• Applied Chemistry for Engineering
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• v.25 no.1
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• pp.1-13
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• 2014

Lithium ion batteries (LIBs) are the state-of-the-art technology among electrochemical energy storage and conversion cells, and are still considered the most attractive class of battery in the future due to their high specific energy density, high efficiency, and long cycle life. Rapid development of power-hungry commercial electronics and large-scale energy storage applications (e.g. off-peak electrical energy storage), however, requires novel anode materials that have higher energy densities to replace conventional graphite electrodes. Germanium (Ge) and silicon (Si) are thought to be ideal prospect candidates for next generation LIB anodes due to their extremely high theoretical energy capacities. For instance, Ge offers relatively lower volume change during cycling, better Li insertion/extraction kinetics, and higher electronic conductivity than Si. In this focused review, we briefly describe the basic concepts of LIBs and then look at the characteristics of ideal anode materials that can provide greatly improved electrochemical performance, including high capacity, better cycling behavior, and rate capability. We then discuss how, in the future, Ge anode materials (Ge and Ge oxides, Ge-carbon composites, and other Ge-based composites) could increase the capacity of today's Li batteries. In recent years, considerable efforts have been made to fulfill the requirements of excellent anode materials, especially using these materials at the nanoscale. This article shall serve as a handy reference, as well as starting point, for future research related to high capacity LIB anodes, especially based on semiconductor Ge and Si.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.7
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• pp.858-868
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• 2017

Korea's shipbuilding industry has led the world in the technical area over the last century. Despite this commendable performance, around 2,000 workers experience accidents almost every year with 40 being killed. This raises a question of whether the safety level of our shipbuilding industry, in particular the safety of workers, is actually at the world-class level. Accordingly, this research has analyzed several types of safety training currently provided in the field through investigating statistical data of serious accidents occurring from 2006 to 2015 in the domestic shipbuilding industry, analyzing its occurrence and causes, and conducting a survey targeting employees in the shipbuilding industry. Based on this, it has investigated problems of safety training in the shipbuilding industry and suggested improvements. First, it is essential to create a standard system for safety training in the shipbuilding industry to address problems of different kinds and levels of safety training provided by each shipyard and low quality of training, and operate more organized and systemized training. Second, safety training curriculum specializing in the shipbuilding industry should continue to be developed and standardized based on a standard system for safe training to prevent serious accidents and improve safety awareness of workers. Lastly, both employers and employees should actively provide and participate in safety training to secure safety of workers through preventing serious accidents and ultimately create safety-first culture in workplace.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.191-203
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• 2023

This paper analyzed the overall status of the domestic freight transportation business, established SWOT analysis and strategy through existing literature research and designed an AHP model to derive priorities for each strategy. The SWOT analysis analyzed the management model of the consignment borrowers belonging to a transportation company that did not handle the supplies with the lowest satisfaction with the consignment system. The AHP model was designed by establishing a SWOT strategy through SWOT analysis. As a result of the analysis of the upper class, priorities were derived in the order of WO strategy, SO strategy, ST strategy, and WT strategy. As a result of comprehensive priorities for the development strategy of the domestic freight transportation business, WO strategy's "Improvement of cooperative relations between transportation companies and consignment owners through fair consignment contracts" was first, SO strategy's "Public promotion of the necessity of consignment systems based on high economic feasibility and reliability" was second, and ST strategy's "Proposal of policies to strengthen financial performance through the introduction of freight transport platforms" was fourth, followed by WT strategy's "Improvement of satisfaction with transport services through the introduction of freight transport platforms" and SO strategy's "Expansion of safe freight systems" in sixth, respectively.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.359-365
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• 2020

The purpose of this study was to collect and analyze information on the major scoring paths, success rate by shooting type, assist, steal, block, and goalkeeper defense rate by shooting type in the 2019 World Women's Handball Championship. Based on the research results, the following conclusions were drawn. First, as a result of the analysis of the scoring route, 1st the Netherlands and 2nd Spain had 6m shots and 7m shots the most. Second, in the success rate analysis results by shooting type, the top 6 teams showed 7m shot, breakthrough shot, and fast shot. Third, according to the result of assist frequency analysis, the fourth place in the competition Norway scored the most assists among the six teams. Fourth, as a result of the steel frequency analysis, Norway scored the most with 5.2 on average among the six teams. Fifth, as a result of analyzing the goalkeeper defense rate by shooting type, it was found that all 6 team's goalkeepers had the highest defense against 9m shooting. Therefore, for an in-depth analysis of the game strategy, it is considered that it is necessary to understand the characteristics of individual player's performance and collect information will be followed by a follow up research in the future.

