• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.141-156
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• 2023

Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.40-50
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• 2010

The building process of any geological map involves linking sparse lithological outcrop information with equally sparse geometrical measurements, all in a single entity which is the preferred interpretation of the field geologist. The actual veracity of this interpretative map is partially dependent upon the frequency and distribution of geological outcrops compounded by the complexity of the local geology. Geophysics is commonly used as a tool to augment the distribution of data points, however it normally does not have sufficient geometrical constraints due to: a) all geophysical inversion models being inherently non-unique; and b) the lack of knowledge of the physical property contrasts associated with specific lithologies. This contribution proposes the combined use of geophysical edge detection routines and 'three point' solutions from topographic data as a possible approach to obtaining geological contact geometry information (strike and dip), which can be used in the construction of a preliminary geological model. This derived geological information should first be assessed for its compatibility with the scale of the problem, and any directly observed geological data. Once verified it can be used to help constrain the preferred geological map interpretation being developed by the field geologist. The method models the contacts as planar surfaces. Therefore, it must be ensured that this assumption fits the scale and geometry of the problem. Two examples are shown from folded sequences at the Bathurst Mining Camp, New Brunswick, Canada.

• Ecology and Resilient Infrastructure
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• v.3 no.3
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• pp.189-200
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• 2016

The species distribution model would be a useful tool for understanding how invasive alien species spread over the country and what environmental variables contribute to their distributions. This study is focused on the potential distribution of two invasive alien species, the common ragweed (Ambrosia artemisiifolia) and knotgrass (Paspalum distichum) in the Korean Peninsula. The maximum entropy (Maxent) model was used for the prediction of their distribution by inferring their climatic environmental requirements from localities where they are currently known to occur. We obtained their presence data from the Global Biodiversity Information Facility and the Korean plant species databases and bioclimatic data from the WorldClim dataset. As a results of the modelling, the potential distribution predicted by global occurrence data was more accurate than that by native occurrence data. The variables determining the common ragweed distribution were precipitation of the driest month and annual mean temperature. Both annual and the coldest quarter mean temperatures were critical factors in determining the knotgrass distribution. The Maxent model could be a useful tool for the prediction of alien species invasion and the management of their expansion.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1095-1105
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• 2014

A spatial downscaling method using the Support Vector Machine (SVM) Regression for 25 km Tropical Rainfall Measuring Mission (TRMM) Monthly precipitation is proposed. The nonlinear relationship among hydrometeorological variables and precipitation was effectively depicted by the SVM for predicting downscaled grid precipitation. The accuracy of spatially downscaled precipitation was estimated by comparing with rain gauge data from sixty-four stations and found to be improved than the original TRMM data in overall. Especially the positive bias of the original TRMM data was effectively removed after the downscaling procedure. The spatial distributions of 25 km and 1 km grid precipitation were generally similar, while the local spatial trend was better detected by 1 km grid precipitation. The downscaled grid data derived from the proposed method can be applied in hydrological modelling for higher accuracy and further be studied for developing optimized downscaling method incorporation other regression methods.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.289-293
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• 2008

In this study, in order to find out the movement of polluted substance that is flown into the river and the characteristics of dispersion, the experiment that used the RI (Radio Isotope) tracer in the river was undertaken, and by using the experiment result, the figure modelling was undertaken to analyze the general type of pollutant dispersion. In addition, in order to calculate more accurate dispersion range and moving time, the experiment was done in about 2km from the measuring points of Namdaecheon around the Yongdam Dam of the upper Geum River to the lower stream, and modeling was undertaken for the 20km zone from the measuring points to the lower stream. In order to find out the flow of river and dispersion of polluted substance, RMA (Resource Modeling Associates)-2 and RMA-4 program are used in study. The site experiment using the RI was implemented for the experiment in the applied area and the same area, and the distance between each zone was set for 1㎞ with the slight difference for site situation and measured the density date of one second distance through the NaI apparatus to measure the density data of one second interval. On the basis of this measured data, it is compared and analyzed with the result of figure copy of RMA-2 and RMA-4 models to make the comparison and analysis of density distribution following the change in expansion coefficient that makes great influence on expansion range and dispersion in natural rivers. The influence of expansion coefficient on river can be researched and the measured density data, the maximum and minimum density time, and the basic data to calculate the expansion coefficient was prepared in this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.52-61
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• 2004

Korea Water Resources Corporation carried out LiDAR survey to construct detailed terrain data for flood mapping and it is expected that much money is required in flood mapping of all over the country. Therefore, it is desirable to use NGIS digital map to construct preliminary modelling data for selection of flood mapping area. And the analysis of DEM error with respect to scale of digital map is necessary for the sake of applying digital map as the input data of flood mapping. We compared DEM from digital map with DEM from LiDAR survey. Especially we analyzed DEM error characteristics that is occurred with respect to the interpolation method that is used to construct DEM from TIN of digital map. As a result of analysis, digital map(1:1,000) showed smaller error than digital map(1:5,000) and DEM applying linear interpolation showed smaller error than DEM applying quintic interpolation. Especially, variation of DEM error by cell resolution was evaluated as very slight because urban district was composed of gentle slope.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.77-83
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• 2019

Stream water qualities have been predicted in the year 2002 and 2014 through providing the Hwangguji Stream Rectification Plan. However, the reliability of result for predicted water quality was relatively lower by applying conventional values of the parameters in model. In this study deoxygenation coefficients between Sema bridge(HGJ2) and Sujik bridge(HGJ3) have been evaluated based on the observed data of water quality and travelling time to compare with the applied value of coefficients in predicting water quality model. The values of deoxygenation coefficient $0.078day^{-1}{\sim}0.748day^{-1}$ for normal period and $0.053day^{-1}{\sim}0.505day^{-1}$ for drought period have been calculated based of observed data between Sema bridge and Sujik bridge. The values of coefficients $0.02day^{-1}{\sim}3.4day^{-1}$ have been applied in predicting water quality model in the year 2002 and $0.043day^{-1}$ 2014. Thus, the simulated results of stream water quality were better than the observed data in 2002, and worse in 2014. It has shown that values of deoxygenation coefficient should be properly estimated based on observed data to predict proper stream water quality by model.

• Journal of the Geological Society of Korea
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• v.54 no.5
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• pp.545-556
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• 2018

This study was conducted to estimate the deep percolation using numerical modeling and field observation data based on rainfall in Haean basin. Soil moisture sensors were installed to monitoring at 30, 60 and 90 cm depths in four sites (YHS1-4) and automatic weather station was installed to around YHS3. Soil moisture and meteorological data was observed from March 25, 2017 to March 25, 2018 and May 06, 2016 to May 06, 2018, respectively. Numerical analysis was performed from June to August, 2017 using the HYDRUS-1D. Average soil moisture contents were high to generally in YHS3 for 0.300 to $0.334m^3/m^3$ and lowest in YHS1 for 0.129 to $0.265m^3/m^3$ during the soil moisture monitoring period. The results of soil moisture flow modeling showed that field observations and modeling values were similar but the peak values were larger in the modeling result. Correlation analysis between observation and modeling data showed that r, $r^2$ and RMSE were 0.88, 0.77, and 0.0096, respectively. This show high correlation and low error rate. The total deep percolation was 744.2 mm during the period of modelling at 500 cm depth. This showed that 61.3% of the precipitation amount (1,214 mm) was recharged in 2017. Deep percolation amount was high in the study area. This study is expected to provide basic data for the estimation of groundwater recharge through unsaturated zone.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.