• Journal of Technologic Dentistry
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• v.30 no.1
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• pp.57-63
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• 2008

The purpose of this study was to ascertain the effect of different abutment height and different taper of abutment on retention force of cemented implant-supported prostheses. Test specimens consisted of different abutment height group(3mm, 4mm, 5mm, 6mm, 7mm) and different taper(degrees) abutment group($4^{\circ},\;5^{\circ},\;6^{\circ},\;7^{\circ},\;8^{\circ}$). The surfaces of abutments and crowns were manufactured and finished by automatic lathe(CNC). Luting cement(Tokuso Ionomer) was prepared according to the manufacturer's instruction. And the cylinders were sealed onto the abutments and loaded in compression at 5kg for 10minutes. Excess cement was removed from the abutment-cylinder junction and the specimens were stored at room temparature for 24 hours. Specimens were tested in tension using a universal testing machine. Within the limits of this study, the following conclusions were drawn: 1. The increase in abutment height result in improvement in retention strength(P<0.05). 2. The increase in taper of abutment result in decrease in retention strength(P<0.05). 3. The decrease in abutment height result in decrease in retention strength, besides has a significantly lower retention strength at 3mm abutment height. 4. The increase in taper of abutment result in decrease in retention strength, besides has a significantly lower retention strength at $7^{\circ}$ abutment.

• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.161-169
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• 2017

We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.

• Journal of IKEEE
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• v.23 no.4
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• pp.1360-1365
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• 2019

In this paper, we describe a sound source localization(SSL) system which can be applied to mobile robot and automatic control systems. Usually the SSL method finds the Interaural Time Difference, the Interaural Level Difference, and uses the geometrical principle of microphone array. But here we proposed another approach based on the deep neural network to obtain the horizontal directional angle(azimuth) of the sound source. We pick up the sound source signals from the two microphones attached symmetrically on both sides of the robot to imitate the human ears. Here, we use difference of spectral distributions of sounds obtained from two microphones to train the network. We train the network with the data obtained at the multiples of 10 degrees and test with several data obtained at the random degrees. The result shows quite promising validity of our approach.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.23-30
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• 2020

The use of increased computing power, machine learning, and deep learning techniques have dramatically increased in various sectors. In particular, image detection algorithms are broadly used in forestry and remote sensing areas to identify forest types and tree species. However, in South Korea, machine learning has rarely, if ever, been applied in forestry image detection, especially to classify tree species. This study integrates the application of machine learning and forest image detection; specifically, we compared the ability of two machine learning data collection methods, namely image data captured by forest experts (D1) and web-crawling (D2), to automate the classification of five trees species. In addition, two methods of characterization to train/test the system were investigated. The results indicated a significant difference in classification accuracy between D1 and D2: the classification accuracy of D1 was higher than that of D2. In order to increase the classification accuracy of D2, additional data filtering techniques were required to reduce the noise of uncensored image data.

• Physical Therapy Korea
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• v.10 no.2
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• pp.71-84
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• 2003

The purpose of this study was to provide information on driving characteristics in persons with spinal cord injury through basic statistic analysis of the survey results. The survey was administered to 44 drivers with spinal cord injury. The subjects' general, neurologic and driving characteristics were analyzed, as well as the degree of difficulty in using their vehicles between tetraplegia and paraplegia. The results were as follows: thirty-five (79.6%) of forty-four respondents was men. The average age was 35.0 years old and the age at the time of injury was 29.0 years old. Their neurologic characteristics were tetraplegics 12 (27.3%) and paraplegics 32 (72.2%). Among complete lesions, the highest level those who could drive independently was C7. All the vehicles were equipped with special devices, including "power steering", "automatic transmission" and "hand controls". The vehicles for cervical cord injury were equipped with "grip bars" as well as for the degree of difficulty in using their vehicles, all the subjects felt that "moving the wheelchair in and out of their vehicles" was too difficult for them to do. We suggest that the driver training should be an essential part of the rehabilitation program for patients with spinal cord injuries to maximize their mobility in the community. This training seems to be essential in order to modify the standards of the Handicapped Drivers Ability Test and to aid the driver rehabilitation program in the health insurance payment system. Also, the driver rehabilitation training program should include instruction in that moving wheelchairs in and out of vehicles.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.12
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• pp.185-193
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• 2002

