• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.1-4
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• 2000

In this paper, A useful nonlinear function for the RBF(Radial Basis Function) equalization is proposed. This proposed function need not calculate an exponential function that is generally used for conventional RBF equalizer and uses the only four rules of arithmetic. Therefore the computational requirement for the RBF equalizer with the proposed function is decreased. As a computer simulation result, the equalizer with the proposed function effectively reduce nonlinear intersymbol interference, caused by nonlinear communication channel.

• Journal of Hospice and Palliative Care
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• v.19 no.1
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• pp.1-4
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• 2016

As advance care planning is taking center stage in the field of end-of-life care, various tools have been developed to aid in the often emotional and difficult decision-making process. Video decision support tools are one of the most promising means of assistance, of which the modus operandi is to provide more comprehensive and precise information of medical procedures to patients and their families, allowing them to make better informed decisions. Despite such value, some are concerned about its potential negative impact. For example, video footages of some procedures may be shocking and unpalatable to non-medical professionals, and patients and families may refuse the procedures. One approach to soften the sometimes unpleasant visual of medical procedures is to show less aggressive or more relaxing scenes. Yet another potential issue is that the objectivity of video decision support tools might be vulnerable to the very stakeholders who were involved in the development. Some might argue that having multiple stakeholders may function as checks and balances and provide collective wisdom, but we should provide more systematic guarantee on the objectivity of the visual decision aids. Because the decision of the modality of an individual's death is the last and most significant choice in one's life, no party should exert their influence on such a delicate decision. With carefully designed video decision support tools, our patients will live the last moments of their lives with dignity, as they deserve.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.642-652
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• 2007

Many contracting firms and project managers in the construction industry have started to utilize multi objective optimization methods to handle multiple conflicting goals for completing the project within the stipulated time and budget with required quality and safety. These optimization methods have increased the pressure on decision makers to search for an optimal resources utilization plan that optimizes simultaneously the total project cost, completion time, and crashing cost by considering indirect cost, contractual penalty cost etc., practically charging them in terms of direct cost of the project which is fuzzy in nature. This paper presents a multiple fuzzy goal programming model (MFGP) that supports decision makers in performing the challenging task. The model incorporates the fuzziness which stems from the imprecise aspiration levels attained by the decision maker to these objectives that are quantified through fuzzy linear membership function. The membership values of these objectives are then maximized which forms the fuzzy decision. The problem is solved using LINGO 8 optimization solver and the best compromise solution is identified. Comparison between solutions of MFGP, fuzzy multi objective linear programming (FMOLP) and multiple goal programming (MGP) are also presented. Additionally, an interactive decision making process is developed to enable the decision maker to interact with the system in modifying the fuzzy data and model parameters until a satisfactory solution is obtained. A case study is considered to demonstrate the feasibility of the proposed model for optimization of project network parameters in the construction industry.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.141-152
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• 2006

This paper presents the development of a decision model to examine the optimal repair action for a deteriorating system. In order to make a reasonable decision, it is necessary to perform an analysis of the uncertainties embedded in deterioration and to evaluate the repair actions based on the expected future cost. Focusing on the power law failure model, the uncertainties related to deterioration are analyzed based on the Bayesian approach. In addition, we develop a decision model for the optimal repair action by applying a repair cost function. A case study is given to illustrate a decision-making process by analyzing the loss incurred due to deterioration.

• Korean System Dynamics Review
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• v.16 no.2
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• pp.75-100
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• 2015

The purpose of this study is to analyze causation of Ecosystem service's trade-off(ES trade-off) and to establish baseline data for wise spatial planning and management. In order to understand why and how ES trade-off occurs, systems thinking and causal loops were employed. The causal loop of ecosystem service creation cycle includes profits quantification process, decision making process, spatial planning and management process, and ecosystem services creation process. The profits quantification process has a limitation that all ecosystem service categories were not included in profits quantification, because quantification method for cultural services is insufficient. These problems led to unequal discussion opportunity in decision making process. ES trade-off occurs through transition of ecosystem function in spatial scale and temporal scale. In spatial scale, land-use variation and resource-use variation contribute to change an ecosystem function for different ES category by spatial planning and management. In temporal scale, a change of an ecosystem function for different ES category is influenced by ecological succession, seasonal change and land cover variation, which are parameter from environmental features. This study presented that spatial planning and management should ecosystem service assessment in order to enhance balanced ecosystem services.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.91-102
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• 2000

Nowadays, every kind of system is changed so complex and enormous, it is necessary to assure system reliability, product liability and safety. Fault tree analysis(FTA) is a reliability/safety design analysis technique which starts from consideration of system failure effect, referred to as “top event”, and proceeds by determining how these can be caused by single or combined lower level failures or events. So in fault tree analysis, it is important to find the combination of events which affect system failure. Minimal cut sets(MCS) and minimal path sets(MPS) are used in this process. FTA-I computer program is developed which calculates MCS and MPS in terms of Gw-Basic computer language considering Fussell's algorithm. FTA-II computer program which analyzes importance and function cost of VE consists. of five programs as follows : (l) Structural importance of basic event, (2) Structural probability importance of basic event, (3) Structural criticality importance of basic event, (4) Cost-Failure importance of basic event, (5) VE function cost analysis for importance of basic event. In this study, a method of initiation such as failure, function and cost in FTA is suggested, and especially the priority rank which is calculated by computer-aided decision analysis program developed in this study can be used in decision making determining the most important basic event under various conditions. Also the priority rank can be available for the case which selects system component in FMEA analysis.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.224-228
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• 2007

It is focused on a methodology to establish a optimal inspection cycle of electrical facility of railroad Decision method of optimal inspection cycle is a process which establishes maintenance plan for facilities' immanent function as using reliability theory in operation term In order to ensure normal operation in a given condition, the decision method is logical for selecting effective maintenance plan to consider characteristic of system In estimation of failure rate, critical facility is selected firstly. After that, proper distribution function on each facility is decided to investigate distribution function for extraction of failure rate. Next, cost produced by the case that facility's failure is occurred is surveyed. Finally, maintenance method developed until now is investigated, before suitable model for the facility applying maintenance method is developed, and that maintenance decision is made. Therefore, this process is the method to find optimal inspection cycle for reasonable cost and effective reliability on facility.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.35-40
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• 2014

Many researches have been conducted to achieve improvement in distributed system that connects multiple computer systems via communication lines. Among others, the load balancing and file migration are considered to have significant impact on the performance of distributed system. The dynamic file migration algorithm common in distributed processing system involved complex calculations of decision function necessary for file migration and required migration of control messages for the performance of decision function. However, the performance of this decision function puts significant computational strain on computer. As one single network is shared by all computers, more computers connected to network means migration of more control messages from file migration, causing the network to trigger bottleneck in distributed processing system. Therefore, it has become imperative to carry out the research that aims to reduce the number of control messages that will be migrated. In this study, the learning automata was used for file migration which would requires only the file reference-related information to determine whether file migration has been made or determine the time and site of file migration, depending on the file conditions, thus reflecting the status of current system well and eliminating the message transfer and additional calculation overhead for file migration. Moreover, mathematical model for file migration was described in order to verify the proposed model. The results from mathematical model and simulation model suggest that the proposed model is well-suited to the distributed system.

• ETRI Journal
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• v.39 no.5
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• pp.621-631
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• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.