• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.1
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• pp.103-118
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• 2018

Ecosystems in subalpine regions are recognized as areas vulnerable to climatic changes because rainfall and the possibility of flora migration are very low due to the characteristics of topography in the regions. In this context, habitat niche was formulated for representative species of arbors in subalpine regions in order to understand the effects of climatic changes on alpine arbor ecosystems. The current potential habitats were modeled as future change areas according to the climatic change scenarios. Based on the growth conditions and environmental characteristics of the habitats, the study was conducted to identify direct and indirect causes affecting the habitat reduction of Abies nephrolepis. Diverse model algorithms for explanation of the relationship between the emergence of biological species and habitat environments were reviewed to construct the environmental data suitable for the six models(GLM, GAM, RF, MaxEnt, ANN, and SVM). Weights determined through TSS were applied to the six models for ensemble in an attempt to minimize the uncertainty of the models. Based on the current climate determined by averaging the climates over the past 30years(1981~2010) and the HadGEM-RA model was applied to fabricate bioclimatic variables for scenarios RCP 4.5 and 8.5 on the near and far future. The results of models of the alpine region tree species studied were put together and evaluated and the results indicated that a total of eight national parks such as Mt. Seorak, Odaesan, and Hallasan would be mainly affected by climatic changes. Changes in the Baekdudaegan reserves were analyzed and in the results, A. nephrolepis was predicted to be affected the most in the RCP8.5. The results of analysis as such are expected to be finally utilizable in the survey of biological species in the Korean peninsula, restoration and conservation strategies considering climatic changes as the analysis identified the degrees of impacts of climatic changes on subalpine region trees in Korean peninsula with very high conservation values.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.388-402
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• 2003

In this paper, we carried out the word, 4 continuous digits. continuous, and task-independent word recognition experiments to verify the effectiveness of the re-defined phoneme-likely units (PLUs) for the phonetic decision tree based HM-Net (Hidden Markov Network) context-dependent (CD) acoustic modeling in Korean appropriately. In case of the 48 PLUs, the phonemes /ㅂ/, /ㄷ/, /ㄱ/ are separated by initial sound, medial vowel, final consonant, and the consonants /ㄹ/, /ㅈ/, /ㅎ/ are also separated by initial sound, final consonant according to the position of syllable, word, and sentence, respectively. In this paper. therefore, we re-define the 39 PLUs by unifying the one phoneme in the separated initial sound, medial vowel, and final consonant of the 48 PLUs to construct the CD acoustic models effectively. Through the experimental results using the re-defined 39 PLUs, in word recognition experiments with the context-independent (CI) acoustic models, the 48 PLUs has an average of 7.06%, higher recognition accuracy than the 39 PLUs used. But in the speaker-independent word recognition experiments with the CD acoustic models, the 39 PLUs has an average of 0.61% better recognition accuracy than the 48 PLUs used. In the 4 continuous digits recognition experiments with the liaison phenomena. the 39 PLUs has also an average of 6.55% higher recognition accuracy. And then, in continuous speech recognition experiments, the 39 PLUs has an average of 15.08% better recognition accuracy than the 48 PLUs used too. Finally, though the 48, 39 PLUs have the lower recognition accuracy, the 39 PLUs has an average of 1.17% higher recognition characteristic than the 48 PLUs used in the task-independent word recognition experiments according to the unknown contextual factor. Through the above experiments, we verified the effectiveness of the re-defined 39 PLUs compared to the 48PLUs to construct the CD acoustic models in this paper.

• Geomechanics and Engineering
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• v.39 no.3
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• pp.241-256
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• 2024

