• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.250-258
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• 2007

Current speech recognition technology s achieved high performance with the development of hardware devices, however it is insufficient for some applications where high reliability is required, such as voice control of powered wheelchairs for disabled persons. For the system which aims to operate powered wheelchairs safely by voice in real environment, we need to consider that non-voice commands such as user s coughing, breathing, and spark-like mechanical noise should be rejected and the wheelchair system need to recognize the speech commands affected by disability, which contains specific pronunciation speed and frequency. In this paper, we propose non-voice rejection method to perform voice/non-voice classification using both YIN based fundamental frequency(F0) extraction and reliability in preprocessing. We adopted a multi-template dictionary and acoustic modeling based speaker adaptation to cope with the pronunciation variation of inarticulately uttered speech. From the recognition tests conducted with the data collected in real environment, proposed YIN based fundamental extraction showed recall-precision rate of 95.1% better than that of 62% by cepstrum based method. Recognition test by a new system applied with multi-template dictionary and MAP adaptation also showed much higher accuracy of 99.5% than that of 78.6% by baseline system.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.148-153
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• 1999

Baikdu-san was a very active volcano during the Cenozoic era and is believed to be formed in late Cenozoic era. Recently it was also reported that there was a major eruption in or around 1002 A.D. and there are evidences which indicate that it is still an active volcano and a potential volcanic hazard. Remote sensing techniques have been widely used to monitor various natural hazards, including volcanic hazards. However, during an active volcanic eruption, volcanic ash can basically cover the sky and often blocks the solar radiation preventing any use of optical sensors. Synthetic aperture radar(SAR) is an ideal tool to monitor the volcanic activities and lava flows, because the wavelength of the microwave signal is considerably longer that the average volcanic ash particle size. In this study we have utilized several sets of SAR data to evaluate the utility of the space-borne SAR system. The data sets include JERS-1(L-band) SAR, and RADARSAT(C-band) data which included both standard mode and the ScanSAR mode data sets. We also utilized several sets of auxiliary data such as local geological maps and JERS-1 OPS data. The routine preprocessing and image processing steps were applied to these data sets before any attempts of classifying and mapping surface geological features. Although we computed sigma nought ($\sigma$$^{0}$) values far the standard mode RADARSAT data, the utility of sigma nought image was minimal in this study. Application of various types of classification algorithms to identify and map several stages of volcanic flows was not very successful. Although this research is still in progress, the following preliminary conclusions could be made: (1) sigma nought (RADARSAT standard mode data) and DN (JERS-1 SAR and RADARSAT ScanSAR data) have limited usefulness for distinguishing early basalt lava flows from late trachyte flows or later trachyte flows from the old basement granitic rocks around Baikdu-san volcano, (2) surface geological structure features such as several faults and volcanic lava flow channels can easily be identified and mapped, and (3) routine application of unsupervised classification methods cannot be used for mapping any types of surface lava flow patterns.

• Journal of the Korean association of regional geographers
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• v.1 no.1
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• pp.93-101
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• 1995

Nam Khane watershed, in the Northern Laos, consists of limestone plateau surrounded with steep slope(above 1000m), wide piedmont hill land(300-700m) and narrow alluvial plain. Opium on the plateau and up-land rice on the hill-side are cultivated for each, but its shifting agricultural activity, which degrades the forest and soil, has caused the serious environmental problems. MOS-1 satellite image and 40 points of soil samples are analyzed to identify the distribution of the shifting cultivation and to evaluate the environmental problems for Nam Khane watershed. The land use classification map is presented on the photo 2, and the value of each land use area by elevation level and soil property are showed on the table 2 and 3, respectively. Excessive agricultural activity of shifting cultivation in the Nam Khane watershed not only decreased the forest area, but also changed the primary forest of tree into secondary woodland of shrub. On the phase of soil property, it accelerated the soil and gully erosion, and acidification. To solve these environmental problems, the most important step is to settle the agriculture from shifting cultivation to permanent cropping.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.57-62
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• 2011

On the view point of renewable energies as energy sources of district heating and cooling plant, the purpose of this study is to develop, classify and map the 500m${\times}$500m mesh, of which is treated as normal size in DHC regulations for evaluation process. Followings are the results. Various building and geographical informations including 13 districts and 108 counties are re-defined to create 500m${\times}$500m meshes, and it is find out that 3,289 meshes among 8,463 meshes have meaningful floor areas. Only 59 meshes(1.8%) are evaluated as mesh which has more than 50% of building volume ratio per mesh. 5 clusters classified by principal analysis and cluster analysis with building needs' characteristics are defined. Gwang-an Dong is representative of cluster 1 characterized as commercial area, and the cluster 4, 5 which has mainly residential needs are distributed in Yong-ho dong. Because there are a lot of cluster 3 meshes, which has complex needs area based on residential, cluster 3 could be defined as representative of Busan metropolitan city.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.9-17
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• 2020

