• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• Fire Science and Engineering
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• v.29 no.2
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• pp.84-91
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• 2015

The purpose of this study is to suggest the reasonable model of the caliber in suction nozzle, the pressure of suction nozzle, and the flow rate about foam system of line proportioner type using in the pumpcar. To test this, the experimental study was accomplished on the ground of the standards for the Performance Certification and Product Inspection of Foam Fire-extinguishing Chemical Mixing Machine. Aqueous Film Forming Foam in 3% and pipe type air foam nozzle with line proportioner FE 40 type were used. Test result showed that the pressure of suction nozzle within the limits between 0.25 MPa and 0.35 MPa was appropriate when the caliber in suction nozzle is 4 mm. Also, the pressure of suction nozzle within the limits between 0.45 MPa and 0.60 MPa was appropriate in the higher pressure than 4 mm when the caliber in suction nozzle is 5 mm.

• Land and Housing Review
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• v.8 no.3
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• pp.189-200
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• 2017

The work breakdown structure and the precedence relations by work activity are very important because they are the basic data for estimating the construction duration in the construction work. However, there is no standard to accurately estimate the construction duration since the size of the school facilities construction is smaller than the general construction work. Therefore, some schools are unable to open in March or September and the delay of the construction duration can cause damage to the students. To solve this problem, this study developed a work breakdown structure of school facilities construction work and analyzed the precedence relations by work activities. The work breakdown structure of the school facilities construction is composed of three steps. The operations corresponding to level 1 and level 2 are as follows. (1) 2 preparatory work categories; preparation period and temporary construction. (2) 17 architectural work categories; temporary construction, foundation & pile work, reinforced concrete work, steel roof work, brick work, plaster work, tile work, stone work, waterproof construction, wood work, interior construction, floor work, metal work, roof work, windows construction, glazing work and paint construction. (3) 7 mechanic and fire work categories; outside trunk line work, plumbing work, air-conditioning equipment work, machine room work, city gas plumbing work, sanitation facilities and inspection & test working. (4) 4 civil work categories; wastewater work, drainage work, pavement work and other work. (5) 1 landscaping work categories; planting work. The work breakdown structure was derived from interviews with experts based on the milestones and detailed statements of existing school facilities. The analysis of precedence relations by school facilities work activity utilized PDM(Precedence Diagramming Method)which does not need a dummy and the relations were applied using FS(Finish to Start), FF(Finish to Finish), SS(Start to Start), SF(Start to Finish). The analysis of this study shows that if one work activity is delayed, the entire construction duration may be delayed because the majority of the works are FS relations. Therefore, it is necessary to use the Lag at the appropriate time to estimate the standard construction duration of the school facility construction. Lag is a term used only in the PDM method and it is used to define the relationship between the predecessor and the successor in creating the network milestone. And it means the delay time applied to the two work activities. The results of this study can reasonably estimate the standard construction duration of school facilities and it will contribute to the quality of the school facilities construction.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.215-221
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• 2019

The respiratory medical device is a medical device that delivers optimal oxygen or a certain amount of humidification to a patient by delivering artificial respiration to a patient through a machine when the patient has lost the ability to breathe spontaneously. These include respirators for use in chronic obstructive pulmonary disease and anesthesia or emergency situations, and positive airway pressure devices for treating sleep apnea, and as the population of COPD (chronic obstructive pulmonary disease) and elderly people worldwide surge, the market for the respiratory medical devices it is getting bigger. As the demand for both airway pressure devices, there is a problem that the ventilator standard is applied because the reference standard has not been established. Therefore, the boundaries between the items are blurred due to the purpose, intended use, and method of use overlapping similar items in a respiratory medical device. In addition, for both airway pressure devices, there is a problem that the ventilator standard is applied because the reference standard has not been established. Therefore, in this study, we propose clear classification criteria for the respiratory medical devices according to the purpose, intended use, and method of use and provide safety and performance evaluation guidelines for those items to help quality control of the medical devices. And to contribute to the rapid regulating and improvement of public health. This study investigated the safety and performance test methods through the principles of the respiratory medical device, national and international standards, domestic and international licensing status, and related literature surveys. The results of this study are derived from the safety and performance test items in the individual ventilator(ISO 80601-2-72), the International Standard for positive airway pressure device (ISO 80601-2-70), The safety and performance of humidifiers (ISO 80601-2-74) and the safety evaluation items related to home healthcare environment (IEC 60601-1-11), In addition, after reviewing the guidelines drawn up through expert consultation bodies including manufacturers and importers, certified test inspection institutions, academia, etc., the final guidelines were established through revision and supplementation. Therefore, in this study, we propose guidelines for evaluating the safety and performance of the respiratory medical device in accordance with growing technology development.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.93-108
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• 2023

