• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.2
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• pp.128-137
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• 2011

The purpose of the FAPIS(Forest Aerial Photograph Interpretation System) development is to increase accuracy and efficiency of the digital forest cover type mapping for improving conventional analog-based mapping procedures by optimizing work-flow and mapping technology. The database models including digital forest cover type map, aerial photograph, and topographic map were designed for use in this system construction. The interface configured concisely to connect with functions such as search engine, display control, conversion to stereo interpretation mode, modification tools, automation of print layout and database models. It is expected that the standardization methodology based on this system can be applied and extended in making all kinds of digital thematic maps, providing decision-making and information of forest resources.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.158-169
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• 2012

Sabah has experienced a rapid decline in the extent of forest cover. The precise impact of habitat loss on the conservation status of the plants of Sabah is uncertain. In this study we use the niche modelling algorithm MAXENT to construct preliminary, revised and final ecological niche models for Dipterocarpus lamellatus and Dipterocarpus ochraceus and combined these models with data on current land-use to derive conservation assessments for each species. Preliminary models were based on herbarium data alone. Ground surveys were conducted to evaluate the performance of these preliminary models, and a revised niche model was generated from the combined herbarium and ground survey data. The final model was obtained by constraining the predictions of the revised models by filters. The range overlap between the preliminary and revised models was 0.47 for D. lamellatus and 0.39 for D. ochraceus, suggesting poor agreement between them. There was substantial variation in estimates of habitat loss for D. ochraceus, among the preliminary, revised and constrained models, and this has the potential to lead to incorrect threat assessments. From these estimates of habitat loss, the historic distribution and estimates of population size we determine that both species should be classified as Critically Endangered under IUCN Red List guidelines. Our results suggest that ground-truthing of ecological niche models is essential, especially if the models are being used for conservation decision making.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.77-88
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• 2017

This study developed an inventory, using GIS-based resource assessment, for assisting forest management planning. The major inventory contents were relationally integrated, using field sample plots, to extract and calculate attributes such as general status, forest stand condition, forest site condition, forest site and soil area (ha) and growing stock (weight, in tons). Evaluating the efficiency of forest management plan implementations is critical to effective health and sustainability at a larger functional level, specifically in bamboo forests. This inventory is a valuable tool for decision-making, such as developing a long-term management plan for sustainably managing bamboo resources.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.183-183
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• 2019

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• Journal of Korean Society of Forest Science
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• v.97 no.3
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• pp.255-265
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• 2008

This study is aiming at providing the useful information for successful implementation of the forest resource promotion policy through analyzing the impacting factors and their relationships on policy accomplishment and policy effectiveness in executing the forest resource promotion policy. Interview survey was conducted on the stake-holders such as government officers and civilians who have direct or indirect influences on the establishment and execution of the forest resource promotion policy. The forest resource promotion policy was heuristically analyzed by using the LISREL (Linear Structural RELations) Model. The policy accomplishments of forest resource promotion significantly showed the positive relations with the suitability of problem recognition, the suitability of policy decision-making and rationality of policy execution, while the degree of policy support and the policy accomplishments were having significant impacts upon the policy effectiveness. Since the path coefficients between the latent variables and observed variables and among the latent variables mostly showed the positive relations significantly, the forest resource promotion policy was evaluated very successful in terms of policy accomplishments and policy effectiveness.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• Journal of Forest and Environmental Science
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• v.29 no.1
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• pp.49-57
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• 2013

Malaysia possesses about 1.56 million ha of Peat Swamp Forest (PSF). The PSF safeguard enormous biological diversity, while providing crucial benefits for the sustainable development of human communities. Numbers of threatened plant species are associated with the PSF, including the commercially important Gonystylus bancanus timber. To prevent significant losses of biodiversity, it is important to manage the PSF for both biological conservation and sustainable use. Equally important is to restore all degraded PSF in an attempt to ensure the PSF ecosystem is suitable for the vegetation to grow and rehabilitate back to the normal condition. Prior to plan any forest restoration program, there is a need to properly map the degraded PSF in order to estimate the forest conditions and determine the vegetations status. Most of the time this need to be done at a landscape level and requires a technology that can provide accurate, timely and reliable information for the planner to make decision. This paper describes a study using geospatial technology in combination with ground survey to classify the degraded PSF in South East Pahang Peat Swamp Forest (SEPPSF), Malaysia, into different degree of vegetation classes. With map accuracy of about 83%, the technique proved to be useful in delineating the different degree of PSF degradation from which the information can be used to properly plan forest restoration program in the area. The final output which is in the form of map can be used in developing a Restoration Master Plan for the degraded PSF areas.

• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.291-301
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• 2016

One of the most concerned topics in ecology is the relationship between biodiversity and ecosystem functioning. However, there are few field studies, carried out in forests, although many studies have been done in controlled experiments in grasslands. In this paper, we describe the relationship pattern between three facets of diversity and productivity at Ratargul Fresh Water Swamp Forest (RFWSF) in Bangladesh, which is the only remaining fresh water swamp forest of the country. Sixty sample plots were selected from RFWSF and included six functional traits including leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), tree height, bark thickness and wood density. In analyzing TD, we used Shannon diversity and richness indices, functional diversity was measured by Rao's quadratic entropy (Rao 1982) and Faith's (1992) index was used for phylogenetic diversity (PD). It was found that, TD, FD and PD were positively related with productivity (basal area) due to resource use complementarity but surprisingly the best predictor of tree productivity was FD. The results contribute to the understanding the effects of biodiversity loss and it is essential for conservation decision-making and policy-making of Ratargul Fresh Water Swamp Forest.