• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.4
• /
• pp.529-540
• /
• 2018

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2013.07a
• /
• pp.11-13
• /
• 2013

무선 센서 네트워크는 개방된 환경에서 무작위로 배치되어 악의적인 공격자들에게 쉽게 노출된다. 센서 노드는 한정된 에너지 자원과 손쉽게 훼손된다는 단점을 통해 허위 보고서와 투표 삽입 공격이 발생한다. Li와 Wu는 두 공격을 대응하기 위해 확률적 투표기반 여과기법을 제안하였다. 확률적 투표기반 여과기법은 고정적인 검증 경로를 결정하기 때문에 특정 노드의 에너지 자원고갈 위험이 있다. 본 논문에서는 센서 네트워크에서 보고서 여과 확률 향상을 위하여 퍼지 시스템을 기반으로 다음 노드 선택을 약 6% 효율적인 경로 선택 방법을 제안한다. 제안 기법은 전달 경로 상의 노드 중 상태정보가 높은 노드를 검증 노드로 선택하고, 선택된 검증 노드는 허용 범위 경계 값을 기준으로 공격 유형을 판별하고 여과한다. 실험결과를 통해 제안기법은 기존기법과 비교하였을 때 에너지 효율이 향상되었다.

• Journal of the Korean association of regional geographers
• /
• v.13 no.4
• /
• pp.381-395
• /
• 2007

After the democratization process since 1988, the national scale voting behavior in congressional elections has changed from a rural-government party and urban-opposite party connection to a political regionalism oriented pattern. In this context, the case study with provincial border regions aims to investigate possible party identification change of the region, and to find a relationship between polling score ratio and socio-political characteristics of the candidates. As a result, Yeongdong shows a strong negation to the presumed Chungcheong local party and shows a continuous party identification with the Kyungsang local party. Muju reveals a more or less weakened identification with the Jeolla local party, on the contrary, Kimcheon shows a unchanged strong identification with the Kyungsang local party. The regional neighborhood effect was verified quite partly between the subdivision districts of the border regions. With a application of linear fitting method, it is certified that voters have attached great importance to the belonging party, native place, as well as political career of the candidates as a voting criterion.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.7
• /
• pp.1340-1345
• /
• 2001

In this paper, we proposed the method computing the optimal solution of Optical Flow(OF) representing the moving information of moving object and improving the operating speed. In order to do that, we computed the optimal solution of OF using the Combinatorial Hough Transform(CHT) and Voting accumulation and simply searched the moving object compared to conventional method.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.760-762
• /
• 2001

정보 사회의 급속한 발전을 통해 유.무선 환경에서 다양한 응용 분야들이 창출되고 있다. 그 중에서 전자 투표는 그 효용성 측면에서 새로이 관심을 가지는 분야이다. 그러나, 아직까지는 공개 네트워크를 이용하여 투표를 수행할 경우 보안 측면에서 여러 고려 사항들이 존재하며, 이들이 만족되지 않을 경우 투표의 신뢰성을 떨어뜨리게 된다. 본 논문은 전자 투표를 위해 필수적으로 요구되는 보안 사항들을 일반 요구 사항과 특수 요구 사항으로 분류하고, 이들 요구사항을 만족하는데 필요한 Screw method와 Magic Sticker 방식을 제안한다. 동시에 이들을 기초로 새로운 전자 투표 기법을 제안하고, 요구 사항을 만족하는지 평가할 것이다.

• Journal of Broadcast Engineering
• /
• v.18 no.3
• /
• pp.401-408
• /
• 2013

In this paper, we propose a method that automatically detects organs having similar appearances in medical images by learning both context and appearance features. Since only the appearance feature is used to learn the classifier in most existing detection methods, detection errors occur when the medical images include multiple organs having similar appearances. In the proposed method, based on the probabilities acquired by the appearance-based classifier, new classifier containing the context feature is created by iteratively learning the characteristics of probability distribution around the interest voxel. Furthermore, both the efficiency and the accuracy are improved through 'region based voting scheme' in test stage. To evaluate the performance of the proposed method, we detect femur and tibia which have similar appearance from SKI10 knee joint dataset. The proposed method outperformed the detection method only using appearance feature in aspect of overall detection performance.

• Survey Research
• /
• v.8 no.1
• /
• pp.81-104
• /
• 2007

This paper discusses the central issues of election studies and in this contort, suggests the panel survey method as an alternative to general opinion surveys. In doing so, it also explains the advantages and benefits that a panel survey provides, and discusses the weakness of the 2006 panel survey. East Asia Institutes, constructing the consortium which includes JoongAng Ilbo, SBS, and Hankook Research, traces the change in voting decisions during the 2006 Korean Local Elections. Four regional panels, focusing on the gubernatorial elections in Seoul, Pusan, Kwangju, and Chungnam, enable researchers to study the critical issues of election studies such as the causal relations among a set of voting determinants, the impact of campaigns, and the characteristics of floating voters.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.10
• /
• pp.35-41
• /
• 2012

We propose a new marker driven Watershed algorithm for automated segmentation of clustered cell from microscopy image with less over segmentation. The Watershed Transform is able to segment extremely complex objects which are highly touched and overlapped each other. The success of the Watershed Transform depends essentially on the finding markers for each of the objects of interest. For extracting of markers positioning around center of each cell we used radial symmetry and iterative voting algorithms. With synthetic and real images, we quantitatively demonstrate the performance of our method and achieved better results than the other compared methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1264-1286
• /
• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.6009-6027
• /
• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.