• The Journal of the Korea Contents Association
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• v.11 no.1
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• pp.184-192
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• 2011

Cultural heritage is the historic symbol implying the life of people and regionality. Accordingly, the study on cultural heritage along the border between Korea and China is a very critical and urgent task for educational purposes as well as to secure historic and cultural awareness and national identity. The border area between Korea and China is classified into the areas along Aprok River and areas along Tumen River where the cultural heritage of various ethnicities is scattered. Accordingly, this study tried to find the approaches to implement and use the exhibition contents for educational purposes as well as visual applications rather than a literary study on cultural heritage in the border area between Korea and China. The results of this study will be the opportunity to enhance the practical understanding of the modern states as well as to learn the cultural awareness of the territory of a modern state and people's awareness of the importance of world heritages. Furthermore, the results of this study will be used as the resources for historic and cultural tours on the web or applications and help to understand the cultural features of Korea and China in the northeastern region as well as historic awareness in educational programs using exhibition materials in a museum.

• Convergence Security Journal
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• v.17 no.5
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• pp.143-151
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• 2017

The purpose of this study is to provide the future development of the PRC Marine Corps by analyzing the changing functions via its historical development. The PRCMC is an elite regular military forces and was established by the Central Military Commission(China) in 1953 in order to project the national military power toward the enemy's territory by overcoming maritime obstacles such as seas and lakes. The PRCMC is relatively smaller personnel strength compared with the whole size of the PRC military forces. Thus its functions were limited in the areas of amphibious and land operations, island and land defense against Taiwan before the PRC pursues expanding policy toward outside. However, in the 2000s, China pursues its policy for obtaining absolute national interest so that its functions are rapidly enlarged into defense of the forward naval bases, and those for evolving its power toward outside according to not only the island territorial dispute with Japan, but also Xi Jinping's active expanding policy what we call 'the one belt and one road'. So its personnel strength is slowly increased. If the increasement of the PRC Navy and Marine Corps would develop into the level of which they can contain the status and influence of the US military power in Asia-Pacific area, it is possible that the security environment of the North-east Asia including the Korean peninsula will be fluctuated. Consequently, the ROK also needs to reevaluate the functions and the size of the joint strength of the ROK Navy and Marine Corps considering the transition of the changing security environment.

• Maritime Security
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• v.5 no.1
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• pp.1-53
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• 2022

One manifestation of the contemporary era of renewed great power competition has been the deepening relationship between China and Russia. Their strengthening military ties, notwithstanding their lack of a formal defense alliance, have been especially striking. Since China and Russia deploy two of the world's most powerful navies, their growing maritime cooperation has been one of the most significant international security developments of recent years. The Sino-Russian naval exercises, involving varying platforms and locations, have built on years of high-level personnel exchanges, large Russian weapons sales to China, the Sino-Russia Treaty of Friendship, and other forms of cooperation. Though the joint Sino-Russian naval drills began soon after Beijing and Moscow ended their Cold War confrontation, these exercises have become much more important during the last decade, essentially becoming a core pillar of their expanding defense partnership. China and Russia now conduct more naval exercises in more places and with more types of weapons systems than ever before. In the future, Chinese and Russian maritime drills will likely encompass new locations, capabilities, and partners-including possibly the Arctic, hypersonic delivery systems, and novel African, Asian, and Middle East partners-as well as continue such recent innovations as conducting joint naval patrols and combined arms maritime drills. China and Russia pursue several objectives through their bilateral naval cooperation. The Treaty of Good-Neighborliness and Friendly Cooperation Between the People's Republic of China and the Russian Federation lacks a mutual defense clause, but does provide for consultations about common threats. The naval exercises, which rehearse non-traditional along with traditional missions (e.g., counter-piracy and humanitarian relief as well as with high-end warfighting), provide a means to enhance their response to such mutual challenges through coordinated military activities. Though the exercises may not realize substantial interoperability gains regarding combat capabilities, the drills do highlight to foreign audiences the Sino-Russian capacity to project coordinated naval power globally. This messaging is important given the reliance of China and Russia on the world's oceans for trade and the two countries' maritime territorial disputes with other countries. The exercises can also improve their national military capabilities as well as help them learn more about the tactics, techniques, and procedures of each other. The rising Chinese Navy especially benefits from working with the Russian armed forces, which have more experience conducting maritime missions, particularly in combat operations involving multiple combat arms, than the People's Liberation Army (PLA). On the negative side, these exercises, by enhancing their combat capabilities, may make Chinese and Russian policymakers more willing to employ military force or run escalatory risks in confrontations with other states. All these impacts are amplified in Northeast Asia, where the Chinese and Russian navies conduct most of their joint exercises. Northeast Asia has become an area of intensifying maritime confrontations involving China and Russia against the United States and Japan, with South Korea situated uneasily between them. The growing ties between the Chinese and Russian navies have complicated South Korean-U.S. military planning, diverted resources from concentrating against North Korea, and worsened the regional security environment. Naval planners in the United States, South Korea, and Japan will increasingly need to consider scenarios involving both the Chinese and Russian navies. For example, South Korean and U.S. policymakers need to prepare for situations in which coordinated Chinese and Russian military aggression overtaxes the Pentagon, obligating the South Korean Navy to rapidly backfill for any U.S.-allied security gaps that arise on the Korean Peninsula. Potentially reinforcing Chinese and Russian naval support to North Korea in a maritime confrontation with South Korea and its allies would present another serious challenge. Building on the commitment of Japan and South Korea to strengthen security ties, future exercises involving Japan, South Korea, and the United States should expand to consider these potential contingencies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• 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.