Earticle

In this paper, we propose spectrum sensing using cooperative relay to solve problem of sensing performance degradation due to CPE (Customer-Primise equipments) which causes low SNR (signal-to-noise ratio) problem. In cooperative communication systems, AF (amplify-and-forward) and DF (decoded-and-forward) is widely used for relay mechanism. Also, it is expected that a cooperative relay scheme guarantees a high sensing performance by its diversity gain. Based on these backgrounds, we applied a cooperative relay scheme to CR (cognitive radio) system, and simulation results show a sensing performance comparison between AF and DF.

In order to acquire a precise and quick response to an analytical query, proper selection of the views to materialize in the data warehouse is crucial. In traditional view selection algorithms, all relations are considered for selection as materialized views. However, materializing all relations rather than a part results in much worse performance in terms of time and space costs. Therefore, we present an improved algorithm for selection of views to materialize using the clustering method to overcome the problem resulting from conventional view selection algorithms. In the presented algorithm, ASVMRT (Algorithm for Selection of Views to Materialize using Reduced Table), we first generate reduced tables in the data warehouse using clustering based on attribute-values density, and then we consider the combination of reduced tables as materialized views instead of a combination of the original base relations. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs are approximately 1.8 times better than conventional algorithms.

The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (${\eta}$) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.

Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on the optimal threshold was statistically validated with other algorithms.

A reporting guideline can be defined as “a checklist, flow diagram, or structured text to guide authors in reporting a specific type of research, developed using explicit methodology.” A reporting guideline outlines the bare minimum of information that must be presented in a research report in order to provide a transparent and understandable explanation of what was done and what was discovered. Many reporting guidelines have been developed, and it has become important to select the most appropriate reporting guideline for a manuscript. Herein, I propose an algorithm for the selection of reporting guidelines. This algorithm was developed based on the research design classification system and the content presented for major reporting guidelines through the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. This algorithm asks 10 questions:“is it a protocol,” “is it secondary research,” “is it an in vivo animal study,” “is it qualitative research,” “is it economic evaluation research,” “is it a diagnostic accuracy study or prognostic research,” “is it quality improvement research,” “is it a non-comparative study,” “is it a comparative study between groups,” and “is it an experimental study?” According to the responses, 16 appropriate reporting guidelines are suggested. Using this algorithm will make it possible to select reporting guidelines rationally and transparently.

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

통신시스템에서 한정된 자원에서 최대의 용량을 얻기 위해서 안테나의 개수를 늘리는 것이 효과적이다. 주어 진 주파수 자원에서 안테나 개수의 증가는 또 다른 차원의 신호의 송수신을 가능하게 하고, 추가적인 채널자원을 확보하여, 통신 용량을 증대시킬 수 있기 때문이다. 다수의 안테나가 존재하는 상황에서는 안테나를 한정된 공간에 효과적으로 집적하는 문제가 매우 중요해지게 된다. 이러한 상황에서 채널 자원과 안테나 자원을 효율적으로 분배하 는 문제가 발생하여, 본 논문에서는 주어진 다수의 안테나 자원을 안테나간의 상관성을 고려하여 다수의 사용자에게 효과적으로 분배하는 문제를 해결하였다. 사용자를 선택하는 문제와 선택된 사용자에게 안테나를 할당하는 문제로 나누어 Semi-definite Relaxation 방식을 사용하여 최적해를 빠르게 구할 수 있도록 고안되었고, 시뮬레이션 결과 를 통하여 편파가 사용된 상황에서는 공간 집적도가 증가함을 보였고, 안테나의 자원이 늘어남에 따라 평균적으로 34% 감소된 전력을 사용하여 전력 효율적인 전송을 하는 결과를 보였다.

In order to ensure maximum capacity at a given frequency resource, the number of antennas must be increased. The increase in antennas means that such guaranteed channel resources can be used as an increase in channel capacity by aquiring another channel resource. In order to aggregate antennas in such a situation where there are a plurality of antennas, a problem of miniaturizing and integrating antennas must be accompanied. In this situation, in order to efficiently allocate channel resources and antenna resources in limited device resources, the problem of antenna selection and user scheduling was considered and solved together. By numerical simulation results, the proposed algorithm was proven to effectively reduce 34 % power consumption in averagewith increase in antennas.

