• Journal of Biosystems Engineering
• /
• v.29 no.1
• /
• pp.29-36
• /
• 2004

The geometric characteristics of the Altari radish were measured for the purpose of mechanization of the kimchi processing. In this study, geometric characteristics such as the sectional area and volume of the radishes(pyeong-gang and sa-chul) were calculated using the image processing method, and physical properties such as the compressive strength, the cutting force of the radish and the torsional moment of the radish leaf-stems were measured by using a universal testing machine. In case of the radish(pyeong-gang), the weight was ranged 215.0∼465.0 g, the length of the radishes(body) was 86.3∼129.2 mm, the diameters were 43.3∼58.1 mm, and the length of the leaves was 261.3-368.2 mm. And the vertical compressive strengths were ranged 83.8∼171.7 N/$\textrm{cm}^2$, the horizontal compressive strengths were 113.0∼176.3 N/$\textrm{cm}^2$, the shearing forces were 86.0∼114.6 N, and the surface hardness was ranged 51.1∼52.1 N/$\textrm{cm}^2$. In case of the radish(sa-chul), the weight was ranged 203.5∼412.2 g, the length of the bodies was 67.5∼127.0 mm, the diameters were 22.3∼59.8 mm and the length of the leaves was 245.6∼312.6 mm respectively. And the vertical compressive strengths were ranged 91.3∼168.3 N/mm, the horizontal compressive strengths were 132.6∼186.9 N/$\textrm{cm}^2$, the shearing forces were 89.4∼116.5 N, and the surface hardness was ranged 52.4∼67.8 N/$\textrm{cm}^2$, respectively.

• Nuclear Engineering and Technology
• /
• v.50 no.2
• /
• pp.223-228
• /
• 2018

AREVA NP (Courbevoie, Paris, France) is actively developing several enhanced accident-tolerant fuels cladding concepts ranging from near-term evolutionary (Cr-coated zirconium alloy cladding) to long-term revolutionary (SiC/SiC composite cladding) solutions, relying on its worldwide teams and partnerships, with programs and irradiations planned both in Europe and the United States. The most advanced and mature solution is a dense, adherent chromium coating on zirconium alloy cladding, which was initially developed along with the CEA and EDF in the French joint nuclear R&D program. The evaluation of the out-of-pile behavior of the Cr-coated cladding showed excellent results, suggesting enhanced reliability, enhanced operational flexibility, and improved economics in normal operating conditions. For example, because chromium is harder than zirconium, the Cr coating provides the cladding with a significantly improved wear resistance. Furthermore, Cr-coated samples exhibit extremely low corrosion kinetics in autoclave and prevents accelerated corrosion in harsh environments such as in water with 70 ppm Li leading to improved operational flexibility. Finally, AREVA NP has fabricated a physical vapor deposition prototype machine to coat full-length cladding tubes. This machine will be used for the manufacturing of full-length lead test rods in commercial reactors by 2019.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.124-130
• /
• 2023

In this paper, we intend to improve the network lifetime by improving the energy efficiency of sensor nodes in a wireless sensor network by utilizing machine learning using K-means clustering algorithm. A wireless sensor network is a wireless network composed of physical devices including batteries as physical sensors. Due to the characteristics of sensor nodes, all resources must be efficiently used to minimize energy consumption to maximize network lifetime. A cluster based approach is used to manage groups of relatively large numbers of nodes. In the proposed protocol, by improving the existing LEACH algorithm, we propose a clustering algorithm that selects a cluster head using a cluster based approach and a location based approach. The performance results to be improved were measured using Matlab simulation. Through the experimental results, K-means clustering was applied to the energy efficiency part. By utilizing K-means, it is confirmed that energy efficiency is improved and the lifetime of the entire network is extended.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Clean Technology
• /
• v.17 no.2
• /
• pp.117-123
• /
• 2011

In this study, evaluation tests for cleaning performance of various cleaning agents and selection of optimal ultrasonic cleaning parameters were executed to develop an efficient cleaning process in remanufacturing of laser copy machine. Cleaning performance tests were executed with 8 cleaning agents (A~H) to remove the contaminants of oil-ink, toner particles, and shoe polish. Physical properties and foamability tests were also applied. For 3 types of contaminants, cleaning agent G showed superior cleaning performance compared to agent A which has being used at a remanufacturing of laser copy machine in Korea. With cleaning agents selected in pre-tests, ultrasonic cleaning tests were executed to remove real contaminants on the parts of used digital laser copy machine parts. Cleaning agent G at 28 kHz ultrasonic frequency showed faster cleaning performance compared to agent A and other frequencies. The productivity and economic efficiency in remanufacturing of laser copy machine are expected to increase by adapting agent G and 28 kHz frequency at ultrasonic cleaning process.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.3
• /
• pp.261-270
• /
• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• The Journal of Korean Physical Therapy
• /
• v.13 no.3
• /
• pp.585-596
• /
• 2001

