error is established. A numerical example is given to show that the developed DPLL has superior robustness against quantization and incorrect noise compared to the existing DPLLs.

Particle filtering approach to membership function adjustment in fuzzy logic systems

The fuzzy logic system has been a popular tool for modeling nonlinear systems in recent years. In the fuzzy logic system, the shape of the membership function has a significant effect on the modeling accuracy. Thus, membership function adjustment methods have been studied and developed. However, in highly nonlinear systems, the existing membership function adjustment method based on the extended Kalman filter (EKF) may exhibit poor performance due to the linearization error. In this paper, to overcome the drawback of the EKF-based membership function adjustment (EKFMFA), we propose a new membership function adjustment method based on the particle filter (PF). The proposed PF-based membership function adjustment (PFMFA) does not suffer from performance degradation due to the linearization error. We demonstrate that the PFMFA outperforms the EKFMFA through the simulation of a fuzzy cruise control system.

Novel vehicle detection system based on stacked DoG kernel and AdaBoost

This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions.

Smart air condition load forecasting based on thermal dynamic model and finite memory estimation for peak-energy distribution

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

Vehicle roll angle and bank angle estimation using FIR filtering

This paper proposes a novel estimation method using finite impulse response (FIR) filter for the vehicle roll and bank angles. A vehicle model was constructed using the bicycle model and roll motion model and has parameter uncertainties. The proposed method ensures robust to vehicle parameter uncertainties and has no risk of divergence. Furthermore, the proposed method does not need to use expensive sensors such as differential global positioning sensor (DGPS) or tilt angle sensors. Simulation results are provided to demonstrate the effectiveness and performance of the proposed method.

Vision-based humanoid robot control using FIR filter

In this paper, we propose a novel vision-based humanoid control method and visual tracking based on constant velocity (CV) model using the finite impulse response (FIR) filter. The proposed method has robust performance even if a sampling time or noise information is inaccurate. Furthermore, even when the movement of the detected ball or the ambient illuminance changes suddenly, the proposed method shows robust performance. The robust performance of the proposed method is verified through experimental results.