• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.442-445
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• 2023

최근 딥러닝 및 로봇기술의 발전으로 인해 대량의 데이터를 빠르게 수집하고 처리하는 연구 분야들로 확대되었다. 이와 관련된 한 가지 분야로써 다중 로봇을 이용한 분산학습 연구가 있으며, 이는 단일 에이전트를 이용할 때보다 대량의 데이터를 빠르게 수집 및 처리하는데 용이하다. 본 연구에서는 기존 Distributed Neural Network Optimization (DiNNO) 알고리즘에서 제안한 정적 분산 학습방법과 달리 단계적 분산학습 방법을 새롭게 제안하였으며, 모델 성능을 향상시키기 위해 원시 변수를 근사하는 단계수를 상수로 고정하는 기존의 방식에서 통신회차가 늘어남에 따라 점진적으로 근사 횟수를 높이는 방법을 고안하여 새로운 알고리즘을 제안하였다. 기존 알고리즘과 제안된 알고리즘의 정성 및 정량적 성능 평가를 수행하기 MNIST 분류와 2 차원 평면도 지도화 실험을 수행하였으며, 그 결과 제안된 알고리즘이 기존 DiNNO 알고리즘보다 동일한 통신회차에서 높은 정확도를 보임과 함께 전역 최적점으로 빠르게 수렴하는 것을 입증하였다.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.215-224
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• 2018

The Purpose of this study was to examine the relations between nurse image, self-esteem, major satisfaction and career identify of nursing students perceive. The participants of 410 subjects, data was analyzed using t-test, ANOVA, multiple linear regression with the SPSS/WIN 20.0 program. The results: The mean score of nurse image(3.82), self-esteem(2.64), major satisfaction(3.76), career identity(2.64) were abve the average. Nurse image, self-esteem, major satisfaction and career identity showed a positive correlation. The results of multiple linear regression analysis showed that the variables of self - esteem, major satisfaction, and career identity had an explanatory power(Adj R2=33.2% )on image of nurses. Study results suggested that, we propose a program for establishing a positive nurse image to improve the self - esteem, major satisfaction and career identity of nursing students.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.494-499
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• 2015

As the size of data increases, it becomes important to identify properties by analyzing big data. In this paper, we propose a k-Means based efficient clustering technique, called MCSKMeans (Multi centroid set k-Means), using distributed parallel processing framework MapReduce. A problem with the k-Means algorithm is that the accuracy of clustering depends on initial centroids created randomly. To alleviate this problem, the MCSK-Means algorithm reduces the dependency of initial centroids using sets consisting of k centroids. In addition, we apply the agglomerative hierarchical clustering technique for creating k centroids from centroids in m centroid sets which are the results of the clustering phase. In this paper, we implemented our MCSK-Means based on the MapReduce framework for processing big data efficiently.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.187-194
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• 2011

IEEE 802.11 wireless LAN has become the center of attention for one of the most dominant wireless networking technologies nowadays. In densely deployed wireless LANs, mobile stations are exposed to a number of AP, thus it is needed to select the best AP to associate with. The most common approach is to select the AP with the highest received signal strength. However it does not consider traffic load imposed to each AP so that it may cause the poor network performance. Therefore, in this paper, we propose a throughput estimation scheme for neighboring APs by sniffing the traffic within 802.11 networks. We devise a tool, named "VirtFrame", which is to estimate the station's capable throughput from neighbor APs based on the channel access time by virtually combining the sniffed frames. Simulation results show that our proposed scheme well matches that there exists correlation between the channel access time and the actual throughput of the APs.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.427-449
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• 2015

Network governance can be defined as collaborative process to develop a new socio-political order through civil society centered networking with government and market, and the term 'collaborative governance' can be used in a sense that the basis of governance is collaborative process. In particular, it can be stressed that collaborative governance between regions need double collaborative processes, that is, collaboration between local governments and collaboration between local government and local civil society within a region. Yet, the collaboration as a core element of collaborative governance should not be seen as a pure normativity presupposing confidence and reciprocity, but as a strategy based on competition and antagonism. The normativity implied in the concept of collaborative governance may not realized in actual process, and tends to be mobilized as a rationale for justifying neoliberal strategies. In order to overcome such limits of collaborative governance, the concept of collaborative governance should be reconstructed. This paper suggests that collaborative governance can be seen as hegemonic governing process in a Gramcian sense operating in the government plus civil society, and that, radicalizing Ostrom's concept, it also can be seen as a governing process producing polycentricity by self-regulating subjects. Finally, collaborative governance between regions needs expansion of material basis for economic complementarity and construction of infrastructure as well as a discursive process in order to enhance connectivity between them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4A
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• pp.403-410
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• 2008

