• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.113-116
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• 2004

Assisted by high technologies of information and communication in storing and collecting moving object information, many applications have been developing technical methods to exploit databases of moving objects effectively and variously. Among them, today, Current and Anticipated Future Position Indexing methods manage current positions of moving objects in order to anticipate future positions of them or more complex future queries. They, however, strongly demand update performance as fast enough to guarantee certainty of queries as possible. In this paper, we propose a new indexing mettled derived from the TPR-tree that should has update performance considerably improved, we named it BUR-tree. In our method, index structure can be inserted, deleted, and updated with a number (or bulk) of objects simultaneously rather than one object at a time as in conventional methods. This method is intended to be applied to a traffic network in which vast number of objects, such as cars, pedestrians, moves continuously.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.815-816
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• 2009

Owing to the development of computer technologies, to process data derived from various sensors is recently demanding. It is also increasing to demand the moving object based servies like the services of location based and mobile application services. That's why it is needed the processing of visualizing the services for education aspects. In this paper, we show the implemented results about $R^*$ tree algorithm to insert, delete and search a object in two dimension environment.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.177-179
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• 1999

We present a simple and efficient spatial object indexing scheme based on the minimum bounding rectangles (MBR) of the objects for use in applications in geographic information system (GIS). We also provide the rationale behind the simple indexing scheme instead of other complex hierarchical indexing approaches such as the R-tree and its variants.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.5
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• pp.271-280
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• 2014

In wireless sensor networks, there have been two methods for sensing continuously moving object tracking: user-query based method and periodic report based method. Although the former method requires overhead for user query rather than the latter method, the former one is known as an energy-efficient method without transferring unnecessary information. In the former method, a virtual tree, consisting of sensor nodes, is exploited for the user querying and sensor reporting. The tree stores the information about mobile objects; the stored information is triggered to report by the user query. However, in case of fast moving object, the tracking accuracy reduces due to the time delay of end-to-end repeated query. To solve the problem, we propose a query relaying method reducing the time delay for mobile object tracking. In the proposed method, the nodes in the tree relay the query to the adjacent node according to the movement of mobile object tracking. Relaying the query message reduces the end-to-end querying time delay. Simulation results show that our method is superior to the existing ones in terms of tracking accuracy.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.427-432
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• 2020

In order to establish an effective forest management plan, it is necessary to investigate tree management information such as tree height and DBH(Diameter at breast height). However, research on convergence and application of data acquisition technology to improve the efficiency of existing forest survey methods is insufficient. Therefore, in this study, tree management information was constructed and analyzed using point cloud data acquired through a 3D scanner. Data on the study site was acquired using fixed and mobile 3D scanners, and the efficiency of the mobile 3D scanner was presented through comparison of working hours. In addition, tree management information for object management was constructed by classifying vegetation by object using point cloud data, and by constructing information on chest height diameter and height. As a result of the accuracy evaluation compared with the conventional measurement method, the difference in tree height was 0.02-0.09m and DBH was 0.01-0.04m. If information on the location of vegetation and crowns of each object is constructed through additional research in the future, the efficiency of the work related to forest management information construction can be greatly increased.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.171-177
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• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• Journal of KIISE
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• v.42 no.3
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• pp.390-398
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• 2015

Spatial web objects refer to web documents that contain geographic information. Recently, services that create spatial web objects have increased greatly because of the advancements in devices such as smartphones. For services such as Twitter or Facebook, simple texts posted by users is stored along with information about the post's location. To search for such spatial web objects, a method that uses spatial information and text information simultaneously is required. Conventional spatial web object search methods mostly use R-tree and inverted file methods. However, these methods have a disadvantage of requiring a large volume of space when building indices. Furthermore, such methods are efficient for searching with many keywords but are inefficient for searching with a few keywords.. In this paper, we propose a spatial web object search method that uses a quad-tree and a patricia-trie. We show that the proposed technique is more effective than existing ones in searching with a small number of keywords. Furthermore, we show through an experiment that the space required by the proposed technique is much smaller than that required by existing ones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1653-1659
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• 2013

In this paper, we propose a new ECAD software for efficiently analyzing and debugging of digital architecture implemented in Verilog HDL or VHDL codes. This software firstly elaborates HDL codes so as to extract internal architecture structure, then generates several graphical aids such as hierarchical schematics by applying placement and routing algorithm, object tree to show configuration of each module, instance tree to show hierarchical structure of instances, and SPD (Signal Propagation Diagram) to show internal interconnections. It is more important function that same objects in different views(HDL codes, object tree, instance tree, SPD, waveform etc.) can be highlighted at the starting any object. These functions are sure to improve efficiency of manual job to fix bugs or to analyze HDL codes.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.217-219
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• 2001

이동 기기의 발달로 이동 객체의 위치 정보에 대한 연구가 진행되고 있다. 그러나 시공간 데이타베이스에서 요구하는 질의를 효과적으로 처리하기 위한 인덱스 구조는 많지 않다. 사용자는 먼저 범위 질의를 던져 이동 객체를 뽑아낸 후 궤적 질의를 던지게 되는데 이것을 결합 질의라 한다. 기존 방법인 TB-tree는 MBR의 겹침이 심해 성능이 저하된다. 본 논문에서는 결합 질의를 효과적으로 처리할 수 있는 CR-tree 인덱스 구조를 제안한다. CR-tree는 R-tree와 연곁노드로 구성된다. R-tree 부분은 MBR의 겹침을 최소화해 범위 질의를 효과적으로 처리하고 연결 노드 부분은 이동 객체별로 궤적의 부분을 차례로 저장해서 궤적 질의를 빠르게 저리한다. 즉 결합 질의시 R-tree를 이용해 범위에 속하는 이동 객체를 검색하고 연결 노드를 이용해 그 객체의 궤적을 찾는다.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.