• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.143-151
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• 2008

Execute IP traceback at this paper as target an intruder's attacking that Bypass Attack in order to avoid an exposure of own Real IP address Design IP traceback server and agent module, and install in Internet network system for Real IP traceback. Set up detection and chase range aggressive loop around connection arbitrariness, and attack in practice, and generate Real IP data cut off by fatal attacks after data and intrusion detection accessed general IP, and store to DB. Generate the Forensic data which Real IP confirms substance by Whois service, and ensured integrity and the reliability that buy to early legal proof data, and was devoted to of an invader Present the cyber criminal preventive effect that is dysfunction of Ubiquitous Information Society and an effective Real IP traceback system, and ensure a Forensic data generation basis regarding a judge's robe penalty through this paper study.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.297-304
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• 2003

Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.187-196
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• 2006

In 3D animation most people adjust the moving motion of their characters on various terrains by using motion data acquired with the motion capture equipment. The motion data can be used to present real human motions naturally, but the data must be captured again to apply to the different terrains from those given at acquiring mode. In addition, there would be a difficulty when applying the data to other characters, in that case the motion data must be captured newly or the existing motion data must be heavily edited manually. In this paper we propose a unified method to generate human motions of walking and running for various terrains such as flat plane, inclined plane, stairway and irregular face. With these methods we are able to generate human motions controlled by the parameters : body height, moving speed, stride, etc. In the proposed methods, the positions and angles of joint can be calculated by using inverse kinematics, and we calculate the trajectory of the swing leg and pelvis according to the cubic spline. With these methods we were presented moving motions using a model of a human body.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.228-233
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• 2021

In this study, we analyze the credit information (loan, delinquency information, etc.) of individual business owners to generate voluminous training data to establish a bankruptcy prediction model through a partial synthetic training technique. Furthermore, we evaluate the prediction performance of the newly generated data compared to the actual data. When using conditional tabular generative adversarial networks (CTGAN)-based training data generated by the experimental results (a logistic regression task), the recall is improved by 1.75 times compared to that obtained using the actual data. The probability that both the actual and generated data are sampled over an identical distribution is verified to be much higher than 80%. Providing artificial intelligence training data through data synthesis in the fields of credit rating and default risk prediction of individual businesses, which have not been relatively active in research, promotes further in-depth research efforts focused on utilizing such methods.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.343-354
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• 2023

Recently, there have been many improvements in general language models using architectures such as GPT-3 proposed by Brown et al. (2020). Nevertheless, training complex models can hardly be done if the number of data is very small. Data augmentation that addressed this problem was more than normal success in image data. Image augmentation technology significantly improves model performance without any additional data or architectural changes (Perez and Wang, 2017). However, applying this technique to textual data has many challenges because the noise to be added is veiled. Thus, we have developed a novel method for performing data augmentation on text data. We divide the data into signals with positive or negative meaning and noise without them, and then perform data augmentation using k-doc augmentation to randomly combine signals and noises from all data to generate new data.

• Proceedings of the Acoustical Society of Korea Conference
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• 1987.11a
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• pp.82-85
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• 1987

Preliminary results of using the LPC parameter for text-independent speaker identification problem are presented. The idetification process includes log likelihood ratio for distance measure and dynamic programming for time normalization. To generate the data base for experiments, ten times. Experimental results show 99.4% of identification accuracy, incorrect identification were made when the speaker uses a dialect.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.507-512
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• 1987

Input/output equations in SDINS IMU are modeled from survey of IMU data flow. Given without precise equipments which can generate acceleration and angular velocity, a simple method is derived to calibrate the parameters of i/o eqijations. Also in order to upgrade ins performance, methods to estimate variant magnitudes of time variant parameters are surveyed.

• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.417-425
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• 2016

Especially for resolving BIM data compatibility issue, as one of recently raised BIM technology issues, has also been improved by using open BIM, representatively using IFC (Industry Foundation Classes) format. As shown in many case studies, usefulness of BIM technology is increasing day by day, and the IFC-based open BIM technology is essential in recent AEC projects where the productive collaboration is of importance. One of current problems in actual projects is that there is a conflict between conventional ways and newly developed BIM ways. Using both conventional and new technologies leads construction workers to having more work loads, consequently the efficiency and productivity of on-site workers have been decreased. Thus, it is strongly necessary to facilitate 3D BIM models to extract and generate 2D precision drawings in automation, especially using open BIM technologies. Some native BIM authoring tools have limitations in there automatic generation of 2D drawings, and there is no standardized mechanism to generate 2D drawings from heterogeneous applications. For this reason, this paper aims to develop an automated stand-alone program to generate 2D drawings in automation using IFC file, totally independent from various BIM authoring tools. By using the application described in this paper, any type of general drawings such as plan, section and elevation can be extracted without additional efforts. The development approach described in this paper, based on the open BIM technologies, has a strong impact to the current process especially in the perspective of enhancing productivity when we need to find out a trade-off in-between conventional and new approaches.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.45-56
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• 2015

In this paper, we present a method that can detect context-sensitive word errors and generate correction candidates. Spelling error detection is one of the most widespread research topics, however, the approach proposed in this paper is adjusted for an automated English scoring system. A common strategy in context-sensitive word error detection is using a pre-defined confusion set to generate correction candidates. We automatically generate a confusion set in order to consider the characteristics of sentences written by second-language learners. We define a word error that cannot be detected by a conventional grammar checker because of part-of-speech ambiguity, and propose how to detect the error and generate correction candidates for this kind of error. An experiment is performed on the English writings composed by junior-high school students whose mother tongue is Korean. The f1 value of the proposed method is 70.48%, which shows that our method is promising comparing to the current-state-of-the art.