• Steel and Composite Structures
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• v.43 no.6
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• pp.689-706
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• 2022

Thin-walled square concrete-filled double steel tubular (CFDST) columns composed of the inner circular tube filled with concrete can be used to carry the large axial loads or strengthen existing CFST columns in composite constructions. This paper reports an experimental program carried out on short square CFDST columns loaded concentrically. The influences of important column parameters on the post-buckling performance of such columns are investigated. Test results exhibit that the inner circular tube significantly improves the ultimate loads and the ductility of such columns compared to conventional concrete-filled steel tubular (CFST) and double-skin CFST (DCFST) columns with an inner void. A mathematical model developed is used to simulate the ultimate strengths and load-strain curves of such columns loaded axially. Furthermore, the ultimate strengths of such columns are predicted using existing codified design models for conventional CFST columns as well as the formulas proposed by previous researchers and compared against a large database comprising 500 CFDST columns. Lastly, an accurate artificial neural network model is developed for the practical applications of such columns under axial loading.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Journal of Sensor Science and Technology
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• v.20 no.1
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• pp.19-24
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• 2011

Brain disorders such as epilepsy is a condition that affects an estimated 2.7 million Americans, 50,000,000 worldwide, approximately 200,000 new cases of epilepsy are diagnosed each year. Of the major chronic medical conditions, epilepsy is among the least understood. Scientists are conducting research to determine appropriate treatments, such as the use of drugs, vagus nerve stimulation, brain stimulation, and Peltier chip-based focal cooling. However, brain stimulation and Peltier chip-based stimulation processes cannot effectively stop seizures. This paper presents simulation of a novel heat enchanger network(HEN) technique designed to stop seizures by using a neural cooling probe to stop focal and spontaneous seizures by cooling the brain. The designed probe was composed of a U-shaped tube through which cold fluid flowed in order to reduce the temperature of the brain. The simulation results demonstrated that the neural probe could cool a 7 $mm^2$ area of the brain when the fluid was flowing atb a velocity of 0.55 m/s. It also showed that the neural cooling probe required 23 % less energy to produce cooling when compared to the Peltier chip-based cooling system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.67-68
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• 2018

본 논문에서는 다수의 음향방출 센서로부터 취득한 원 데이터를 관리하고 보일러 튜브에 대한 상태를 실시간으로 진단하고 모니터링을 할 수 있는 네트워크 기반의 보일러 튜브 모니터링 시스템을 제안한다. 본 시스템을 화력발전소 보일러 튜브에 설치한다면 보일러의 불시정지를 예방하는데 효과적일 것으로 기대된다.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.182-188
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• 2000

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.582-588
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• 2018

Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2109-2118
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• 1996

The neural convolutional decoder(NCD) was proposed as a method of decoding convolutional codes. In this paper, simulation results are presented for coherent BPSK in memoryless AWGN channels and coherent QPSK in the satellite channels. The NCD can learn the nonlinear distortion caused by the charactersitics of the satellite channel including the filtering effects and the nonlinear effects of the travling wave tube amplifier(TWTA). Thus, as compared with the AWGN channel, the performance difference in the satellite channel between the NCD for the systematic code and the Viterbi decoder for the nonsystematic code is reduced.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.338-341
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• 2020

Cases of syringomyelia associated with spinal dysraphism are distinct from those associated with hindbrain herniation or arachnoiditis in terms of the suspected pathogenetic mechanism. The symptoms of terminal syringomyelia are difficult to differentiate from the symptoms caused by spinal dysraphism. Nonetheless, syringomyelia has important clinical implications, as it is an important sign of cord tethering. The postoperative assessment of syringomyelia should be performed with caution.