• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.214-221
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• 2015

In this paper, we present robust estimation and detection algorithms for discrete-time switched linear systems whose output measurements are corrupted by noises. First, a mode estimation algorithm is proposed based on the minimum distance criterion. Then, state variables are also observed under the active mode estimate. Second, a detection algorithm is constructed to detect the mode switching of the switched system. With the boundedness of measurement noise, the proposed estimation algorithm returns the exact active mode and approximate state information of the switched system. In addition, the detection algorithm can detect the switching time within a pre-determined time interval after the actual switching occurred.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• Journal of Power Electronics
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• v.9 no.4
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• pp.654-666
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• 2009

Direct torque control (DTC) of induction machines (IM) is a well-known strategy of these drives control which has a fast dynamic and a good tracking response. In this paper a nonlinear DTC of speed sensorless IM drives is presented which is based on input-output feedback linearization control theory. The IM model includes iron losses using a speed dependent shunt resistance which is determined through some effective experiments. A stator flux vector is estimated through a simple integrator based on stator voltage equations in the stationary frame. A novel method is introduced for DC offset compensation which is a major problem of AC machines, especially at low speeds. Rotor speed is also determined using a rotor flux sliding-mode (SM) observer which is capable of rotor flux space vector and rotor speed simultaneous estimation. In addition, stator and rotor resistances are estimated using a simple but effective recursive least squares (RLS) method combined with the so-called SM observer. The proposed control idea is experimentally implemented in real time using a FPGA board synchronized with a personal computer (PC). Simulation and experimental results are presented to show the capability and validity of the proposed control method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3136-3150
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• 2015

A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9C
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• pp.661-668
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• 2008

In this paper, a channel estimation technique based on pilot tones is proposed for cooperative SFBC-OFDM systems with a STO(Symbol Timing Offset). In a cooperative SFBC-OFDM system, the STO between RS(Relay Station) and MS(Mobile Station) varies depending on the location of MS. Since the STO causes distortion in the form of phase rotation, a channel estimation technique based on linear interpolation with respect to phase and amplitude is proposed for the case of orthogonal pilot allocation. Also, a channel estimation technique is proposed by solving nonlinear equation for the case of pilot structure with orthogonal code. It is shown by computer simulation that the performance of channel estimation can be significantly improved when the proposed techniques are applied to cooperative SFBC-OFDM systems with STO.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1361-1369
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• 1996

Motion estimation is one of the key components for high quality video coding. In this paper, a new motion estimation scheme for MPEG-like video coder is suggested. The proposed temporally adaptive motion estimation scheme consists of five functional blocks: Temporal subband analysis (TSBA), extraction of temporal information, scene change detection (SCD), picture type replacement (PTR), and temporally adapted block matching algorithm (TABMA). Here all the functional components are based on the temporal subband analysis. In this papre, we applied the analysis part of subband decompostion to the temporal axis of moving picture sequence, newly defined the temporal activity distribution (TAD) and average TAD, and proposed the temporally adapted block matching algorithm, the scene change detection algorithm and picture type replacement algorithm which employed the results of the temporal subband analysis. A new block matching algorithm TABMA is capable of controlling the block matching area. According to the temporal activity distribution of objects, it allocates the search areas nonuniformly. The proposed SCD and PTR can prevent unavailable motion prediction for abrupt scene changes. Computer simulation results show that the proposed motion estimation scheme improve the quality of reconstructed sequence and reduces the number of block matching trials to 40% of the numbers of trials in conventional methods. The TSBA based scene change detection algorithm can detect the abruptly changed scenes in the intentionally combined sequence of this experiment without additional computations.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.387-390
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• 2000

This paper proposes an efficient and accurate power estimation method for Application Specific Instruction set Processors (ASIPs). Proposed method takes advantage of the data types of variables in application program to be executed on the ASIP. According to the experimental results, the efficiency of proposed method was more than 1000 times as high as that of conventional RTL based power estimation method, and the estimation error was within 10% compared to a conventional gate-level accurate power estimation method

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.473-495
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• 2019

In this paper, a new extension of Lindley distribution has been introduced. Certain characterizations based on truncated moments, hazard and reverse hazard function, conditional expectation of the proposed distribution are presented. Besides, these characterizations, other statistical/mathematical properties of the proposed model are also discussed. The estimation of the parameters is performed through different classical methods of estimation. Bayes estimation is computed under gamma informative prior under the squared error loss function. The performances of all estimation methods are studied via Monte Carlo simulations in mean square error sense. The potential of the proposed model is analyzed through two data sets. A modified goodness-of-fit test using the Nikulin-Rao-Robson statistic test is investigated via two examples and is observed that the new extension might be used as an alternative lifetime model.