• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.185-191
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• 2022

This paper proposed a division algorithm of performance complexity $O{\frac{n(n+1)}{2}}$ for a change-making problem(CMP) in which polynomial time algorithms are not known as NP-hard problem. CMP seeks to minimize the sum of the x_j number of coins exchanged when a given amount of money C is exchanged for c_j,j=1,2,⋯,n coins. Known polynomial algorithms for CMPs are greedy algorithms(GA), divide-and-conquer (DC), and dynamic programming(DP). The optimal solution can be obtained by DP of O(nC), and in general, when given C>2ⁿ, the performance complexity tends to increase exponentially, so it cannot be called a polynomial algorithm. This paper proposes a simple algorithm that calculates quotient by dividing upper triangular matrices and main diagonal for k×n matrices in which only j columns are placed in descending order of c_j of n for c_j ≤ C and i rows are placed k excluding all the dividers in c_j. The application of the proposed algorithm to 39 benchmarking experimental data of various types showed that the optimal solution could be obtained quickly and accurately with only a calculator.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.263-272
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• 2017

The concept of Internet of Things (IoT) has been increasingly popular these days, and its areas of application have been broadened. However, if the data stored in an IoT system is damaged and cannot be recovered, our society would suffer considerable damages and chaos. Thus far, most of the studies on fault-tolerance have been focused on computer systems, and there has not been much research on fault-tolerance for IoT systems. In this study, therefore, a fault-tolerance method in IoT environments is proposed. In other words, based on the EVENODD method, one of the traditional fault-tolerance methods, a fault-tolerance storage and recovery method for the data stored in the IoT server is proposed, and the method is implemented on an oneM2M IoT system. The fault-tolerance method proposed in this paper consists of two phases - fault-tolerant data storage and recovery. In the fault-tolerant data storage phase, some F-T gateways are designated and fault-tolerant data are distributed in the F-T gateways' storage using the EVENODD method. In the fault-tolerant recovery phase, the IoT server initiates the recovery procedure after it receives fault-tolerant data from non-faulty F-T gateways. In other words, an EVENODD array is reconstructed and received data are merged to obtain the original data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.11-22
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• 2009

Tunnel excavation with several sections and appropriate auxiliary measures such as face bolt and pre-grouting are widely used in case of weak and less rigid ground for the stability of a tunnel face during excavation. This papers first described the evaluation methods proposed in technical literature to maintain the tunnel face stable, and then studied by FEM analysis whether face reinforcement is need in what degree of ground deformation and strength features for the stability of a tunnel face when excavating by full excavation with sub-bench. Lastly, a three dimensional FEM analysis was performed to study how the tunnel face itself and the ground around the tunnel behave depending on different bolt layouts, length of bolts, number of bolts. There were relative differences in comparison of results on the stability of a tunnel face by a theoretical evaluation methods and FEM analysis, but the same in reinforced effect of face. It was found that the stability of a tunnel face can be obtained with face bolt installed longer than 1.0D (tunnel width), bolt density of about 1 bolt per every $1.5\;m^2$ (layout of grid type), and reinforcement area of $120^{\circ}$ arch area of upper section.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.551-556
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• 2023

This study was conducted to evaluate in vitro antioxidant activity of goat meat hot water extracts and the changes in apoptosis-related protein expression levels in the cancer cells treated with these extracts. Goat meat hot water extracts were prepared using different cuts of goat meat, including foreleg, hindleg, loin, and rib. Among these extracts, the foreleg and hindleg extracts displayed higher (P<0.05) ABTS radical scavenging activity than the other two extracts. Protein expression levels of BAX, p53, and p21 were not different in the cells treated with the extracts from different cuts, regardless of the cell type. Only p53 expression in HT-29 cells was elevated (P<0.05) after loin extract treatment. These results suggest that antioxidant activity and apoptosis-related effects of goat meat hot water extract varied with cut of meat under in vitro conditions. Because all data was obtained from the in vitro experiment, the ability to generalize conclusions is limited. Additional in vivo studies are necessary.

• Sim-seong Yeon-gu
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• v.39 no.1
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• pp.61-91
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• 2024

Alchemy is the process of producing worthless substances into the best substances through chemical opus(work). On the surface, many of the alchemist's experiments can be depicted as work on transforming substances, but in reality, the alchemist's result is a product of the Unconscious. This study aims to explain the three phases of alchemy, Nigredo, Albedo, and Rubedo, through Michael Mayer's alchemical text, Atalanta Fugiens, and understand the transformation process by utilizing images that appeared from clients' sand play therapy. This study first described why alchemy, as the foundation for the psychology of the Unconscious, is important in sand play that deals with images. Next, Nigredo (blackening), the first phase of the alchemical process, is briefly described, and how the contents of Nigredo, such as chaos, dissolution, separation, division, corruption, death, and calcination, appear in sand play therapy. Next, the second phase, albedo (whitening), is described, and how the images of water and fire, which are representative images of albedo in the form of purification, sublimation, distillation, separation, descension, and coagulation, are revealed in sand play. Lastly, the phase of rubedo (reddening) in alchemy is described, and how the form of union (mandala or central image) in rubedo, which appears in the form of conjunction and rebirth, is revealed in sand play. The symbols revealed in alchemy are very valuable in amplifying the images that appeared in sand play therapy or dream analysis. In particular, the procedures found in alchemical opus are helpful in understanding the transformation process of personality.