• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.10
• /
• pp.309-316
• /
• 2020

We predict the mortality of the elderly patients visiting the emergency department who are over 65 years old using Feed Forward Neural Network (FFNN) and Convolutional Neural Network (CNN) respectively. Medical data consist of 99 features including basic information such as sex, age, temperature, and heart rate as well as past history, various blood tests and culture tests, and etc. Among these, we used random forest to select features by measuring the importance of features in the prediction of mortality. As a result, using the top 80 features with high importance is best in the mortality prediction. The performance of the FFNN and CNN is compared by using the selected features for training each neural network. To train CNN with images, we convert medical data to fixed size images. We acquire better results with CNN than with FFNN. With CNN for mortality prediction, F1 score and the AUC for test data are 56.9 and 92.1 respectively.

• Journal of the Korean Society for Railway
• /
• v.12 no.5
• /
• pp.641-648
• /
• 2009

Concrete structures become more common in railway systems with an advancement of high speed train technologies. As the service life of concrete structures increases, structural strengthening for concrete structures may be necessary. There are several typical strengthening techniques using steel plate and fiber reinforced polymer (FRP) materials, which have their own inherent shortcomings. In order to enhance greater durability and resistance to fire and other environmental attacks, basalt fiber material attracts engineer's attention due to its characteristics. This study investigates bonding performance of basalt fiber sheet as a structural strengthening material. Experimental variables include bond width, length and number of layer. From the bonding tests, there were three different types of bonding failure modes: debonding, rupture and rip-off. Among the variables, bond width indicated more significant effect on bonding characteristics. In addition the bond length did not contribute to bond strength in proportion to the bond length. Hence this study evaluated effective bond length and effective bond strength. The effective bond strength was compared to those suggested by other researches which used different types of FRP strengthening materials such as carbon FRP.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.4
• /
• pp.259-266
• /
• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of Digital Convergence
• /
• v.16 no.12
• /
• pp.293-299
• /
• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.4
• /
• pp.42-49
• /
• 2018

Pilot Aptitude Research Equipment (PARE) is a simulator developed to measure or research pilot aptitude and train for student pilots. Design of an ergonomic PARE operation console is required to operate the equipment effectively. This study carried out five steps : (S1) operator questionnaire survey, (S2) anthropometric design formula development, (S3) usability evaluation, (S4) improvement design, and (S5) validation considering both Physical User Interface (PUI) and Graphic User Interface (GUI) of PARE operation console. The operator questionnaire surveyed needs for each PUI and GUI part of the console from two PARE actual operators. In terms of PUI, the anthropometric design formula was developed by using design variables, body dimensions, target population characteristics, and reference posture related to the PARE console. In terms of GUI, the usability evaluation was conducted by three usability testing experts with a 7-point scale (1 : very low, 4 : neutral, 7 : very high) on GUI of the PARE operation console by seven usability criteria. The improved PARE operation console was designed to reflect the optimal values of design variables calculated from design formula, the results from usability testing, and the operator's needs. The improvement effect was observed by 20 people who had experience with the PARE operation console. As a result of the validation, monitor visibility and cockpit visibility for the improved PUI design and visibility and efficiency for the improved GUI design were significantly increased by more than 90% respectively. The improved design of the PARE operation console in this study can contribute to enhance operation performance of the PARE.

• Korean Journal of Plant Resources
• /
• v.35 no.2
• /
• pp.192-200
• /
• 2022

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.341-348
• /
• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.2
• /
• pp.369-390
• /
• 2023

Technologies for disaster management are highly sought areas for research and commercial deployment. Landslides, Flood, cyclones, earthquakes, forest fires and road/train accidents are some causes of disasters. Capturing video and accessing data in real time from the disaster site can help first responders make split second decisions which may save human lives and valuable resource destructions. In this context the communication technologies performing the task should have high bandwidth and low latency which only 5G can deliver. But unfortunately in India, deployment of the 5G mobile communication systems is yet to give a shape and again in remote areas unavailability of 4G signals is still severe. In this situation the authors have proposed, simulated and experimented a 4G-5G communication scheme where from the disaster site the signals will be transmitted by a 5G terminal to a nearby 4G-5G gateway installed in a mobile vehicle. The received 5G signal will be further relayed by the 4G-5G gateway to the fixed 4G base station for onward transmission towards the disaster management station for decision making, deployment and relief monitoring. The 4G-5G gateway acts as a relay and converter of 5G signal to 4G signal and vice versa. This relayed system can be further mounted on a vehicle mounted relay (VMR) as proposed by 3GPP in Release 18. The scheme is also in the same line of context with Verizon's, "Tactical Humanitarian Operations Response" (THOR) vehicle concept. The performance of the link is studied in different channel conditions, the throughput achieved is superb. The authors have implemented the above mentioned system towards smart campus networking and monitoring landslides activities which are common in their regions.

• Geomechanics and Engineering
• /
• v.31 no.6
• /
• pp.545-556
• /
• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.34-34
• /
• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.