• Journal of IKEEE
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• v.25 no.3
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• pp.420-424
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• 2021

The massive MIMO system, which is a core technology of 5G communication systems, has a problem that it is difficult to implement in a frequency division duplex system based on limited channel feedback because a large amount of channel information is required at the transmitting end. In order to solve this problem, the Joint Spatial Division and Multiplexing (JSDM) technique that dramatically reduces the channel information requirement by removing interference between the user groups using channel correlation information that does not change for a long time has been proposed. Recently, a regularized JSDM technique has been proposed to further improve performance by allowing residual interference between the user groups. However, such JSDM-related studies were mainly designed to focus on inter-group interference cancellation, and thus performance analysis was not performed in a more realistic environment assuming limited feedback in the intra-group interference cancellation phase. In this paper, we analyze the performance of the JSDM and regularized JSDM techniques according to the number of groups and users in a limited feedback environment, and through the simulation results, demonstrate that the regularized JSDM technique shows a more remarkable advantage compared to the existing JSDM in a limited feedback environments.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.1-8
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• 2003

Various methods have been studying to maintain and apply the multimedia inform abruptly increasing over all social fields, in recent years. For retrieval of still images, we is implemented content-based image retrieval system in this paper that make possible to retrieve similar objects from image database after segmenting query object from background if user request query. Query image is processed median filtering to remove noise first and then object edge is detected it by canny edge detection. And query object is segmented from background by using convex hull. Similarity value can be obtained by means of histogram intersection with database image after securing color histogram from segmented image. Also segmented image is processed gray convert and wavelet transform to extract spacial gray distribution and texture feature. After that, Similarity value can be obtained by means of banded autocorrelogram and energy. Final similar image can be retrieved by adding upper similarity values that it make possible to not only robust in background but also better correct object retrieval by using object segmentation method.

• Korean Journal of Construction Engineering and Management
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• v.1 no.3 s.3
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• pp.75-80
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• 2000

In scheduling multi-unit projects, several alternatives can exist in network construction due to repetitiveness of their activities. Project duration is affected not only by the duration of each activity but also by the arrangement of repeating activities in such projects. This paper provides a network compression method that assigns predecessors to each activity to minimize its float time. Different to the previous efforts that utilized line of balance as the base scheduling-model, this research adopts precedence diagram arranged in two coordinates, the space axis and the resource one. This method seeks the most appropriate predecessors for each activity in each direction of the two coordinates for the purpose of minimizing the idle resource and space. This activity arrangement method was applied to a multi-unit apartment-construction project, to prove its capability of network compression. The result shows that the method successfully sought room for saving construction duration by changing the activity arrangement. The network compression method presented in this research can be utilized in multi-unit construction projects such as apartment complex projects.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.167-167
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• 2017

스마트팜 내부의 3차원 공간의 온도, 습도, 기압, 공기질 분석을 통한 돈사 미세 조절 기술에 대한 연구가 진행 중이다. 해당 특성 중에서 기압을 제외한 환경인자들은 돈사 내의 구조 특성상 위치별로, 시간별로 매우 상이한 변이의 형태를 보인다. 일정 시점을 기준으로 계측 지점 이외의 지점에 대한 환경인자들을 공간적으로 추정하는 기술은 대표적으로 컴퓨터 분석 기술에 의존하고 있다. 시간 복잡도가 매우 높은 CFD(Computer Fluid Dynamics) 방식은 정밀도 측면에서 유리하나, 상응하는 제어 기술/하드웨어 등의 부재로 모델링 결과의 활용도가 낮다고 볼 수 있다. 본 연구에서는 CFD를 수행하는 과정에 있어 실질적으로 유효한 단위로 공간 분해능을 낮추고, 동등한 크기의 세부 공간에 대한 모델링을 병렬적으로 수행하기 위한 방안을 연구하였다. 실험적으로 돈사 환경을 3차원으로 구성하기 위하여, 공기 흡입구, 배출구, 기둥, 덕트 요소를 포함시켰다. 실내 공간을 1차적으로 가로, 세로, 높이방향으로 $3{\times}3{\times}3$ 균등 분배한 후 3차원 행렬로 분할하였다. 각 분할된 행렬에 대한 연산 수행을 위하여 현재까지 대중에 공개된 SBC(Single Board Computer) 중 가장 높은 연산 수행 능력이 있는 Odroid-XU4(Hardkernel, AnYang, Korea) 16식을 병렬 클러스터링 기술로 연동하였다. 하나의 AP당 8개의 코어가 내장되어 있으므로, 총 128개의 코어를 이용하여 동시에 128개의 3D 정방행렬 연산이 가능하도록 구성하였다. 모델링을 위한 수학적 모델로는 실험적으로 Steady turbulent model (Newtonian coefficient)을 이용하였다. 클러스터링을 이용한 병렬 처리의 특성상 균등한 정보량을 동시에 배분해야 하므로 108 ($27{\times}4$)개의 코어를 이용하여 1차적으로 나뉜 공간을 다시 4등분하여 동시에 $12{\times}12{\times}12$에 해당하는 공간 분해능에 대한 처리를 동시에 수행할 수 있도록 하였다. 2단계에 걸쳐 분할한 공간 세그먼트에 대한 클러스터링 연산 수행 결과 초당 15회 정도의 연산을 수행할 수 있었으며, 시간 분해능을 100으로 설정한 경우 약 5초가 수행되었다. 선행적으로 수행하였던 CFD 모델링 (OpenFOAM)과 비교하였을 때 상대적으로 정밀도가 낮은 3차원 모델링 결과를 얻을 수 있었다. 모델링에 소요되는 시간을 비약적으로 경감 시킨 장점을 살려 적정한 공간 분할 기법과 추가로 발생하는 다수의 바운더리 조건을 근사적으로 추정할 수 있는 데이터 마이닝 기술이 보완되어야 할 것이다.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.11-22
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• 2004

