• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.12
• /
• pp.363-370
• /
• 2023

Object detection technology that accurately recognizes the road and surrounding conditions is a key technology in the field of autonomous driving. In the field of autonomous driving, object detection technology requires real-time performance as well as accuracy of inference services. Task offloading technology should be utilized to apply object detection technology for accuracy and real-time on resource-constrained devices rather than high-performance machines. In this paper, experiments such as performance comparison of task offloading, performance comparison according to input image resolution, and performance comparison according to camera object resolution were conducted and the results were analyzed in relation to the application of task offloading for real-time object detection of autonomous driving in resource-constrained devices. In this experiment, the low-resolution image could derive performance improvement through the application of the task offloading structure, which met the real-time requirements of autonomous driving. The high-resolution image did not meet the real-time requirements for autonomous driving due to the increase in communication time, although there was an improvement in performance. Through these experiments, it was confirmed that object recognition in autonomous driving affects various conditions such as input images and communication environments along with the object recognition model used.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2003.10a
• /
• pp.163-166
• /
• 2003

Hull form designers have to design a ship with satisfying an economical, technical and environmental demand. When it is concerned by a technical and environmental demand, there will be a economical demand left to criticize optimization. In this case, there were used to be requirements which needs to meet only a best performance not concerning about input of Human resource. Life cycle's cost contains building cost and operation cost so that now we need to check Man Hour cost in building a ship. This research shows a correlation between hull form information, i.e. curvature, length, breadth and thickness of surface and Man Hour of the Shell plating manufacture with using Artificial Neural Network and Gentic Programming. This study will support to classify initial work, to have a high assumption possible through predicting a Man Hour and to provide a guide book to infer a building cost and a economical optimization hull form.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.2
• /
• pp.125-133
• /
• 2009

RDF is widely used as the ontology language for representing metadata on the semantic web. Since ontology models the real-world, ontology changes overtime. Thus, it is very important to detect and analyze changes in knowledge base system. Earlier studies on detecting changes between RDF models focused on the structural differences. Some techniques which reduce the size of the delta by considering the RDFS entailment rules have been introduced. However, inferencing with RDF models increases data size and upload time. In this paper, we propose a new change detection using RDF reasoning that only computes a small part of the implied triples using backward chaining strategy. We show that our approach efficiently detects changes through experiments with real-life RDF datasets.

• Journal of information and communication convergence engineering
• /
• v.6 no.3
• /
• pp.255-260
• /
• 2008

In the recently years, there has been a big interest in ad hoc wireless network as they have tremendous military and commercial potential. An Ad hoc wireless network is composed of mobile computing devices that use having no fixed infrastructure of a multi-hop wireless network formed. So, the fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. And the ontology clustering adopts a tree structure to enhance resilient against mobility and routing complexity. This proposed multicast routing protocol utilizes node locality to be improve the flexible connectivity and stable mobility on local discovery routing and flooding discovery routing. Also attempts to improve route recovery efficiency and reduce data transmissions of context-awareness. We also provide simulation results to validate the model complexity. We have developed that proposed an algorithm have design multi-hierarchy layered networks to simulate a desired system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.10
• /
• pp.1194-1199
• /
• 2010

In this paper, for a relay-based cellular network, a interference avoidance beamforming technique is proposed to enhance direct link capacity while minimizing loss in the capacity of concurrent relaying link. A direct link is transmitted by beamforming at the transmitter, and the relaying link with the least interference to the direct link is scheduled to transmit data by a collision avoidance scheduling algorithm. Simulation results show that the proposed IA beamforming provides a considerable direct link capacity enhancement while minimizing relaying link capacity loss by effectively mitigating inference between concurrent direct and relaying links only with limited feedback.

• International Journal of Knowledge Content Development & Technology
• /
• v.3 no.1
• /
• pp.81-95
• /
• 2013

Through a series of precise text handling processes, including automatic extraction of information from documents with knowledge from various fields, recognition of entity names, detection of core topics, analysis of the relations between the extracted information and topics, and automatic inference of new knowledge, the most efficient knowledge base of the relevant field is created, and plans to apply these to the information knowledge management and service are the core requirements necessary for intellectualization of information. In this paper, the knowledge base, which is a necessary core resource and comprehensive technology for intellectualization of science and technology information, is described and the usability of academic information services using it is evaluated. The knowledge base proposed in this article is an amalgamation of information expression and knowledge storage, composed of identifying code systems from terms to documents, by integrating terminologies, word intelligent networks, topic networks, classification systems, and authority data.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.13-15
• /
• 2006

과학기술 연구분야에서 인력, 기관 등의 연구 주체와 논문, 과제, 지적재산권 등의 성과에 대한 온톨로지는, 시맨틱 웹 환경에서 이질적 과학기술 연구정보의 의미적 통합과 자동화된 유통, 그리고 암묵적 지식의 추론을 가능케 할 것이다. 이 논문에서는 현재 한국과학기술정보연구원에서 개발 중인 국가과학기술 R&D 기반정보 은톨로지를 소개하고, 그의 응용으로써 은톨로지에 내재된 암묵적 지식들을 규칙을 사용하여 추론하는 과정의 기술에 중점을 둔다. 상기 은톨로지는 인스턴스의 유일성 확보를 위해 URI(Uniform Resource identifier)서버에 기반하여 온톨로지 인스턴스에 고유한 URI를 할당하는 데 중점을 두고 설계되었으며, 논문의 특정순위저자를 모델링한 저작자정보 클래스를 은톨로지 스키마 상에 명시적으로 표현한다는 특징이 있다.

• Smart Media Journal
• /
• v.11 no.3
• /
• pp.18-25
• /
• 2022

Neuromorphic computing generally shows significantly better power, area, and speed performance than neural network computation using CPU and GPU. These characteristics are suitable for resource-constrained IoT environments where energy consumption is important. However, there is a problem in that it is necessary to modify the source code for environment setting and application operation according to heterogeneous IoT devices that support neuromorphic computing. To solve these problems, NAAL was proposed and implemented in this paper. NAAL provides functions necessary for IoT device control and neuromorphic architecture abstraction and inference model operation in various heterogeneous IoT device environments based on common APIs of NAAL. NAAL has the advantage of enabling additional support for new heterogeneous IoT devices and neuromorphic architectures and computing devices in the future.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.4
• /
• pp.141-150
• /
• 2023

Currently, various companies are actively participating in research and development of drone delivery services. Existing studies do not comprehensively provide integrated functions for future drone delivery services such as mission automation, customer verification, and overcoming performance limitations, which can lead to high manpower demand, reduced user service trust, and potentially overloading low-end devices. Therefore, this study proposes a drone mission automation system (DMAS) using EdgeCPS technology to provide the three aforementioned functions in an integrated manner. Real-world experiments were conducted to evaluate the proposed system, demonstrating that the DMAS components operate according to the specified roles in the delivery scenario. In addition, the system achieved user verification with a similarity of more than 90% in the process of receiving the product, and verified a faster inference speed and a lower resource share than the existing method.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.30-33
• /
• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.