Most of NC machines are operated by Is06983 standard called G-code, which was developed in the early days of machine tools. This G-code limits hardware performance of the currently developed high-performance hardware & machine tools. By describing only movements of tool, almost all of information of previous production departments is lost, and the machining department cannot exchange information with other departments. For adjusting new hardware environment and direct communication of CNC machines with CAD/CAM software, ISO 14649, STEP -NC is researched. This new standard stores CAD/CAM information as well as operation commands of CNC machines. In this research, the new CNC machine operated by STEP-NC was built and tested. Unlike other STEP-NC milling machines, this system uses the STEP-NC file in form of XML as data input. It makes possible for STEP-NC machines to exchange information to other databases using XML. The mentioned system of this paper loads the XML file, analyzes it, makes tool paths of two5D features with information of STEP-NC, and machines automatically without making G-code. All of software is programmed with Visual C＋＋, and the milling machine is made with table milling machine, step motors, and motion control board for PC that can be directly controlled by C＋＋ commands. All modules of software and hardware were independent, it allows convenient for substitution and expansion of the milling machine. The example 1 in ISP14649-11 that had all information about geometry and machining and the example 2 that has only geometry and tool information were used to test automatic machining by the open-architecture milling machine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.681-685
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• 2000

A computer software was developed in our laboratory to automatically control the pigs environment by the image analysis system (IAS), which monitors and analyzes the pig's behavior and feeds the results back to the computer hardware. Three feeding trials were conducted with growing pigs ($L{\times}Y$) to test the effectiveness of the IAS under various seasons. In all three trials, the open-sided conventional pens with half-slatted floor were used as controls; for the IAS treatment, fully-slatted floors were used in the windowless pens. Experiment 1 was conducted in the winter for 30 d with 24 growing pigs. There were two treatments (Conventional vs. IAS), and three pens (replicates) per treatment. During the growing period, the feed efficiency was significantly (p<0.05) improved by the IAS. In addition, the pigs reared under the IAS during the growing period displayed better growth rate during the finishing period than did the pigs reared under the conventional conditions. Experiment 2 was conducted in the summer for 30 d with 24 growing pigs. The experimental design was the same as Experiment 1. During the finishing period, all the pigs were kept in conventional open-sided pens until their market weights to evaluate their carcass characteristics. During the growing period, the growth rate and feed efficiency of the pigs in the IAS was better than those of the control pigs. In addition, various carcass characteristics were significantly improved by the IAS rearing during the growing period. Experiment 3 was conducted with 30 growing pigs for 30 d in the spring. The experimental design was the same as Experiment 1. No difference was found in growing performance between the control and IAS pigs. It could be concluded that the IAS is effective in providing optimum conditions for the growing pigs in summer and winter seasons. In addition, providing an optimum environment during the growing period results in improved growth rate, feed efficiency, and carcass qualities for the finishing pigs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.86-94
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• 2015

In recent days, a Connected Car has caught an attention of the motor companies and various industrial institutes such as communication company. An automobile is regarded as a device and has been developed as an interactive system because the system is connected with various device. This drives a new business item, too. As a new automatic car technology is emerging, a new type of car accident is appeared, too. So, many researches for preventing car accident comes from the driver's are carried out in many car related institutes. In this paper, we study a driver's driving workload and develop an algorithm that measures the driver's driving behavior. We can see that the developed algorithm runs well by the experiment of road test. This results affects various road condition, driver's driving behavior and load that reflects the driver's status.