In manufacturing productivity analysis, understanding the intricate interplay among factors like facility performance, layout design, and workforce allocation within the production line is imperative. This paper introduces a simulation-based methodology tailored to food manufacturing, progressively expanding scenarios to analyze production enhancement. The target system is a food processing plant, encompassing production processes, including warehousing, processing, subdivision, packaging, inspection, loading, and storage. First, we analyze the target system and design a simulation model according to the actual layout arrangement of equipment and workers. Then, we validate the developed model reflecting the real data obtained from the target system, such as the workers' working time and the equipment's processing time. The proposed model aims to identify optimal factor values for productivity gains through incremental scenario comparisons. To this end, three stages of simulation experiments were conducted by extending the equipment and worker models of the subdivision and packaging processes. The simulation experiments have shown that productivity depends on the placement of skilled workers and the performance of the packaging machine. The proposed method in this study will offer combinations of factors for the specific production requirements and support optimal decision-making in the real-world field.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.27-43
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• 2024

The market share of online platform services in the used car market continues to expand. And The used car online platform service provides service users with specifications of vehicles, accident history, inspection details, detailed options, and prices of used cars. SUV vehicle type's share in the domestic automobile market will be more than 50% in 2023, Sales of Hybrid vehicle type are doubled compared to last year. And these vehicle types are also gaining popularity in the used car market. Prior research has proposed a used car price prediction model by executing a Machine Learning model for all vehicles or vehicles by brand. On the other hand, the popularity of SUV and Hybrid vehicles in the domestic market continues to rise, but It was difficult to find a study that proposed a used car price prediction model for these vehicle type. This study selects a used car price prediction model by vehicle type using vehicle specifications and options for Sedans, SUV, and Hybrid vehicles produced by domestic brands. Accordingly, after selecting feature through the Lasso regression model, which is a feature selection, the ensemble model was sequentially executed with the same sampling, and the best model by vehicle type was selected. As a result, the best model for all models was selected as the CBR model, and the contribution and direction of the features were confirmed by visualizing Tree SHAP Value for the best model for each model. The implications of this study are expected to propose a used car price prediction model by vehicle type to sales officials using online platform services, confirm the attribution and direction of features, and help solve problems caused by asymmetry fo information between them.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.91-111
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• 2024

Most of utility cable tunnels are constructed utilizing shield TBM as part of the underground transmission line project. The TBM chamber is the only space inside the tunnel that encounters rock and soil, and is the place with the highest frequency of accident exposure, such as collapse and collision accidents. Since there is currently no way to measure the disc cutter wear from outside the chamber, frequent inspection by workers is essential. Accordingly, in this study, in order to prevent safety accidents inside the TBM chamber and expect the effect of shortening the construction period by reducing the number of chamber openings, the concept of disk cutter wear measurement technology was established and a prototype was produced. By considering prior technology and determining that magnetic sensors are most suitable for the excavation environment, wear measurement sensor package were developed integrating magnetic sensors, wireless communication modules, power supply, external casing, and monitoring systems. To verify the performance of the prototype in an actual excavation environment, a full-scale tunnelling test was performed using a 3.6 m EPB shield TBM. Based on the full-scale tests, five prototypes were operated normally among eight prototypes. It was analyzed that sensor measurement, wireless communication, and durability performance were secured within a maximum thrust of 3,000 kN and a rotation speed of 1.5 RPM.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.