본 논문에서는 대규모 종합감시시스템 환경에서 비디오의 특징을 분석하여 비디오가 촬영되는 환경에 적합한 지능형 영상분석 알고 리즘 선택 기준을 제안하였다. 대규모 종합감시시스템 환경의 예로 도시철도 감시시스템을 사용하였다. 본 시스템에 설치된 카메라 에 입력되는 비디오의 특징을 위치와 용도 및 상황별로 분석하였다. 분석을 기반으로 하여 적합한 비디오 감시 알고리즘을 선택하는 기준을 제시하였으며 상황 처리 시나리오를 제시하였다.

In this paper we proposed an algorithm selection method based on the features of video inputs which are acquired from the large scale integrated surveillance system. The example of integrated surveillance system is the metro railway system. Automated surveillance system at large area saves the human resource and minimizes the non-observing spots. We have analyzed the input video under this system in order to apply adequate video analytics algorithms in each installing places and for each situation. Based on the analysis, we suggested event processing scenarios and video analytic algorithm selection.

The conventional channel selection method for a ZigBee communication network basically detects the energy values in all channels. In the ISM band, no license is required to use channels in this band, so there may exist various interference factors in this band. It is well known that WLAN is the major interference factor degrading the performance of ZigBee in the ISM band. In this paper, we propose an efficient channel selection algorithm which uses the pattern of WLAN channel uses as the a priori information. By using the proposed algorithm, we may save the time required to select channels for the ZigBee communications.

동영상 압축에 관한 국제 표준화 기구인 JCT-VC (Joint Collaborative Team on Video Coding) 에서는 High Efficiency Video Coding (HEVC)의 확장 표준으로 스크린 콘텐츠 영상의 부호화 기술을 위한 HEVC Screen Content Coding(HEVC SCC)표준을 제정하였다. 현재까지 개발된 HEVC SCC 기술들 중 가장 높은 부호화 성능을 보이는 화면 내 블록 카피(IBC, Intra Block Copy)기술은 현재 프레임 내에서 복원된 블록들 중 현재 블록과 가장 유사한 블록을 예측하는 과정이다. IBC에서는 전송되는 블록 벡터의 데이터양을 줄이기 위해 예측 블록 벡터 값과 그 차이 값을 부호화 한다. 본 논문에서는 HEVC SCC 참조 소프트웨어인 SCM-2.0과 SCM-4.0을 이용하여 IBC의 블록 벡터 예측 과정에 대해 분석하였다. 또한 분석한 내용을 바탕으로 HEVC SCC IBC의 IBC의 예측 성능 향상을 위해 예측 블록 벡터(BVP, Block Vector Predictor) 후보 선정 과정에서 기존의 공간적 BVP 후보 외에 인접한 BV에 대한 탐색 과정을 추가하여 개선된 BVP 후보 선정 방법을 제안한다. 제안 방법의 실험 결과는 부호화 속도 저하 없이 최소 0.2%부터 최대 1%의 BD-rate 감소를 보인다.

Joint Collaborative Team on Video Coding (JCT-VC) of ISO/IEC MPEG and ITU-T developed the HEVC Screen Content Coding (HEVC SCC) standard as the HEVC extension for the screen content video coding. The Intra Block Copy (IBC) is the most effective tool adopted in HEVC SCC and predicts current block from already reconstructed neighboring blocks in the same picture. To reduce the amount of data in BV (Block Vector) to be transmitted, a BV predictor (BVP) is used to generate the BV differences in the IBC BV coding. In this paper, we analyze the current BV prediction process using HEVC reference software SCM-2.0 and SCM-4.0. Based on the analysis results, we propose an improved BVP candidate selection algorithm by adding a search process for adjacent BVs in addition to the existing spatial BVP candidates. Experimental results show that the BD-rate reduction of our proposed improvements ranges from 0.2% to 1%.

Quality control is essential to the final product in BGA-type PCB fabrication. So, many automatic vision systems have been developed to achieve speedy, low cost and high quality inspection. A multiple threshold selection algorithm is a very important technique for machine vision based inspection. In this paper, an inspected image is modeled by using fuzzy sets and then the parameters of specified membership functions are estimated to be in maximum fuzzy entropy with the probability of the fuzzy sets, using the exhausted search method. Fuzzy c-partitions with the estimated parameters are automatically generated, and then multiple thresholds are selected as the crossover points of the fuzzy sets that form the estimated fuzzy partitions. Several experiments related to flip chip BGA images show that the proposed algorithm outperforms previous ones using both entropy and variance, and also can be successfully applied to AVI systems.

