• The KIPS Transactions:PartD
• /
• v.13D no.7 s.110
• /
• pp.997-1008
• /
• 2006

For extracting and processing information explicitly from given data, Formal Concept Analysis(FCA) is provided a method which is widely used for data analysis and clustering. The data can be structured into concepts, which are formal abstractions human thought allowing meaningful comprehensible interpretation. However, most FCA tools mainly focus on analyzing one-valued contexts that represent objects, attributes and binary relations between them. There we few FCA tools available that provide scaling and analyzing many-valued contexts representing objects, attributes and relations with attributes' values. In this paper, we propose not only a scaling algorithm for interpreting and simplifying the multivalued input data, but also an algorithm to generate concepts and build concept hierarchy from given raw data as well. Based on these algorithms, we develop an automate tool, FCA Wizard, for concept analysis and concept hierarchy. We also present FCA Wizard based applications in medical domain.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.5
• /
• pp.999-1008
• /
• 2017

In this paper, we propose a pipeline network coding (PNC) scheme for efficient data transmission in wireless networks, a data authentication scheme for verifying the integrity of data, and a node authentication scheme for a virtual source. PNC is a technique that improves the overall network performance by relaying data such that the relay node performing network coding transmits to the sender instead. However, network coding is vulnerable to a pollution attack, which is an attack by a malicious attacker to inject modified data into the network. To prevent this, hash-based message authentication code (HMAC) is used. For this purpose, in order to generate a tag used for data authentication, a key must be distributed to the nodes performing authentication. We applied a hash chain to minimize the overhead of key distribution. A null vector is used as the authentication scheme for the virtual source. Finally, we analyze the safety and complexity of the proposed scheme and show he performance through simulation.

• International Journal of Automotive Technology
• /
• v.5 no.1
• /
• pp.33-46
• /
• 2004

A finite element model development of a 50th percentile male cervical spine is presented in this paper. The model consists of rigid, geometrically accurate vertebrae held together with deformable intervertibral disks, facet joints, and ligaments modeled as a series of nonlinear springs. These deformable structures were rigorously tuned, through failure, to mimic existing experimental data; first as functional unit characterizations at three cervical levels and then as a fully assembled c-spine using the experimental data from Duke University and other data in the NHTSA database. After obtaining satisfactory validation of the performance of the assembled ligamentous cervical spine against available experimental data, 22 cervical muscle pairs, representing the majority of the neck's musculature, were added to the model. Hill's muscle model was utilized to generate muscle forces within the assembled cervical model. The muscle activation level was assumed to be the same for all modeled muscles and the degree of activation was set to correctly predict available human volunteer experimental data from NBDL. The validated model is intended for use as a post processor of dummy measurement within the simulated injury monitor (SIMon) concept being developed by NHTSA where measured kinematics and kinetic data obtained from a dummy during a crash test will serve as the boundary conditions to "drive" the finite element model of the neck. The post-processor will then interrogate the model to determine whether any ligament have exceeded its known failure limit. The model will allow a direct assessment of potential injury, its degree and location thus eliminating the need for global correlates such as Nij.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.4
• /
• pp.1248-1266
• /
• 2012

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.3
• /
• pp.41-50
• /
• 2012

In the monitoring system based on web service application, it is faced with the problems like code reproduction, difficult scalability and error recovery derived from the frequent change of data structure. So we propose a technique of monitoring system by DTD(Document Type Definition) automatic generation. This technique is to use dynamic server-side script to cope with the change of sensor data structure, generate the DTD dynamically. An it also adapt the AJAX(Asynchronous JavaScript and XML) for XML data parsing, it can support mass data transmission and exception processing for data loss and damage. This technique shows the result of recovery time is decreased about 44.8ms in case of temporary data failure by comparing to the conventional XML method.

• The Journal of Information Systems
• /
• v.18 no.4
• /
• pp.59-86
• /
• 2009

Data modeling can be regarded as a series of processes to abstract real-world business concerns. The conceptual modeling phase is often regarded as the most difficult stage in the entire modeling process, because quite different conceptual models may be produced even for similar business domains based on users' varying requirements and the data modelers' diverse perceptions of the requirements. This implies that an object considered as an entity in one domain may be considered as an attribute in another, and vice versa. However, many traditional knowledge-based automated database design systems unfortunately fail to construct appropriate Entity-Relationship Diagrams(ERDs) for a given set of requirements due to the rigid assumption that an object should be classified as an entity if it has been classified as an entity in previous applications. In this paper, we propose an alternative automation system which can generate ERDs from business descriptions using association rule mining technique. Our system can be differentiated from the traditional ones in that our system can perform data modeling only based on business description written by domain workers, rather than relying on any kind of knowledge base. Since the proposed system can produce various versions of ERDs from the same business descriptions simultaneously, users can have the opportunity to choose one of the ERDs as being the most appropriate, based on their business environment and requirements. We performed a case study for personnel management in a university to evaluate the practicability of the proposed system This paper summarizes the result of it in the experiment section.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.05a
• /
• pp.266-273
• /
• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

• KIISE Transactions on Computing Practices
• /
• v.22 no.5
• /
• pp.201-208
• /
• 2016

Collection and storing of multiple time series data in real time requires large memory space. To solve this problem, the usage of varying sample size is proposed in the compression scheme using discrete cosine transform technique. Time series data set has characteristics such that a higher compression ratio can be achieved with smaller amount of value changes and lower frequency of the value changes. The coefficient of variation and the variability of the differences between adjacent data elements (VDAD) are presumed to be very good measures to represent the characteristics of the time series data and used as key parameters to determine the varying sample size. Test results showed that both VDAD-based and the coefficient of variation-based scheme generate excellent compression ratios. However, the former scheme uses much simpler sample size decision mechanism and results in better compression performance than the latter scheme.

• Journal of Korea Multimedia Society
• /
• v.16 no.1
• /
• pp.67-74
• /
• 2013

In this paper, we propose the pointing and correction algorithm for optimized performance based on Bluetooth communication. The error from the accelerometer sensor's output must be carefully managed as the accelerometer sensor is more sensitive to data change compared to that of the gyroscope sensor. Thus, we minimize the noise by applying the Kalman filter to data for each axis from the accelerometer. In addition, we can also obtain effect compensating the hand tremor by applying the Kalman filter to the data variation for x and y. In this study, we extract data through the Quaternion mapping process on data from the accelerometer and gyroscope. In turn, we can obtain a tilt compensation by applying a compensation algorithm with acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensor due to the rapid movement of a device, we propose a adaptive pointing and correction algorithm using the genetic approach to generate the initial population depending on the user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.235-239
• /
• 2010

Use of mobile phones reached saturation point, the recent use of the iPhone, including the Smart Phone is increasing rapidly. How to extract forensic data from current mobile phones and SYN, JTAG, Revolving There are three ways. Mobile phone and Smart Phone, but the technology and how to use forensic data because of the difference must have different extraction methods. In this paper, in the Smart Phone will study how to extract forensic data. Commonly used in the Google Android Smart Phone and Windows Mobile Smart Phone OS in the specification and analysis for analysis, the data analysis. Also, Google Android and Windows Mobile Smart Phone to extract forensic data to generate evidence. The present study tested the Mobile Smart Phone technology research will contribute to the development of forensic techniques.