• Analytical Science and Technology
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• v.28 no.3
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• pp.187-195
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• 2015

The heat of sublimation (HOS) is an essential parameter used to resolve environmental problems in the transfer of organic contaminants to the atmosphere and to assess the risk of toxic chemicals. The experimental measurement of the heat of sublimation is time-consuming, expensive, and complicated. In this study, quantitative structural property relationships (QSPR) were used to develop a simple and predictive model for measuring the heat of sublimation of organic compounds. The population-based forward selection method was applied to select an informative subset of descriptors of learning algorithms, such as by using multiple linear regression (MLR) and the support vector machine (SVM) method. Each individual model and consensus model was evaluated by internal validation using the bootstrap method and y-randomization. The predictions of the performance of the external test set were improved by considering their applicability to the domain. Based on the results of the MLR model, we showed that the heat of sublimation was related to dispersion, H-bond, electrostatic forces, and the dipole-dipole interaction between inter-molecules.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.97-101
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• 2017

X-ray diffractometers are used to characterize material properties, such as the phase, texture, lattice constant and residual stress, based on the diffracted beams obtained from specimens. Quantitative analyses using X-rays are typically conducted by measuring the peak positions of the diffracted beams. However, the long-term use of the diffractomer, like any other machine, results in errors associated with the mechanical parts, which can deteriorate the accuracy of the quantitative analyses. In this study, the process of correcting systematic errors in the $2{\theta}$ range of $30{\sim}90^{\circ}$ is discussed, for which strain-free Si powders from NIST were used as the standard specimens. For the evaluation of the impact of such error correction, we conducted a quantitative analysis of the true lattice constant for tungsten thin films.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.145-154
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• 2015

The objective of this research is to evaluate the usability test of 'Paldokangsan3' which has been developed as a serious game for the elderly to improve their physical and mental health. This game machine has been installed in a silver house for one month that the elderly could play the game as they like in their convenient times. To promote their participations to practice the game, we set 3 contests with gifts for the high scores and collect their data through inspection, questionnaire and interviews by the researchers as well as in-game measurement for the play. Eight people volunteered to join the project. While the result analysis for the usability area of easiness of control, learnability of the game play, memorability and challenge didn't show the statistical confident t-value, most elderly players participated 2~3 times a day for a month even though most of them are suffering mild cognition impairment. They showed good subjective satisfactions in their interviews that we could go on the project further to expand its applications.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.1
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• pp.81-88
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• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.9
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• pp.839-849
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• 2015

This paper deals with an experimental technique for monitoring machining conditions by analyzing cutting-force vibration measured at a milling machine. This technique is based on the relationship of the cutting-force vibrations with the feed rate and cutting depth as reported earlier. The measurement system consists of dynamic force transducers and a signal amplifier. The analysis system includes an oscilloscope and a computer with a LabVIEW program. Experiments were carried out at various feed rates and cutting depths, while the rotating speed was kept constant. The magnitude of the cutting force vibration component corresponding to the number of cutting edges multiplied by the frequency of rotation was linearly correlated with the machining conditions. When one condition of machining is known, another condition can be identified by analyzing the cutting-force vibration.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.24 no.2
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• pp.317-326
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• 1994

Panoramic radiographs of a dry skull were used to evaluate the accuracy of four panoramic dental X-ray systems in determining tooth angulations in the buccal segments. The four machines evaluated were the Philips Oralix Pan DC, the GE panelipse, the SS White panorex 1, and the SS White Panorex 2. Panoramic radiographs were taken at six different patient positions for each machine to evaluate the effects of patient positioning on determining tooth angulations in the buccal segments. All of the machines studied showed a significant correlation between the mean radiographic estimates and the actual measurements regardless of positioning(p<0.05). With correct placement of the skull, the results were analyzed for an error tolerance of +/-3/sup 0/ between the actual measurements and the radiographic estimates for tooth angulation. The SS White Panorex 1 was accurate 74% of the time, the GE Panelipse was accurate 67% of the time, the Philips Oralix Pan DC and the SS White Panorex 2 were accurate 64% of the time. When an error tolerance of +/-5/sup 0/ was permitted, the accuracy was 89% for the SS White Panorex 1 and the GE Panelipse, 85% for the Philips Oralix Pan DC, and 81% for the SS White Panorex 2.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.4
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• pp.6-13
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• 2011

Due to its robustness to illumination change, normalized cross-correlation based similarity measurement is widely used in many machine vision applications. However, its inefficient computation structure is not adequate for real-time embedded vision system. In this paper, we present an efficient hardware architecture based on a normalized cross correlation (NCC) for fast image similarity measure. The proposed architecture simplifies window-sum process of the NCC using the integral-image. Relieving the overhead to constructing integral image, we make it possible to process integral image construction at the same time that pixel sequences are inputted. Also the proposed segmented integral image method can reduce the buffer size for storing integral image data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.729-736
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• 2017

In this study, the physics-based model and machine learning technique were used to conduct model-assisted probability of detection (MAPOD) experiments. The possibility of using in-service cracked parts was also investigated. Bolt hole shaped specimens with fatigue crack on the hole surface were inspected using eddy current inspection. Owing to MAPOD, the number of experimental factors decreased significantly. The uncertainty in the crack length measurement for in-service cracked parts was considered by the application of Monte Carlo simulation.