• Speech Sciences
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• v.12 no.4
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.724-732
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• 2016

In this paper, a sampled-data observer-based decentralized fuzzy control technique is proposed for a class of nonlinear large-scale systems, which can be represented to a Takagi-Sugeno fuzzy system. The premise variable is assumed to be measurable for the design of the observer-based fuzzy controller, and the closed-loop system is obtained. Based on an exact discretized model of the closed-loop system, the stability condition is derived for the closed-loop system. Also, the stability condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed techniques.

• The Journal of Engineering Geology
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• v.25 no.3
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• pp.395-401
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• 2015

An investigation of the tunnel face, as well as related measurement data collected during tunneling, is necessary for rock classification and to determine tunnel stability and the cost efficiency of tunneling. However, systematic management and efficient use of such data have yet to be successfully implemented domestically, and the number of experts in this field in Korea is limited. Thus, measures to develop and implement systematic management and effective use of data and expertise are urgently needed. This study aimed to develop measures to efficiently provide online tunnel design and construction data using a building information model (BIM)-based data visualization approach, based on an integrated 3D tunnel model generation module and a web viewer module. The development technology was verified through ○○ tunnel design and construction. Directions for future study and system improvement are proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4228-4236
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• 2015

Due to the rapid advance in web and mobile computing technologies, normal users have become to produce, provide, and share a varied form of spatial data and information. In the domain of spatial information, numerous researches on GIS have been conducted to provide spatial information services based on a geo-sensor network and a data integration and processing technology. However, to provide user-oriented information, a context information model is necessary to associate GIS data with web and sensor data. Context awareness services is designed to provide specific information, minimizing users' interference. For which, the context information model expresses the relationship of various data from sensor networks and mobile applications and provides a user-specific information considering location and area of interest. Thus, this research aims to develops a context information model based air-pollution information system that obtains and analyses air pollution data and reflects the analysis results on an air-pollution policy. Also, this system aims to raise citizens' awareness on air-pollution and to promote citizens' participatory to improve city's air quality.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.2
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• pp.101-114
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• 2016

A model-based systems engineering (MBSE) approach to implementing hardware-based network cybersecurity controls for APR1400 non-safety data network is presented in this work. The proposed design was developed by implementing packet filtering and deep packet inspection functions to control the unauthorized traffic and malicious contents. Denial-of-Service (DoS) attack was considered as a potential cybersecurity issue that may threaten the data availability and integrity of DCS gateway servers. Logical design architecture was developed to simulate the behavior of functions flow. HDL-based physical architecture was modelled and simulated using Xilinx ISE software to verify the design functionality. For effective modelling process, enhanced function flow block diagrams (EFFBDs) and schematic design based on FPGA technology were together developed and simulated to verify the performance and functional requirements of network security controls. Both logical and physical design architectures verified that hardware-based cybersecurity controls are capable to maintain the data availability and integrity. Further works focus on implementing the schematic design to an FPGA platform to accomplish the design verification and validation processes.

• Journal of Information Technology and Architecture
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• v.10 no.3
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• pp.315-321
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• 2013

The ROK military has established the MND-AF (Ministry of National Defense- Architecture Framework) and is applying it when developing architecture for Enterprise (MND), Segment (component) and Solution (weapons and force support system). The AMM (Architecture Meta model) of the MND-AF supports the establishment of interoperability by systematizing the architecture information structure defined by the Architecture artifact, describing the relationship between data and also by liking reference models and Information technology standard, common components, and MND-Meta data. which are interoperability standards. However, the MND-AMM has insufficient linkage with the standards, which places constraints on applying interoperability standards when developing Solution architecture. This study suggests a logical data model (OV-7c) from the MND-Meta-data-based Architecture meta model that will enhance the application of MND meta data managed by the MND interoperability standard when developing Solution architecture.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.113-127
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• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.