• Applied Chemistry for Engineering
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• v.22 no.4
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• pp.358-366
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• 2011

A homologous series of linear liquid crystal dimers, the ${\alpha},{\omega}$-bis(4-cyano-azobenzene-4'-oxy)alkanes (CATWETn, where n, the number of methylene units in the spacer, is 2~10) were synthesized, and their thermotropic liquid crystalline phase behavior were investigated. The CATWETn with n of 3 and 6 exhibited monotropic nematic phases, whereas other derivatives showed enantiotropic nematic phases. The nematic-isotropic transition temperatures of the dimers and their entropy variation at the phase transition showed a large odd-even effect as a function of n. This phase transition behavior was rationalized in terms of the change in the average shape of the spacer on varying the parity of the spacer. The thermal stability and degree of order in the nematic phase and the magnitude of the odd-even effect of CATWETn were similar to those for the methoxy-, nitro-, and pentyl-substituted dimers, while they were significantly different from those for the monomesogenic compounds, 1-{4-(4'-cyanophenylazo)phenoxy}alkylbromides and the side-chain liquid-crystalline polymers, the poly[1-{4-(4'-cyanophenylazo)phenoxyalkyloxy}ethylene]s. The results were discussed in terms of 'virtual trimer model' by Imrie.

• The Journal of Bigdata
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• v.5 no.2
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• pp.53-67
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• 2020

This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.4
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• pp.483-491
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• 2021

The identification of regional collision risks in water areas is significant for the safety of navigation. This paper introduces a new method of collision risk assessment that incorporates a clustering method based on the distance factor - hierarchical clustering - and uses real-time data in case of several surrounding vessels, group methodology and preliminary assessment to classify vessels and evaluate the basis of collision risk evaluation (called HCAAP processing). The vessels are clustered using the hierarchical program to obtain clusters of encounter vessels and are combined with the preliminary assessment to filter relatively safe vessels. Subsequently, the distance at the closest point of approach (DCPA) and time to the closest point of approach (TCPA) between encounter vessels within each cluster are calculated to obtain the relation and comparison with the collision risk index (CRI). The mathematical relationship of CRI for each cluster of encounter vessels with DCPA and TCPA is constructed using a negative exponential function. Operators can easily evaluate the safety of all vessels navigating in the defined area using the calculated CRI. Therefore, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of life and property. To illustrate the effectiveness of the framework proposed, an experimental case study was conducted within the coastal waters of Mokpo, Korea. The results demonstrated that the framework was effective and efficient in detecting and ranking collision risk indexes between encounter vessels within each cluster, which allowed an automatic risk prioritization of encounter vessels for further investigation by operators.

• Journal of the Korean Geosynthetics Society
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• v.19 no.4
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• pp.41-50
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• 2020

Since cracks in concrete structures expedite corrosion of reinforced concrete over a long period of time, regular on-site inspections are essential to ensure structural usability and prevent degradation. Most of the safety inspections of facilities rely on visual inspection with naked eye, so cost and time consuming are severe, and the reliability of results differs depending on the inspector. In this study, a portable measuring device that can be used for safety diagnosis and maintenance was developed as a device that measures the width and length of concrete cracks through image analysis of cracks photographed with a camera. This device captures the cracks found within a close distance (3 m), and accurately calculates the unit pixel size by laser distance measurement, and automatically calculates the crack length and width with the image processing algorithm developed in this study. In measurement results using the crack image applied to the experiment, the measurement of the length of a 0.3 mm crack within a distance of 3 m was possible with a range of about 10% error. The crack width showed a tendency to be overestimated by detecting surrounding pixels due to vibration and blurring effect during the binarization process, but it could be effectively corrected by applying the crack width reduction function.

• Journal of the Korean Chemical Society
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• v.66 no.2
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• pp.124-135
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• 2022

The purpose of this study was to analyze the chemisry education contents of South Korea and North Korea for understanding chemistry education of North Korea. Chemistry education in South and North Korea was investigated in terms of learning period and learning quantaty. Especially, what content North Korea learned prior to South Korea and what contents learned more were analyzed. The subjects of this study were South Korean 2015 revised National Science Curriculum and North Korean science textbooks in Kim Jong-un era. The North Korean textbooks analyzed are 'Nature' for North Korean elementary school 3, 'Natural Science' for North Korean middle school 1 and 2, and 'Chemistry' for North Korean high school 1 and 2. The analysis results are as follows. First, the content elements to be learned in advance in North Korean textbooks were density, oxidation and reduction, battery, and atomic weight. Second, the content elements additionally learned in North Korean textbooks include separation of mixtures, fuels, oxidation and reduction, metals, organic and inorganic substances, metals and non-metal oxides and hydroxides, inorganic substances used as fertilizers, nutritional substances, and salt reaction and utilization, atomic orbitals, hybridization of orbitals, coordination bonds and complexes. As a future research task, a qualitative analysis of the elements of North Korean chemistry, the activities of textbooks, and an experimental analysis were proposed.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.183-192
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• 2022

