• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.2
• /
• pp.18-27
• /
• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• Journal of Ocean Engineering and Technology
• /
• v.33 no.3
• /
• pp.222-228
• /
• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.9
• /
• pp.57-64
• /
• 2021

In this paper, we propose a device that can control the primary control surface of an aircraft by recognizing speech commands. The speech command consists of 19 commands, and a learning model is constructed based on a total of 2,500 datasets. The training model is composed of a CNN model using the Sequential library of the TensorFlow-based Keras model, and the speech file used for training uses the MFCC algorithm to extract features. The learning model consists of two convolution layers for feature recognition and Fully Connected Layer for classification consists of two dense layers. The accuracy of the validation dataset was 98.4%, and the performance evaluation of the test dataset showed an accuracy of 97.6%. In addition, it was confirmed that the operation was performed normally by designing and implementing a Raspberry Pi-based control device. In the future, it can be used as a virtual training environment in the field of voice recognition automatic flight and aviation maintenance.

• Journal of IKEEE
• /
• v.25 no.1
• /
• pp.193-200
• /
• 2021

In order to prevent illegal fishing and reduce lost fishing gear, it is necessary to develop a constant and continuous fishing gear monitoring system in the marine environment. In this paper, we design a long-term operational, reliable system model with communication coverage of more than 25Km considering the reality of gradually expanding fishing activity due to the depletion of fishery resources and marine environments. The design results are implemented to verify the operability of the system by separating the communication success rate of SKT and private LoRa networks and verifying the control function of each control system through the collected location information, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.146-152
• /
• 2022

Information collected from temperature, humidity, inclination, and pressure sensors using Raspberry Pi or Arduino is used in automatic constant temperature and constant humidity systems. In addition, by using it in the agricultural and livestock industry to remotely control the system with only a smartphone, workers in the agricultural and livestock industry can use it conveniently. In general, temperature and humidity are expressed in a line graph, etc., and the change is monitored in real time. The technology to visually express the temperature has recently been used intuitively by using an infrared device to test the fever of Corona 19. In this paper, the information collected from the Raspberry Pi and the DHT11 sensor is used to predict the temperature change in space through intuitive visualization and to make a immediate response. To this end, an algorithm was created to effectively visualize temperature and humidity, and data representation is possible even if some sensors are defective.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.151-158
• /
• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• Imaging Science in Dentistry
• /
• v.52 no.1
• /
• pp.43-51
• /
• 2022

Purpose: The aim of this study was to evaluate the reliability of the Vectra M3 (3D Imaging System; Canfield Scientific, Parsippany, NJ, USA) in detecting chin asymmetry, and to assess whether the automatic markerless tracking function is reliable compared to manually plotting landmarks. Materials and Methods: Twenty subjects (18 females and 2 males) with a mean age of 42.5±10.5 years were included. Three-dimensional image acquisition was carried out on all subjects with simulated chin deviation in 4 stages (1-4 mm). The images were analyzed by 2 independent observers through manually plotting landmarks and by Vectra software auto-tracking mode. Repeated-measures analysis of variance and the Tukey post-hoc test were performed to evaluate the differences in mean measurements between the 2 operators and the software for measuring chin deviation in 4 stages. The intraclass correlation coefficient (ICC) was calculated to estimate the intra- and inter-examiner reliability. Results: No significant difference was found between the accuracy of manually plotting landmarks between observers 1 and 2 and the auto-tracking mode (P=0.783 and P=0.999, respectively). The mean difference in detecting the degree of deviation according to the stage was <0.5 mm for all landmarks. Conclusion: The auto-tracking mode could be considered as reliable as manually plotted landmarks in detecting small chin deviations with the Vectra^® M3. The effect on the soft tissue when constructing a known dental movement yielded a small overestimation of the soft tissue movement compared to the dental movement (mean value<0.5 mm), which can be considered clinically non-significant.

• Journal of Aerospace System Engineering
• /
• v.16 no.5
• /
• pp.70-77
• /
• 2022

This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.2
• /
• pp.9-14
• /
• 2022

In this study, we propose a method for moving a device such as a flexible air sculpture while stably maintaining the user's desired posture. To accomplish this, a robot system with a structure of a mobile robot capable of running according to a given trajectory was studied by applying the PI algorithm and horizontal maintenance posture control using IMU. The air sculptures used in this study often use thin strings in a fixed posture. Another method is to put a load on the center of gravity to maintain the posture, and it is a system with flexibility because it uses air pressure. It is expected that these structures can achieve various results by combining flexible structures and mobile robots through the convergence process of digital sensor technology. In this study, posture control was performed by fusion of the driving technology of AGV(Automatic Guided Vehicle),, a field of robot, and technologies applying various sensors. For verification, the given performance evaluation was performed through an accredited certification test, and its validity was verified through an experiment.

• The Korea Journal of Herbology
• /
• v.34 no.3
• /
• pp.37-44
• /
• 2019

Objectives : Acne is a common disease that affects more than 70% of adolescents. Acne patients have a poor quality of life compared to patients with other skin diseases. We tried to demonstrate the effectiveness of acne treatment using cleansing soap which is easily used in daily life. Methods : We selected 20 students with acne symptoms on their facial skin. We made herbal Cp (Cold process) soaps using Seosiokyongsan, Kyungohkgo, Hwangryunhaedoktang and Baeksoooh and distributed them to experiment participants. We let them wash their face in the morning and evening for 6 weeks using herbal Cp soap. Prior to the experiment, their skin condition was checked and assessed using A-ONE Smart One-Click Automatic Facial Diagnosis System three times at 3-week intervals. Acne status was classified into 6 stages according to KAGS and acne status was also measured 3 times in total. After the experiment, the changes of skin were analyzed through facial analysis test. Results : Based on the KAGS classification, the condition of acne has improved as a whole. The state of moisture was gradually increased and the state of skin oil was significantly decreased after 6 weeks of using soap compared to before using soap. Conclusions : Cp soaps made from four kinds of herbal medicine are believed to improve the condition of acne by increasing the moisture of the facial skin and decreasing the skin oil content.