• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.3
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• pp.11-28
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• 2021

Due to its size and complex characteristics, it is not often to newly create a large park within an existing urban area. Also, there has been a lack of research on the planting design methodologies for a large park. This study aims to elucidate how ecological ideas can be applied to planting practice from a designer's perspective, and eventually suggest a planting design framework in the actual case, the Central Park in the City of Gwangju. This framework consists of spatial structure of planting area in order to connect and unite the separated green patches, to adapt to the changes of existing vegetation patterns, to maintain the visual continuity of landscape, and to organize the whole open space system. The framework can be provided for the spatial planning and planting design phase in which the landscape designer flexibly uses it with the design intentions as well as with an understanding of the physical, social, and aesthetic characteristics of the site. The significance of this approach is, first that it can maintain ecological and visual consistency of the both existing and introduced landscapes as a whole in spite of its intrinsic complexity and largeness, and second that it can help efficiently respond to the unexpected changes in the landscape. In the case study, comprehensive site analysis is conducted before developing the framework. In particular, wetlands and grasslands have been identified as potential wildlife habitat which critically determines the vegetation patterns of the green area. Accordingly, the lists of plant communities are presented along with the planting scheme for their shape, layout, and relations. The model of the plant community is developed responding to the structure of surrounding natural landscape. However, it is not designed to evolve to a specific plant community, but is rather a conceptual model of ecological potentials. Therefore, the application of the model has great flexibility by using other plant communities as an alternative as long as the characteristics of the communities are appropriate to the physical conditions. Even though this research provides valuable implications for landscape planning and design in the similar circumstances, there are several limitations to be overcome in the further research. First, there needs to be more sufficient field surveys on the wildlife habitats, which would help generate a more concrete planting model. Second, a landscape management plan should be included considering the condition of existing forest, in particular the afforested landscapes. Last, there is a lack of quantitative data for the models of some plant communities.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.107-127
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• 2022

Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.614-629
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• 2001

As the information-intensive society in 21c based on the environment of global internet is promoted, the software is getting more large and complex, and the demand for the software is increasing briskly. So, it becomes an important issue in academic and industrial field to activate reuse by developing and exchanging the standardized component. Currently, the information services as a product type of each company are provided in foreign market place for reusing a commercial component, but the components which are serviced in each market place are different, insufficient and unstandardized. That is, construction for Component Data Registry based on ISO 11179, is not accomplished. Hence, the national government has stepped up the plan for sending out public component at 2001. Therefore, the systems as a tool for sharing and exchange of data, have to support the meta-information of standardized component. In this paper, we will propose the C_MDR system: a tool to register and manage the standardized meta-information, based upon ISO 11179, for the commercialized common component. The purpose of this system is to systemically share and exchange the data in chain of acceleration of reusing the component. So, we will show the platform of specification for the component meta-information, then define the meta-information according to this platform, also represent the meta-information using XML for enhancing the interoperability of information with other system. Moreover, we will show that three-layered expression make modeling to be simple and understandable. The implementation of this system is to construct a prototype system of the component meta-information through the internet on www, this system uses ASP as a development language and RDBMS Oracle for PC. Thus, we may expect the standardization of the exchanged component metadata, and be able to apply to the exchanged reuse tool.

• The Journal of Korean Academy of Prosthodontics
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• v.49 no.4
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• pp.316-323
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• 2011

Purpose: The purpose of this study was to investigate the effect of different thread designs on the marginal bone stresses around dental implant. Materials and methods: Standard ITI implant(ITI Dental Implant System; Straumann AG, Waldenburg, Switzerland), 4.1 mm in diameter and 10 mm in length, was selected as control. Test implants of four different thread patterns were created based on control implant, i.e. maintaining all geometrical design of control implant except thread pattern. Four thread designs used in test implants include (1) small V-shape screw (model A), (2) large V-shape screw (model B), (3) buttress screw (model C), and (4) trapezoid screw (model D). Surface area for unit length of implant was 14.4 $mm^2$ (control), 21.7 (small V-shape screw), 20.6 (large V-shape screw), 17.0 (buttress screw) and 28.7 $mm^2$ (trapezoid screw). Finite element models of implant/bone complex were created using an axisymmetric scheme with the use of NISA II/DISPLAY III (Engineering Mechanics Research Corporation, Troy, MI, USA). A load of 100 N applied to the central node on the crown top either in parallel direction or at 30 degree to the implant axis (in order to apply non-axial load to the implant NKTP type 34 element was employed). Quantification and comparison of the peak stress in the marginal bone of each implant model was made using a series of regression analyses based on the stress data calculated at the 5 reference points which were set at 0.2, 0.4, 0.6, 0.8 and 1.0 mm from implant wall on the marginal bone surface. Results: Results showed that although severe stress concentration on the marginal bone cannot be avoided a substantial reduction in the peak stress is achievable using different thread design. The peak marginal bone stresses under vertical loading condition were 7.84, 6.45, 5.96, 6.85, 5.39 MPa for control and model A, B, C and D, respectively. And 29.18, 26.45, 25.12, 27.37, 23.58 MPa when subject to inclined loading. Conclusion: It was concluded that the thread design is an important influential factor to the marginal bone stresses.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.312-326
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• 2020

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.87-87
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• 2010

