• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.165-172
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• 2005

Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.744-750
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• 2014

In this paper, I have measured NCO(Network Centric Operation) Effectiveness of Anti-piracy Operation at the Chunghae Unit. For quantitative analysis, Network Centric Operations Conceptual Framework(U.S Office of Force Transformation) is applied. In accordance with the framework, the Chunghae unit anti-piracy operation scenario is analysed. The scenario is devided with two case(only voice communication and networking). The element of analysis be composed of the organic information, networking, share-ability, and individual information. As a result of analysis, the individual information of first case(only voice) gets 0.59 points. The other side, second case (networking) gets 1 points. This means that NCO has effect on the Chunghae Unit's mission. In addition, I stated the tactics advantage of NCO related a fighting power.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.271-280
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• 2011

In this paper, the relationships between code-share networks and performances in airline industry were analyzed by using Social Network Analysis (SNA). We first analyzed the schedule data from OAG (Official Airline Guide) to obtain core-share information of airline industries. SNA was, then, applied to the code-share information. Finally, statistical analysis was conducted to analyze the relationships between code-share social networks and performances. The result shows that the size and out-degree centrality have relatively significant effects on the performance of airline industries, while in-degree and betweenness centrality has less significant effects.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.591-603
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• 2014

In this paper, we have described the characteristics of the Underwater Distributed Sensor Network (UDSN) and proposed the conceptual design guideline by an effectiveness analysis. To perform the effectiveness analysis, we defined an battlefield environment, and then analyzed principal components which compose the UDSN to find out simulation parameters and system constraints. We have chosen a measure of effectiveness based on a target trajectory, which could enhance intuitive understanding about current status, and performed various simulations to reveal critical design parameters in terms of sensor node types, arrangement, cost and combination of detection information.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.109-116
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• 2022

This study conducted YouTube network analysis on YouTube video related with prevention of COVID-19 and COVID-19 vaccine to explores how government's policy is spread via social media in the condition of COVID-19. As a result of network analysis on the Mask chaos, A surge in confirmed cases, supply of vaccine, the influence of media like YTN and KBS is large, their view count is high. Government highlights to inform correct information actively to face negative massage and misinformation. The media has to fact check on the misinformation and disinformation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2606-2626
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• 2016

This article investigates the distributed data storage problem in the space information network (SIN) using distributed fountain codes. Since space nodes in the SIN are resource-limited, in order to reduce energy consumption while improving the storage reliability, an efficient distributed storage based on fountain codes and probabilistic broadcasting (DSFPB) strategy is proposed. In the proposed strategy, source packets are disseminated among the entire network according to probabilistic broadcasting (PBcast), and the final degree distribution is close to the desired robust soliton distribution (RSD), this is benefited from the appropriate packets encoding procedure of the proposed strategy. As presented by the analysis and simulations, the total cost of data dissemination is greatly reduced compared with existing representative strategies, while improving the decoding performance.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.226-235
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• 1999

Applying digital network technology for advanced nuclear plant requires deterministic communication for tight safety requirements, timely and reliable data delivery for operation-critical and mission-critical characteristics of nuclear plant. Communication protocols, such as IEEE 802/4 Token Bus, IEEE 802/5 Token Ring, FDDI, and ARCnet, which have deterministic communication capability are partially applied to several nuclear power plants. Although digital communication technologies have many advantages, it is necessary to consider the noise immunity from electromagnetic interference (EMI), electrical interference, impulse noise, and heat noise before selecting specific digital network technology for nuclear plant. In this paper, we consider the token frame loss and data frame loss rate due to the link error event, frame size, and link data rate in different protocols, and evaluate the possibility of failure to meet the hard real-time requirement in nuclear plant.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.95-103
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• 2017

The PCI Express is a widely used system bus technology that connects the processor and the peripheral I/O devices. The PCI Express is nowadays regarded as a de facto standard in system area interconnection network. It has good characteristics in terms of high-speed, low power. In addition, PCI Express is becoming popular interconnection network technology as like Gigabit Ethernet, InfiniBand, and Myrinet which are extensively used in high-performance computing. In this paper, we designed and implemented a evaluation platform for interconnect network using PCI Express between two computing nodes. We make use of the non-transparent bridge (NTB) technology of PCI Express in order to isolate between the two subsystems. We constructed a testbed system and evaluated the performance on the testbed.