In this paper, we propose a variant of the QRM-MLD signal detection method that is used for spatially multiplexed multiple antenna system. The original QRM-MLD signal detection method combines the QR decomposition with the M-algorithm, thereby significantly reduces the prohibitive hardware complexity of the ML signal detection method, still achieving a near ML performance. When the number of transmitter antennas and/or constellation size are increased to achieve higher bit rate, however, its increased complexity makes the hardware implementation challenging. In an effort to overcome this drawback of the original QRM-MLD, a number of variants were proposed. A most strong variant among them, in our opinion, is the ranking method, in which the constellation points are ranked and computation is performed for only highly ranked constellation points, thereby reducing the required complexity. However, the variant using the ranking method experiences a significant performance degradation, when compared with the original QRM-MLD. In this paper, we point out the reasons of the performance degradation, and we propose a novel variant that overcomes the drawbacks. We perform a set of computer simulations to show that the proposed method achieves a near performance of the original QRM-MLD, while its computational complexity is near to that of the QRM-MLD with ranking method.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.111-118
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• 2019

In this age where the social environment is changing rapidly and unpredictably, interest in safety from crime is increasing in Korean society. As the desire to live a life free from the fear of crime increases, interest in the construction of safe cities is also rising nationwide. For this, it is important to develop precision-positioning technology and support-service and intelligent security-service technology based on spatial information. Therefore, this study analyzes cases of multiple CCTV collaboration technology from among the intelligent-security technologies, and evaluates the technology's guarantee system through the evaluation system of the Technology Guarantee Fund, and evaluates continuity based on innovation, spreadability, usability, and proposed commercialization in order to enable utilization and commercialization. As a result of analyzing multiple CCTV collaborative technologies through the evaluation system of the Technology Guarantee Fund, the technology with the highest outlook was given five points, and the others were rated as excellent in terms of spreadability, usability, and differentiation. For innovation, the score was three points lower than the other evaluation items, but we expect to overcome that by introducing the latest technology and converging it with other technologies, such as the Internet of Things.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.11
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• pp.545-561
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• 2007

Location management and message delivery protocol is fundamental to the further development of mobile agent systems in a multi-region mobile agent computing environment in order to control mobile agents and guarantee message delivery between them. However, previous works have some problems when they are applied to a multi-region mobile agent computing environment. First, the cost of location management and message delivery is increased relatively. Second, a tracking problem arises. finally, cloned mobile agents and parent-child mobile agents do not get dealt with respect to location management and message delivery. In this paper, we present a HB (Home-Blackboard) protocol, which is a new location management and message delivery protocol in a multi-region mobile agent computing environment. The HB protocol places a region server in each region and manages the location of mobile agents by using intra-region migration and inter-region migration. It also places a blackboard in each region server and delivers messages to mobile agents when a region server receives location update form them. The HB protocol can decrease the cost of location update and message passing and solve the tracking problem with low communication cost. Also, this protocol deals with the location management and message passing of cloned mobile agents and parent-child mobile agents, so that it can guarantee message delivery of these mobile agents and pass messages without passing duplicate messages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2251-2259
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• 2009

We propose an algorithm and system which generates 3D stereo image by composition of 2D image from 4 multiple clusters which 1 cluster was composed of 4 multiple cameras based on network. Proposed Schemes have a network-based client-server architecture for load balancing of system caused to process a large amounts of data with real-time as well as multiple cluster environments. In addition, we make use of JPEG compression and RAM disk method for better performance. Our scheme first converts input images from 4 channel, 16 cameras to binary image. And then we generate 3D stereo images after applying edge detection algorithm such as Sobel algorithm and Prewiit algorithm used to get disparities from images of 16 multiple cameras. With respect of performance results, the proposed scheme takes about 0.05 sec. to transfer image from client to server as well as 0.84 to generate 3D stereo images after composing 2D images from 16 multiple cameras. We finally confirm that our scheme is efficient to generate 3D stereo images in multiple view and multiple clusters environments with real-time.