• Journal of the Korea Convergence Society
• /
• v.6 no.6
• /
• pp.213-219
• /
• 2015

This study examines how the features of communication on Social Network Service(SNS) affect building faculty trust and long-term orientation in professor-student relationships. The research model was developed based on the previous research about communication, SNS and relationship development. The researcher surveyed 210 students to collect research data, and 195 questionnaires were analyzed using SmartPLS. The results indicate that the quality, frequency, interactivity and openness of communication on SNS affect positively on faculty trust. Moreover, the quality, frequency and openness of communication on SNS affect positively on long-term orientation, whereas interactivity does not. This mean that faculty trust plays a mediating role between interactivity and long-term orientation.

• Journal of Digital Convergence
• /
• v.14 no.8
• /
• pp.269-276
• /
• 2016

Public Safty-LTE(Long Term Evolution) is being deployed in the direction of reducing cost by using both of municipal network and commercial network. However, LTE Network is difficult to ensure the survivability during the information communication infrastructure failures. In addition, it is vulnerable in communication coverage of inside buildings and underground. In this study, we propose to implement effectively the network survivability technique through the convergence to the proven technology. As the advent of the IoT Age, smart sensors which are embedded in the environment and the things will be able to provide a useful infrastructure for ensuring the network survivability. Based on the feature of the smart sensor, we designed the sink node architecture to guarantee the network survivability in disaster situation through the convergence of the small cell technology and extension of wireless network coverage technology. The computing power inherent in the environment is a valuable resource that can be utilized in the disaster situation.

• Journal of Korea Water Resources Association
• /
• v.57 no.2
• /
• pp.127-137
• /
• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Information and Communications Magazine
• /
• v.3 no.1
• /
• pp.52-63
• /
• 1986

This paper discusses the network evolution strategies, worldwide trends in fiber optics systems, fiber hub in KTA access network, positioning the access network for new digital services - DLC(Digital Loop Carrier), CSA(Carrier Serving Area), and fiber optics systems overlay in the existing access network and its evolution toward near term ISDN.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.10a
• /
• pp.135-139
• /
• 2000

This research proposes a nonlinear sliding mode control. The sliding mode control is designed according to Lyapunov function. The equivalent control term is estimated by neural network. To estimate the unknown part in the control law in on-line fashion, A recurrent neural network is given as on-line estimator. The stability of the control system is guaranteed owing to the on-line learning ability of the recurrent neural network. It is certificated through simulation results to be applied to nonlinear system that the function approximation and the proposed control scheme is very effective.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1759-1772
• /
• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• The Journal of the Korea Contents Association
• /
• v.11 no.6
• /
• pp.1-7
• /
• 2011

The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.

• Journal of Korean Society of Water and Wastewater
• /
• v.31 no.1
• /
• pp.51-62
• /
• 2017

For the asset management of a water pipe network, it would be necessary to understand the extent of the maintenance cost required for the water pipe network for the future. This study would develop a method to draw the optimum cost required for the maintenance of the water pipe network in waterworks facilities to maintain the aim revenue water ratio and to achieve the target revenue water ratio, considering the water service providers' waterworks condition and revenue water ratio comprehensively. This study conducted a survey with 96 water service providers as of the early 2015 and developed models to estimate the optimum maintenance cost of the water pipe network, considering the characteristics of the water service providers. Since the correlation coefficient of all the developed models was higher than 0.95, it turned out that it had significant reliability, which was statistically significant. As a result of applying the developed models to the actual water service providers, it was drawn that increasing revenue water ratio to more than a certain level can reduce the maintenance cost of the water pipe network by a great deal. In other words, it is judged that it would be the most efficient to secure the reliability of waterworks management by increasing the short-term revenue water ratio to more than a certain level and gradually increase the revenue water ratio from the long-term perspective. It is expected that the proposed methodology proposed in this study and the results of the study will be used as a basic research for planning the maintenance of water pipe network or establishing a plan for waterworks facilities asset management.

• Journal of KIISE:Databases
• /
• v.32 no.6
• /
• pp.616-627
• /
• 2005

In the shared storage file system, systems can directly access the shared storage device through specialized data-only subnetwork unlike in the network attached file server system. In this shared-storage architecture, data consistency is maintained by some designated set of lock servers which use control network to send and receive the lock information. Furthermore, lease mechanism is introduced to cope with the control network failure. But when the control network is partitioned, participating systems can no longer make progress after the lease term expires until the network recovers. This paper addresses this limitation and proposes a method that allows partitioned systems to make progress under the partition of control network. The proposed method works in a manner that each participating system is rotationally given a predefined lease term periodically. It is also shown that the proposed mechanism always preserves data consistency.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.11
• /
• pp.5580-5593
• /
• 2019

A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.