• Journal of Science and Technology Studies
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• v.4 no.1 s.7
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• pp.31-57
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• 2004

Controversies over NEIS(Network of Education Information System) began with very deep concern about infringement of human rights stemming from NEIS. A large information system which accumulates and uses huge size of individual information is always able to deeply infringe on human rights. But the ministry of education would not do the best not to be 'Big Brother' being dazzled by instrumental efficiency of information technology. NEIS has demonstrated problems of the information policy of Korea strongly driven in the name of 'E-goverment'. It has very strong characteristic of the statist economic growth policy focusing on more economic possibility than other. In this situation, making money is easily considered more important than protecting human rights. Information capitalism is nurtured at the sacrifice of human rights. So, we have to face problems of 'E-goverment' in order to correct the NEIS problem, The most important task to correct the NEIS problem is to make an element law protecting privacy and to establish an independent national institute protecting privacy

• Journal of the Korean Ceramic Society
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• v.50 no.3
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• pp.195-200
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• 2013

Here, we report preparation of cyan ceramic nano-pigment for inkjet printing and the Ni substitutional effects on the cyan color. $MgAl_2O_4$ was selected as the crystalline host network for the synthesis of nickel-based cyan ceramic nano-pigments. Various compositions of $Ni_xMg_{1-x}Al_2O_4$ ($0{\leq}x{\leq}1$) powders were prepared using the polymerized complex method. The powder was then preheated at $400^{\circ}C$ for 5 h and finally calcined at $1000^{\circ}C$ for 5 h. XRD patterns of $Ni_xMg_{1-x}Al_2O_4$ showed a single phase of the spinel structure in all the compositions. The particle sizes ranged from 20 to 50 nm in TEM observations. The characteristics of the color tones of $Ni_xMg_{1-x}Al_2O_4$ were analyzed by UV-Visible spectroscopy and CIE $L^*a^*b^*$ measurement. CIE $L^*a^*b^*$ measurement results indicate that the pigment color changes from light cyan to deep cyan due to the decrease of the $a^*$ and $b^*$ values with an increase of an Ni substitutional amount. In addition, the thermal stability and the binding nature of $Ni_xMg_{1-x}Al_2O_4$ are also discussed using TG-DSC and FT-IR results respectively.

• Asia pacific journal of information systems
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• v.22 no.4
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• pp.31-49
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• 2012

In this essay, we problematize the problematics of the KMIS SOLOMO research agenda. We propose that the SOLOMO agenda is a conditioned product of the various assumptions, biases, premises, and presuppositions that the field of IS collectively succumbs to and shares, and thus needs to be problematized to arrive at a new set of research questions for the field. The problematization begins with the ontology that underlies the agenda. We argue that the agenda is largely drawn from a dichotomic, deep ontology of Human vs. Technology. While such ontology is neither right nor wrong in its own right, we suggest it is what underlies and influences the field's whole mode of inquiry including its research agenda. We propose an alternative ontology, the Actor-Network Theory (ANT), which as a flat ontology provides a radically different set of research questions for the field. Next we take issue with the teleology of the SOLOMO agenda, and suggest that the telos of the agenda, and of the field of IS and the whole of Management discipline together, are anchored upon the capitalist episteme so that it creates a significant hole in its teleological scape. While not in any sense calling for an ideological demagogue, we propose that the field of IS should open itself to an alternative teleology including a leftist perspective. We draw upon the Critical Management Studies (CMS) to explore how further problematization can be made on the SOLOMO agenda, generating questions about its performativity, denaturalization, and reflexivity. As a result of the discussions, a list of new problematized research questions for the SOLOMO agenda is generated. In the end we state the motivation of the essay and call for a critical refurbishing of the field of IS.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.83-93
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• 2020

With the rapid increase of vehicles, various traffic problems, e.g., car crashes, traffic congestions, etc, frequently occur in the road environment of the urban area. To overcome such traffic problems, intelligent transportation systems have been developed with a traffic flow analysis. The traffic flow, which can be estimated by the vehicle counting scheme, plays an important role to manage and control the urban traffic. In this paper, we propose a novel vehicle counting method based on predicted centers of each lane. Specifically, the centers of each lane are detected by using the accumulated movement of vehicles and its filtered responses. The number of vehicles, which pass through extracted centers, is counted by checking the closest trajectories of the corresponding vehicles. Various experimental results on road CCTV videos demonstrate that the proposed method is effective for vehicle counting.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.525-532
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• 2013

Recently. smartphones have been popularized rapidly and now located deep in our daily life, providing a variety of services from banking, SNS (Social Network Service), and entertainment to smart-work mobile office through apps. Such smartphone apps can be easily downloaded from what is known as app store which, however, bears many security issues as software developers can just as easily upload to it. Military apps will be exposed to a myriad of security threats if distributed through internet-basis commercial app store. In order to mitigate such security concerns, this paper suggests a security guidelines for establishing a military-excusive app store and security verification system which prevent the security hazards that can occur during the process of development and distribution of military-use mobile apps.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.592-601
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• 2016

The recurrent neural network (RNN) is a deep learning model which is suitable to sequential or length-variable data. The Long Short-Term Memory (LSTM) mitigates the vanishing gradient problem of RNNs so that LSTM can maintain the long-term dependency among the constituents of the given input sequence. In this paper, we propose a LSTM based language model which can predict following words of a given incomplete sentence to generate a complete sentence. To evaluate our method, we trained our model using multiple Korean corpora then generated the incomplete part of Korean sentences. The result shows that our language model was able to generate the fluent Korean sentences. We also show that the word based model generated better sentences compared to the other settings.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.1
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• pp.9-16
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• 2020

The k-anonymity scheme has been widely used to protect private information when Big Data are distributed to a third party for research purposes. When the scheme is applied, an optimal k value determination is one of difficult problems to be resolved because many factors should be considered. Currently, the determination has been done almost manually by human experts with their intuition. This leads to degrade performance of the anonymization, and it takes much time and cost for them to do a task. To overcome this problem, a simple idea has been proposed that is based on machine learning. This paper describes implementations and experiments to realize the proposed idea. In thi work, a deep neural network (DNN) is implemented using tensorflow libraries, and it is trained and tested using input dataset. The experiment results show that a trend of training errors follows a typical pattern in DNN, but for validation errors, our model represents a different pattern from one shown in typical training process. The advantage of the proposed approach is that it can reduce time and cost for experts to determine k value because it can be done semi-automatically.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.