• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.161-171
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• 2009

In this paper, we propose an automatic method for setting data constraints of a data cleansing service, which is for managing the quality of data exchanged between composite services based on SOA(Service-Oriented Architecture) and enables to minimize human intervention during the process. Because it is impossible to deal with all kinds of real-world data, we focus on business data (i.e. costumer order, order processing) which are frequently used in services such as CRM(Customer Relationship Management) and ERP(Enterprise Resource Planning). We first generate an extended-element vector by extending semantics of data exchanged between composite services and then build a rule-based system for setting data constraints automatically using the decision tree learning algorithm. We applied this rule-based system into the data cleansing service and showed the automation rate over 41% by learning data from multiple registered services in the field of business.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.6
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• pp.411-423
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• 2020

To remove phosphorus from the effluent of public wastewater treatment facilities, hundreds of enhanced phosphorus treatment processes have been introduced nationwide. However, these processes have a few problems including excessive maintenance cost and sludge production caused by inappropriate coagulant injection. Therefore, the optimal decision of coagulant dosage and automatic control of coagulant injection are essential. To overcome the drawbacks of conventional phosphorus removal processes, the integrated sedimentation and dissolved air flotation(SeDAF) process has been developed and a demonstration plant(capacity: 100 ㎥/d) has also been installed. In this study, various jar-tests(sedimentation and / or sedimentation·flotation) and multiple regression analyses have been performed. Particularly, we have highlighted the decision-making algorithms of optimal coagulant dosage to improve the applicability of the SeDAF process. As a result, the sedimentation jar-test could be a simple and reliable method for the decision of appropriate coagulant dosage in field condition of the SeDAF process. And, we have found that the SeDAF process can save 30 - 40% of coagulant dosage compared with conventional sedimentation processes to achieve total phosphorus (T-P) concentration below 0.2 mg/L of treated water, and it can also reduce same portion of sludge production.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4C
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• pp.241-253
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• 2011

In this paper, a CFNC (Complex Field Network Coding) scheme is presented, which focuses on improving transmission efficiency by reducing time slots that are needed to exchange data frames. Conventional CFNC scheme has the advantage of minimizing the number of time slots required for information exchanges. However, there exists serious performance deterioration because the transmit signals are interfered with each other. Moreover, when CFNC scheme is applied, the estimation and compensation performance of fine frequency offset severely deteriorates due to the multiple frequency offsets in received signal of relay node. In order to overcome these critical problems, we propose an improved CFNC scheme with modified maximum likelihood decision method which uses uplink transmit diversity. Also, we propose an enhanced fine frequency offset estimation method and a corresponding compensation method to deal with the multiple frequency offsets. Corresponding simulation results verify that the proposed methods are able to effectively solve the problems of CFNC scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.15-20
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• 2016

The existing multiple modulus-based self-recovering equalization type has not been applied to initial equalization. Instead, it was used for steady-state performance improvement. In this paper, for the self-recovering equalization type that considers the multiple modulus as a desired response, the initial convergence performance was improved by extending the dynamics of the errors using error boosting and their characteristics were analyzed. Error boosting in the proposed method was carried out in proportion to a symbol decision for the equalizer output. Furthermore, having the initial convergence capability by extending the dynamics of errors, it showed excellent performance in the initial convergence rate and steady-state error level. In particular, the proposed method can be applied to the entire process of equalization through a single algorithm; the existing methods of switching over or the selection of other operation modes, such as concurrent operating with other algorithms, are not necessary. The usefulness of the proposed method was verified by simulations performed under the channel conditions with multipath propagation and additional noise, and for performance analysis of self-recovering equalization for high-order signal constellations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.115-122
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• 2006

The most methods for intrusion detection are based on the misuse detection which accumulates hewn intrusion information and makes a decision of an attack against any behavior data. However it is very difficult to detect a new or modified aoack with only the collected patterns of attack behaviors. Therefore, if considering that the method of anomaly behavior detection actually has a high false detection rate, a new approach is required for very huge intrusion patterns based on sequence. The approach can improve a possibility for intrusion detection of known attacks as well as modified and unknown attacks in addition to the similarity measurement of intrusion patterns. This paper proposes a method which applies the multiple sequence alignments technique to the similarity matching of the sequence based intrusion patterns. It enables the statistical analysis of sequence patterns and can be implemented easily. Also, the method reduces the number of detection alerts and false detection for attacks according to the changes of a sequence size.

