• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• Proceedings of The Korean Society of Health Promotion Conference
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• 1999.07a
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• pp.129-147
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• 1999

The National Health Promotion Act passed in 1995 was a milestone for initiating a national and local health promotion program in Korea. And since then local governments and health centers have been developing and providing health promotion programs for the community population. To apply the effectiveness of community health promotion program, it is important to understand the key issue related to health education and the role of health education personnel. The purpose of this study was to define the responsibility and competency of health education specialist, and to develop the activity areas of health promotion program in Korea. Those who provide the service for health promotion and health education should be properly qualified and professionally trained. However, the skills and responsibilities of those who are in charge of providing health education program have not yet been clearly defined in Korea because the areas of health promotion and health education are composed of multi-academic fields. In case of United States, health education specialist is being developed through professional preparation in colleges and graduate schools, and certified through the examination. Also health education specialist is in charge of the planing, implementing and evaluation of health education program in school, hospital, health center, workplace and health food company. Therefore it is important to develop the programs to train and certify health education specialist. Also to extend the activity areas, the government should support continuously program development for health promotion and health education personnel.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.11
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• pp.9-16
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• 2011

We need to establish standard for the ICT based on train control system. In order to solve the ISI problem, this paper evaluate the performance of OFDM and FDE system. We seem that OFDM system is better than FDE system. In order to solve ISI problem, SC System is needed a equalizer. And another method is OFDM System. If system is used SC with a equalizer, It is better than OFDM in terms of PAPR, but this system is not easy to use Multi-Antenna technique, i.e., beam-forming and MIMO-multiplexing. And If system is used high-order modulation, BER performance is worse than OFDM. If we think about in terms of PAPR problem, considerations are considered not significant because the size of relays is not considered in the communication between trains and ground.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.367-370
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• 2016

QFN(Quad Flat No-leads package) is one of the SMD(Surface Mount Device). Since there is no lead in QFN, there are many defects on solder. Therefore, we propose an efficient mechanism for QFN solder defect detection at this paper. For this, we employ Convolutional Neural Network(CNN) of the Machine Learning algorithm. QFN solder's color multi-layer images are used to train CNN. Since these images are 3-channel color images, they have a problem with applying to CNN. To solve this problem, we used each 1-channel grayscale image(Red, Blue, Green) that was separated from 3-channel color images. We were able to detect QFN solder defects by using this CNN. Later, further research is needed to detect other QFN.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.7
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• pp.711-716
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• 2012

The purpose of this study was to investigate how the modulation method affects the effectiveness of eliciting tactile sensations by electrical stimulation. Two methods were employed and the results were compared and analyzed; pulse amplitude modulation (PAM) and pulse width modulation (PWM). Thirty-five healthy subjects participated in the experiments to measure the stimulation intensity that began to elicit a tactile sensation - activation threshold (AT). Constant-current monophasic rectangular pulse trains were employed, and the stimulation intensity was varied from zero until the subject felt any uncomfortable sensation. The step size of the stimulation intensity was 100nC/pulse. After each experiment, the subject described the sensation both quantitatively and qualitatively. The two modulation methods did not make a significant difference as far as the AT values were concerned, but most of the subjects showed 'intra-individual' consistency. Also, it was confirmed that our range of the stimulation parameters enabled us to obtain three major tactile sensations; tickling, pressure and vibration. The results suggested that the stimulation parameters and the modulation type should be selected for each individual and that selective electrical stimulation of the mechanoreceptors needs more diversified researches on the electrode design, multi-channel stimulation protocol, waveforms of the pulse train, etc.

• Journal of KIISE
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• v.42 no.9
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• pp.1124-1132
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• 2015

In order to train a supervised machine learning algorithm, it is necessary to have non-biased labels and a sufficient amount of training data. However, it is difficult to collect the required non-biased labels and a sufficient amount of training data to develop an automatic English Composition scoring system. In addition, an English writing assessment is carried out using a multi-faceted evaluation of the overall level of the answer. Therefore, it is difficult to choose an appropriate machine learning algorithm for such work. In this paper, we show that it is possible to alleviate these problems through ensemble learning. The results of the experiment indicate that the ensemble technique exhibited an overall performance that was better than that of other algorithms.

• KNOM Review
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• v.23 no.1
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• pp.34-42
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• 2020

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

• Steel and Composite Structures
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• v.47 no.2
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• pp.167-184
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• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.

• Phonetics and Speech Sciences
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• v.14 no.4
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• pp.35-43
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• 2022

This paper studies how to improve the identification rate of laryngeal disability speech data by convolutional neural network (CNN) and machine learning ensemble learning methods. In general, the number of laryngeal dysfunction speech data is small, so even if identifiers are constructed by statistical methods, the phenomenon caused by overfitting depending on the training method can lead to a decrease the identification rate when exposed to external data. In this work, we try to combine results derived from CNN models and machine learning models with various accuracy in a multi-voting manner to ensure improved classification efficiency compared to the original trained models. The Pusan National University Hospital (PNUH) dataset was used to train and validate algorithms. The dataset contains normal voice and voice data of benign and malignant tumors. In the experiment, an attempt was made to distinguish between normal and benign tumors and malignant tumors. As a result of the experiment, the random forest method was found to be the best ensemble method and showed an identification rate of 85%.