• Journal of the military operations research society of Korea
• /
• v.34 no.2
• /
• pp.143-162
• /
• 2008

This paper aims to formulate the method of estimating the wartime stockpile requirement of 155mm self-propelled artillery including intelligent ammunition for armored vehicles, currently being developed. The usual method of utilizing war-game simulation results in considerable margins in expected occupancy ratio between ground forces and air forces for each weapon system for armored vehicles. Also, the method tends to produce excessive output greater than the minimal stockpile requirements; therefore, the study aims to overcome limitations like these by the allocation method for each weapon system according to targets. This allocation method is better than war-game simulation method.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.181-193
• /
• 2017

The paper introduces bottom-up and optimal combination methods that can analyze and forecast hierarchical time series. These methods allow forecasts at lower levels to be summed consistently to upper levels without any ad-hoc adjustment. They can also potentially improve forecast performance in comparison to independent forecasts. We forecast regional traffic accident counts as time series data in order to identify efficiency gains from hierarchical forecasting. We observe that bottom-up or optimal combination methods are superior to independent methods in terms of forecast accuracy.

• Journal of KIISE:Databases
• /
• v.28 no.4
• /
• pp.606-619
• /
• 2001

This paper presents an efficient algorithm to compute difference between old and new versions of an SGML/XML document. The difference between the two versions can be considered to be an edit script that transforms some document tree into another The proposed algorithm is based on hybridization of bottom-up and top-down methods: matching relationships between nodes in the two versions are producted in a bottom-up manner and top-down breadth -first search computes an edit script. Because the algorithm does not need to investigate possible existence of matchings for all nodes, faster matching can be achieved . Furthermore, it can detect more structurally meaningful changes such as subtree move and copy as well as simple changes to the node itself like insert, delete, and update.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.2
• /
• pp.266-277
• /
• 2019

In modern warfare, securing time for preemptive response is recognized as an important factor of victory. The naval combat system, the core of naval forces, also strives to increase the effectiveness of engagement by improving its real-time information processing capabilities. As part of that, it is considered to use the TPR-tree in the naval combat system's target indexing because spatio-temporal searches can be performed quickly even as the number of target information increases. However, because the TPR-tree is slow to process updates, there is a limitation to handling frequent updates. In this paper, we present a method for improving the update performance of TPR-tree by applying the bottom-up update scheme, previously proposed for R-tree, to the TPR-tree. In particular, we analyze the causes of overlaps occurring when applying the bottom-up updates and propose ways to limit the MBR expansion to solve it. Our experimental results show that the proposed technique improves the update performance of TPR-tree from 3.5 times to 12 times while maintaining search performance.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.393-396
• /
• 2008

센서 네트워크에서 병합 질의를 효율적으로 처리하기 위한 다양한 인-네트워크 질의 처리 기법이 제안되었다. 스카이라인 질의는 일반적인 병합 질의와 달리 다차원 데이터에 대한 비교를 요구하므로 인-네트워크 처리가 쉽지 않다. 스카이라인 질의를 에너지 효율적으로 처리하기 위해서 불필요한 데이터의 전송을 제거하는 것이 중요하다. 기존에 제안된 스카이라인 처리 기법은 전체 네트워크에 필터를 배포함으로써 불필요한 데이터 전송을 차단한다. 하지만 많은 False Positive 발생에 따른 불필요한 데이터 전송과 필터 배포시 발생하는 에너지 소모로 인해 네트워크의 수명이 단축된다. 본 논문에서는 필터 배포에 따른 에너지 소모를 줄이기 위한 방법으로 상향식 필터 설정을 통한 스카이라인 질의 처리 기법과 필터링 성능을 향상시키는 기법을 제안한다. 제안하는 기법은 데이터를 수집하는 과정에서 스카이라인 필터테이블(SFT)설정하는 상향식 필터링을 수행한다. 그리고 선-필터링(Pre-filtering) 기법을 통해 필터효과를 증가시킨다. 제안하는 알고리즘의 우수성을 보이기 위해 시뮬레이션을 통해 기존에 제안된 MFTAC기법과 비교하였으며, 그 결과 평균 False Positive가 평균 84.44% 감소하였고, 네트워크 수명이 약 75.99% 증가하였다.

