• Journal of the Korea Convergence Society
• /
• v.6 no.5
• /
• pp.241-248
• /
• 2015

Nursing clinical practice, especially because it is required to reproduce this fusion education is very urgent. This Study was done to examine the effect of action learning techniques in simulation class. The study was designed using a nonequivalent control group pretest-posttest design. The participants consisted of control group 92, experimental group 92. The data analyzed using SPSS 18.0 program. Professional self-concept are higher than in the control group were measured.(t=-5.118, p=>.001). communication competence and self-directed learning capability of experimental group increased significantly from those control group. This result means that can help to significantly improve the professional nursing students learning techniques to simulate the application of an action class. In other words, if the act of creative training techniques such as future action learning hands-on training to be a big help.

• Journal of information and communication convergence engineering
• /
• v.8 no.4
• /
• pp.421-426
• /
• 2010

This paper presents a method of design the counter using sequential system based on synchronous techniques. For the design the counter, first of all, we derive switching algebras and their operations. Also, we obtain the next-state functions, flip-flop excitations and their input functions from the flip-flop. Then, we propose the algorithm which is a method of implementation of the synchronous sequential digital logic circuits. Finally, we apply proposed the sequential logic based on synchronous techniques to counter.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.149-149
• /
• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• Journal of Korea Multimedia Society
• /
• v.20 no.11
• /
• pp.1811-1819
• /
• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• Journal of Korea Multimedia Society
• /
• v.22 no.2
• /
• pp.136-145
• /
• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Journal of the Korean Society of Industry Convergence
• /
• v.15 no.1
• /
• pp.29-36
• /
• 2012

Reinforced concrete shear walls and X-type steel braces were applied in seismic retrofit techniques for seismic performance evaluation of school buildings constructed in accordance with standard design announced by the ordinance of the ministry of construction in 1980s. Seismic performance evaluation was based on FEMA 356 using response spectrum as elastic analysis and conducted to pushover analysis with nonlinear static analysis. The maximum displacement ratio between floors in 4th and 3rd floors of the existing school buildings was less than 1.0%, which was functioning level in FEMA 356. However, because plastic hinge occurs somewhat in structural members according to the results of pushover analysis, partial reinforcement will be required. X-direction of the maximum lateral displacement of reinforced concrete shear walls than X-type steel braces was 45% and 32% in 4th and 3rd floors of school buildings, and Y-direction was 18% and 17%, respectively.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1015-1033
• /
• 2020

With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics. The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.

• Journal of the Korean Society of Industry Convergence
• /
• v.18 no.1
• /
• pp.45-52
• /
• 2015

In this paper, the repeated learning technique of neural network was used for gripping force control algorithm. The hybrid control system was introduced and the manipulator's finger reorganized form 2 ea to 3 ea for comfortable gripping. The data was obtained using the gripping force of repeated learning techniques. In the fucture, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.

• Journal of Information Technology Services
• /
• v.18 no.2
• /
• pp.123-141
• /
• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Mass Spectrometry Letters
• /
• v.3 no.1
• /
• pp.4-9
• /
• 2012