본 논문은 레이다로 다중경로 상황에서 고각 추정을 할 때 고각 추정 오차가 커지는 문제를 극복하기 위한 방법을 제시하였다. 다중경로 상황 이란, 동일한 표적에서 반사된 레이다의 수신신호가 여러경로에서 오는 것을 의미한다. 다중경로 상황이 아닐 때는 모노펄스 방식이 정확하고, 그 반대 상황이면 최소 제곱 오차 추정 방식이 정확하다. 다중경로 상황이면서 고각이 매우 낮을 경우, 최소 제곱 오차 추정이 발산하는 특이 경우가 발생한다. 이 특이경우를 전파 인자 기반으로 판별하여, 모노펄스와 최소 제곱 오차 추정 방식을 선택적으로 운용했다. 그 결과, 고각 추정의 정확도를 높이는 데 성공했다. 본 논문에서 제안한 방법을 검증하기 위하여 매트랩 시뮬레이션을 수행했다.

This paper presents a method to overcome the problem of increasing elevation estimation error when estimating elevation in a multipath situation with radar. A multipath situation means that radar reception signals reflected from the same target come from multiple paths. In non-multipath, the monopulse method is accurate. For the opposite case, the least square error method is accurate. In multipath situation and when the elevation angle is very low, a singular occurs where the least square error estimate diverges. This singular was identified based on the propagation factor, and monopulse and least square error estimation methods were selectively used. As a result, we succeeded in increasing the accuracy of elevation estimation. MATLAB simulations were performed to verify the method proposed in this paper.

최근 영상 장비와 기술이 발전함에 따라 우리 군에서도 작전지역에 대한 정보를 획득하고, 효율적인 지휘통제에 활용하기 위해 수많은 영상정보가 생산되어 활용되고 있다. 이때 현재 군내에서 활용하고 있는 영상 재생장비는 저성능의 전술용 다기 능 단말기(TMFT)로부터 고성능의 영상서버에 이르기까지 매우 다양하고, 영상이 전송되는 네트워크 또한 저속의 전술정보통 신체계(TICN)로부터 초고속의 국방광대역통합망(M-BcN)에 이르기까지 다양하다. 따라서 이기종(異機種) 통신장비 및 네트 워크 환경에서 국방 영상정보를 효율적으로 전송할 수 있는 스트리밍 기법이 필요하다. 이 문제를 해결하기 위해 본 논문에서 는 확장비디오코딩(SVC) 및 균형선택 알고리즘 기반의 피투피(P2P) 스트리밍 기법을 제안한다. 비토렌트 기반의 시뮬레이터 를 활용한 성능분석결과 우리가 제안하는 균형선택 알고리즘 기반의 스트리밍 기법은 순차선택이나 희소선택 알고리즘에 비해 우수한 성능을 보였다.

Recently, with the rapid development of video equipment and technology, tremendous video information is produced and utilized in military domain to acquire battlefield information or for effective command control. Note that the video playback devices currently used in the military domain ranges from low-performance tactical multi-functional terminals (TMFT) to high-performance video servers and the networks where the video information is transmitted also range from the low speed tactical information and communication network (TICN) to ultra-high speed defense broadband converged networks such as M-BcN. Therefore, there is a need for an efficient streaming technique that can efficiently transmit defense video information in heterogeneous communication equipment and network environments. To solve the problem, this paper proposes a Scalable Video Coding (SVC) and balanced selection algorithm based Peer-to-Peer (P2P) streaming technique and the feasibility of the proposed technique is verified by simulations. The simulation results based on our BitTorrent simulator show that the proposed balanced selection scheme outperforms the sequential or rarest selection algorithm.