In this paper, we proposed an SNS review analysis method based on deep learning for user tendency. The existing SNS review analysis method has a problem that does not reflect a variety of opinions on various interests because most are processed based on the highest weight. To solve this problem, the proposed method is to extract the user's personal tendency from the SNS review for food. It performs classification using the YOLOv3 model, and after performing a sentiment analysis through the BiLSTM model, it extracts various personal tendencies through a set algorithm. Experiments showed that the performance of Top-1 accuracy 88.61% and Top-5 90.13% for the YOLOv3 model, and 90.99% accuracy for the BiLSTM model. Also, it was shown that diversity of the individual tendencies in the SNS review classification through the heat map. In the future, it is expected to extract personal tendencies from various fields and be used for customized service or marketing.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.3
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• pp.343-357
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• 2012

The Seoul metropolitan subway system can be represented by a graph which consists of nodes and edges. In this paper, we study classification of subway stations and trip behaviour of subway passengers through partitioning the graph of the subway system into roughly equal groups. A weight of each edge of the graph is set to the number of passengers who pass the edge, where the number of passengers is extracted from the transportation card transaction database. Since the graph partitioning problem is NP-complete, we propose a heuristic algorithm to partition the subway graph. The heuristic algorithm uses one of two alternative objective functions, one of which is to minimize the sum of weights of edges connecting nodes in different groups and the other is to maximize the ratio of passengers who get on the subway train at one subway station and get off at another subway station in the same group to the total subway passengers. In the experimental results, we illustrate the subway stations and edges in each group by color on a map and analyze the trip behaviour of subway passengers by the group origin-destination matrix.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.187-195
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• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.

• Korean Journal of Soil Science and Fertilizer
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• v.4 no.2
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• pp.187-192
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• 1971

This paper deals mainly with the genesis and classification of the Jeonnam series. These soils have brown to dark brown silt loam and silty clay loam A horizon(strong brown or reddish brown where eroded). Argillic B horizons are dominantly red or yellowish red silty clay loam to silty clay with moderately developed subangular blocky structure and with thin clay cutans on the ped faces. The C horizons are strongly and very deeply weathered strong brown, yellowish brown, pale brown and reddish yellow silty clay loam and sandy loam granitic saprolite. Content of clay increases with depth to a maximum between 100cm. Percolating water seems to be responsible for transportation and oriented deposition of clay. Chemically, soil reaction is strongly acid to medium acid throughout the profile. The content of organic matter is 1 to 2 percent, and decreases regularly with depth. Base saturation is low, based on amount of extractable cations. Characterisltically the Jeonnam series are similar to Red-Yellow Podzolic soils of the United States and are similar to Red-Yellow soils of the Japan. In the writer's opinion the Jeonnam soils are classified as Red Yellow soils. According to USDA 7th approximation, this soil can be classified as Typic Hapludults and in the FAO/UNESCO World Soil Map as Helvic Acrisols.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.509-516
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• 2012

In this research, it was intended to examine the vulnerability of Pinus rigida to climate changes, a major planting species in Korea. For this purpose, the distribution of Pinus rigida and its changes caused by climate changes were estimated based on the 'A1B' climate change scenario suggested by IPCC. Current distribution of Pinus rigida was analyzed by using the $4^{th}$Forest Type Map and its potential distribution in the recent year (2000), the near future (2050) and the further future (2100) were estimated by analyzing the optimized ranges of three climate indices - warmth index(WI), minimum temperature index of the coldest month (MTCI) and precipitation effectiveness index(PEI). The results showed that the estimated potential distribution of Pinus rigida declines to 56% in the near future(2050) and 15% in the further future (2100). This significant decline was found in most provinces in Korea. However, in Kangwon province where the average elevation is higher than other provinces, the area of potential distribution of Pinus rigida increases in the near future and the further future. Also the result indicated that the potential distribution of Pinus rigida migrates to higher elevation. The potential distributions estimated in this research have relatively high accuracy with consideration of classification accuracy (44.75%) and prediction probability (62.56%).