• Journal of the Korean Society for Nondestructive Testing
• /
• v.19 no.1
• /
• pp.25-33
• /
• 1999

Since concrete is an inhomogeneous material consisting of larger aggregates and sand embedded in a cement paste matrix, it relatively shows a complex failure mechanism. In order to assure the reliability of concrete structure. microscopic fracture behavior and internal damage progress of concrete under the loading should be fully understood. In this study, an acoustic emission(AE) technique has been used to clarify microscopic failure mechanism and their corresponding AE signal characteristics of concrete under three-point bending test. In addition 2-dimensional AE source location has been performed to monitor the progress of an internal damage and the successive crack growth behavior during the loading. The relationship between AE signal characteristics and microscopic fracture mechanism is discussed.

• Economic and Environmental Geology
• /
• v.38 no.2 s.171
• /
• pp.187-196
• /
• 2005

Structural sites which cations can occupy in garnet structure are centers of the tetrahedron, octahedron, and distorted cube sharing edges with the tetrahedron and octahedron. Among them, the size of cation occuping at tetrahedral site (the center of tetrahedron) is closely related with the size of a unit cell of garnet. Accordingly, garnet containing iron with relative large ionic radii in tetrahedral site can be considered as a promising matrix for the immobilization of the elements with large ionic radii, such as actinides in radioactive wastes. We synthesized several garnets with the batch composition of $Ca_{1.5}GdCe_{0.5}ZrFeFe_3O_{12}$, and studied their properties and phase relations under various conditions. Mixed samples were fabricated in a pellet form under a pressure of $200{\~}400{\cal}kg/{\cal}cm^2$ and were sintered in the temperature range of $1100\~1400^{\circ}C$ in air and under oxygen atmospheres. Phase identification and chemical analysis of synthesized samples were conducted by XRD and SEM/EDS. In results, garnet was obtained as the main phase at $1300^{\circ}C$, an optimum condition in this system, even though some minor phases like perovskite and unknown phase were included. The compositions of garnet and perovskite synthesized from the batch composition of $Ca_{1.5}GdCe_{0.5}ZrFeFe_3O_{12}$ were ranged $[Ca_{l.2-1.8}Gd_{0.9-1.4}Ce_{0.3-0.5}]^{VIII}[Zr_{0.8-1.3}Fe_{0.7-1.2}]^{VI}[Fe_{2.9-3.1}]^{IV}O_{12}$ and $Ca_{0.1-0.5}Gd_{0.0-0.8}Ce_{0.1-0.5}\;Zr_{0.0-0.2}Fe_{0.9-1.1}O_3$, respectively. Ca content was exceeded and Ce content was depleted in the 8-coordinated site, comparing to the initial batch composition. This phenomena was closely related to the content of Zr and Fe in the 6-coordinated site.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2009.06a
• /
• pp.441-441
• /
• 2009

Dye-sensitized solar cells (DSSCs) have been widely investigated as a next-generation solar cell because of their simple fabrication process and low coats. The cells use a porous nanocrystalline TiO2 matrix coated with a sensitizer dye that acts as the light-harvesting element. The photo-exited dye injects electrons into the $TiO_2$ particles, and the oxide dye reacts with I- in the electrolyte in regenerative cycle that is completed by the reduction of $I_3^-$ at a platinum-coated counter electrode. Since $TiO_2$ porous film plays a key role in the enhancement of photoelectric conversion efficiency of DSSC, many scientists focus their researches on it. Especially, a high light-to-electricity conversion efficiency results from particle size and crystallographic phase, film porosity, surface structure, charge and surface area to volume ratio of porous $TiO_2$ electrodes, on which the dye can be sufficiently adsorbed. Effective treatment of the photoanode is important to improve DSSC performance. In this paper, to obtain properties of surface and dispersion as nitric acid treated $TiO_2$ photoelectrode was investigate. The photovoltaic characteristics of DSSCs based the electrode fabricated by nitric acid pre-treatment $TiO_2$ materials gave better performances on both of short circuit current density and open circuit voltage. We compare dispersion of $TiO_2$ nanoparticles before and after nitric acid treatment and measured Ti oxidized state from XPS. Low charge transfer resistance was obtained in nitric acid treated sample than that of untreated sample. The dye-sensitized solar cell based on the nitric acid treatment had open-circuit voltage of 0.71 V, a short-circuit current of 15.2 mAcm-2 and an energy conversion efficiency of 6.6 % under light intensity of $100\;mWcm^{-2}$. About 14 % increases in efficiency obtained when the $TiO_2$ electrode was treated by nitric acid.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.89-105
• /
• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.6
• /
• pp.573-582
• /
• 2010