컴퓨터 기반 적응적 검사는 이전에 제시된 문항에 대한 반응을 기반으로 피검사자의 잠재 특질 수준을 추정하기에 가장 적절한 다음 문항을 선택해 출제함으로써 피검사자별 맞춤형 검사를 제시하는 컴퓨터 기반 검사형태다. 컴퓨터 기반 적응적 검사 제작의 핵심 요소 중 하나는 문항 선정 알고리즘이라 할 수 있으며, 최근 결정-트리를 이용한 적응적 심리 검사 구성에 대한 관심과 적용 사례가 늘어나고 있다. 결정-트리는 기계학습 분야에서 주로 연구되어온 예측 모형 중 하나로서 쉽게 해석 가능한 트리-구조를 가진다는 장점에도 불구하고 과적합 문제에 매우 취약하다는 것이 알려져 있다. 본 연구의 목적은 기계학습 분야에서 결정-트리의 대안으로 제시된 앙상블 모형 중에서 해석 가능한 트리-구조를 지닌 Alternating Model Tree (AMT)가 컴퓨터 기반 적응적 심리 검사 제작에 활용될 수 있는지 탐색하는 데 있다. 이를 위해 먼저 AMT의 작동 방식을 적응적 검사의 특징에 비추어 상술하였고, 검사 점수를 예측하는 AMT 기반의 적응적 검사와 결정-트리 기반의 적응적 검사의 예측 성능을 두 개의 심리 검사를 대상으로 비교하였다. 그 결과, AMT는 적응적 검사의 특징을 가지는 것으로 확인되었고, AMT 기반 적응적 심리 검사의 성능은 결정-트리 기반 적응적 심리 검사의 성능과 유사하거나 더 나은 결과를 보였다. 이러한 결과를 바탕으로 본 연구의 의의와 한계, 후속연구에 대한 제언 등을 논의하였다.

Computerized adaptive testing (CAT) is a computer-administered test where the next question for estimating the examinee’s trait level is selected depending on his or her reponses to the previous items, resulting in tailored testing for each individual examinee. A defining feature of CAT stems from its item selection algorithms, among which both research interest and practical applications of decision-tree based CAT (DT-based CAT) have been rising recently. In the field of machine learning, however, it is well known that decision-trees, as a form of predictive models with simple and interpretable tree structures, can be vulnerable to the problem of overfitting or the problem of creating overly complex trees that do not generalize to newly observed data. Among various ensemble techniques developed to adequately address this problem, we the authors paid attention to the Alternating Model Tree (AMT) due to its interpretable tree-like structure. The purpose of this article is to investigate the viability of the Alternating Model Tree (AMT) as an item selection algorithm for constructing CAT. To this end, we first presented a detailed exposition of how AMT-based CAT can be constructed and then compared its performance with DT-based CAT using two sets of publicly available psychological test scores. The results provided supportive evidence that AMT-based CAT is viable, and that AMT-based CAT can predict test scores at least as accurate as DT-based CAT does. Based on our findings, we discuss implications, limitations, and directions of future studies.

TETRA(TErrestrial Trunked RAdio)는 항상 기지국을 통해서 통신을 하는 TMO(Trunked Mode Operation)가 기지국 없이 단말기간 직접 통신을 하는 DMO(Direct Mode Operation) 보다 주파수 채널의 할당에 있어 우선순위를 가지고 있다. 이 때문에 단말간 통신인 DMO를 안정적으로 운용하기 위해서는 단말이 TMO로 할당된 주파수 채널을 회피할 수 있는 알고리즘이 필요하나 현재의 TETRA DMO 표준에는 이에 관한 사항이 반영되어 있지 않다. 따라서 본 논문에서는 이웃 노드(기지국 혹은 단말)로부터 TMO 주파수 채널 현황 및 DMO 주파수 이용률에 관한 추가 정보를 제공받아 단말 스스로 최적의 DMO 주파수 채널을 선택할 수 있는 알고리즘을 제안한다. 성능평가를 통하여 제안 알고리즘이 기존의 DMO 주파수 채널 할당 방식에 비해 평균 전송효율이 우수함을 증명하였다.

In TETRA(TErrestrial Trunked RAdio) standard, TMO(Trunked Mode Operation) communicates with the UE through the base station and it has a higher priority than DMO(Direct Mode Operation) which communicates with UE directly for channel allocation. As a result, the UE needs an algorithm which restricts the frequency allocation by TMO so that DMO can have a successful communication within the network. However, the TETRA DMO standard does not consider this issue. In this paper, we propose an active DMO channel selection algorithm which allocates a channel based on some additional information such as channel usage state of TMO and channel utilization of DMO. The experimental results show that the proposed algorithm outperforms existing DMO channel allocation scheme in terms of the transmission efficiency.