• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1445-1462
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• 2022

Compact Advanced Satellite 500-4 (CAS500-4), which is scheduled to be launched in 2025, is a mid-resolution satellite with a 5 m resolution developed for wide-area agriculture and forest observation. To utilize satellite images, it is important to establish a precision sensor model and establish accurate geometric information. Previous research reported that a precision sensor model could be automatically established through the process of matching ground control point (GCP) chips and satellite images. Therefore, to improve the geometric accuracy of satellite images, it is necessary to improve the GCP chip matching performance. This paper proposes an improved GCP chip matching scheme for improved precision sensor modeling of mid-resolution satellite images. When using high-resolution GCP chips for matching against mid-resolution satellite images, there are two major issues: handling the resolution difference between GCP chips and satellite images and finding the optimal quantity of GCP chips. To solve these issues, this study compared and analyzed chip matching performances according to various satellite image upsampling factors and various number of chips. RapidEye images with a resolution of 5m were used as mid-resolution satellite images. GCP chips were prepared from aerial orthographic images with a resolution of 0.25 m and satellite orthogonal images with a resolution of 0.5 m. Accuracy analysis was performed using manually extracted reference points. Experiment results show that upsampling factor of two and three significantly improved sensor model accuracy. They also show that the accuracy was maintained with reduced number of GCP chips of around 100. The results of the study confirmed the possibility of applying high-resolution GCP chips for automated precision sensor modeling of mid-resolution satellite images with improved accuracy. It is expected that the results of this study can be used to establish a precise sensor model for CAS500-4.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Weed & Turfgrass Science
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• v.2 no.1
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• pp.38-46
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• 2013

With increasing environmental issues from synthetic chemical herbicides, microbe-originated herbicides could be a fascinating alternative in current agriculture. We isolated Streptomyces strains that produced herbicidal active metabolite(s) against a grass weed Digitaria sanguinalis. According to the result from 16S rDNA sequence comparison with the close strains, the best isolate (Code name MS-80673) was identified as Streptomyces scopuliridis KR-001. The closest type strain was Streptomyces scopuliridis RB72 which was previously reported as a bacteriocin producer. The optimal culture condition of S. scopuliridis KR-001 was $28^{\circ}C$, pH 7.0 and culture period 4 to7 days. Both of soil and foliar application of the crude culture broth concentrate was effective on several troublesome or noxious weed species such as a Sciyos angulatus in a greenhouse and field condition. Phytotoxic symptoms of the culture broth concentrate of S. scopuliridis KR-001 by foliar application were wilting and burndown of leaves, and stems followed by discoloration and finally plant death. In crops such as rice, wheat, barley, hot pepper and tomato, growth inhibition was observed. These results suggest that the new S. scopuliridis KR-001 strain producing herbicidal metabolites may be a new bio-herbicide candidate and/or may provide a new lead molecule for a more efficient herbicide.

• Journal of Animal Environmental Science
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• v.14 no.3
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• pp.201-210
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• 2008

Farmers directly spread the livestock manure compost on their arable land as an organic fertilizer. However, there are some difficult problems to solve. first, we are unsure of whether the livestock manure compost can meet the nutritional demand of plant. Second, application of the current powered livestock manure compost to crop land is very difficult work due to heavy weight of compost and its powdered shape. For this reason, this study was carried out to develope high quality pelletized livestock manure compost. In pelletizing process with composted manure, the optimal water content for pelletizing was around 30$\sim$40%. When rice bran was mixed with 5% as a bonding agent on volume basis, the pelletizing effect was remarkably improved. On a dry matter basis, the contents of N and P of manure compost were 1.31%, and 0.58%, respectively. After pelletizing, the contents of compost pelleted were 1.37% and 0.54%, respectively. The same parameters of pelletized compost made by screw type Instrument were 1.37% and 0.53%, respectively. The other hand, N and P content of pelletized compost made by pellet mill type instrument were 1.06% and 0.18%, respectively.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.197-202
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• 2010

Purpose: Recently, many hospitals have been tried to increase the satisfaction of the outpatients through blood-gathering, exam, result notice and process in a day. Each laboratory has been used the automatic equipment for the rapid requests of the result notice and the increase of the reliability and efficiency. Current automatic equipments that have been limited short TAT(Turn-Around Time)because of the restricted batch lists and 1 tip-5 detectors. The Gamma Pro which is made in Korea to improve the shortcomings of existing automation equipment, complemented with capacity to perform a wide range of domestic automation equipment. In this study, we evaluated the usefulness and reliability of short TAT by comparing Gamma Pro with current automatic equipment. Materials and Methods: We studied the correlation between Gamma Pro and RIA-mat 280 using the respective 100 specimens of low or high density to the patients who were requested the thyroid hormone test (Total T3, TSH and Free T4) in Samsung Medical Center Sep. 2009. To evaluate the split-level Gamma Pro, First, we measured accuracy and carry over on the tips. Second, the condition of optimal incubation was measured by the RPM (Revolution Per Minute) and revolution axis diameter on the incubator. For the analysis for the speed of the specimen-processing, TAT was investigated with the results in a certain time. Result: The correlation coefficients (R2) between the Gamma Pro and RIA-mat 280 showed a good correlation as T3 (0.98), TSH (0.99), FT4 (0.92). The coefficient of variation (C.V) and accuracy was 0.38 % and 98.3 % at tip 1 and 0.39 % and 98.6 % at tip 2. Carry over showed 0.80 % and 1.04% at tip 1 and tip 2, respectively. These results indicate that tips had no effect on carry over contamination. At the incubator condition, we found that the optimal condition was 1.0mm of diameter at 600RPM in 1.0mm and 1.5mm of at 500RPM or 1.0mm and 1.5 mm of diameter at 600 RPM. the Gamma Pro showed that the number of exam times were increased as maximum 20 times/day comparing to 6 times/day by current automatic equipment. These results also led to the short TAT from 4.20 hour to 2.19 hours in whole processing. Conclusion: The correlation of between the Gamma Pro and RIA-mat 280 was good and has not carry over contamination in tips. The domestic automation equipment (Gamma Pro) decreases the TAT in whole test comparing to RIA-280. These results demonstrate that Gamma Pro has a good efficiency, reliability and practical usefulness, which may contribute to the excellent skill to process the large scale specimens.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.1-18
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• 2014