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. Consequently, reducing cache misses emerges as the most important issue in current main memory databases, in which CPU speeds have been increasing at 60% per year, compared to the memory speeds at 10% per you. In this paper, we design and implement a main-memory database system for real-time mobile GIS. Our system is composed of 5 modules： the interface manager provides the interface for PDA users； the memory data manager controls spatial and non-spatial data in main-memory using virtual memory techniques； the query manager processes spatial and non-spatial query : the index manager manages the MR-tree index for spatial data and the T-tree index for non-spatial index : the GIS server interface provides the interface with disk-based GIS. The MR-tree proposed propagates node splits upward only if one of the internal nodes on the insertion path has empty space. Thus, the internal nodes of the MR-tree are almost 100% full. Our experimental study shows that the two-dimensional MR-tree performs search up to 2.4 times faster than the ordinary R-tree. To use virtual memory techniques, the memory data manager uses page tables for spatial data, non- spatial data, T-tree and MR-tree. And, it uses indirect addressing techniques for fast reloading from disk.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11B
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• pp.1510-1521
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• 2001

In this paper a subspace-based blind interference cancellation is proposed and its performance is analyzed. Then the blind adaptive implementation is devolped using the improved natural power method which is the signal subspace tracking algorithm. The theoretical analysis shows that when the exact covariance matrix is kown the performance of the proposed detector is the same as that of the decorrelating detector. And when the covariance matrix is estimated the asymptotic results are examined. The results of computer simulation demonstrate that the proposed detector outperforms the previous blind adaptive RLS MOE detector.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.147-153
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• 2011

In this paper, we present a new video management technique using forward timeline to efficiently store and delete the videos on a local server. The proposed method is based on capturing the changing preference of the videos according to recentness, frequency, and playback length of the requested videos. For this purpose, we utilize the forward timeline which represents the time area within a number of predefined intervals. The local server periodically measures time popularity and request segment of all videos. Based on the measured data, time popularity and request segment, the local server calculates the mean time popularity and mean request segment of a video using forward timeline. Using mean time popularity and mean request segment of video, we estimate the ranking and allocated storage space of a video. The ranking represents the priority of deletion when the storage area of local server is running out of space and the allocated storage space means the maximum size of storage space to be allocated to a video. In addition, we propose an efficient storage space partitioning technique in order to stably store videos and present a time based free-up storage space technique using the expected variation of video data in order for avoiding the overflow on a local server in advance. The simulation results show that the proposed method performs better than other methods in terms of hit rate and number of deletion. Therefore, our video management technique for local server provides the lowest user start-up latency and the highest bandwidth saving significantly.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.221-228
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• 2008

To estimate array shape with reference sources in SONAR systems, nearfield signal modeling is required for the reference sources near a towed array. Array shape estimation method based on the nearfield signal modeling generally exploits the spatial covariance matrix of the received reference sources. Among those method, nearfield eigenvector method uses the eigenvector corresponding to the maximum eigenvalue as a steering vector of the reference source. In this paper, we propose a simplified subspace fitting method based on the nearfield signal modeling with spherical wave modeling. Furthermore, we analyze performance of the array shape estimation methods based on the nearfield signal modeling for various environments. The results of the numerical experiments indicate that the simplified subspace fitting method and the nearfield eigenvector method with single reference source shows almost similar performance. Furthermore, the simplified subspace fitting method with 2 reference sources consistently estimates the shape of the array regardless of the incident angle of the reference sources, whereas the nearfield eigenvector method cannot apply for the case of 2 reference sources.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.217-227
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• 2022

Quantile regression is widely used in many fields based on the advantage of providing an efficient tool for examining complex information latent in variables. However, modern large-scale and high-dimensional data makes it very difficult to estimate the quantile regression model due to limitations in terms of computation time and storage space. Divide-and-conquer is a technique that divide the entire data into several sub-datasets that are easy to calculate and then reconstruct the estimates of the entire data using only the summary statistics in each sub-datasets. In this paper, we studied on a variable selection method using Bayes information criteria by applying the divide-and-conquer technique to the penalized quantile regression. When the number of sub-datasets is properly selected, the proposed method is efficient in terms of computational speed, providing consistent results in terms of variable selection as long as classical quantile regression estimates calculated with the entire data. The advantages of the proposed method were confirmed through simulation data and real data analysis.