• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.108-116
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• 2019

In this paper, we propose a method to apply artificial intelligence technology efficiently to integrated security control technology. In other words, by applying machine learning learning to artificial intelligence based on big data collected in integrated security control system, cyber attacks are detected and appropriately responded. As technology develops, many large capacity Is limited to analyzing individual logs. The analysis method should also be applied to the integrated security control more quickly because it needs to correlate the logs of various heterogeneous security devices rather than one log. We have newly proposed an integrated security service model based on artificial intelligence, which analyzes and responds to these behaviors gradually evolves and matures through effective learning methods. We sought a solution to the key problems expected in the proposed model. And we developed a learning method based on normal behavior based learning model to strengthen the response ability against unidentified abnormal behavior threat. In addition, future research directions for security management that can efficiently support analysis and correspondence of security personnel through proposed security service model are suggested.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.24-27
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• 2001

Nowadays, increasing demand of the customized products has led to an increased usage of rapid prototyping in the product development. However, the acquisition price of a rapid prototyping equipment is still too high that not every body can afford to buy one. To offer a wide access to the users who need physical prototypes, a connection of the rapid prototyping equipment to the Internet is a viable step. It would allow a large group of customers all over the world to use the manufacturing capability of a service provider offering this kind of manufacturing service. To realize how such an e-manufacturing concept can look like, a LOM-type 3D printer developed at KIST has been used as test site and connected to the Internet. A possible user can log on to the server of the equipment and view his STL file and start the building operation from a remote place. To see whether the operation runs properly, a CCD camera is used to transmit the actual state of operation online. The result so far proves the feasibility of rapid prototyping on the Internet as well as an order-adaptive manufacturing system via web.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• Transactions of The Korea Fluid Power Systems Society
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• v.4 no.3
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• pp.13-21
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• 2007

A forklift is a motive machine powered by LPG, diesel engine or electric motors. The internal combustion engine type forklift is equipped with automatic transmission to meet the required drive load as well as the easy operation of the vehicle. This paper deals with the shift control and endurance test controller which is developed for the functional test of the newly designed automatic transmission on a dynamometer test bench. Its major function is to control the proportional solenoid currents, which is directly related to clutch pressures, for the given reference current trajectory during shift and sequential operation of shift schedule designed for the durability test at each gear. It also has the ability to monitor all the necessary test data through RS232 communication and log them to disk files. The current controller of embedded system is designed from the identified dynamics of solenoid coil and the current reference can be easily modified with a user interface software on PC so as to match the shift data by experiments.

• Journal of Korean Society of Forest Science
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• v.96 no.4
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• pp.448-454
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• 2007

Mathematical models for predicting the ground and semi-ground skidding force have been developed. The skidding force is expressed as a function of log geometry, total weight and coefficient of skidding. The coefficient of skidding was determined under field tests. The validity of the model developed was examined by comparing the predicted and measured skidding forces. Calculated ground skidding force, using the model developed can be predicted well with that measured experimentally. The semi-ground skidding force calculated from the model, however, does not predict well due to its confined conditions experimentally.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• Neonatal Medicine
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• v.18 no.2
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• pp.301-309
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• 2011

Purpose: Point-of-care tests (POCTs) have the potential to significantly influence management of neonates. The aim of this study was to assess the clinical usefulness of the POCT chemistry analyzer in a neonatal intensive care unit (NICU). Methods: Blood samples of neonates admitted to the NICU were tested using a POCT chemistry analyzer (Piccolo Xpress Chemistry Analyzer, Abaxis, Union City, CA, USA) and a central laboratory chemical analyzer (Chemistry analyzer 7600-110, Hitachi Ltd., Tokyo, Japan) from March to September, 2010. Correlation of 15 analytes between the POCT and the central laboratory machine was evaluated. For consistency of the POCT, three consecutive samplings were performed. Differences among the three tests were recorded. The causes of performance errors were checked through log files. Results: One hundred of 112 pairs of tests for accuracy performed in 54 neonates showed a high correlation between the two machines. Twelve performance errors occurred during the 112 tests. The most common error was insufficient sample error. Eighteen triplet tests performed in 18 patients for consistency revealed a difference range of 3-10%, which was considered to be acceptable. No error occurred during the 54 tests. Conclusion: The POCT is capable of analyzing multiple analytes with a minimal amount of whole blood in a short time. The few performance errors noted presently are likely preventable. This POCT is concluded to be suitable for use as a simple and rapid diagnostic method in the NICU with a minimal amount of blood collected in a less invasive manner.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.