• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.427-439
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• 2013

With the fast development of complex science, lots of social networks are studied. We know that the social network is widely applied in analyzing issues in human culture, economics and web sciences. Recently we witness that some researchers began to compare the social network constructed from fiction literatures(literature social network) and the real social network obtained from practice. But we point that previous approaches for literature social network have some drawbacks since they completely depend on the biographical dictionary constructed for a designated literature. So since the previous approach focus on the few important characters and peoples around them, we can not understand the global structure of all characters appeared in the literature at least once. We propose one method to extract all characters appeared in the literature and how to make the social network from that information. Also we newly propose K-critical network by applying frequency of the named characters and the strength of relationship among all textual characters. Our experiment shows that the K-critical measure could be one crucial quantitative measure to compute the relationship strength among characters appeared in the object literature.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.23-37
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• 1994

In this paper, we design and implement the prototype system, and propose the Extended Faceted Classification. Scheme and the Hybrid Retrieval Method that support classifying the software components, storing in library, and efficient retrieval according to user's request. In order to designs the classification scheme, we identify several necessary items by analyzing basic classes of software components that are to be classified. Then, we classify the items by their characteristics, decide the facets, and compose the component descriptors. According to their basic characteristics, we store software components in the library by clustering their application domains and are assign weights to the facets and its items to describe the component characteristics. In order to retrieve the software components, we use the retrieval-by-query model, and the weights and similarity for easy retrieval of similar software components. As the result of applying proposed classification scheme and retrieval model, we can easily identify similar components and the process of classification become simple. Also, the construction of queries becomes simple, the control of the size and order of the components to be retrieved possible, and the retrieval effectiveness is improved.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.108-108
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• 2015

준설이란, 물리적으로 수저의 퇴적물을 제거함으로써 하도관리에서 가장 확실하게 퇴적물을 제거하고 통수 단면적을 증가시킬 수 있는 방법 중 하나이다. 우리나라에서는 주로 해안이나 항만에서 주를 이루어져 왔으며, 하천에서는 주로 골재채취를 목적으로 하는 소규모 준설사업이 대부분이었다. 그러나 4대강 살리기 사업으로 인해서 건설된 다기능보는 수위 및 유량을 조절할 수 있다는 장점이 있는 반면, 흐름 및 유사의 연속성을 차단하여 유사퇴적이 발생할 가능성이 높아졌음으로 이를 위한 대책으로 유지준설이 이루어져야 한다. 준설은 대규모의 사업비가 투입되는 건설공사이면서, 수저의 퇴적물을 물리적으로 제거함에 따라 고탁도를 발생시키고 생태계를 교란시키는 등의 문제가 있다. 준설선진국인 미국의 경우, 이러한 문제점을 최소화하기 위한 일환으로 환경창(EWs; Environmental Windows)을 개발하여 미국 준설사업의 약 80%에 적용하여 관리하고 있다. 환경창이란, 준설 및 준설토 처분에 관한 작업이 이루어질수 있는 기간을 의미하여, 결정적으로 사회 환경적으로 준설에 따른 영향의 강도가 상대적으로 작은 기간을 선정하여 준설을 허용하는 기간이다. 본 연구에서는 이러한 환경창를 국내에 적용하기 위하여 어류, 조류, 친수시설 이용빈도, 홍수기를 이용하였다. 연구대상지역은 낙동강 유역의 금호강 합류점이며, 홍수기에는 준설하지 않는 것을 대전제로 하였다. 어류는 대표어종을 선정하여 연구를 진행하였고, 그 외 조류는 법적보호종인 흑두루미, 친수시설 이용빈도는 4대강 방문객 통계자료를 사용하였다. 엔트로피 가중치 산정방법을 통하여 각 속성별 가중치를 산정하여 최종적으로 한국형 환경창을 제시하였다. 본 연구에서 제시한 한국형 환경창은 기존의 환경창과 비교하였을 때, 영향의 정도를 수치로 표현하여 의사결정권자가 간편하게 환경창을 결정할 수 있도록 의사결정지원을 한다는 장점이 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.114-114
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• 2018

과거 수십년간 댐의 운영방법은 과거 관측 유입량 자료를 바탕으로 결정되었지만, 미래 기후변화의 불확실성을 고려하면 기존 운영방법이 더 이상 유효하지 않을 수 있다. 따라서, 이에 대응하여 수자원을 적절히 운용하기 위해서는 기후변화의 불확실성을 고려한 댐의 운영방법에 대한 연구가 필요하다. 본 연구는 예측 유입량의 불확실성을 고려하기 위하여 로버스트(Robust) 의사결정 방법을 댐 운영 최적화에 접목한 다목적 로버스트 최적화(Multi-Objective Robust Optimization) 방법을 제안한다. 이는 기존의 다목적 로버스트 의사결정이론(MORDM, Multi Objective Robust Decision Making)과 로버스트 최적화이론(Robust Optimization)을 결합한 의사결정 방법이다. 로버스트 최적화의 목적함수는 로버스트 항(Robust Term)을 신뢰도, 심각도, 그리고 회복도 등의 여러 관점으로 구성할 수 있으며, 이는 다목적 최적화의 일종으로 볼 수 있다. 본 연구는 신뢰도와 심각도 관점으로 로버스트 항을 적절히 구성하고 그 가중치들을 조절하며, 그에 따라 기후변화의 상황에서 댐 운영의 수행결과가 어떻게 변하는지 의사결정자들이 파악할 수 있도록 가시화한다. 그리고 동시에, 목표하는 댐 운영의 안정성이 다양한 미래 기후변화 시나리오 상에서 유지되도록 하는 로버스트 항과 각 항의 가중치들을 결정하는 방법을 제시한다. 이를 통해 의사결정자는 여러 측면에서 안정적인 다목적 로버스트 최적화의 해를 찾아갈 수 있다. 댐 운영을 위한 로버스트 최적화를 진행하기 위해서 본 연구는 Robust-SDP(Stochastic Dynammic Programming)을 수행하였으며, 대상유역인 보령댐이 이수기동안 인근지역의 수요량만큼 물을 충분히 공급함을 목적으로 로버스트 최적화를 진행하였다. 아울러, 저수지 용량이 로버스트 최적화에 미치는 영향을 분석하기 위해서 남강댐에 동일한 최적화 방법을 적용하고 이를 비교하였다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.11
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• pp.45-54
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• 2008

