• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.11
• /
• pp.13-20
• /
• 2008

In this paper, an 1.8V 12-bit 10MSPS CMOS A/D converter (ADC) is described. The architecture of the proposed ADC is based on a folding and interpolation using an even folding technique. For the purpose of improving SNR, cascaded-folding cascaded-interpolation technique, distributed track and hold are adapted. Further, a digital encoder algorithm is proposed for efficient digital process. The chip has been fabricated with $0.18{\mu}m$ 1-poly 4-metal n-well CMOS technology. The effective chip area is $2000{\mu}m{\times}1100{\mu}m$ and it consumes about 250mW at 1.8V power supply. The measured SNDR is about 46dB at 10MHz sampling frequency.

• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1315-1324
• /
• 2016

Recently, the increasing interest in drones has resulted in development of new related technologies. Attention has been focused toward research on swarm flight which controls drones simultaneously without collision. Thus, complicated missions can be completed rapidly through collaboration between drones. Due to low position accuracy, GPS is not appropriate for the outdoor mission involving accurate flight. In addition, the inaccurate position estimation of GPS gives rise to the serious problem of collision, since many drones are controlled in a narrow space. In this study, we increased the accuracy of position estimation through various sensors with Real-Time Kinematic-GPS (RTK-GPS). The mode switching algorithm was proposed to minimize the problem of sensor error. In addition, we introduced the outdoor swarm flight system based on the proposed position estimation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.2
• /
• pp.164-170
• /
• 2002

Sliding mode control, as a typical method of variable structure control, has the robust characteristics for the uncertainty and the disturbance of the nonlinear system. Because, however, sliding mode control input includes a sign function that Is discontinuous on the predefined switching surface, its applications are primarily limited by the need of alleviation or reduction of chattering. In this paper, we propose a chattering alleviation strategy based on a special nonlinear function and a fuzzy system. By using the proposed control scheme, we can reduce the steady state error. Its tracking performance is as fast as that of conventional method using the fixed boundary layer. Especially, in the proposed method, we can adjust the trade-off between the steady state error and the degree of chattering by regulating the proper range of the output variable of the fuzzy system. To verify the validity of the proposed algorithm, the analysis of the control method using the fixed boundary layer and the computer simulations are shown to compare with them.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.5
• /
• pp.162-168
• /
• 2004

Service restoration is an emergency control in distribution control centers to restore out-of-service area as soon as possible when a fault occurs in distribution networks. Therefore, it requires fast computation time and high quality solutions for load balancing. In this paper, a load balance index and heuristic guided best-first search are proposed for these problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to identify the most effective the set of switches using proposed search technique and a feeder load balance index. Test results on the KEPCO's 108 bus distribution system show that the performance is efficient and robust.

• Journal of KIISE
• /
• v.42 no.6
• /
• pp.748-754
• /
• 2015

This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.1B
• /
• pp.1-10
• /
• 2007

We propose a new approach to accommodate bidirectional asymmetric traffic demands as well as unexpected dynamic internet traffic variation in the WDM mesh network by using optical networking technologies. In the proposed scheme, an intermediate node determines the optical path based on the switching statistics of IP router of the node which characterizes the Internet traffic variation, which in effect provides a dynamic and distributed traffic control over the network. It is expected to reduce the efficiency deterioration of RWA(Routing and Wavelength Assignment) due to the real-time variation of Internet traffic so that expandability and flexibility of the network can be enhanced. In this paper, we describe a methodology for traffic behavior analysis at a node, and the decision policy of the establishment/release of optical path. In addition, we evaluate the performance of the proposed scheme through the computer simulations.

• KIEE International Transactions on Power Engineering
• /
• v.5A no.4
• /
• pp.357-365
• /
• 2005

This research investigates the influences of distributed generations (DG), which are interconnected to the bus by the dedicated lines, on the overcurrent relays (OCR) of the neighboring distribution feeders and also proposes a novel method to reduce the negative effects on the feeder protection. Due to the grid connected DG, the entire short-circuit capacity of the distribution networks increases, which may raise the current of the distribution feeder during normal operations as well as fault conditions. In particular, during the switching period for loop operation, the current level of the distribution feeder can be larger than the pickup value for the fault of the feeder's OCR, thereby causing the OCR to perform a mal-operation. This paper proposes the adaptive setting algorithm for the OCR of the distribution feeders having the neighboring dedicated feeders for the DG to prevent the mal-operations of the OCR under normal conditions. The proposed method changes the pickup value of the OCR by adapting the power output of the DG monitored at the relaying point in the distribution network. We tested the proposed method with the actual distribution network model of the Hoenggye substation at the Korea Electric Power Co., which is composed of five feeders supplying the power to network loads and two dedicated feeders for the wind turbine generators. The simulation results demonstrate that the proposed adaptive protection method could enhance the conventional OCR of the distribution feeders with the neighboring dedicated lines for the DG.

• Journal of Power Electronics
• /
• v.18 no.2
• /
• pp.558-572
• /
• 2018

In addition to its modular nature, a Hybrid Modular Multilevel Converter (HMMC) assembled from half-bridge and full-bridge sub-modules, is able to block DC faults with a minimum number of switching devices, which makes it attractive for high power applications. This paper introduces a control strategy based on the Root-Least Square (RLS) algorithm to estimate the capacitor voltages instead of using direct measurements. This action eliminates the need for voltage transducers in the HMMC sub-modules and the associated communication link with the central controller. In addition to capacitor voltage balancing and suppression of circulating currents, a fault tolerant control unit (FTCU) is integrated into the proposed strategy to modify the parameters of the HMMC controller. On advantage of the proposed FTCU is that it does not need extra components. Furthermore, a fault detection unit is adapted by utilizing a hybrid estimation scheme to detect sub-module faults. The behavior of the suggested technique is assessed using PSCAD offline simulations. In addition, it is validated using a real-time digital simulator connected to a real time controller under various normal and fault conditions. The proposed strategy shows robust performance in terms of accuracy and time response since it succeeds in stabilizing the HMMC under faults.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.57-64
• /
• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.4
• /
• pp.2058-2074
• /
• 2017

Dense Heterogeneous Cellular Networks(HCNs) offer a promising approach to meet the target of 1000x increase in aggregate data rates in 5G wireless communication systems. However how to best utilize the available radio resources at densely deployed small cells remains an open problem as those small cells are typically unplanned. In this paper we focus on balancing loads across macro cells and small cells by offloading users to small cells, as well as dynamically switching off underutilized small cells. We propose a joint user association and base station(BS) sleep mangement(UA-BSM) scheme that proactively offloads users to a fraction of the densely deployed small cells. We propose a heuristic algorithm that iteratively solves the user association problem and puts BSs with low loads into sleep. An interference relation matrix(IRM) is constructed to help us identify the candidate BSs that can be put into sleep. User associations are then aggregated to selected small cells that remain active. Simulation results show that our proposed approach achieves load balancing across macro and small cells and reduces the number of active BSs. Numerical results show user signal to interference ratio(SINR) can be improved by small cell sleep control.