• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.81-91
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• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.257-262
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• 2015

As software grows more complex, it contains more bugs that are not recognized by developers. Attackers can then use exploitable bugs to penetrate systems or spread malicious code. As a representative method, attackers manipulated documents or multimedia files in order to make the software engage in unanticipated behavior. Recently, this method has gained frequent use in A.P.T. In this paper, an automatic analysis method to find software security bugs was proposed. This approach aimed at finding security bugs in the software which can arise from input data such as documents or multimedia. Through dynamic taint analysis, how input data propagation to vulnerable code occurred was tracked, and relevant instructions in relation to input data were found. Next, the relevant instructions were translated to a formula and vulnerable input data were found via the formula using an SMT solver. Using this approach, 6 vulnerable codes were found, and data were input to crash applications such as HWP and Gomplayer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.603-613
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• 2015

Smart-phone Applications are consists of UI(User Interface). During using applications, UI events such as button click and scroll down are transmitted to Smart-phone system with many changes of UI. In these UI events, various information including user-input data are also involved. While Keylogging, which is a well-known user-input data acquisition technique, is needed a restrictive condition like rooting to obtain the user-input data in android environment, UI events have advantage which can be easily accessible to user-input data on user privileges. Although security solutions based keypad in several applications are applied, we demonstrate that these were exposed to vulnerability of application security and could be obtained user-input data using UI events regardless of presence of any security system. In this paper, we show the security threats related information disclosure using UI events and suggest the alternative countermeasures by showing the replay-attack example based scenarios.

• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.77-84
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• 1997

This paper is concerning the development of multiple neural networks system of problem domains where the complete input space can be decomposed into several different regions, and these are known prior to training neural networks. We will adopt oblique decision tree to represent the divided input space and sel ect an appropriate subnetworks, each of which is trained over a different region of input space. The overall architecture of multiple neural networks system, called the federated architecture, consists of a facilitator, normal subnetworks, and tile networks. The role of a facilitator is to choose the subnetwork that is suitable for the given input data using information obtained from decision tree. However, if input data is close enough to the boundaries of regions, there is a large possibility of selecting the invalid subnetwork due to the incorrect prediction of decision tree. When such a situation is encountered, the facilitator selects a tile network that is trained closely to the boundaries of partitioned input space, instead of a normal subnetwork. In this way, it is possible to reduce the large error of neural networks at zones close to borders of regions. The validation of our approach is examined and verified by applying the federated neural networks system to the configuration design of a midship structure.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.1 s.20
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• pp.39-48
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• 2005

After the development of the Firefly, flight tests have been performed to verify the performance and get the parameters for the mathematical model of the aircraft. The flight test data is used to get parameters for the mathematical model of the aircraft through the parameter identification process. An arbitrary control input is applied to the test flight which is a part of parameter identification process. A square wave has been used a control input which is called Doublet signal. The aspect of the signal is same length and magnitude in both (+) and (-) directions such as sine wave. The Doublet signal is composed of a dominant frequency and many high frequencies, so that it is appropriate signal to excite the motion of an aircraft. In this paper, the control input of the flight test data has been analyzed to check the efficiency of the control input using DFT(Discrete Fourier Transform). From the result of analysis, an alternative input was extracted.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Asia pacific journal of information systems
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• v.7 no.3
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• pp.167-180
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• 1997

Data semantics is an well recognized issue in areas of information systems research. It provides indispensable information for management of data, It describes what data mean, how they are created, where they can be applied to, to name a few. Because of these diverse nature of data semantics, it has been described from different perspectives of formalization. This article proposes to formalize data semantics by the processes that data are created or transformed, A scheme is proposed to describe the structure that data are created and transformed, which is called Bill of Data. Bill of Data is a directed graph, whose leaves are primary input data and whose internal nodes are output data objects produced from input data objects. Using Bill of Data, algorithms are developed to compare data semantics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1401-1414
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• 2018

The k-means clustering algorithm groups input data with the number of groups represented by variable k. In fact, this algorithm is particularly useful in market segmentation and medical research, suggesting its wide applicability. In this paper, we propose a privacy-preserving clustering algorithm that is appropriate for outsourced encrypted data, while exposing no information about the input data itself. Notably, our proposed model facilitates encryption of all data, which is a large advantage over existing privacy-preserving clustering algorithms which rely on multi-party computation over plaintext data stored on several servers. Our approach compares homomorphically encrypted ciphertexts to measure the distance between input data. Finally, we theoretically prove that our scheme guarantees the security of input data during computation, and also evaluate our communication and computation complexity in detail.