• Korean Journal of Ecology and Environment
• /
• v.38 no.4 s.114
• /
• pp.445-453
• /
• 2005

Relative importance between bottom-up and top-down controls on phytoplankton dynamics was investigated in the Juam Reservoir, Chonnam based on the results from statistical analyses including regression and artificial neural network (ANN) modeling. Effects of nutrients on size-structured phytoplankton dynamics were explored by simple linear regression analysis and relative importance between bottom-up and top-down controls was estimated based on results from the artificial neural network analyses. Although there is a limitation in determining direct grazing effects since chlorophyll a : pheopigments ratios, indirect index for grazing activity rather than grazing rates or herbivores biomass were used, the results from regression analysis showed that nutrients especially orthophosphates were positively correlated with the phytoplankton biomass and chlorophyll a : pheopigments ratios were also positively correlated with the phytoplankton biomass at lower coefficient of determination ($r^2$) compared to orthophosphates. The simulation results from ANN suggested that the bottom-up mechanisms including water temperature and availability of nutrients, especially orthophosphates were more important than top-down mechanisms such as grazing in the phytoplankton dynamics.

• Journal of Korean Society of Disaster and Security
• /
• v.9 no.2
• /
• pp.87-91
• /
• 2016

In recent years, lots of damages appear frequently due to cyber terrorism and natural disasters such as typhoon 'Chaba', earthquake at Kyeongju. The damages lead to people's anxiety and public institutions connected directly with them also can't be free from the threat situation. In addition, public institutions must prepare business continuity issues with the highest priority in the event of disasters because they are closest with people's safety. However, preceding researches suggest needs of disaster mitigation activities management system or central office oriented top-down BCP establishing system. In this study, We grasp the current status of public institutions's business continuity plan(BCP) against disaster and we propose an efficient bottom-up method for structuring the BCP of public institutions.

• Journal of Broadcast Engineering
• /
• v.25 no.5
• /
• pp.750-757
• /
• 2020

One of the basic tasks for robots to interact with humans is to quickly and accurately grasp human behavior. Therefore, it is necessary to increase the accuracy of human pose recognition when the robot is estimating the human pose and to recognize it as quickly as possible. However, when the human pose is estimated using deep learning, which is a representative method of artificial intelligence technology, recognition accuracy and speed are not satisfied at the same time. Therefore, it is common to select one of a top-down method that has high inference accuracy or a bottom-up method that has high processing speed. In this paper, we propose two methods that complement the disadvantages while including both the advantages of the two methods mentioned above. The first is to perform parallel inference on the server using multi GPU, and the second is to mix bottom-up and One-class Classification. As a result of the experiment, both of the methods presented in this paper showed improvement in speed. If these two methods are applied to the entertainment robot, it is expected that a highly reliable interaction with the audience can be performed.

• Journal of KIISE:Databases
• /
• v.29 no.5
• /
• pp.367-380
• /
• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• Journal of KIISE:Databases
• /
• v.36 no.6
• /
• pp.476-485
• /
• 2009

In sensor networks, many methods have been proposed to process in-network aggregation effectively. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approach like MFTAC restricts unnecessary data transitions by deploying filters to whole sensors. However, network lifetime is reduced by energy consumption for many false positive data and filters transmission. In this paper, we propose a bottom up filtering-based skyline query processing algorithm of in-network for reducing energy consumption by filters transmission and a PBFiltering technique for improving performance of filtering. The proposed algorithm creates the skyline filter table (SFT) in the data gathering process which sends from sensor nodes to the base station and filters out unnecessary transmissions using it. The experimental results show that our algorithm reduces false positives and improves the network lifetime over the existing method.