• International Journal of Highway Engineering
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• v.7 no.4 s.26
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• pp.21-30
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• 2005

Traffic volume data have been used for the plan, the design, and the operation of highway. Since 1955, traffic survey has been nation- widely carried out at national highway and the regular survey in national highway has been conducted at the intersections of highways. However, it is critical issue to select the priority of the regular survey because it is almost impossible to conduct regular survey at all intersections of national highways. In this study, MCDM(Multiple Criteria Decision Making) using AHP(Analytic Hierarchy Process) was applied to decide the priority of the regular survey. The following standard variables for determining the priority was selected the highway plan variables[AADT, VKT, Peak Hourly Volume, Location of highway from Urban], the highway design variables[Volume(pcu), Directional Traffic Volume, Heavy Vehicle Rate], and the highway operation variables[Speed, Density, V/C]. The standard variables were quantified and normalized. Using the Eigen vector method, the weighted values of each hierarchy based on the pair-wise comparison values from the questionnaire survey were calculated. The selection of the priority of regular survey was dependent on the size of the product of the weighted values for each hierarchy and the normalized values for the standard variables. Finally, the priority of regular survey of the intersections of national highways was determined according to the order in the size of the product of two values.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2001.11a
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• pp.99-117
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• 2001

In the past, The selection of individual method of construction was done by head of construction site or an experienced person very frequently. By doing this, The wrong selection of construction method without exact adjudication of construction site situation lead to increasing of cost and extension of construction term. Finally it will effect all over the construction process. Especially, In case of Underground construction in the beginning, there are a lot of a variable factor and it also effect on the entire construction process and it need rely careful process. The purpose of this study is to present the best suitable methodology fer selection of construction method by considering potential risk of construction method and variables together with external condition for Underground construction. The purpose of this study is to select the most suitable construction method by analysing potential conditions(Construction site situation and Client. Request in designing) To do this, We prepared arrangement rule to arrangement conditions for construction method. And then make Checklist the analyzing construction method. Though above process, To expect the risk of individual construction method using above risk checklist and using Analytic Hierarchy Process among Multiple-Criteria Decision Making, the professional opinions is to be adapted. By doing this, It can lead and select the most suitable considering method considering the data which get from risk density test.

• Spatial Information Research
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• v.21 no.2
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• pp.23-33
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• 2013

W ith the recent advancement of IoT (Internet of Things) technology, there has been much interest in the spatial query processing which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. Therefore, various kinds of spatial query processing algorithms and distributed spatial indexing methods have been proposed. They can minimize energy consumption of sensor nodes by reducing wireless communication among them using in-network spatial filtering technology. However, they cannot optimize multiple spatial queries which w ill be w idely used in IoT, because most of them have focused on a single spatial query optimization. Therefore, we propose a new multiple spatial query optimization algorithm which can energy-efficiently process multiple spatial queries in a sensor network. The algorithm uses a concept of 'query merging' that performs the merged set after merging multiple spatial queries located at adjacent area. Here, our algorithm makes a decision on which is better between the merged and the separate execution of queries. For such the decision making, we additionally propose the cost estimation method on the spatial query execution. Finally, we analyze and clarify our algorithm's distinguished features using the spatial indexing methods of GR-tree, SPIX, CPS.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.563-577
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• 2006

A stream processor uses resource sharing method for efficient of limited resource in multiple continuous queries. The previous methods process aggregate queries to consist the level structure. So insert operation needs to reconstruct cost of the level structure. Also a search operation needs to search cost of aggregation information in each size of sliding windows. Therefore this paper uses linear structure for optimization of sliding window aggregations. The method comprises of making decision, generation and deletion of panes in sequence. The decision phase determines optimum pane size for holding accurate aggregate information. The generation phase stores aggregate information of data per pane from stream buffer. At the deletion phase, panes are deleted that are no longer used. The proposed method uses resources less than the method where level structures were used as data structures as it uses linear data format. The input cost of aggregate information is saved by calculating only pane size of data though numerous stream data is arrived, and the search cost of aggregate information is also saved by linear searching though those sliding window size is different each other. In experiment, the proposed method has low usage of memory and the speed of query processing is increased.