• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.11a
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• pp.240-241
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• 2013

The maintenance management in buildings has got more important by the increasing complexity of building sizes and use. Nowadays an expectation and a possibility of BIM technology become accepted as a new construction management method, therefore many studies and legal systems of it are being suggested actively. Although orders for BIM projects are supposed to be increasing, at present the BIM information accumulated from planning and design still doesn't have its continuity at the maintenance step after completion of construction in terms of LCC. Therefore according to bim information, we set a goal of developing apartment maintenance system which is able to maintain by user viewpoint.

• Journal of Applied Reliability
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• v.15 no.1
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• pp.1-5
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• 2015

This note suggests three approaches to improve reliability in developing weapon systems. The high complexity of the weapon systems make it hard to analyze and predict of those reliability. Current situations of the reliability have been reviewed in terms of logistics support analysis (LSA), warranty policy, maintenance and development. Three suggestions are notified to improve the reliability considering the complexity of the weapon systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.24-31
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• 2018

As a system complexity increases and technology innovation progresses rapidly, leasing the equipment is considered as an important issue in many engineering areas. In practice, many engineering fields lease the equipment because it is an economical way to lease the equipment rather than to own the equipment. In addition, as the maintenance actions for the equipment are costly and need a specialist, the lessor is responsible for the maintenance actions in most leased contract. Hence, the lessor should establish the optimal maintenance strategy to minimize the maintenance cost. This paper proposes two periodic preventive maintenance policies for the leased equipment. The preventive maintenance action of policy 1 is performed with a periodic interval, in which their intervals are the same until the end of lease period. The other policy is to determine the periodic preventive maintenance interval minimizing total maintenance cost during the lease period. In addition, this paper presents two decision-making models to determine the preventive maintenance strategy for leased equipment based on the lessor's preference between the maintenance cost and the reliability at the end of lease period. The structural properties of the proposed decision-making model are investigated and algorithms to search the optimal maintenance policy that are satisfied by the lessor are provided. A numerical example is provided to illustrate the proposed model. The results show that a maintenance policy minimizing the maintenance cost is selected as a reasonable decision as the lease term becomes shorter. Moreover, the frequent preventive maintenance actions are performed when the minimal repair cost is higher than the preventive maintenance cost, resulting in higher maintenance cost.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.221-232
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

• The Transactions of the Korea Information Processing Society
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• v.2 no.3
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• pp.314-323
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• 1995

Recently, as users' understanding about software is raised, software developers devoted all their energy to satisfy users' needs. Accordingly, software is getting increase in volume and becoming complicated. As account of it, development and maintenance costs of software have been increased, and a large number of developers were projected in maintenance and development of new software. Software with good maintenability has a function in a module and is easy to read and simple. In this paper, for effective management of software complexity, I designed and implemented the system which accepts source program by input and measures product metrics and produce measurement value of factor items.

• Smart Structures and Systems
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• v.30 no.6
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• pp.571-582
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• 2022

Optimal life-cycle management is a challenging issue for deteriorating regional bridges. Due to the complexity of regional bridge structural conditions and a large number of inspection and maintenance actions, decision-makers generally choose traditional passive management strategies. They are less efficiency and cost-effectiveness. This paper suggests a deep reinforcement learning framework employing double-deep Q-networks (DDQNs) to improve the life-cycle management of deteriorating regional bridges to tackle these problems. It could produce optimal maintenance plans considering restrictions to maximize maintenance cost-effectiveness to the greatest extent possible. DDQNs method could handle the problem of the overestimation of Q-values in the Nature DQNs. This study also identifies regional bridge deterioration characteristics and the consequence of scheduled maintenance from years of inspection data. To validate the proposed method, a case study containing hundreds of bridges is used to develop optimal life-cycle management strategies. The optimization solutions recommend fewer replacement actions and prefer preventative repair actions when bridges are damaged or are expected to be damaged. By employing the optimal life-cycle regional maintenance strategies, the conditions of bridges can be controlled to a good level. Compared to the nature DQNs, DDQNs offer an optimized scheme containing fewer low-condition bridges and a more costeffective life-cycle management plan.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.147-159
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• 2011

Recently, the military service period has been shortened continuously. Meanwhile, more skilled airmen are needed as the complexity of weapon systems increase. This phenomenon could lead to a disastrous result such as deteriorating the level of the readiness and the fighting power. We suggest a method to improve recruit's maintenance capability rapidly by assigning airmen to jobs appropriate to their characteristics using Datamining methods (K-menas and CART). We focus on the assigning method for air force's air-munition maintenance airmen since they are requested more skilled than other airmen. Grouping airmen with k-means method and devising classification rule with CART algorithm, we found that airmen's proficiency arrival period could be shortened by 1.79 months when they are assigned in the suggested way.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.720-731
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• 2001

Complexity metrics have been developed for the structured paradigm of software development are not suitable for use with the object-oriented(OO) paradigm, because they do not support key object-oriented concepts such as inheritance, polymorphism. message passing and encapsulation. There are many researches on OO software metrics such as program complexity or design metrics. But metrics measuring the complexity of classes at the OO analysis phase are needed because they provide earlier feedback to the development project. and earlier feedback means more effective developing and less costly maintenance. In this paper, we propose the new metrics to measure the complexity of analysis classes which draw out in the analysis based on RUP(Rational Unified Process). By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and determine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We verify theoretically the suggested metrics for Weyuker's nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the user using the text mining technique. As a result of the comparison of CC and CBO and WMC suggested by Chidamber and Kemerer, the class that have highly the proposed metric value maintain the high complexity at the design phase too. And the complexity can be represented by CC and IC more than CBO and WMC. We can expect that our metrics may provide us the earlier feedback and hence possible to predict the efforts, costs and time required to remainder processes. As a result, we expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC(Software Development Life Cycle).

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.281-289
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• 2006

Even though software developing environment has been changing to Web basis very fast, there are just few studies of quality metric or estimation model for Web software. In this study after analyzing the correlation between the risk level and property of objects using linear regression, six middle sized industrial system has been used to propose the correlation model of size and Number of Classes(NOC), size and Number of Methods(NOM), complexity and NOC, and complexity and NOM. Among of six systems 5 systems(except S06) have high correlation between size(LOC) and NOM, and four systems(except S04 & S06) have high correlation between complexity and NOC / NOM. As Web software architecture with three sides of Server, Client and HTML, complexity of each sides has been compared, two system(S04, S06) has big differences of each sides compleity values and one system(S06) has very higher complexity value of HTML, So the risk level could be estimated through NOM to improve maintenance in case of that the system has no big differences of each sides complexity.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.