The twenty one individuals with total knee replacement who were admitted to Kang-Dong Hospital for comprehensive physical therapy were studied in order to demonstrate the effectiveness of an isokinetic test program using the Cybex 6000 machine. The subjects were divided into three groups with the isometric exercise group receiving isometric exercise, the isokinetic eccentric exercise group, and isokinetic concentric exercise group receivind isokinetic exercise(eccentric, concentric) to knee flexors and extensors muscles for a six weeks' period using the Cybex 6000. The results are follow: 1. The extensors were increased significantly at all groups after 6 weeks training(p<.05). The flexors were increased significantly at isokinetic eccentric and isokinetic concentric group but no significantly differences at isometric group(p>.05) 2. At the effect of extensors and flexors after 6 weeks training. higher to 30$^{\circ}$ /sec of isokinetic concentric exercise, lower to 120$^{\circ}$ /sec of isometric exercise. 3. The peak torque was more increased significantly in the flexors and extensors of the isokinetic conccentric exercise among three groups. 4. The total work was more increased significantly in the flexors and extensors of the isokinetic conccentric exercise among three groups. 5. The ratio of peak torque to body weight were more increased significantly in the flexors and extensors of the isikinetic conccentric exercise among three groups. 6. The average power was more increased significantly in the flexors and extensors of the isikinetic conccentric exercise among three groups. 7. The average R.O.M in the pre-exercise and post-exercise was not different significantly in all three groups. According to the above results, In the muscle strength recovery for total knee replacement patients, isokinetic concentric exercise group was significantly greater than the isokinetic eccentric and isometric exercise groups after a six weeks training.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
• /
• v.11 no.2
• /
• pp.13-18
• /
• 2005

The ulnar nerve extends down the arm, across the elbow, and into the hand. It provides sensation to the little and ring fingers and activates many of the small muscles in the hand. The determination of peripheral nerve conduction velocity is an important part of ulnar nerve evaluation. The electrodiagnostic value as neurophysiologic investigative procedure has been known for many years but normal value of digital nerve was not reported in Korea. The purpose of this investigation was to measure the digital nerve conduction velocity of ulnar nerve for obtain clinically useful reference value and compare difference in each fingers and then compare with the other countries. 71 normal Korean volunteers (age, 19-65 years; 142 hands) examined who has no history of peripheral neuropathy, diabetic mellitus, chronic renal failure, endocrine disorders, anti-cancer medicine, anti-tubercle medicine, alcoholism, trauma, radiculopathy. Nicolet Viking II (EMG machine) was use for detected conduction velocity and amplitude of digital nerves in ulnar nerve. Data analysis was performed using SPSS. Descriptive analysis was used for obtain mean and standard deviation and independent t-test was used to compare with ring and little finger. Conduction velocity of the right ring finger was 57.44m/sec and little finger was 55.32msec. The left ring finger was 55.55msec and little finger was 54.11msec. Amplitude of the right ring finger was $30.28{\mu}V$ and little finger was $48.36{\mu}V$. The left ring finger was $30.67{\mu}V$ and little finger was $52.76{\mu}V$. There were significantly difference between ring and little in amplitude (p<.05) but there were no statistically difference between conduction velocity of ring and little finger (p>.05). The amplitude of little finger are greater than ring finger. The present results revealed that electodiagnosis can easily perform in little finger for digital nerve of ulnar nerve study.

• ETRI Journal
• /
• v.36 no.5
• /
• pp.741-751
• /
• 2014

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).

• Proceedings of the Korean Vacuum Society Conference
• /
• 2015.08a
• /
• pp.190.1-190.1
• /
• 2015

Graphite has excellent mechanical and physical properties. It is known to advanced materials and is used to materials for molds, thermal treatment of furnace, sinter of diamond and cemented carbide tool etc. SiC materials are coated on the surface and holes of graphite to protect particles emitted from porous graphite with 5%~20% porosity and make graphite hard surface. SiC materials have high durability and thermal stability. Thermal CVD method is widely used to manufacture SiC thin films but high cost of machine investment and production are required. SiC thin films manufactured by Si reaction liquid and vapore with carbon are effective because of low cost of machine and production. SiC thin films made by vapor silicon infiltration into porous graphite can be obtained for shorter time than liquid silicon. Si materials are evaporated to the graphite surface in about $10^{-2}$ torr and high temperature. Si materials are melted in $1410^{\circ}C$. Si vapor is infiltrated into the surface hole of porous graphite and $Si_xC_y$ compound is made. $Si_x$ component is proportional to the Si vapor concentration. Si diffusion coefficient is estimated from quadratic equation obtained by Fick's second law. The steady stae is assumed. Si concentration variation for the depth from graphite surface is fitted to quadratic equation. Diffusion coefficient of Si vapor is estimated at about $10^{-8}cm^2s^{-1}$.