• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.119-128
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• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.81-90
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• 2022

There are several ways to improve software performance by using existing software. So, the developments of some programs are the most promising ways. However, traditional part programming studies usually assume that the components are recycled "as is". Existing models of component objects only provide limited support for partial adjustments, namely white box technologies ( copy-paste & inheritance) and the black-box methods (such as mixing and encapsulation). These technologies have problems related to recovery, efficiency, implementation of indirect costs, or their own problems. This paper suggests as JALTREE, The Black Box adaptation technology, which allows us for the implementation of previous components, but we need configurable the interface types, for measuring the adaptability. In this article we discussed the types of adjustments including component interfaces and component composition. An example of customizing JALTREE and component can be illustrated in several examples

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.292-302
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• 2022

The radar tomographic imaging is based on the Radar Cross-Section "RCS" of the materials of a shape under examination and investigation. The RCS varies as the conductivity and permittivity of a target, where the target has a different material profile than other background objects in a scene. In this research paper, we use Hierarchical Performance Modeling "HPM" and a framework developed earlier to determine/spot bottleneck(s) for pattern recognition of materials using a combination of the Single Layer Perceptron (SLP) technique and tomographic images in radar systems. HPM provides mathematical equations which create Objective Functions "OFs" to find an average performance metric such as throughput or response time. Herein, response time is used as the performance metric and during the estimation of it, bottlenecks are found with the help of OFs. The obtained results indicate that processing images consumes around 90% of the execution time.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.225-232
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• 2022

When we drive a vehicle in an IoT environment, there is a problem in that information of car users is collected without permission. The security measures used in the existing wired network environment cannot solve the security problem of cars running in the Internet of Things environment. Information should only be shared with entities that have been given permission to use it. In this paper, we intend to propose a method to prevent the illegal use of vehicle information. The method we propose is to use attribute-based encryption and dynamic threshold encryption. Real-time processing technology and cooperative technology are required to implement our proposed method. That's why we use fog computing's proxy servers to build smart gateways in cars. Proxy servers can collect information in real time and then process large amounts of computation. The performance of our proposed algorithm and system was verified by simulating it using NS2.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.51-59
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• 2024

In this work, we focused on reducing the amount of image data to be sent by extracting and progressively sending prominent image features to high-performance computing systems taking into consideration the right amount of image data required by object identification application. We demonstrate that with our technique called Progressive Object Detection over a Lossless Network using Fragmented DCT Coefficients (Proficient), object identification applications can detect objects with at least 70% combined confidence level by using less than half of the image data.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.154-162
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• 1999

This paper introduces the research and development of automatic generation technology to develop the character agent. The R&D of this technology includes three major elements-body model generation, automatic motion generation and synthetic human generation. Main areas of application would be cyber space- 3D game, animation, virtual shopping, on line chatting, virtual education system, simulation and security system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.75-85
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• 2010

This paper presents a robust 3D model hashing based on object feature vector for 3D content authentication. The proposed 3D model hashing selects the feature objects with highest area in a 3D model with various objects and groups the distances of the normalized vertices in the feature objects. Then we permute groups in each objects by using a permutation key and generate the final binary hash through the binary process with the group coefficients and a random key. Therefore, the hash robustness can be improved by the group coefficient from the distance distribution of vertices in each object group and th hash uniqueness can be improved by the binary process with a permutation key and a random key. From experimental results, we verified that the proposed hashing has both the robustness against various mesh and geometric editing and the uniqueness.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.33-43
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• 2011

Recently, advances in mobile communication and location identifying technology of the moving object is evolving. Therefore, the location-based services based on request for service have increased and a variety of the indexing for the position management of moving objects has been studied. Because the index based on Euclidean space are no restriction of movement, it is difficult to apply to the real world. Also, there is additional cost to find adjacent road segments in road networks-based indexing. Existing studies of fixed static objects such as buildings or hospitals are not considered. In this paper, we propose an efficient road networks-based indexing for management of current positions. The proposed indexing partitions road networks by grids and has integrated road connection informations and manage separated extra indexing for fixed static objects. Through the experiment, we show that the proposed indexing based on road networks improves the performance of operation for search or update than existing indexing.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.551-562
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• 2024

This study relates to a method and device for extracting and tracking moving objects. In particular, objects are extracted using different images between adjacent images, and the location information of the extracted object is continuously transmitted to provide accurate location information of at least one moving object. It relates to a method and device for extracting and tracking moving objects based on tracking moving objects. People tracking, which started as an expression of the interaction between people and computers, is used in many application fields such as robot learning, object counting, and surveillance systems. In particular, in the field of security systems, cameras are used to recognize and track people to automatically detect illegal activities. The importance of developing a surveillance system, that can detect, is increasing day by day.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.437-445
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• 2017

Necessity of security reinforcement has been increased these days. As a result, surveillance systems using CCTV and the patrol system by person have been applied to security zone. However, CCTV has a blind spot and CCTV itself cannot handle emergency situation. Patrol system by person is economically expensive. To overcome these disadvantages, this paper presents an image-based surveillance system using unmanned aircraft. As a default, unmanned aircraft traces lines installed on ground which represents path for patrolling. If the aircraft detects objects, then it keeps a constant distance between object without missing the object in the image. Simulation results show that the proposed system can be operated even with unmanned aircraft with low specification.