With the growth of the packaging industry, demand on the packaging printing comes in various forms. Customers' orders are diversifying and the standards for quality are increasing. Offset printing is mainly used in the packaging printing since it is easy to print in large quantities. However, productivity of the offset printing decreases when printing various order. This is because it takes time to change colors for each printing unit. Therefore, scheduling that minimizes the color replacement time and shortens the overall makespan is required. By the existing manual method based on workers' experience or intuition, scheduling results may vary for workers and this uncertainty increase the production cost. In this study, we propose an automated scheduling method of parallel offset printing process for packaging printing. We decompose the original problem into assigning and sequencing orders, and ink arrangement for printing problems. Vehicle routing problem and assignment problem are applied to each part. Mixed integer programming is used to model the problem mathematically. But it needs a lot of computational time to solve as the size of the problem grows. So guided local search algorithm is used to solve the problem. Through actual data experiments, we reviewed our method's applicability and role in the field.

• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.160-177
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• 2021

The Solar and Space Environment Division of the Korean Space Science Society investigated the use and possession of ground and satellite observations and models of solar and planetary data operated by domestic research institutes and universities. Based on the findings, we would like to introduce observational instruments, data, and models in solar and interplanetary fields in this paper to improve understanding and use of each data and explore opportunities for interdisciplinary research. The ground and satellite observations, which require a lot of investment, were mainly held by research institutes (National Meteorological Satellite Center, Polar Research Institute, Korean Space Weather, Korea Astronomy and Space Science Institute and KAIST Satellite Research Institute), and model development was overwhelmingly carried out at Kyung Hee University. In solar and interplanetary fields, we introduce Fast Imaging Solar Spectrograph (FISS), neutron monitors, and the analysis models [for the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) and Hinode/X-Ray Telescope (XRT) observations] in nonequilibrium ionization state as representatives. Survey on solar and interplanetary fields can be downloaded from the website of the Korean Space Science Society (http://ksss.or.kr/). The paper makes know the importance of long-term and continuous management of space science-related materials, and hopes to contribute to enhancing the status of domestic space science data by utilizing locally produced data by various personnel participating in space science research.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.597-610
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• 2022

Knowledge of the failure behavior of friction materials considering their intermediate principal stress is related to an understanding of situations where these materials might be used: for example, the stability of deep-seated boreholes and fault slip analysis. This study designed equipment for physically implementing true triaxial compression and used it to assess specimens of plaster, a friction material. The material's mechanical behaviors are discussed based on the results. The applicability of the 3D failure criteria are also reviewed. The tested specimens were molded cuboids of width, length, and height 52, 52, and 104 mm, respectively. A total of 24 true triaxial compression tests were performed under various combinations of 𝜎₃ and 𝜎₂ conditions. Conventional uniaxial and triaxial compression tests were employed to estimate the mechanical properties of the plaster for use as parameters for 3D failure criteria. Examining the stress-strain relations of the plaster materials showed that a large difference between the intermediate principal stress and the minimum principal stress indicated strong brittle behavior. The mechanical behavior of the plaster used here reflects the change of intermediate principal stress. Nonlinear multiple regression analysis on the test data in the principal space showed that the modified Wiebols-Cook failure criterion and the modified Lade failure criterion were the most suitable 3D failure criteria for the tested plaster.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.1-11
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• 2022

In this paper, we propose an NFT(Non-Fungible Token)-based access control method for safely sharing data between users in blockchain environment. Since all data stored in the blockchain can be accessed by anyone due to the nature of the technology, it is necessary to control access except for authorized users when sharing sensitive data. For that, we generate each data as NFT and controls access to the data through the smart contract. In addition, in order to overcome the limitations of single ownership of the existing NFT, we separated the NFT into ownership and use rights, so that data can be safely shared between users. Ownership is represented as an original NFT, use rights is represented as a copied NFT, and all data generated as NFT is encrypted and uploaded, so data can be shared only through the smart contract with access control. To verify this approach, we set up a hypothetical scenario called Building Information Modeling (BIM) data trade, and deployed a smart contract that satisfies 32 function call scenarios that require access control. Also, we evaluated the stability in consideration of the possibility of decryption through brute-force attack. Through our approach, we confirmed that the data can be safely shared between users in blockchain environment.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.