• IEMEK Journal of Embedded Systems and Applications
• /
• v.3 no.2
• /
• pp.102-108
• /
• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.428-435
• /
• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• Korean Journal of Geomatics
• /
• v.5 no.1
• /
• pp.27-34
• /
• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• Journal of Sasang Constitutional Medicine
• /
• v.22 no.1
• /
• pp.17-25
• /
• 2010

1. Objectives: Voice diagnosis has been used to classify individuals into the Sasang constitution in SCM(Sasang Constitution Medicine) and to recognize his/her health condition in TKM(Traditional Korean Medicine). In this paper, we purposed a new speech classification algorithm for Sasang constitution. 2. Methods: This algorithm is based on the SVM(Support Vector Machine) technique, which is a classification method to classify two distinct groups by finding voluntary nonlinear boundary in vector space. It showed high performance in classification with a few numbers of trained data set. We designed for this algorithm using 3 SVM classifiers to classify into 4 groups, which are composed of 3 constitutional groups and additional indecision group. 3. Results: For the optimal performance, we found that 32.2% of the voice data were classified into three constitutional groups and 79.8% out of them were grouped correctly. 4. Conclusions: This new classification method including indecision group appears efficient compared to the standard classification algorithm which classifies only into 3 constitutional groups. We find that more thorough investigation on the voice features is required to improve the classification efficiency into Sasang constitution.

• Journal of KIISE:Information Networking
• /
• v.29 no.4
• /
• pp.397-404
• /
• 2002

We propose a heuristic distributed unicast routing algorithm for minimizing the total cost of the path in a point to point network with do]ay constraint. The algorithm maintains a delay vector and a cost vector about the network states and finds the path using this information. In this paper, we show that our algorithm always finds a delay-constrained path if such a path exists and has O(│E│) message complexity(│E│is the number of links in the network). Also, simulation results show that the proposed algorithm has better cost performance than other delay-constrained routing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.6
• /
• pp.1127-1136
• /
• 1997

A vector timestamp is used to satisfy message ordering in a group communications. In this paper, we propose a new vector timestamp compression method which is applicable to a single process group environment where one process belongs to only one precess group. An existing compression method compares the fields of the previously sent vector timestamp with thouse of the currently updated vector timestamp, then sends only the modified fields of the vector timestamp. Unlike the previous one, a proposed compression method performs individual compression for each process using the locally maintained vector timestamp information on other processes. Also, we logicallyproved the causal ordering algorithm using the new compression method and compared the performance of the proposed method with one of the previous compression method by computer simulation. Using the proposed compression method, the message overhead required for causal ordering can be reduced.

• Journal of Power Electronics
• /
• v.10 no.3
• /
• pp.277-284
• /
• 2010

This paper presents a novel hybrid carrier based space vector modulation for cascaded multilevel inverters. The proposed technique inherits the properties of carrier based space vector modulation and the fundamental frequency modulation strategy. The main characteristic of this modulation are the reduction of power loss, and improved harmonic performance. The carrier based space vector modulation algorithm is implemented with a TMS320F2407 digital signal processor. A Xilinx Complex Programmable Logic Device is used to develop the hybrid PWM control algorithm and it is integrated with a digital signal processor for hybrid carrier based space vector PWM generation. The inverter offers less weighted total harmonic distortion and it operates with equal electrostatic and electromagnetic stress among the power devices. The feasibility of the proposed technique is verified by spectral analysis, simulation, and experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.9
• /
• pp.133-141
• /
• 1994

Conventional competitive learning algorithms compute the Euclidien distance to determine the winner neuron out of all predetermined output neurons. In such cases, there is a drawback that the performence of the learning algorithm depends on the initial reference(=weight) vectors. In this paper, we propose a new competitive learning algorithm that dynamically generates output neurons. The proposed method generates output neurons by dynamically changing the class thresholds for all output neurons. We compute the similarity between the input vector and the reference vector of each output neuron generated. If the two are similar, the reference vector is adjusted to make it still more like the input vector. Otherwise, the input vector is designated as the reference vector of a new outputneuron. Since the reference vectors of output neurons are dynamically assigned according to input pattern distribution, the proposed method gets around the phenomenon that learning is early determined due to redundant output neurons. Experiments using speech data have shown the proposed method to be superior to existint methods.

• Journal of Information Processing Systems
• /
• v.15 no.2
• /
• pp.422-432
• /
• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.6
• /
• pp.1140-1145
• /
• 2011

This paper proposes a new loss minimization control method for the vector controlled induction motors. The aim of the proposed loss minimization method is how to determine the optimal flux reference to minimize the total loss of induction motor. Even though the proposed algorithm is based on the equivalent circuit of induction motor including iron loss and leakage inductance, the algorithm is easy to be found and simple to be implemented. Futhermore, the proposed loss minimization algorithm can be applied easily to the traditional vector control system without any additional hardware. Simulation and experimental results are given to validate the effectiveness of the proposed method.