• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.449-456
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• 2022

This study proposes a deep learning algorithm to predict crop yield using GIS (Geographic Information System) to extract soil properties from Soilgrids and soil suitability class maps. The proposed model modified the structure of a published CNN-RNN (Convolutional Neural Network-Recurrent Neural Network) based crop yield prediction model suitable for the domestic crop environment. The existing model has two characteristics. The first is that it replaces the original yield with the average yield of the year, and the second is that it trains the data of the predicted year. The new model uses the original field value to ensure accuracy, and the network structure has been improved so that it can train only with data prior to the year to be predicted. The proposed model predicted the yield per unit area of autumn cabbage for kimchi by region based on weather, soil, soil suitability classes, and yield data from 1980 to 2020. As a result of computing and predicting data for each of the four years from 2018 to 2021, the error amount for the test data set was about 10%, enabling accurate yield prediction, especially in regions with a large proportion of total yield. In addition, both the proposed model and the existing model show that the error gradually decreases as the number of years of training data increases, resulting in improved general-purpose performance as the number of training data increases.

• Korean small business review
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• v.42 no.4
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• pp.251-283
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• 2020

The purpose of this study is to provide assistance to the establishment of related policies to improve the level of acceptance and use of smart factories for SMEs in Korea. To this end, the Unified Technology Acceptance Model (UTAUT) was extended to select additional factors that could affect the intention to accept technology, and to demonstrate this. To achieve the research objective, a questionnaire composed of 7-point Likert scales was prepared, and a survey was conducted for manufacturing-related companies. A total of 136 questionnaires were used for statistical processing. As a result of the hypothesis test, performance expectation and social influence had a positive (+) positive effect on voluntary use, but effort expectation and promotion conditions did not have a significant effect. As an extension factor, the network effect and organizational characteristics had a positive (+) effect, and the innovation resistance had a negative effect (-), but the perceived risk had no significant effect. When the size of the company is large, the perceived risk and innovation resistance are low, and the level of influencing factors for veterinary intentions, veterinary intentions, and veterinary behaviors are excluded. Through this study, factors that could have a positive and negative effect on the adoption (reduction) of smart factory-related technologies were identified and factors to be improved and factors to be reduced were suggested. As a result, this study suggests that smart factory-related technologies should be accepted.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.751-757
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• 2022

Objective : Tumours of the brain are a rare occurrence accounting for approximately 2% of all neoplasms in adults. Few studies have been done in Nigeria on the profile of brain tumours. The aim of this study is to determine the profile of brain tumours in general and determine the change in Kanofsky Performance Score (KPS) after treatment. Methods : This is a prospective hospital-based study in Kaduna. All consecutive patients over 18 years of age with diagnosis of brain tumours from January 2016 to December 2019 were included in the study. Demographic and clinical data was collected using a proforma during the study. Patients who received treatment were followed up for 12 months. The primary outcome data was the difference in the quality of life as measured by KPS at the point of first contact and at 1-month after treatment and at 12-month follow up. Data obtained was analysed with SPSS version 25.0 for Windows. Descriptive statistics was done to determine the profile. Paired t-test at 95% confidence interval was done to check for significant correlation between the mean KPS. Results : A total of 39 consecutive patients were included in the study. There was a slight male preponderance with a M : F of 1.17 : 1. Meningioma and metastasis were more common in females while gliomas and pituitary tumours were more common in males. The mean age of patients was 49.8 years and standard deviation of 11.8 years. Pituitary tumours were the most common tumours. The most common location of the tumour was frontal lobe followed by the pituitary gland. The mean duration of symptoms before neurosurgical consultation was 38 weeks. The most common presenting symptoms of patient with brain tumour was headache. The quality of life improve compare to the baseline in 81% of patient at discharge and at 1 year follow up. The overall mortality rate was 25.6%. Conclusion : The most common brain tumour in our study is pituitary tumour. Most patients present late. The most common presenting symptoms is headache. There is significant improvement in the KPS of patients following treatment. The overall mortality rate at 1-year post treatment is 25.6%.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.869-875
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• 2009

Safety is a big issue in the construction sites. For safe and secure management, tracking locations of construction resources such as labors, materials, machineries, vehicles and so on is important. The materials, machineries and vehicles could be controlled by computer, whereas the movement of labors does not have fixed pattern. So, the location and movement of labors need to be monitored continuously for safety. In general, Global Positioning System(GPS) is an opt solution to obtain the location information in outside environments. But it cannot be used for indoor locations as it requires a clear Line-Of-Sight(LOS) to satellites Therefore, indoor location monitoring system could be a convenient alternative for environments such as tunnel and indoor building construction sites. This paper presents a case study to investigate feasibility of Wi-Fi based indoor location monitoring system in construction site. The system is developed by using fingerprint map of gathering Received Signal Strength Indication(RSSI) from each Access Point(AP). The signal information is gathered by Radio Frequency Identification (RFID) tags, which are attached on a helmet of labors to track their locations, and is sent to server computer. Experiments were conducted in a shield tunnel construction site at Guangzhou, China. This study consists of three phases as follows: First, we have a tracking test in entrance area of tunnel construction site. This experiment was performed to find the effective geometry of APs installation. The geometry of APs installation was changed for finding effective locations, and the experiment was performed using one and more tags. Second, APs were separated into two groups, and they were connected with LAN cable in tunnel construction site. The purpose of this experiment was to check the validity of group separating strategy. One group was installed around the entrance and the other one was installed inside the tunnel. Finally, we installed the system inner area of tunnel, boring machine area, and checked the performance with varying conditions (the presence of obstacles such as train, worker, and so on). Accuracy of this study was calculated from the data, which was collected at some known points. Experimental results showed that WiFi-based indoor location system has a level of accuracy of a few meters in tunnel construction site. From the results, it is inferred that the location tracking system can track the approximate location of labors in the construction site. It is able to alert the labors when they are closer to dangerous zones like poisonous region or cave-in..