The shear strength (SS) of soil is a critical parameter utilized in the design of civil engineering projects. The SS parameters, including cohesion (c) and friction angle (𝜑), can be determined through methods conducted either in the field or within a laboratory environment. However, the traditional method for determining SS parameters are not only costly but also time-consuming. Recently, the application of machine learning (ML) in geotechnical problems has received increasing attention. In order to select an appropriate ML model and assess the effect of physical properties on the SS of soil. This research endeavors to predict critical SS parameters of soil through the application of five machine learning (ML) models, integrating easily-available physical soil index, including specific gravity (G), saturation degree (Sr), liquid limit (LL), silt content (SC), and clay content (CC). The used ML techniques include Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). A range of metrics, encompassing the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R²) were used to measure the predictive efficacy of the employed models as well as compare the performance of the used ML models. The values of R² range from 0.769 to 0.987 indicate that all ML models exhibit excellent predictive capabilities for estimating SS parameters, in which the XGBoost, and CNN techniques show outperforming results compared to the other models. The study uses decision tree feature importance (DTFI) and coefficient feature importance (CFI) techniques to investigate how various physical properties impact the predictive capabilities of the model and indicates that both G and LL have a substantial impact on the predictive accuracy of cohesion and friction angle.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.2
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• pp.126-135
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• 2015

This study was conducted to develop a large scale map of species-specific plantation sites based on selected environmental variables such as topography, soil, and climatic factors in Namwon city. Site index equations by tree species were first regressed to 27 environmental variables that could influence the productivity of forest sites using digital forest site maps, digital climate maps, and the 5th National Forest Inventory data. Site index equations by tree species were all evaluated to estimate site productivity using 4-5 environmental variables, and the models' reliability was confirmed based on evaluation statistics. The determination coefficients of site index equations by species ranged from 0.42 to 0.76. With the site index equations, the site conditions appropriate for productive sites by species were considered to assess spatial distribution of productive areas for each species. The final map for optimal plantation in Namwon city was produced based on both site index equations and site conditions appropriate for productive sites by each species using GIS technique. Field survey was conducted to evaluate the suitability of selected species on the map of species-specific plantation sites. Results showed that the plantation map provides relatively reasonable spatial distribution of productive areas for selected species. It was revealed, however, that the sites evaluated as 'not suitable' for any tree species should be revised and complemented with additional information, especially with the site conditions appropriate for productive sites by species of interest. The outcomes of this study are expected to provide information for making customized species-specific plantation maps.

• Journal of the Society of Naval Architects of Korea
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• v.38 no.2
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• pp.79-86
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• 2001

This study shows an attempt to generate an assembly sequence and its model for a ship block assembly using the graph theory and graph algorithms. To generate the ship block assembly, we propose four levels of the ship block assembly model such as "geometry mode1", "relational model", "sequential mode1", and "hierarchical model". To obtain the relational model, we used surface and surface intersection algorithm. The sequential model that represents a possible assembly sequence is made by using several graph algorithms from the relational model. The hierarchical model will be constructed from the sequential model in order to represent the block assembly tree and so forth. The purpose of the hierarchical model is to define an assembly tree and to generate the Bill Of Material(BOM). Lastly, the validity of the method proposed in this study is examined with application to ship block assembly models of a single type and double type according to four models mentioned above.

• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.1-13
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• 2000

While various types of information can be mixed in a continuous video stream without any cleat boundary, the meaning of a video scene can be interpreted by multiple levels of abstraction, and its description can be varied among different users. Therefore, for the content-based retrieval in video data it is important for a user to be able to describe a scene flexibly while the description given by different users should be maintained consistently This paper proposes an effective way to represent the different types of video information in conventional database models such as the relational and object-oriented models. Flexibly defined attributes and their values are organized as tree-structured dictionaries while the description of video data is stored in a fixed database schema. We also introduce several browsing methods to assist a user. The dictionary browser simplifies the annotation process as well as the querying process of a user while the result browser can help a user analyze the results of a query in terms of various combinations of Query conditions.

• Journal of Forest and Environmental Science
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• v.17 no.1
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• pp.62-73
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• 2001

This study was conducted to predict the tractive resistance for tree length logs being skidded by ground based logging machine. The mathematical models for predicting the tractive resistance of tree length log have been developed. The tractive resistance is expressed as a function of log weight, skidding coefficient, and ground gradient. The skidding coefficients for four species of Korean pine, Japanese larch, mongolian oak, and cork oak were determined under laboratory condition using universal testing machine and small soil bin, Three different tractive resistance models were applied to four species and compared with each other. The ratios (T/Wt) of skidding-line tensions to the skidding log weight increased linearly with increment in ground gradient. Semi-ground skidding generally required smaller tensions than ground skidding under given condition. Results of this study can be utilized as basic information for logging machine selection and power requirement of skidding winch.