• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.731-742
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• 2007

Self-growing software is a software system that has the capability of evolving its functionalities and configurations by itself based on dynamically monitored situations. Self-growing software is especially necessary for intelligent service robots, which must have the capability to monitor their surrounding environments and provide appropriate behaviors for human users. However, it is hard to anticipate all situations that robots face with, and it is hard to make robots have all functionalities for various environments. In addition, robots have limited internal capacity. To support self-growing software for intelligent service robots, we are developing a cell-based distributed repository system that allows robots and developers transparently to share robot functionalities. To accomplish the creation of evolutionary repositories, we invented the concept of a cell, which is a logical group of distributed repositories based upon the functionalities of components. In addition, a cell can be used as a unit for the evolutionary growth of the components within the repositories. In this paper, we describe the requirements and architecture of the cell-based repository system for self-growing software. We also present a prototype implementation and experiment of the repository system. Through the cell-based repositories, we achieve improved performance of self-growing actions for robots and efficient sharing of components among robots and developers.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.21-29
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• 2009

An intelligent service robot is a robot that monitors its surroundings, and then provides a service to meet a user's goal. It is normally impossible for a robot to anticipate all the needs of its user and various situations in the surroundings ahead, and to prepare for all the necessary functions to cope with them. Therefore, it is required to support the self-growing capability by which robots can extend their functionality based on users' needs and external conditions. In this paper, as an enabler of the self-growing capability, we propose a method that allows a robot to select a component-composition pattern represented in an architectural form (called a sub-architecture), and to extend its functionality by obtaining a set of software components that are prescribed in the pattern. Sub-architecture is selected and instantiated not only based on the functionality required but also based on quality requirements of a user and the surrounding environment. To provide this method, we constructed a quality-attributes-in-use ontology and developed a brokering mechanism that matches quality requirements of users and surroundings against quality attributes of sub-architectures. The ontology provides the common vocabularies to represent quality requirements and attributes, and enables the semantically-based reasoning in matching and instantiating appropriate sub-architectures in supporting services to users. This ontology-based approach contributes to provide a great flexibility in extending robot functionality based on available software components, and to narrow the gap between users' Quality requirements and the Quality of the actual services provided by a robot.

• Korean Management Science Review
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• v.15 no.2
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• pp.59-70
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• 1998

According to the Gartner Group, the market for ERP software in Korea is growing rapidly. However, the number of successful ERP implementations is quite few. Standard(unmodified) ERP's are built based on best practices. Improvements expected from successful implementation are based on best practices built into the software. Many improvements are lost through modification to "standard" software. Even minor changes in software can significantly reduce benefits. Both implementation time and risk factors are increased with modifications. We introduce a methodology, called package Enable Re-engineering (PER) and the main components of change management program by Ernst & Young. "To-Be" model could be developed through the software capabilities. And change management processes such as continuous education and self-developments are required. The philosophy of the change management processes is to let the software package drive the re-engineering practices and avoid moving the software toward the "As-IS" process. Extensive top management involvement, major focus on speed, extensive communication program, and "clear" picture of the future are essential components of change management. We are sure that the complied experiences and model have implications for practice and for academicians for their endeavors in their fields.

• Journal of Information Processing Systems
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• v.5 no.2
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• pp.69-76
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• 2009

Self healing systems are considered as cognation-enabled sub form of fault tolerance system. But our experiments that we report in this paper show that self healing systems can be used for performance optimization, configuration management, access control management and bunch of other functions. The exponential complexity that results from interaction between autonomic systems and users (software and human users) has hindered the deployment and user of intelligent systems for a while now. We show that if that exceptional complexity is converted into self-growing knowledge (policies in our case), can make up for initial development cost of building an intelligent system. In this paper, we report the application of AHSEN (Autonomic Healing-based Self management Engine) to in OKKAM Project infrastructure backbone cluster that mimics the web service based architecture of u-Zone gateway infrastructure. The 'blind' load division on per-request bases is not optimal for distributed and performance hungry infrastructure such as OKKAM. The approach adopted assesses the active threads on the virtual machine and does resource estimates for active processes. The availability of a certain server is represented through worker modules at load server. Our simulation results on the OKKAM infrastructure show that the self healing significantly improves the performance and clearly demarcates the logical ambiguities in contemporary designs of self healing infrastructures proposed for large scale computing infrastructures.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.235-237
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• 2006

