• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.191-199
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• 2005

Space-time turbo codes have been studied extensively as a powerful and bandwidth efficient error correction code over the wireless communication environment. In this paper, the efficient algorithm for decoding space-time turbo codes is proposed. The proposed method reduces the computational complexity by approximating a prior information for a iterative decoder. The performance of space-time turbo codes is also analyzed by using the fixed point implementation and the efficient method for approximating the Log-MAP algorithm is proposed. It is shown that the BER performance of the proposed method is close to that of the Log-MAP algorithm.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.83-90
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• 1994

In this paper, we propose a new learning algorithm, ASOFM(Adaptive Self Organizing Feature Map), to solve the defects of Kohonen's Self Organiaing Feature Map. Kohonen's algorithm is sometimes stranded on local minima for the initial weights. The proposed algorithm uses an object function which can evaluate the state of network in learning and adjusts the learning rate adaptively according to the evaluation of the object function. As a result, it is always guaranteed that the state of network is converged to the global minimum value and it has a capacity of generalized learning by adaptively. It is reduce that the learning time of our algorithm is about $30\%$ of Kohonen's.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.11
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• pp.863-869
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• 2008

This paper investigated the new calibration algorithm of a straight-type five-hole pressure probe for measuring three-dimensional flow velocity components. This new calibration algorithm was used for velocity data reduction from the calibration map and based on the combination of a look-up, a binary search algorithm and a geometry transformation including the translation and reflection of nodes in a calibration map. The calibration map was expanded up to the application angle, ${\pm}55^{\circ}$ of a probe. This velocity data reduction method showed a perfect performance without any kind of interpolating errors in calculating yaw and pitch angles from the calibration map. Moreover, when it was applied to an actual flow field including a swirling flow, a good result came out on the whole.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.365-371
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• 2014

In the era of big data, algorithms for the existing IT environment cannot accept on a distributed architecture such as hadoop. Thus, new distributed algorithms which apply a distributed framework such as MapReduce are needed. Lloyd's algorithm commonly used for vector quantization is developed using MapReduce recently. In this paper, we proposed a decombined distributed VQ codebook generation algorithm based on a distributed VQ codebook generation algorithm using MapReduce to get a result more fast. The result of applying the proposed algorithm to big data showed higher performance than the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.208-209
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• 2011

In this paper, we introduce a Naive Bayes learning algorithm for learning and reasoning in Map-Reduce model based environment. For this purpose, we use Apache Mahout to execute Distributed Naive Bayes on University of California, Irvine (UCI) benchmark data sets. From the experimental results, we see that Apache Mahout' s Distributed Naive Bayes algorithm is comparable to WEKA' s Naive Bayes algorithm in terms of performance. These results indicates that in the future Big Data environment, Map-Reduce model based systems such as Apache Mahout can be promising for machine learning usage.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.252-258
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• 2012

This paper describes hierarchical semantic map building using the classified area information in home environments. The hierarchical semantic map consists of a grid, CAIG (Classified Area Information in Grid), and topological map. The grid and CAIG maps are used for navigation and motion selection, respectively. The topological map provides the intuitive information on the environment, which can be used for the communication between robots and users. The proposed semantic map building algorithm can greatly improve the capabilities of a mobile robot in various domains, including localization, path-planning and HRI (Human-Robot Interaction). In the home environment, a door can be used to divide an area into various sections, such as a room, a kitchen, and so on. Therefore, we used not only the grid map of the home environment, but also the door information as a main clue to classify the area and to build the hierarchical semantic map. The proposed method was verified through various experiments and it was found that the algorithm guarantees autonomous map building in the home environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.725-732
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• 2002

Block-wise MAP (Maximum A posteriori) decoding algorithm for turbo-codes requires less memory than Log-MAP decoding algorithm. The ER (Bit Error Rate) performance of previous block-wise MAP decoding algorithm depend on the block length and training length. To maximize hardware utilization and perform successive decoding, the block length is set to be equal to the training length in previous MAP decoding algorithms. Simulation result on the BER performance shows that the EBR performance can be maintained with shorter blocks when training length is sufficient. This paper proposes an architecture for area efficient block-wise MAP decoder. The proposed architecture employs the decoding schema for reducing memory by using the training length, which in N times larger than block length. To efficiently handle the proposed schema, a pipelined architecture is proposed. Simulation results show that memory usage can be reduced by 30%~45% in the proposed architecture without degrading the BER performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.2
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• pp.171-179
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• 2010

A fundamental technology of UGV(Unmanned Ground Vehicle) to perform a given mission with success in various environment is a path planning method which generates a safe and optimal path to the goal. In this paper, we suggest a local path-planning method of UGV based on the binary map using world model data which is gathered from terrain perception sensors. In specially, we present three core algorithms such as shortest path computation algorithm, path optimization algorithm and path smoothing algorithm those are used in the each composition module of LPP component. A simulation is conducted with M&S(Modeling & Simulation) system in order to verify the performance of each core algorithm and the performance of LPP component with scenarios.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.983-985
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• 1995

In this paper, we propose a Self Organizing Feature Map (SOFM) Type Neural Computation Algorithm for the Travelling Salesman Problem(TSP). The actual best solution to the TSP problem is computatinally very hard. The reason is that it has many local minim points. Until now, in neural computation field, Hopield-Tank type algorithm is widely used for the TSP. SOFM and Elastic Net algorithm are other attempts for the TSP. In order to apply SOFM type neural computation algorithms to the TSP, the object function forms a euclidean norm between two vectors. We propose a Largrangian for the above request, and induce a learning equation. Experimental results represent that feasible solutions would be taken with the proposed algorithm.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.