Over the past years RFID/SN has been an elementary technology in a diversity of applications for the ubiquitous environments, especially for Internet of Things. However, one of obstacles for widespread deployment of RFID technology is the inherent unreliability of the RFID data streams by tag readers. In particular, the problem of false readings such as lost readings and mistaken readings needs to be treated by RFID middleware systems because false readings ultimately degrade the quality of application services due to the dirty data delivered by middleware systems. As a result, for the higher quality of services, an RFID middleware system is responsible for intelligently dealing with false readings for the delivery of clean data to the applications in accordance with the tag reading environment. One of popular techniques used to compensate false readings is a sliding window filter. In a sliding window scheme, it is evident that determining optimal window size intelligently is a nontrivial important task in RFID middleware systems in order to reduce false readings, especially in mobile environments. In this paper, for the purpose of reducing false readings by intelligent window adaption, we propose a new adaptive RFID data cleaning scheme based on window sliding for a single tag. Unlike previous works based on a binomial sampling model, we introduce the weight averaging. Our insight starts from the need to differentiate the past readings and the current readings, since the more recent readings may indicate the more accurate tag transitions. Owing to weight averaging, our scheme is expected to dynamically adapt the window size in an efficient manner even for non-homogeneous reading patterns in mobile environments. In addition, we analyze reading patterns in the window and effects of decreased window so that a more accurate and efficient decision on window adaption can be made. With our scheme, we can expect to obtain the ultimate goal that RFID middleware systems can provide applications with more clean data so that they can ensure high quality of intended services.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.455-463
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• 2010

In this paper, we present an approach to sentence boundary detection for web documents that builds on statistical-based methods and uses rule-based correction. The proposed system uses the classification model learned offline using a training set of human-labeled web documents. The web documents have many word-spacing errors and frequently no punctuation mark that indicates the end of sentence boundary. As sentence boundary candidates, the proposed method considers every Ending Eomis as well as punctuation marks. We optimize engine performance by selecting the best feature, the best training data, and the best classification algorithm. For evaluation, we made two test sets; Set1 consisting of articles and blog documents and Set2 of web community documents. We use F-measure to compare results on a large variety of tasks, Detecting only periods as sentence boundary, our basis engine showed 96.5% in Set1 and 56.7% in Set2. We improved our basis engine by adapting features and the boundary search algorithm. For the final evaluation, we compared our adaptation engine with our basis engine in Set2. As a result, the adaptation engine obtained improvements over the basis engine by 39.6%. We proved the effectiveness of the proposed method in sentence boundary detection.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.8
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• pp.991-998
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• 2013

The fuel injection pipe of a common rail system used in a clean diesel vehicle plays a role in supplying fuel from a rail to the injector of each cylinder connecting the engine under a repeated internal pressure. The fuel injection pressure is increased to over 200 MPa for satisfying EU emission standards and improving fuel efficiency, and a heading process and an autofrettage process are required for preventing folding defects and improving fatigue life. In this study, the flow stress and SN data of the material of the pipe are obtained through a tensile test and a fatigue test. The heading process for checking the folding defects of pipe ends is performed by using FEA. Furthermore, the optimal design of the autofrettage process for improving fatigue life considering not only the compressive residual stresses of the inner surface but also the tensile residual stresses of the outer surfaces of the pipe under the repeated internal pressure is performed by using FEA. To verify the process design, fatigue analysis for the autofrettaged pipe is performed.

• Korean Journal of Microbiology
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• v.49 no.4
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• pp.353-359
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• 2013

The objectives of this study were to investigate effectiveness of the Compressed Air Jet (CAJ) System for cleaning up shorelines contaminated with crude oils and to examine effects of the system on total petroleum hydrocarbon (TPH) removal and microbial community changes before and after remediation of the oil-contaminated shorelines. These data will lead to better understanding of optimized remediation process. About 66% of TPH reduction was observed when the contaminated site was treated with the CAJ System 2, 3, 4, and 5 times. This treatment system was more efficient than the seawater pumping system under similar treatment conditions (by 40%). By the way, little oil degrader communities were observed despite a potential function of the air jet system to stimulate aerobic oil degraders. The apparent low population density of the oil degraders might be as a result of low concentration of TPH as a carbon source and limiting nutrients such as nitrogen and phosphorus. It was proposed that the CAJ System would contribute significantly to removal of residual oils on the shorelines in combination with addition of these limiting nutrients.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.