• The Research Journal of the Costume Culture
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• v.14 no.1
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• pp.1-15
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• 2006

This study examines the social meaning of the CosPlay, the growth potential of CosPlay culture and its effect on the related industry through the perspectives and language of the youths who enjoy CosPlay, based on the ethnographic research. Also, this study presents a comparative description of Korea and Japan CosPlay culture by the ethnographic methodology whose purpose is to define relationship of cause and effect with phenomenon. For further step, this study plans to emphasize the need to link culture, clothes and related industry in order to create a cultural environment where diversity co-exists. CosPlay is the mania culture of Japan that emulated the Halloween party of the West and that developed the party into a unique form. In Korea, this practice was accepted for the first time among a handful of youths, starting from the 1990s, after which, it was introduced to the masses while holding CosPlay related events. While CosPlay is succeeded as an industry in Japan, CosPlay in Korea is considered childish play due to the Korean culture of considering cartoon as a childish and low class genre which is enjoyed by youths. CosPlay in Korea faces the following changes: aging of the members who comprised the CosPlay culture at the initial stage; population increase, centered on middle and high school students; interest of the government and the businesses that wish to produce economic wealth by organizing CosPlay events into events for youths; and changes in the environment that comprised the surrounding of the CosPlay culture. CosPlay is an honest play that demonstrates one's effort on the stage through performance. Moreover, most of the middle and high school students who comprise the CosPlay culture demonstrate similar characteristics as mania type of people when it came to the reason that they enjoy CosPlay. However, they did not consider CosPlay culture as an important aspect of their lives. Instead, most of them said that they participate to relieve stress. Thus, they have the potential to move onto another form of youth culture that may appear more attractive to them. To them, it is not the CosPlay culture that is important, but the fact that CosPlay provides a forum where they can freely engage in play.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.1-12
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• 2020

This is a study on the direction of the teaching support program contents analysis and teaching support platform of the teaching and learning center. To this end, in April 2020, a literature study was conducted, an analysis of the current status of teaching support at other universities and K universities, an analysis of professor interviews, and expert verification were conducted. The main research results are as follows. First, as a result of examining the current status of teaching support programs at 24 universities, it was confirmed that special lectures on teaching methods, class consulting, teaching method research meetings, and educational resource rooms were operated as representative programs. Second, the basic structure of the platform is composed of a lecture case sharing bulletin board to enable active exchange of opinions on teaching methods among teachers, a teaching support program application bulletin board to enable application for a teaching method program, and Edu-tech to activate the platform, and a professor support menu. Third, the contents of the teaching support platform were implemented based on the basic structure of the teaching support platform. This study analyzed the contents of the teaching support program and conducted a study to suggest the direction of the teaching support platform to discuss the direction of establishing an effective teaching support platform.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Journal of Agricultural Extension & Community Development
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• v.21 no.1
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• pp.35-82
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• 2014

It was main objectives to find the learners characteristics and educational effects of cyber agricultural technology courses in RDA. For the research, it was followed by literature reviews and internet based survey methods. In internet based survey, two staged stratified sampling method was adopted from cyber training members database in RDA along with some key word as open course or certificate course, and enrollment years. Instrument was composed through literature reviews about cyber education effects and educational effect factors. And learner characteristics items were added in survey documents. It was sent to sampled persons by e-mail and 316 data was returned via google survey systems. Through the data cleaning, 303 data were analysed by chi-square, t-test and F-test. It's significance level was .05. The results of the research were as followed; First, the respondent was composed of mainly man(77.9%), and monthly income group was mainly 2,000,000 or 3,000,000 won(24%), bachelor degree(48%), fifty or forty age group was shared to 75%, and their job was changed after learning(12.2%). So major respondents' job was not changed. Their major was not mainly agriculture. Learners' learning style were composed of two or more types as concrete-sequential, mixing, abstract-random, so e-learning course should be developed for the students' type. Second, it was attended at 3.2 days a week, 53.53 minutes a class, totally 172.63 minutes a week. They were very eager or generally eager to study, and attended two or more subjects. The cyber education motives was for farming knowledge, personal competency development, job performance enlarging. They selected subjects along with their interest. A subject person couldn't choose more subjects for little time, others, non interesting subject, but more subject persons were for job performance benefits and previous subjects effectiveness. Most learner was finished their subject, but a fourth was not finished for busy (26.7%). And their entrying behavior was not enough to learn e-course and computer or internet using ability was middle level as software using. And they thought RDA cyber course was comfort in non time or space limit, knowledge acquisition, and personal competency development. Cyber learning group was composed of open course only (12.5%), certificate only(25.7%), both(36.3%). Third, satisfaction and academic achievement of e-learning learners were good, and educational service offering for doing job in learning application category was good, but effect of cyber education was not good, especially, agricultural income increasing was not good because major learner group was not farmer, so they couldn't apply their knowledge to farming. And content structure and design, content comprehension, content amount were good. The more learning subject group responded to good in effects, and both open course and certificate course group satisfied more than open course only group. Based on the results, recommendation was offered as cyber course specialization before main course in RDA training system, support staff and faculty enlargement, building blended learning system with local RDA office, introducing cyber tutor system.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.