Photoelectrochemical (PEC) systems are promising methods of producing H2 gas using solar energy in an aqueous solution. The photoelectrochemical properties of numerous metal oxides have been studied. Among them, the PEC systems based on TiO2 have been extensively studied. However, the drawback of a PEC system with TiO2 is that only ultraviolet (UV) light can be absorbed because of its large band gap (3.2 - 3.4 eV). Two approaches have been introduced in order to use PEC cells in the visible light region. The first method includes doping impurities, such as nitrogen, into TiO2, and this technique has been extensively studied in an attempt to narrow the band gap. In comparison, research on the second method, which includes visible light water splitting in molecular photosystems, has been slow. Mallouk et al. recently developed electrochemical water-splitting cells using the Ru(II) complex as the visible light photosensitizer. the dye-sensitized PEC cell consisted of a dye-sensitized TiO2 layer, a Pt counter electrode, and an aqueous solution between them. Under a visible light (< 3 eV) illumination, only the dye molecule absorbed the light and became excited because TiO2 had the wide band gap. The light absorption of the dye was followed by the transfer of an electron from the excited state (S*) of the dye to the conduction band (CB) of TiO2 and its subsequent transfer to the transparent conducting oxide (TCO). The electrons moved through the wire to the Pt, where the water reduction (or H2 evolution) occurred. The oxidized dye molecules caused the water oxidation because their HOMO level was below the H2O/O2 level. Organic dyes have been developed as metal-free alternatives to the Ru(II) complexes because of their tunable optical and electronic properties and low-cost manufacturing. Recently, organic dye molecules containing multi-branched, multi-anchoring groups have received a great deal of interest. In this work, tri-branched tri-anchoring organic dyes (Dye 2) were designed and applied to visible light water-splitting cells based on dye-sensitized TiO2 electrodes. Dye 2 had a molecular structure containing one donor (D) and three acceptor (A) groups, and each ended with an anchoring functionality. In comparison, mono-anchoring dyes (Dye 1) were also synthesized. The PEC response of the Dye 2-sensitized TiO2 film was much better than the Dye 1-sensitized or unsensitized TiO2 films.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.1
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• pp.49-60
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• 2018

The role of the botanical garden in securing biodiversity, responding to climate change, and sustainable development in modern cities is becoming more important. Private botanical gardens that lead the domestic botanical culture are declining due to complex reasons such as an increase of tourist destinations, lack of policy support, lack of introduction of advanced management strategies, and similar leisure activities to national and public botanical gardens. The Private Botanical Garden not only has a large number of plant species with high conservation value, but also provides various public utilities as a cultural and educational space and government efforts are needed to activate the operation of this amenity. The purpose of this study is to evaluate the value of public functions provided by private botanical gardens using the Contingent Valuation Method (CVM). In this way, the government aims to provide a basis for policy and institutional support for private botanical gardens. The main results are as follows. First, public utility functions provided by private botanical gardens were recognized as 'preservation' (23.4%), 'recreation and tourism' (17.5%) and 'research' (16.6%) in order of analytic hierarchy process (AHP). Second, 'heritage value' (33.0%) and 'existence value' (32.5%) were recognized as significant among the values provided by private botanical gardens. Third, the willingness to pay (WTP) to preserve the public functions of the private botanical garden was 12,234 won. Based on this, the economic value of all private botanical gardens in the whole country was estimated, resulting in about 233.8 billion won. There is a need to revise laws and regulations related to financial support for the revitalization and quality improvement of private botanical gardens. It is also necessary to establish a cooperative operating system between national, public and private botanical gardens.

• International Journal of High-Rise Buildings
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• v.6 no.3
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• pp.227-235
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• 2017

This paper describes some of the challenges for structural design of a mid-story seismic isolated high-rise building, which is located near Tokyo station, completed in 2015. The building is a mixed-use complex and encompasses three volumes: one substructure including basement and lower floors, and a pair of seismic isolated superstructures on the substructure. One is a 136.5m high Main Tower (office use), and the other is a 98.5 m high South Tower (hotel use). The seismic isolation systems are arranged in the $3^{rd}$ floor of the Main Tower and $5^{th}$ floor of the South Tower, so that we call this isolation system as the mid-story seismic isolation. The primary goal of the structural design of this building was to secure high seismic safety against the largest earthquake expected in Tokyo. We adopted optimal seismic isolation equipment simulated by dynamic analysis to minimize building damage. On the other hand, wind-induced vibration of a seismic isolated high-rise building tends to be excited. To reduce the vibration, the following strategies were adopted respectively. In the Main Tower with a large wind receiving area, we adopted a mechanism that locks oil dampers at the isolation level during strong wind. In the South Tower, two tuned mass dampers (TMDs) are installed at the top of the building to control the vibration. In addition, our paper will also report the building performance evaluated for wind and seismic observation after completion of the building. In 2016, an earthquake of seismic intensity 3 (JMA scale) occurred twice in Tokyo. The acceleration reduction rate of the seismic isolation level due to these earthquakes was approximately 30 to 60%. These are also verified by dynamic analysis using observed acceleration data. Also, in April 2016, a strong wind exceeding the speed of 25m/s occurred in Tokyo. On the basis of the record at the strong wind, we confirmed that the locking mechanism of oil damper worked as designed.