• Journal of Digital Convergence
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• v.14 no.9
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• pp.229-239
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• 2016

The aim of this paper is to extend the TOPSIS(Technique for Order Performance by Similarity to Ideal Solution) to the fuzzy environment for solving the new professor selection problem in a university. In order to achieve the goal, the rating of each candidate and the weight of each criterion are described by linguistic terms which can be expressed in trapezoidal fuzzy numbers. In this paper, a vertex method is proposed to calculate the distance between two trapezoidal fuzzy numbers. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all candidates. This research derived; 1) 4 evaluation criteria(research results, education and research competency, personality, major suitability) for new professor selection, 2) the 5 step procedure of the proposed fuzzy TOPSIS method for the group decision, 3) priorities of 4 candidates in the new professor selection case. The results of this paper will be useful to practical expert who is interested in analyzing fuzzy data and its multi-criteria decision-making tool for personal selection problem in personal management. Finally, the theoretical and practical implications of the findings were discussed and the directions for future research were suggested.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.56-61
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• 2022

In this paper, we classify and present AI algorithms and natural language processing methods used in chatbots. A framework that can be used to implement a chatbot is also described. A chatbot is a system with a structure that interprets the input string by constructing the user interface in a conversational manner and selects an appropriate answer to the input string from the learned data and outputs it. However, training is required to generate an appropriate set of answers to a question and hardware with considerable computational power is required. Therefore, there is a limit to the practice of not only developing companies but also students learning AI development. Currently, chatbots are replacing the existing traditional tasks, and a practice course to understand and implement the system is required. RNN and Char-CNN are used to increase the accuracy of answering questions by learning unstructured data by applying technologies such as deep learning beyond the level of responding only to standardized data. In order to implement a chatbot, it is necessary to understand such a theory. In addition, the students presented examples of implementation of the entire system by utilizing the methods that can be used for coding education and the platform where existing developers and students can implement chatbots.

• (The) Research of the performance art and culture
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• no.43
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• pp.229-261
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• 2021

On the musical stage in 2019, there were many works depicting the Japanese colonial period. This is due to 2019 the timeliness of the March 1st Movement and the centennial of the establishment of the Provisional Government of the Republic of Korea. The way of remembering and commemorating historical facts reflects the power relationship between memory subjects and the time, namely the politics of memory. Until now, stage dramas dealing with the era of Japanese rule have focused on the commemoration of modern national and national defense, including feelings of misfortune and respect for patriots. This study analyzed the metaphor of the memorials emphasized to the audience in the commemorative musicals Shingheung Military School, Ku: Songs of the Goblin, and Watch which were performed in 2019, and looked at how to adjust memories and memorials. The above works highlight the narratives of ordinary people as well as those recorded against the backdrop of the Manchurian Independence Movement and Hongkou Park, expanding the object of the commemoration. Through this, active armed resistance efforts, self-reflection and reflection were highlighted. The case of Shingheung Military School revealed the earnestness of ordinary people who led the independence movement through the movement of central figures. Ku: Songs of the Goblin revises memories by reproducing forgotten objects and apologizing through time slip. Watch has strengthened the spectacles of facilities through documentary techniques such as photography, news reels, and newspaper articles, but it also reveals limitations limited to records. In the 3.1 Movement and the 100th anniversary of the establishment of the Provisional Government of the Republic of Korea, devices that actively reveal that the "people's movement" is connected to the present. To this end, the official records reflected the newly produced values and memories and devoted themselves to the daily lives and emotions of the crowd. In addition, both empirical consideration and calligraphy were utilized to increase reliability. These attempts are meaningful in that they have achieved the achievement of forming contemporary empathy.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1149-1154
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• 2003

Due to various requirements for the user access control to large databases in the hospitals and the banks, database security has been emphasized. There are many security models for database systems using wide variety of policy-based access control methods. However, they are not functionally enough to meet the requirements for the complicated and various types of access control. In this paper, we propose a database security system that can individually control user access to data groups of various sites and is suitable for the situation where the user's access privilege to arbitrary data is changed frequently. Data group(s) in different sixes d is defined by the table name(s), attribute(s) and/or record key(s), and the access privilege is defined by security levels, roles and polices. The proposed system operates in two phases. The first phase is composed of a modified MAC (Mandatory Access Control) model and RBAC (Role-Based Access Control) model. A user can access any data that has lower or equal security levels, and that is accessible by the roles to which the user is assigned. All types of access mode are controlled in this phase. In the second phase, a modified DAC(Discretionary Access Control) model is applied to re-control the 'read' mode by filtering out the non-accessible data from the result obtained at the first phase. For this purpose, we also defined the user group s that can be characterized by security levels, roles or any partition of users. The policies represented in the form of Block(s, d, r) were also defined and used to control access to any data or data group(s) that is not permitted in 'read ' mode. With this proposed security system, more complicated 'read' access to various data sizes for individual users can be flexibly controlled, while other access mode can be controlled as usual. An implementation example for a database system that manages specimen and clinical information is presented.

• The Journal of Korean Philosophical History
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• no.56
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• pp.151-180
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• 2018

Since the completion of the theories on human ethics and moral had been established to pursue by Confucian thinkers like Confucius and Mencius, they generally had agreed to present the basic principles for human education which every human could be the sage. In these principles for human ethics and morality there is on the premise that the knowledge about your own ethical and that the completion of the so-called act(爲) and learning(學). They had given to us that how to get a goal for the ethical and moral lives there are several academic oriented methodology will have act and learning set. In the point of achieving complete figures which act and learning for good society, there was named the sage(聖). This concept sage has two major types. One is on for the political figures that completed, and the other one is for the realm of academic side. Confucian as above mentioned the moral human being is equipped with a complete personality and political ability to make man and society perfect. Confucius has been understood as a complete human being. Yes, ideal for these two types of figures will be fulfilled in some way? They take a mystical ability to a priori or a posteriori, such as human effort can reach the sage. There are many thinkers are obvious and logical answer for this major problem in the system of confucian philosophy I have been trying. About the sage(聖), inherently natural learning(生知) occur to the position sage or knowledge (學知), can lead to there are two of the doctrine for that problem. With the study of learning and knowledge on human beings and real society the two systems concerned together. In fact, the main content of the "Analects of Confucius" we have a set of ethical and moral values not the benevolent conversation about Jin(仁) and his disciples a steady emphasis but on in praise of learning (學) for. However, at the time in Han Tang(漢唐) Han Wi(韓愈) and Wang Chung(王充), according to such thinkers the sage is already a priori determined, cannot be reached by human effort. But At the beginning of the Neo-Confucianism, Cheng I(程?) for the pioneer this Song(宋) scholars, regarding this issue could rebirth the thought that every human could be the sage through the learning as the pre-Chin(先秦) times.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.