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.31-41
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• 2016

This study was conducted to develop a handy earth auger for use in sloppy and rugged forest terrains in order to reduce labor cost which comprises a major part of the production costs in forest afforestation projects. The first prototype is developed consist of two parts, the soil-digging screw and the battery power source. The specifications of the first prototype screw are: length of 170mm, a top diameter of 60mm, bottom diameter of 47mm, 23° angle for each helix, and a 50mm awl-head tip. The use of a single line of screw was selected for reduced weight. In addition, a power source of rotary DC Motor(WD-6G2425, WONILL, Korea) with a maximum torque of 30kgf-cm, rotation of 20-30rpm, K6G30C decelerator with a reduction ratio of 30:1 which could be used with no load for 48 was operated. In consideration of its weight, a lithium battery was utilized in line with the goal of developing a lightweight auger. In order to evaluate the performance of the first prototype, test sites were selected as 6 areas. The rotational force was found to be highest in area A(Solid area), followed by areas F(Mounted slope 40° area) and E(Mounted slope 30° area). It was also observed that in general, the rotational force increased along with the increase in soil depth with the maximum rotational force recorded at 10cm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2A
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• pp.169-176
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• 2010

Generally, poly-urea is widely used as waterproof coating material due to its superior adhesiveness, elongation capacity, and permeability resistance. In addition, it can be quickly and easily applied on structure surfaces using spray application. Since it hardens in about 30 seconds after application, its construction efficiency is very high and its usage as a special functional material is also excellent. However, currently, poly-urea is mostly used as waterproof coating material and the researches on its usage as a retrofitting material is lacking at best. Therefore, basic studies on the use of poly-urea as a general structural retrofitting material are needed urgently. The objective of this study is to develop most optimum poly-urea composition for structure retrofitting purpose. Moreover, the structural strengthening capacity of the developed poly-urea is evaluated through flexural capacity experiments on RC beams and RC slabs. From the results of the flexural test of poly-urea strengthened RC beam and slab specimens, the poly-urea and concrete specimen showed monolithic behavior where ductility and ultimate strength of the poly-urea strengthened specimen showed slight increase. However, the doubly reinforced specimens with FRP sheet and poly-urea showed lower capacity than that of the specimen reinforced only with FRP sheet.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.551-561
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• 2019

The purpose of this study was to investigate the relationship between entrance exam stress and oral care self-efficacy in girls high school. From June to July 2018, A self-reported questionnaire was administered to 192 high school students in the G area. The data were analyzed for independent t-test, one-way ANOVA and Pearson's correlation coefficient by using SPSS/WIN 21.0 program. Among the sub-sectors of entrance exam stress, exam tension/poor was the highest with 3.07 points, followed by Insufficient spare time stress 2.83 points, future uncertainty stress 2.57 points, and parent pressure stress 2.44 points. the variables related to exam tension/poor stress were academic performance (p<.01), family income level (p<.05), Subjective oral health status(p<.05), and daily brushing frequency(p<.01). The highest level of oral care self-efficacy was 3.13 points for brushing self-efficacy, followed by dental visits 2.80 points and interdental hygiene 2.64 points. As a result of analyzing the general characteristics and oral care self-efficacy, subjects related to brushing self-efficacy were subjective oral health status, caries snacking(daily), and caries drinks(daily)(p<.01). There were negative correlations between entrance exam stress and oral care self-efficacy. The higher the entrance stress, the lower the oral care self-efficacy. To prevent oral disease and increase oral care self-efficacy of students with high entrance stress, it is necessary to provide school oral health education programs that can facilitate oral health behaviors.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.392-400
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• 2009

In this paper, we propose an effective speech reinforcement technique base on ITU-T G.729A CS-ACELP codec under the near-end background noise environments. In general, since the intelligibility of the far-end speech for the near-end listener is significantly reduced under near-end noise environments, we require a far-end speech reinforcement approach to avoid this phenomena. In contrast to the conventional speech reinforcement algorithm, we reinforce the excitation signal of the codec's parameters received from the far-end speech signal based on the G.729A speech codec under various background noise environments. Specifically, we first estimate the excitation signal of ambient noise at the near-end through the encoder of the G.729A speech codec, reinforcing the excitation signal of the far-end speech transmitted from the far-end. we specially propose a novel approach to directly reinforce the excitation signal of far-end speech signal based on the decoder of the G.729A. The performance of the proposed algorithm is evaluated by the CCR (Comparison Category Rating) test of the method for subjective determination of transmission quality in ITU-T P.800 under various noise environments and shows better performances compared with conventional SNR Recovery methods.