• Journal of the Daesoon Academy of Sciences
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• v.37
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• pp.329-355
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• 2021

The objective of this paper is to reveal that the formation of Deleuze's system is a result of a back flow of the 'ideal of pure reason' in Kant's system. I will try to seize upon the keyword in his main book, Difference and Repetition, and examine the aspect of mutual transformation between Deleuze's transcendental empiricism and Kant's transcendentalism. When analyzing Deleuze's system, most researchers tend to focus on anti-Hegelianism, but it is proper that Kant be adopted as the start when tracing the way of deployment directly. Fundamentally, Deleuze is different from Hegel in his approach to observing entire ground of thought. Even if Deleuze surely has the capability of becoming in the dialectical context, his systemic environment wherein dialectics is applied is different even at the onset. While Hegel follows the way of origin and copy or a system that begins from a preceding point of origin, Deleuze follows a way of copy and recopy or a system that begins without a point of origin. This characteristic of Deleuze's system originates directly from idealistic play. In fact, we can anticipate and identify in his book that he refers to Kant who accepted the tradition of empiricism. Therefore, the main contents of this paper is to present an overview of Kant's influence on Deleuze's system. While tracing ideas back to Kant's system, the cohabitation of empiricism and rationalism, which Kant felicitously revoiced, there emerges a definitude of world recognition. This occurs through cohabitation, and this is both deconstructed and integrated by Deleuze, and therein definitude is turned into a vision of prosperity. To the vision of prosperity that spans definitude to recognition, a philosopher has the right to select a philosophical system because selection methodology in philosophy is not a problem of legitimacy so much as the needs of the times. Deleuze's choice resulted in the opening of pandora's box in an abyss and secret contents have in turn risen sharply.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.180-185
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• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Journal of Distribution Science
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• v.15 no.5
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• pp.75-83
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• 2017

Purpose - With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively persue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business(IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology - The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results - Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions - In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• Research in Community and Public Health Nursing
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• v.12 no.2
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• pp.397-405
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• 2001

The vision disturbances of school- age children has been recognized as and important school health problem. As the visual disturbances of the school-age children is recognized as the nation's health problem. the importance of the development of educational program for visual health should be emphasized. Recently, eyeball movement and other visual health management method has been introduced for prevention or recovery of decrease in visual acuity. But, the effect of eyeball movement was not confirmed yet. And, the controversy around the treatment effect is continued. The decrease of visual acuity is one of the important school health problem as well as it causes discomfort in daily life of the students. So, it should be considered as an important subject for school health and there is a need to develop an effective intervention program for visual health. The purpose of this study is to develop and evaluate the program with the recognition of the need of the intervention for visual health. The visual health promotion program was developed by the researcher and the program was initiated by the school. Nonequivalent control group pretest-posttest design was applied for study which examined the effect of the visual health promotion program. The subjects were 742 children (experimental group: 398; control group: 344). The experiment was composed of health education and eyeball movement. Health education was provided 5 times to the children in the class room. Children of experimental group exercised eyeball movement in the class, watching video for 10 minutes two times a day. The exercise was continued for 10 weeks. The result of the study were as follows. 1) change of visual acuity Before the intervention, mean of the visual acuity was .86 for the experimental group and .91 for control group. After the intervention, mean of visual acuity was .95 for the experimental group and. 90 for the control group. There was no significant difference in the change of visual acuity between experimental and control group. 2) change of refraction. In the experimental group, 327 eyes (41.08%) were normal vision and 469 eyes (58.98%) were eyes of refraction errors, 38.82 % of the total eyes were myopia. There was no significant change in the refraction in the children with myopia after the intervention. 3) Awareness of visual acuity, change of knowledge, behavior. and attitude (1) After the intervention, there was a significant difference in the awareness of visual acuity (experimental group: 70.10%. control group: 50.97%, p<.01). (2) After the intervention, there was a significant knowledge increase in the experimental group (pp<.01). (3) There was no significant difference in the visual health behavior after the intervention. (4) There was a significant positive change in the attitude related to visual health in the experimental group ( pp<.05). 4) There was a significant positive change in the subjective discomfort of the students. But, there was no significant change in the objective eye symptom after the intervention. Even though there was no effect in the visual acuity and the change of the refraction. subjective visual health as well as the attitude and knowledge' of the children and parents toward visual health was improved significantly. Also, there was an increase in the intention of change and the awareness for the visual health management. It is suggested that various educational strategies for visual health promotion should be developed and examined for the visual health promotion of the students.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.