• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.147-156
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• 2017

This paper analyzes the performance limit of WAVE system for the platooning service requirements which is referred from the de facto standards. The performance of the packet error rate and mean delay as key parameters in the wireless communication systems should be satisfied to provide safety to the platooning vehicles. The test scenarios are conducted by considering the following vehicle groups: platooning vehicles, vehicles within a hop distance and vehicles within two hop distance( called hidden node vehicles). The models of packet error rate and delay deals with the topology of aforementioned vehicle groups, vehicle speed and communication range. The numerical results are obtained in terms of packet size, packet arrival rate and data transmission rate. Finally, this paper suggests the robust range of packet error rate and delay for the WAVE system to provide the platooning vehicle service.

• Korean Journal of Head & Neck Oncology
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• v.27 no.1
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• pp.38-41
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• 2011

Background and Objectives : Although it is well established that patients with papillary thyroid microcarcinoma (PTMC) have a highly favorable prognosis, the extent of thyroid surgery for PTMC remains unclear. According to the 2011 revised Korean Thyroid Association guideline, the choice of surgical strategy(total thyroidectomy versus lobectomy) for PTMC depends on solely preoperative diagnostic scrutinies-ultrasonography and fine needle aspiration cytology. We want to know how accurately these preoperative diagnostic scrutinies define the choice of surgical strategy for PTMC. Materials and Methods : For 119 patients who underwent total thyroidectomy with central neck dissection for PTMC, retrospectively, we compared the choice of surgery according to preoperative work up and postoperative pathologic findings. Results : Overall accuracy of the choice of surgery by preoperative work up was 61%. Among patients recommended lobectomy on preoperative work up, completion thyroidectomy on postoperative pathology might be necessary for 60% of patients and hidden central node metastasis was revealed in 31% of patients. Conclusions : The results of this study compel us to reinvestigate the current treatment guideline for PTMC. On current guideline according to the sonography and fine needle aspiration cytology, it might be thought to be better to choose more aggressive surgical strategy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.677-680
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• 2005

In numerical analysis on the thermal performance of the heat exchanger with phase change fluids, the numerical values of thermodynamic properties are needed. But the steam table should be modeled properly as the direct use of thermodynamic properties of the steam table is impossible. In this study the function approximation characteristics of neural networks was used in modeling the saturated vapor region of refrigerant R12. The neural network consists of one input layer with one node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. Input can be both saturation temperature and saturation pressure and two cases were examined. The proposed model gives percentage error of ${\pm}$0.005% for enthalpy and entropy, ${\pm}$0.02% for specific volume and ${\pm}$0.02% for saturation pressure and saturation temperature except several points. From this results neural network could be a powerful method in function approximation of saturated vapor region of R12.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.641-655
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• 1992

In this paper, the performance of the various multiple accesstechniques for the mobile computer network has been studied in the consideration of the characteristics. Of the mobile communication channel. In the case of the hidden node occurring, it could be seen that the performance of the code division multiple access(CDMA) technique. With simultaneous access function is better than that of the other packet access methods such as carrier sensed multiple access(CAMA). Busy tone mulitiple access(BTMA)and idle signal muitiple access(ISMA) in the view of the throuhtput and mean delay time. Also it has been shown that the performance of the CDMA method is superior to that of other packet access techniques such as multiple access(CSMA).etc when the fading effect or impulsive noise exists in the mobile channel. Especially in the case of the distributed mobile network. It has been shown that the receiver-transmitter based CDMA method using the characteristics of CDMA effectively has better throughput and less mean delay time than the commontransmitter based CDMA technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2008-2013
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• 2006

In numerical analysis on evaluating the thermal performance of the thermal equipment, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table itself cannot be used without modelling. In this study applicability of neural networks in modelling the wide temperature range of wet saturated vapor region was examined. the multi-layer neural network consists of a input layer with 1 node, two hidden layers with 10 and 20 nodes respectively and a output layer with 6 nodes. Quadratic and cubic spline interpoations methods were also applied for comparison. Neural network model revealed similar percentage error to spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the wide range of the steam table.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.359-366
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• 2020

In this study, a simulation-based damage estimation method for helidecks is proposed using an artificial neural network. The structural members that share a connecting node in the helideck are regarded as a damage group, and a total of 37,400 damage scenarios are numerically generated by applying randomly assigned damage to up to three damage groups. Modal analysis is then performed for all the damage scenarios, which are selectively used as either training or validation or verification sets based on the purpose of use. An artificial neural network with three hidden layers is constructed using a PyTorch program to recognize the patterns of the modal responses of the helideck model under both damaged and undamaged states, and the network is successively trained to minimize the loss function. Finally, the estimated damage rate from the proposed artificial neural network is compared to the actual assigned damage rate using 400 verification scenarios to show that the neural network is able to estimate the location and amount of structural damage precisely.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.491-498
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• 2022

In this paper, as a liberal arts course for non-major students, a case study of recurrent neural network SW practice, which is essential for designing a basic recurrent neural network subject curriculum, was developed. The developed SW practice case focused on understanding the operation principle of the recurrent neural network, and used a spreadsheet to check the entire visualized operation process. The developed recurrent neural network practice case consisted of creating supervised text completion training data, implementing the input layer, hidden layer, state layer (context node), and output layer in sequence, and testing the performance of the recurrent neural network on text data. The recurrent neural network practice case developed in this paper automatically completes words with various numbers of characters. Using the proposed recurrent neural network practice case, it is possible to create an artificial intelligence SW practice case that automatically completes by expanding the maximum number of characters constituting Korean or English words in various ways. Therefore, it can be said that the utilization of this case of basic practice of recurrent neural network is high.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.39-57
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• 2018

The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.96-105
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• 1998

The aim of this paper is to propose the method of WDHMM(Weighted DHMM), using the MLP-VQ for the improvement of speaker-independent connect-digit recognition system. MLP neural-network output distribution shows a probability distribution that presents the degree of similarity between each pattern by the non-linear mapping among the input patterns and learning patterns. MLP-VQ is proposed in this paper. It generates codewords by using the output node index which can reach the highest level within MLP neural-network output distribution. Different from the old VQ, the true characteristics of this new MLP-VQ lie in that the degree of similarity between present input patterns and each learned class pattern could be reflected for the recognition model. WDHMM is also proposed. It can use the MLP neural-network output distribution as the way of weighing the symbol generation probability of DHMMs. This newly-suggested method could shorten the time of HMM parameter estimation and recognition. The reason is that it is not necessary to regard symbol generation probability as multi-dimensional normal distribution, as opposed to the old SCHMM. This could also improve the recognition ability by 14.7% higher than DHMM, owing to the increase of small caculation amount. Because it can reflect phone class relations to the recognition model. The result of my research shows that speaker-independent connected-digit recognition, using MLP-VQ and WDHMM, is 84.22%.