• Journal of Korea Society of Industrial Information Systems
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• v.13 no.4
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• pp.64-72
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• 2008

Recently security is being an important issue in almost every field of industry. This situation has affected port logistics industry deeply. Ports are now leaving operational methods that only focus on productivity, and shifting to new ones which focus on safety and customer services on the basis of it. Thus a lot of companies and institutions have offered various solutions as this issue becomes more and more intense. Among them, most typical solutions involve installing special devices to ordinary containers to improve its security, such as CSD (Container Security Device) of GE (General Electric) and eSeal of Savi Networks. On the other hand, these devices focus only on international standards or technical implementation, and this causes inconvenience to actual users like cargo owners, sea carriers, or stevedoring companies. This is considered to be due to lack of sufficient consideration on user demands. This research uses QFD (Quality Function Deployment) method for deducting system requirements in order to solve the problems of previous security devices and to develop a security system that can not only reflect the demands of the users but also considers real-world conditions. According to the QFD results, a total of 21 system CTO's were deducted under 5 categories.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.67-76
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• 2012

In this study, we established objective appraisal standard by applying newly made appraisal standard to those areas equipped with the protection system targeted to the Mobility handicapped announced in this edition (issue 5, volume 9(Oct., 2010)) beyond simple evaluation related to protector satisfaction. Additionally, we achieved efficient budget execution by conducting the preliminary estimation assessment regarding those areas on which recognition devices should be newly deployed. Through the assessment of the system coordination, the maximum safety distance is proved to be 72.2m. On the basis of this result, we applied dangerous grade to the deployment of recognition devices considering both psychological and accidental risk. With this, we proposed valuation basis to enable us to do future business. Based on this assessment standard, the degree of risk is proved to decrease by 35.2% compared to before conducting the demonstration project in terms of evaluation of comprehensive risk regarding intended areas. Additionally, we confirmed the fact that the degree of risk can decrease by 33.1% totally after having recognition devices built according to the deployment standard within budget. Furthermore, comprehensive risk can decrease up to 94% compared to the level of the demonstration project even though we spend 21.9% less of the existing budget. Hence, we can say that the deployment method of recognition devices related to the degree of risk is applied efficiently in the near future in terms of controlling comprehensive risk and cutting down budget through this study.

• International Journal of Automotive Technology
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• v.7 no.7
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• pp.827-832
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• 2006

Occupant classification and position detection have been significant research areas in intelligent safety systems in the automotive field. The detection and classification of seat occupancy open up new ways to control the safety system. This paper deals with a novel algorithm development, hardware implementation and testing of a prototype intelligent safety system for occupant classification and position detection for in-vehicle environment. Borland C++ program is used to develop the novel algorithm interface between the sensor and data acquisition system. MEMS strain gauge hermatic pressure sensor containing micromachined integrated circuits is installed inside the passenger seat. The analog output of the sensor is connected with a connector to a PCI-9111 DG data acquisition card for occupancy detection, classification and position detection. The algorithm greatly improves the detection of whether an occupant is present or absent, and the classification of either adult, child or non-human object is determined from weights using the sensor. A simple computation algorithm provides the determination of the occupant's appropriate position using centroidal calculation. A real time operation is achieved with the system. The experimental results demonstrate that the performance of the implemented prototype is robust for occupant classification and position detection. This research may be applied in intelligent airbag design for efficient deployment.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.36-46
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• 2011

It is thought traffic flow management under the ubiquitous transportation system has great potential in view of individual vehicle data availability and V2V, V2I two-way communication environments. However, it is expected that deployment of the ubiquitous transportation system takes some time. Therefore it is necessary to evaluate the feasibility of the algorithm under the ITS environment. The speed management algorithm proposed in the previous research is revised to fit for the ITS data collection and information provision environment. And the feasibility of the algorithm is evaluated through simulation experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.710-715
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• 2006

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• IE interfaces
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• v.17 no.2
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• pp.149-157
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• 2004

This paper considers a conformity testing problem on EJB(Enterprise JavaBeans). The EJB architecture is a component architecture for the development and deployment of component-based distributed business applications. The objective is to find an optimal test sequence for the conformity test between the EJB specification and an implemented one. For the test sequence, we formulate the problem as a rural Chinese postman tour one and use a linear programming formulation. Based upon the formulations, we suggest a conformance test procedure and show its efficiency by applying the procedure to the CMP(Container- Managed persistency) entity bean of the EJB.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.