• 한국지리정보학회지
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• 제1권2호
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• pp.67-79
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• 1998

대부분의 GIS 응용 프로그램은 다수의 공간객체에 대하여 주로 읽기 연산을 수행하며 접근하는 공간객체가 복합 객체인 경우 그 복합객체와 그 복합객체가 포함하는 공간객체에 모두 접근하게 된다. GIS 응용 프로그램에서 공간객체의 생성, 삭제, 변경연산은 매우 드물게 일어나지만 다수의 공간객체에 대하여 수행된다. 본 논문은 GIS 응용 프로그램의 이러한 특성을 고려하여 다수의 공간객체들을 신속히 탐색하기 위한 다단계 사전인출 질의의 개념을 제시하고 생성하는 영속객체들을 최적의 성능으로 데이타베이스에 반영하기 위한 지연쓰기 기능을 객체지향 데이타베이스 시스템인 바다-III에 설계하고 구현한 내용을 제시한다. 다단계 사전인출 질의는 그 질의를 만족하는 객체들뿐만 아니라 그 객체들이 포함하는 객체들을 사용자가 명시한 단계만큼 데이타베이스로부터 인출하여 클라이언트 캐쉬에 등록하는 기능이다. 지연쓰기 기능은 응용 프로그램이 생성한 객체들에 대하여 서버의 부담을 최소화하고 클라이언트와 서버간의 통신을 최소화하면서 데이타베이스에 반영하는 기법이다. 이들 두 기능은 GIS 응용과 같이 다수의 복합객체를 탐색하고 생성하는 응용에 적합하다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.725-727
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• 2006

The development of GIS and Location-Based Services requires a high-level database that will be able to allow real-time access to moving objects for spatial and temporal operations. MODB.MM is able to meet these requirements quite adequately, providing operations with the abilities of acquiring, storing, and querying large-scale moving objects. It enables a dynamic and diverse query mechanism, including searches by region, trajectory, and temporal location of a large number of moving objects that may change their locations with time variation. Furthermore, MODB.MM is designed to allow for performance upon main memory and the system supports the migration on out-of-date data from main memory to disk. We define the particular query for truncation of moving objects data and design two migration methods so as to operate the main memory moving objects database system and file-based location storage system with.

• 센서학회지
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• 제33권2호
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• pp.78-85
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• 2024

We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of Communications and Networks
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• 제8권3호
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• pp.330-338
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• 2006

Many web cache systems and policies concerning them have been proposed. These studies, however, consider large objects less useful than small objects in terms of performance, and evict them as soon as possible. Even if this approach increases the hit rate, the byte hit rate decreases and the connections occurring over congested links to outside networks waste more bandwidth in obtaining large objects. This paper puts forth a client-cluster approach for improving the web cache system. The client-cluster is composed of the residual resources of clients and utilizes them as exclusive storage for large objects. This proposed system achieves not only a high hit rate but also a high byte hit rate, while reducing outgoing traffic. The distributed hash table (DHT) based peer-to-peer lookup protocol is utilized to manage the client-cluster. With the natural characteristics of this protocol, the proposed system with the client-cluster is self-organizing, fault-tolerant, well-balanced, and scalable. Additionally, the large objects are managed with an index based allocation method, which balances the loads of all clients well. The performance of the cache system is examined via a trace driven simulation and an effective enhancement of the proxy cache performance is demonstrated.

• Journal of Mechanical Science and Technology
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• 제15권3호
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• pp.300-308
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• 2001

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.

• Journal of Communications and Networks
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• 제12권1호
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• pp.52-66
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• 2010

Since large objects consume substantial resources, web proxy caching incurs a fundamental trade-off between performance (i.e., hit-ratio and latency) and overhead (i.e., resource usage), in terms of caching and relaying large objects to users. This paper investigates how and to what extent the current dedicated-server based web proxy caching scheme is affected by large file transfers in a high bandwidth campus network environment. We use a series of trace-based performance analyses and profiling of various resource components in our experimental squid proxy cache server. Large file transfers often overwhelm our cache server. This causes a bottleneck in a web network, by saturating the network bandwidth of the cache server. Due to the requests for large objects, response times required for delivery of concurrently requested small objects increase, by a factor as high as a few million, in the worst cases. We argue that this cache bandwidth bottleneck problem is due to the fundamental limitations of the current centralized web proxy caching model that scales poorly when there are a limited amount of dedicated resources. This is a serious threat to the viability of the current web proxy caching model, particularly in a high bandwidth access network, since it leads to sporadic disconnections of the downstream access network from the global web network. We propose a peer-to-peer cooperative web caching scheme to address the cache bandwidth bottleneck problem. We show that it performs the task of caching and delivery of large objects in an efficient and cost-effective manner, without generating significant overheads for participating peers.

• 한국게임학회 논문지
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• 제1권1호
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• pp.55-60
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• 2001

With rapid development of graphic hardware, researches on Virtual Reality and 3D Games have received more attention than before. For more realistic 3D graphic scene, objects were to be presented with lots of polygons and the number of objects shown in a scene was remarkably increased. Therefore, for effective visualization of large polygon models like this, view-frustum culling method, that visualizes only objects shown in the screen, has been widely used. In general, the bounding boxes that include objects are generated firstly, and the boxes are intersected with view-frustum to check whether object is in the visible area or not. Recently, an algorithm that can check in-out test of objects using OpenGL's selection mode, which is originally used to select the objects in the screen, is suggested. This algorithm is fast because it can use hardware acceleration. In this study, by implementing and applying this algorithm to large polygon models, we showed the efficiency of OpenGL assisted View-Frustum Culling algorithm. If this algorithm is applied to 3D games that have to process more complicated characters and landscapes, performance improvement can be expected.

• 대한수학회논문집
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• 제17권1호
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• pp.71-80
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• 2002

We give a formulation of the large deviation property for rescalings of random measures on the d-dimensional Euclidean space R$^{d}$ . The approach is global in the sense that the objects are Radon measures on R$^{d}$ and the dual objects are the continuous functions with compact support. This is applied to the cluster random measures with Poisson centers, a large class of random measures that includes the Poisson processes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4098-4116
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• 2020

Mobile devices such as smartphones are very attractive targets for augmented reality (AR) services, but their limited resources make it difficult to increase the number of objects to be recognized. When the recognition process is scaled to a large number of objects, it typically requires significant computation time and memory. Therefore, most large-scale mobile AR systems rely on a server to outsource recognition process to a high-performance PC, but this limits the scenarios available in the AR services. As a part of realizing large-scale standalone mobile AR, this paper presents a solution to the problem of accuracy, memory, and speed for large-scale object recognition. To this end, we design our own basic feature and realize spatial locality, selective feature extraction, rough pose estimation, and selective feature matching. Experiments are performed to verify the appropriateness of the proposed method for realizing large-scale standalone mobile AR in terms of efficiency and accuracy.