In this paper, X-band transceiver for high resolution wideband SAR systems is designed and fabricated. Also as a technique for enhancing the performance, error compensation algorithm is presented. The transceiver for SAR system is composed of transmitter, receiver, switch matrix and frequency generator. The receiver especially has 2 channel mono-pulse structure for ground moving target indication. The transceiver is able to provide the deramping signal for high resolution mode and select the receive bandwidth for receiving according to the operation mode. The transceiver had over 300 MHz bandwidth in X-band and 13.3 dBm output power which is appropriate to drive the T/R module. The receiver gain and noise figure was 39 dB and 3.96 dB respectively. The receive dynamic range was 30 dB and amplitude imbalance and phase imbalance of I/Q channel was ${\pm}$0.38 dBm and ${\pm}$3.47 degree respectively. The transceiver meets the required electrical performances through the individual tests. This paper shows the pulse error term depending on SAR performance was analyzed and range IRF was enhanced by applying the compensation technique.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.100-109
• /
• 2019

Maintainability indicates the extent to which maintenance can be done easily and quickly. The consideration of maintainability is crucial to reduce the operation and support costs of weapon systems, but if the maintainability is evaluated after the prototype production is done and necessitates design changes, it may increase the cost and delay the schedule. The evaluation should verify whether maintenance work can be performed, and support the designers in developing a design to improve maintainability. In previous studies, the maintainability index was calculated using the graph theory at the early design phase, but evaluation accuracy appeared to be limited. Analyzing the methods of evaluating the maintainability using fuzzy logic and 3D modeling indicate that the design of a system with good maintainability should be done in an integrated manner during the whole system life cycle. This paper proposes a method to evaluate maintainability using SysML-based modeling and simulation technique and fuzzy logic. The physical design structure with maintainability attributes was modeled using SysML 'bdd' diagram, and the maintainability was represented by an AHP matrix for maintainability attributes. We then calculated the maintainability using AHP-based weighting calculation and fuzzy logic through the use of SysML 'par' diagram that incorporated MATLAB. The proposed maintainability model can be managed efficiently and consistently, and the state of system design and maintainability can be analyzed quantitatively, thereby improving design by early identifying the items with low maintainability.

• Korean Chemical Engineering Research
• /
• v.50 no.6
• /
• pp.1068-1075
• /
• 2012

Poly(butylene terephthalate) (PBT)/Nylon6,12 core/shell micro fiber were prepared by extrusion molding. To investigate their optimum extrusion conditions, compatibility of PBT/Nylon6,12 blend micro fiber in conformity to their weight ratio and manufacture temperature was explored with SEM morphology and DSC. The alterations in their mechanical properties by extrusion speed were compared and analyzed through a UTM. In comparison with SEM figures, the domain sizes of Nylon6,12 were gradually declined by increasing the extrusion temperature of blends. Furthermore, according to these SEM images, the phase separation between Nylon6,12 domain and PBT matrix became indistinct with increasing of weight percentage of Nylon6,12. In case of DSC, the boundaries of two peaks were almost disappeared when increasing the extrusion temperature and also intervals of each two melting peaks became narrow as increasing the Nylon6,12 ratio. The mechanical properties including tensile strength, elongation, flexural strength and flexural modulus were increased as the increase in the extrusion temperature until $260^{\circ}C$. However, the mechanical properties were actually deteriorated over $260^{\circ}C$. The tensile strength, elongation, flexural strength and flexural modulus at $260^{\circ}C$ were 560 $kg_f/cm^2$, 220%, 807 $kg_f/cm^2$ and 22,146 $kg_f/cm^2$, respectively. These values are more than intermediate values of mechanical properties of PBT and Nylon6,12. These results mean that there is compatibility between PBT and Nylon6,12. Based on the extrusion conditions that produced optimum compatibility of blend, as a result, our group obtained micro fibers with the core/shell structure.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.69-88
• /
• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.