• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.128-133
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• 1998

This paper aims to present some ideas for implementation of Uncertain Relational Databases (URD) which are extensions of classical relational databases. Our system firstly is based on possibility distribution and probability theory to represent and manipulate fuzzy and probabilistic information, secondly adopts flexible mechanisms that allow the management of uncertain data through the resources provided by both available relational database management systems and front-end interfaces, and lastly chooses dynamic SQL to enhance versatility and adjustability of systems.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.261-270
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• 2006

Positional data of moving objects can be regularly sampled in order to minimize the cost of data collection in LBS. Since position data which are regularly sampled cannot include the changes of position occurred between sampling periods, sampled position data differ from the data predicted by a time parameterized linear function. Uncertain position data caused by these differences make the accuracy of the range queries for present positions diminish in the TPR tree. In this paper, we propose the uncertainty region to handle the range queries for uncertain position data. The uncertainty region is defined by the position data predicted by the time parameterized linear function and the estimated uncertainty error. We also present the weighted recent uncertainty error policy and the kalman filter policy to estimate the uncertainty error. For performance test, the query processor based by the uncertainty region is implemented in the TPR tree. The experiments show that the Proposed query processing methods are more accurate than the existing method by 15%.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.317-327
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• 2024

Along with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm.

• The Journal of Industrial Distribution & Business
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• v.10 no.1
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• pp.51-58
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• 2019

Purpose - Manufacturers in uncertain environments need to depend on governance mechanisms to reduce the inherent risk in these environments. However, few studies have examined which governance mechanisms a given manufacturers will develop in uncertain environments for managing the relationships with its vertical partner. This study explores how different governance mechanisms function under uncertain environmental circumstances. We also try to investigate the contextual effect of interfirm benevolence as moderator. Research design, data, and methodology - This research provide the conceptual framework of interfirm benevolence on which this research's propositions are predicted. The theoretical background for environmental uncertainty, governance mechanisms and interfirm benevolence will be discussed. Results - The expected results are as follows. Manufacturers in an uncertain environments rely on different governance mechanisms under conditions of high and low interfirm benevolence. In terms of role of interfirm benevolence, interfirm benevolence provides a better understanding of how governance mechanisms can develop in an uncertain supply markets. Conclusions - This research suggests several theoretical and practical implications between channel partners, particularly, this research offers that interfirm benevolence is a crucial competitive factor under environmental uncertainty situation. In future studies, it is necessary to investigate the effect of each governance mechanism structure on performance in an uncertain environment and various level of interfirm benevolence.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.689-696
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• 2007

This paper considers a Bayesian approach to determine an optimal PM policy with random maintenance quality. Thus, we assume that the quality of a PM action is a random variable following a probability distribution. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal PM policy. Finally, the numerical examples are presented for illustrative purpose.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1758-1763
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• 2004

This paper focuses on congestion control for ATM network with uncertain time-variant delays. The time-variant delays can be distinguished into two distinct components. The first one is represented by time-variant queueing delays in the intermediate switches that are occurred in the return paths of RM cells. The next one is a forward path delay. It is solved by the VBR model which quantifies the data propagation from the sources to the switch. Robust $H_8$ control is studied for solving congestion problem with norm-bounded time-varying uncertain parameters. The suitable robust $H_8$ controller is obtained from the solution of a convex optimization problem through LMI technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2986-2999
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• 2019

The uncertainties of the uncertain graph make the traditional definition and algorithms on mining dense graph for certain graph not applicable. The subgraph obtained by maximizing expected density from an uncertain graph always has many low edge-probability data, which makes it low reliable and low expected edge density. Based on the concept of ${\beta}$-subgraph, to overcome the low reliability of the densest subgraph, the concept of optimal ${\beta}$-subgraph is proposed. An efficient greedy algorithm is also developed to find the optimal ${\beta}$-subgraph. Simulation experiments of multiple sets of datasets show that the average edge-possibility of optimal ${\beta}$-subgraph is improved by nearly 40%, and the expected edge density reaches 0.9 on average. The parameter ${\beta}$ is scalable and applicable to multiple scenarios.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.3
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• pp.280-289
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• 2008

This paper presents design of the quantitative feedback control system of the three axes hydraulic road simulator with respect to the dummy wheel for uncertain multiple input-output(MIMO) feedback systems. This simulator has the uncertain parameters such as fluid compressibility, fluid leakage, electrical servo components and nonlinear mechanical connections. This works have reproduced the random input signal to implement the real road vibration's data in the lab. The replaced $m^2$ MISO equivalent control systems satisfied the design specifications of the original $m^*m$ MIMO control system and developed the mathematical method using quantitative feedback theory based on schauder's fixed point theorem. This control system illustrates a tracking performance of the closed-loop controller with low order transfer function G(s) and pre-filter F(s) having the minimum bandwidth for parameters of uncertain plant. The efficacy of the designed controller is verified through the dynamic simulation with combined hydraulic model and Adams simulator model. The Matlab simulation results to connect with Adams simulator model show that the proposed control technique works well under uncertain hydraulic plant system. The designed control system has satisfied robust performance with stability bounds, tracking bounds and disturbance. The Hydraulic road simulator consists of the specimen, hydraulic pump, servo valve, hydraulic actuator and its control equipments

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.139-158
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• 2007

Information sharing is key to effective supply chain management. In reality, however, it is impossible to get perfect information. Accordingly, only uncertain information can be accessed in business environment, and thus it is important to deal with the uncertainties of information in managing supply chains. This study adopts meteorological forecast as a typical uncertain information. The meteorological events may affect the demands for various weather-sensitive goods, such as beer, ices, clothes, electricity etc. In this study, a beer distribution game is modified by introducing meterological forecast information provided in a probabilistic format. The behavior patterns of the modified beer supply chains are investigated. for two conditions using the weather forecast with or without an information sharing. A value score is introduced to generalize the well-known performance measures employed in the study of supply chains, i.e.. inventory, backlog, and deviation of orders. The simulation result showed that meterological forecast information used in an information sharing environment was more effective than without information sharing, which emphasizes the synergy of uncertain information added to the information sharing environment.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.703-705
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• 2004

Conventional support vector machines (SVMs) find optimal hyperplanes that have maximal margins by treating all data equivalently. In the real world, however, the data within a data set may differ in degree of uncertainty or importance due to noise, inaccuracies or missing values in the data. Hence, if all data are treated as equivalent, without considering such differences, the optimal hyperplanes identified are likely to be less optimal. In this paper, to more accurately identify the optimal hyperplane in a given uncertain data set, we propose a membership-induced distance from a hyperplane using membership values, and formulate three kinds of membership-induced SVMs.