• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.49-56
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• 2022

This study conducted to obtain the lateral resistance of a wind power foundation reinforced with piles through an model experiment. In particular, the lateral resistance of the foundation was compared with the existing gravity-type wind power foundation by integrating the pile, the wind power generator foundation, and the rocky ground. In addition, changes in the lateral resistance and bending moment of the pile were analyzed by embeded depths of the pile. As a result, it was found that the lateral resistance increased with the depth of embedment of the piles. In particular, the pile's resistance increase ratio was 2.11 times greater in the case where the pile embedded up to the rock layer than the case where the pile was embedded into the riprap. It was found that the location of the maximum bending moment occurred at the interface between the wind turbine foundation and the riprap layer when the pile embeded to the rock layer. Through this, as the lateral resistance of the wind power foundation reinforced with piles is greater than that of the existing gravity-type wind power foundation, it is understood that it can be a more advantageous construction method in terms of safety.

• The Korean Journal of Archival Studies
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• no.75
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• pp.177-212
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• 2023

Private archives are an important indicator of understanding a society that contains various memories, the lives and experiences of members, daily lives, morality, and values. Recently, as diversity has emerged as an important value in Korean society, a number of individuals and communities have been appeared based on different bases and purposes, and the contents, types, and categories of private archives produced from their voluntary activities have also diversified. These private organizations and communities are potential targets for producing and holding private archives, but most of them do not have the minimum infrastructure or system for management of archives, and the foundation for management of archives is weak only to be supported with the voluntary will and activities of the private sector. Therefore, there is a need for a plan to support activities to manage archives suitable for each organization's level while respecting the unique characteristics and methods of the private sector within the national management system of archives. In addition, since it is difficult to solve all issues related to management of archives in the private sector with only a small number of process topics, a cooperative system should be established to sustain activities to manage archives on its own through networks between private sectors. In this study, we intend to propose a 'private archives information network platform (hereinafter referred to as a platform)' as a way to establish a communication and network foundation between private sectors and share resources with each other. Based on the results of analysis of cases of building network between private sectors and expected user requirements, we would like to establish a vision and target model of the platform and discuss ways to continuously operate the platform.

• Asia Marketing Journal
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• v.6 no.3
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• pp.143-156
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• 2004

This case intorduced E-mart's business philosophy and vision, analyzed E-mart's outline of marketing strategy, and discussed its performance and future task. E-mart took the role of market pioneer by developing discount store market in Korea. It's mission was to provide substantial benefits to the customers by selling quality products at the lowest price in the market. For this purpose, E-mart has conducted a slogan of 'everyday low price discount store-E-mart'. Objective of E-mart's brand strategy was to be No. 1 discount store in Korea or to be a representative brand in the discount store market. To achieve this objective, E-mart has conducted various efforts such as construction of national network, realization of the lowest price, formation of the most reliable discount store image, establishment of competitive edge and so on. E-mart settled a new model for discount store in Korea and took the lead in expanding market potential. With these efforts, E-mart has maintained secure position as a leading company in the discount store market.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Journal of Korea Port Economic Association
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• v.35 no.4
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• pp.1-16
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• 2019

The purpose of this study is to Analyze the problems that container shipping companies exist through the commercialization of container shipping for Non-Arctic countries and the opportunity factors for the transport of the Arctic shipping to improve cooperation cross-border relation Arctic policy and the use of transport. In order to design a hierarchy analysis method study model, four high and 17 low factors were extracted by designing a hierarchy analysis method study model based on results by prior study and in-depth interview. The first of the higher factors is the internal strength of assessing the value of the Arctic, the will and capabilities of the shipping companies in creating new markets with the vision and goals of the shipping companies. Second, the internal constraints associated with the shipping companies advance to the NSR mean the negative factors for the entry into the NSR and the internal weaknesses that cause the shipping companies capacity limitations. Third, the economic benefits from the use of NSR are external factor for shipping companies in cooperation with the future economic value of the Arctic and with respect to Arctic sea and Arctic advance and development from Arctic coastal countries. Finally, external pre-emptive tasks means to respond to use NSR by external restrictions on transport to prepare the possibility of severe weather conditions, the customs policy change of coastal countries.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.