In this paper, we use particle swarm optimization (PSO) to design a randomly excited and randomly spaced linear array with either the lowest side lobe level (SLL) or the narrowest beamwidth. The positions and the excitation amplitudes of the array elements are considered as variables to be controlled. The beam pattern is optimized by controlling the two variables simultaneously and randomly. The best beam patterns are obtained using PSO in the fitness function where performance is improved by the random assignment of weight coefficients to each angular sector of the beam Pattern. The weight coefficients and angles are obtained through several trial runs. Also, an extra term, ${\beta}{\ast}BW$, is added to the fitness function to account for the beamwidth as well as the SLL. Is produces the best result for the beam pattern with either the lowest SLL or the narrowest beamwidth. In the former case, the SLL and beamwidth are about -43dB and $32.2^{\circ}$, respectively, with only 10 elements. In the latter case, the SLL and beamwidth are about -26dB and $24.2^{\circ}$, respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.2
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• pp.47-55
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• 2012

A discrete-time(DT) filter with high-order temporal moving average(TMA) using actively-weighted charge sampling is proposed in this paper. To obtain different weight of sampled charge, the variable transconductance OTA is used prior to charge sampler, and the ratio of charge can be effectively weighted by switching the control transistors in the OTA. As a result, high-order TMA operation can be possible by actively-weighted charge sampling. In addition, the transconductance generated by the OTA is relatively accurate and stable by using the size ratio of the control transistors. The high-order TMA filter has small size, increased voltage gain, and low parasitic effects due to the small amount of switches and sampling capacitors. It is implemented in the TSMC $0.18-{\mu}m$ CMOS process by TMA-$2^2$. The simulated voltage gain is about 16.7 dB, and P1dB and IIP3 are -32.5 dBm and -23.7 dBm, respectively. DC current consumption is about 9.7 mA.

• Spatial Information Research
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• v.19 no.2
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• pp.9-18
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• 2011

The purpose of this research is to evaluate an accurate biotope area ratio model with efficiency and convenience of database management through promoting sustainable development to provide people amenities in a new town. In particular, the biotope area ratio is used not only in the environment impact assessment but Green building certification criteria. But now there is no any index map of biotope. So it is very hard to implement with data for supplement results. In this research, we suggest the model of integrated attributable information. The evaluation of biotope area ratio is to include a basic land use planning map and a building coverage area which is a wall of greening surface and roof. In case of non building coverage area, the evaluation of biotope area ratio is to include water space, artificial ground, natural ground and pervious gap-pave. A weighted value on the spatial information is combined into the information. And then the merged one is given a land use planning information in a block. In the weighted value on the space type information, it is possible to in its circumstances. Therefore, it can be substituted a correspondence of numerical change for various values elastically in this model.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.178-188
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• 2019

When using a distributed system, it is very important to know the intention of a target ship in order to prevent collisions. The action taken by a certain ship for collision avoidance and the action of the target ship it intends to avoid influence each other. However, it is difficult to establish a collision avoidance plan in consideration of multiple-ship situations for this reason. To solve this problem, a Distributed Stochastic Search Algorithm (DSSA) has been proposed. A DSSA searches for a course that can most reduce cost through repeated information exchange with target ships, and then indicates whether the current course should be maintained or a new course should be chosen according to probability and constraints. However, it has not been proven how the parameters used in DSSA affect collision avoidance actions. Therefore, in this paper, I have investigated the effect of the parameters and weight factors of DSSA. Experiments were conducted by combining parameters (time window, safe domain, detection range) and weight factors for encounters of two ships in head-on, crossing, and overtaking situations. A total of 24,000 experiments were conducted: 8,000 iterations for each situation. As a result, no collision occurred in any experiment conducted using DSSA. Costs have been shown to increase if a ship gives a large weight to its destination, i.e., takes selfish behavior. The more lasting the expected position of the target ship, the smaller the sailing distance and the number of message exchanges. The larger the detection range, the safer the interaction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.4
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• pp.11-22
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• 1999

Least-mean-square(LMS) adaptive filters have proven to be extremely useful in a number of signal processing tasks. However LMS adaptive filter suffer from a slow rate of convergence for a given steady-state mean square error as compared to the behavior of recursive least squares adaptive filter. In this paper an efficient signal interference control technique is introduced to improve the convergence speed of LMS algorithm with tap weighted vectors updating which were controled by reusing data which was abandoned data in the Adaptive transversal filter in the scheme with data recycling buffers. The computer simulation show that the character of convergence and the value of MSE of proposed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computation. Adaptive transversal filter with proposed data recycling buffer algorithm could efficiently reject ISI of channel and increase speed of convergence in avoidance burden of computational complexity in reality when it was experimented having the same condition of LMS algorithm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.