로봇 소프트웨어와 같이 사용자의 요구사항과 주변 환경의 잦은 변화에 직면하는 소프트웨어는 스스로 문제 상황을 판단하고, 동적으로 문제 상황을 극복할 수 있는 기능을 검색하고 이용할 수 있어야 한다. 이를 위해 본 논문에서는 아키텍처 기반의 로봇 소프트웨어 환경하에서 온톨로지를 이용하여 로봇의 문제 상황 극복을 위한 소프트웨어의 아키텍처를 재구성 전략과 컴포넌트를 모델링하고 이를 이용하여 문제 상황을 해결 할 수 있도록 적합한 컴포넌트를 선택하는 메커니즘을 기술한다.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.203-208
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• 2023

Recently, with the growing interest in the field of autonomous driving, many researchers have been focusing on developing autonomous driving software platforms. In particular, we have concentrated on developing 3D object detection models that can improve real-time performance. In this paper, we introduce a self-constructed 3D LiDAR dataset specific to domestic environments and propose a VariFocal-based CenterPoint for the 3D object detection model, with improved performance over the previous models. Furthermore, we present experimental results comparing the performance of the 3D object detection modules using our self-built and public dataset. As the results show, our model, which was trained on a large amount of self-constructed dataset, successfully solves the issue of failing to detect large vehicles and small objects such as motorcycles and pedestrians, which the previous models had difficulty detecting. Consequently, the proposed model shows a performance improvement of about 1.0 mAP over the previous model.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.19-28
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• 2016

Internet traffic has been significantly increasing due to the development of information and communication networks and the growing numbers of cell phone users that access networks. This paper connects to this issue by presenting a way to detect and analyze a typical DDoS attack that results in Internet breaches and network attacks, which are on the increase. To achieve this goal, we improve features and GUI of the existing ATMSim analysis package and use it. This package operates on a network flow-based analysis method, which means that normal traffic collected through an internal LAN at the Korean Bible University campus as well as anomaly traffic with DDoS attacks are generated. Self-similarity processes are used to analyze normal and anomaly traffic that are collected and generated from the improved ATMSim. Our numerical results obtained from three Hurst parameter estimate techniques show that there is quantitatively a significant difference between normal traffic and anomaly traffic from a self-similarity perspective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.322-324
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• 2005

자가성장 소프트웨어란 동적인 상황 판단에 의거하여 스스로 자신의 기능과 컴포넌트 구성 능력을 성장시키는 소프트웨어를 말한다. 자가성장 소프트웨어는 자신의 환경을 모니터링 하여 사용자에게 적절한 행동을 제공하여 주는 지능형 로봇에 특히 필요하다. 지능형 로봇은 현재 자신이 가지고 있는 소프트웨어로는 해결할 수 없는 상황에 부딪힐 수 있고 이러한 경우 동적으로 소프트웨어 컴포넌트를 획득하여 이용하게 해주는 것이 자가성장 로봇 소프트웨어의 역할이다. 효율적인 컴포넌트의 획득과 관리를 위해 컴포넌트 저장소의 중요성이 부각되고, 자가성장 로봇 소프트웨어를 실현하기 위한 저장소 프레임워크를 ICU의 SemBots 프로젝트에서 개발 중에 있다. 본 논문에서는 저장소를 위한 요구조건과 구조를 기술하고 저장소 시스템을 위한 프로토타입을 제시한다.

• Smart Structures and Systems
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• v.12 no.2
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• pp.209-233
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• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.