• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.589-594
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• 2015

In this paper, we described the Cramer-Rao Lower Bound (CLRB) performances of Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) methods when there are multiple emitters. The TDOA and FDOA values between two receivers can be simultaneously estimated by using the so-called Complex Ambiguity Function (CAF). In the case of multiple emitters, there exist Inter Symbol Interferences (ISIs) in the measurement data. Therefore, it is required to reduce the effect of ISI and provide a performance evaluation method of TDOA and FDOA estimations. In order to eliminate the ISIs, using of a filter bank before calculating CAF is proposed when the carrier frequencies of the emitters are different to one another. Angle of Arrival (AOA) or Received Signal Strength (RSS) methods before calculating CAF were proposed to reduce the ISIs when the carrier frequencies are the same. In order to evaluate the CRLB of TDOA and FDOA estimations, we employed the conditional probability distribution method and described the numerical comparison results.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.350-356
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• 2004

This paper presents a farce manipulability analysis of multi-legged walking robots, which calculates force or acceleration workspace attainable from joint torque limits of each leg. Based on the observation that the kinematic structure of the multi-legged walking robots is basically the same as that of multiple cooperating robots, we derive the proposed method of analyzing the force manipulability of walking robot. The force acting on the object in multiple cooperating robot systems is taken as reaction force from ground to each robot foot in multi-legged walking robots, which is converted to the force of the body of walking robot by the nature of the reaction force. Note that each joint torque in multiple cooperating robot systems is transformed to the workspace of force or acceleration of the object manipulated by the robots in task space through the Jacobian matrix and grasp matrix. Assuming the torque limits are given in infinite norm-sense, the resultant dynamic manipulability is derived as a polytope. The validity of proposed method is verified by several examples, and the proposed method is believed to be useful for the optimal posture planning and gait planning of walking robots.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.477-486
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• 2001

Many physical systems can be modeled by incorporating continuous and discrete event nature together. Such hybrid systems contain both continuous and discrete states that influence the dynamic be-havior of the systems. There has been an increasing interest in thers types of systems during the last dec-ade, mostly due to the growing usage of computers in the control of physical plants but also as a result of the hybrid nature of physical processes. The stability theory for hybrid systems is considered as extension of Lyapunov theory where the existence of an abstract energy function satisfying certain properties verifies stability, called multiple Lyapunov theory. In this paper, a hybrid stabilizing controller is proposed using the control Lyapunov function method and multiple Lyapunov theory, and the proposed method is applied to lon-gitudinal spacing control in a vehicle platoon for intelligent transportation systems(ITS).

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.502-507
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• 2016

In this paper, we propose a tracking control method for a quadrotor equipped with a 2-DOF manipulator, which is based on the multiple sliding surface control (MSSC) method. To derive the model of a quadrotor equipped with a 2-DOF manipulator, we obtain the models of a quadrotor and a 2-DOF manipulator based on the Lagrange-Euler formulation separately - and include the inertia and the reactive torque generated by a manipulator when these obtained models are combined. To make a quadrotor equipped with a manipulator track the desired path, we design a double-loop controller. The desired position is converted into the desired angular position in the outer controller and the system's angle tracks the desired angular position through the inner controller based on the MSSC method. We prove that the position-tracking error asymptotically converges to zero based on the Lyapunov stability theory. Finally, we demonstrate the effectiveness of the proposed control system through a computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.10
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.609-614
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• 2015

Implanted telemetry systems provide the ability to monitor different species of animals while they move within their cages. Species monitored include mice, rats, rabbits, dogs, pigs, primates, sheep, horses, cattle, and others. A miniature transmitter implanted in each animal measures one or more parameters. Parameters measured include arterial pressure, intra-pleural pressure, left ventricular pressure, intra-ocular pressure, bladder pressure, ECG, EMG, EEG, EOG, temperature, activity, and other parameters and transmits the data via radio frequency signals to a nearby receiver. Every conventional dedicated transmitter contains one or more sensors, cpu and battery. Due to the expected life of the battery, the measuring time is limited. To overcome these problems, electromagnetic inductive coupling based wireless power transmission technology using multiple transmit coils were proposed, with each coil having a different active area driven by the coil driver. In this research, a parallel resonance based coil driver and serial resonance based coil driver are proposed. From the experiments we see that the parallel coil driver shows better performance under a low impedance and multiple coils configuration. However, the serial coil driver is more efficient for high impedance transmit coils.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• The Journal of Korea Robotics Society
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• v.12 no.4
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• pp.441-447
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• 2017

Robot arms are being increasingly used in various fields with special attention given to unmanned systems. In this research, we developed a high payload dual-arm robot, in which the forearm module is replaceable to meet the assigned task, such as object handling or lifting humans in a rescue operation. With each forearm module specialized for an assigned task (e.g. safety for rescue and redundant joints for object handling task), the robot can conduct various tasks more effectively than could be done previously. In this paper, the design of the high payload dual-arm robot with replaceable forearm function is described in detail. Two forearms are developed here. Each of forearm has quite a different goal. One of the forearms is specialized for human rescue in human familiar flat aspect and compliance parts. Other is for general heavy objects, more than 30 kg, handling with high degree of freedom more than 7.