• Proceedings of the KSRS Conference
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• v.2
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• pp.710-712
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• 2006

The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.437-456
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• 2021

In the field of remote sensing in Korea, starting in 2017, deep learning has begun to show efficient research results compared to existing research methods. Currently, research is being conducted to apply deep learning in almost all fields of remote sensing, from image preprocessing to applications. To analyze the research trend of deep learning applied to the remote sensing field, Korean domestic journal papers, published until October 2021, related to deep learning applied to the remote sensing field were collected. Based on the collected 60 papers, research trend analysis was performed while focusing on deep learning network purpose, remote sensing application field, and remote sensing image acquisition platform. In addition, open source data that can be effectively used to build training data for performing deep learning were summarized in the paper. Through this study, we presented the problems that need to be solved in order for deep learning to be established in the remote sensing field. Moreover, we intended to provide help in finding research directions for researchers to apply deep learning technology into the remote sensing field in the future.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.657-664
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• 2007

Stroke patients need regular medical treatments and rehabilitation training from their doctors. However, severe aftereffects caused by stroke allow them minimum activities, which make it difficult for them to visit doctor. Recently, electric brain stimulation treatment has been found to be better way compared to conventional ones and many are interested in using this method for the treatment of stroke. In this study, we have developed a remote medical treatment system using wireless electric brain stimulator that can help the stroke patients to get a treatment without visiting their doctors. The developed remote medical treatment system connects the doctors to the brain stimulator implanted in the patients via the internet and ZigBee communication built in the brain stimulator. Also, the system receives personal information of the connected patients and cumulates the total records of electric stimulation therapy in a database. Doctors can easily access the information for better treatment planning with the help of graphical visualization tools and management software. The developed remote medical treatment system can be applied to the electric stimulation treatments for other brain diseases with a minor change.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.33-40
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• 2015

This paper presents the design and implementation of the educational remote controlled robot system including remote sensing in the embedded environment. The design of sensing information processing, software design and template design mechanism for the programming practice are introduced. LPC1769 using Cortex-M3 core as CPU, LPCXPRESSO as debugging environment, C language as firmware development language and FreeRTOS as OS are used in development environment. The control command is received via RF communication by the server and the robot system which is operated by driving the various sensors. The educational procedure is from robot demo operation program as hands-on practice and then compiling, loading of the basic robot operation program, already supplied. Thereafter the verification is checked by using the basic robot operation to allow demo operation such as hands-on-training procedure. The original protocol is designed via RF communication between server and robot system, and the satisfied performance result is presented by analyzing the robot sensing data processing.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1677-1689
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• 2022

Air pollution is social issue that has long-term and short-term harmful effect on the health of animals, plants, and environments. Chimneys are the primary source of air pollutants that pollute the atmosphere, so their location and type must be detected and monitored. Power plants and industrial complexes where chimneys emit air pollutants, are much less accessible and have a large site, making direct monitoring cost-inefficient and time-inefficient. As a result, research on detecting chimneys using remote sensing data has recently been conducted. In this study, YOLOv5-based chimney detection model was generated using BUAA-FFPP60 open dataset create for power plants in Hebei Province, Tianjin, and Beijing, China. To improve the detection model's performance, data split and data augmentation techniques were used, and a training strategy was developed for optimal model generation. The model's performance was confirmed using various indicators such as precision and recall, and the model's performance was finally evaluated by comparing it to existing studies using the same dataset.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.431-443
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• 2022

The purpose of this study is to share and assess the experiences of the emergency remote teaching method adopted for the medical communication course at a medical school due to the COVID-19 pandemic. The standardized patients hired for this 'Emergency Remote Teaching (hereafter ERT)' course said that students' interactions with them were less enthusiastic and less realistic, However, in the one-on-one virtual practice, the students seemed to be more focused than in the existing face to face practice. There were no differences in the unit practice test scores between ERT and the face-to-face course while in the face-to-face final exam, the test scores of FTF students were statistically higher than those of the ERT students, which might have resulted from the different methodologies of teaching. Further research on the virtual medical communication course is necessary to prepare medical students for the adoption of the telemedicine which could be accelerated in the near future.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.472-479
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• 1999

In this paper, we propose a land cover pattern classifier for remote sensing image by using neuro-fuzzy algorithm. The proposed pattem classifier has a 3-layer feed-forward architecture that is derived from generic fuzzy perceptrons, and the weights are con~posed of h u y sets. We also implement a neuro-fuzzy pattern classification system in the Visual C++ environment. To measure the performance of this, we compare it with the conventional neural networks with back-propagation learning and the Maximum-likelihood algorithms. We classified the remote sensing image into the eight classes covered the majority of land cover feature, selected the same training sites. Experimental results show that the proposed classifier performs well especially in the mixed composition area having many classes rather than the conventional systems.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.75-85
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• 2006

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.689-698
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• 2017

In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.