• Journal of Periodontal and Implant Science
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• v.46 no.6
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• pp.415-425
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• 2016

Purpose: The present study investigated the impact of 2 different suture techniques, the conventional crossed mattress suture (X suture) and the novel hidden X suture, for alveolar ridge preservation (ARP) with an open healing approach. Methods: This study was a prospective randomized controlled clinical trial. Fourteen patients requiring extraction of the maxillary or mandibular posterior teeth were enrolled and allocated into 2 groups. After extraction, demineralized bovine bone matrix mixed with 10% collagen (DBBM-C) was grafted and the socket was covered by porcine collagen membrane in a double-layer fashion. No attempt to obtain primary closure was made. The hidden X suture and conventional X suture techniques were performed in the test and control groups, respectively. Cone-beam computed tomographic (CBCT) images were taken immediately after the graft procedure and before implant surgery 4 months later. Additionally, the change in the mucogingival junction (MGJ) position was measured and was compared after extraction, after suturing, and 4 months after the operation. Results: All sites healed without any complications. Clinical evaluations showed that the MGJ line shifted to the lingual side immediately after the application of the X suture by $1.56{\pm}0.90mm$ in the control group, while the application of the hidden X suture rather pushed the MGJ line slightly to the buccal side by $0.25{\pm}0.66mm$. It was demonstrated that the amount of keratinized tissue (KT) preserved on the buccal side was significantly greater in the hidden X suture group 4 months after the procedure (P<0.05). Radiographic analysis showed that the hidden X suture had a significant effect in preserving horizontal width and minimizing vertical reduction in comparison to X suture (P<0.05). Conclusions: Our study provided clinical and radiographic verification of the efficacy of the hidden X suture in preserving the width of KT and the dimensions of the alveolar ridge after ARP.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.429-438
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• 2006

Neural Networks(NNs) are applied for solving a wide variety of nonlinear problems in several areas, such as image processing, pattern recognition etc. Although NN can be simulated by using software, many potential NN applications required real-time processing. Thus they need to be implemented as hardware. The hardware implementation of multi-layer perceptrons(MLPs) in several kind of NNs usually uses a fixed-point arithmetic due to a simple logic operation and a shorter processing time compared to the floating-point arithmetic. However, the fixed-point arithmetic-based MLP has a drawback which is not able to apply the MLP software that use floating-point arithmetic. We propose a design method for MLPs which has the floating-point arithmetic-based fully-pipelining architecture. It has a processing speed that is proportional to the number of the hidden nodes. The number of input and output nodes of MLPs are generally constrained by given problems, but the number of hidden nodes can be optimized by user experiences. Thus our design method is using optimized number of hidden nodes in order to improve the processing speed, especially in field of a repeated processing such as image processing, pattern recognition, etc.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1324-1330
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• 2007

In this paper, we propose pattern recognition algorithm for activities of daily living by adopting extreme learning machine based on single layer feedforward networks(SLFNs) to the signal from bidirectional accelerometer. For activity classification, 20 persons are participated and we acquire 6, types of signals at standing, walking, running, sitting, lying, and falling. Then, we design input vector using reduced model for ELM input. In ELM classification results, we can find accuracy change by increasing the number of hidden neurons. As a result, we find the accuracy is increased by increasing the number of hidden neuron. ELM is able to classify more than 80 % accuracy for experimental data set when the number of hidden is more than 20.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.602-608
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• 2011

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs $x_i$, i=1~6, the hidden layer consists of two hidden units for hidden outputs $o_j$, j=1~2, and the output layer consists of two units for the outputs ${\tau}_k$, k=1~2. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, ${\pi}$].

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4455-4461
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• 2013

This paper investigates the diagnosis performance of Artificial Neural Network (ANN) depending on the structure and the input vector type of ANN, which has been used to detect the partial discharge to lead to the electric machinery deterioration. The diagnosis performance of one hidden layer and two hidden layer in ANN are compared. The performance using the 2048 time-series data and the performance using the feature input vector are compared. For measuring the partial discharge signal, the tip-to-plate, the sphere-to-sphere, the tip-to-tip, the tip-to-sphere and the sphere-to-plate electrodes are used respectively. For ANN's learning, Matlab and C-code program are used. For evaluating the diagnosis performance of ANNs, the simulation studies are performed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.1
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• pp.63-70
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• 2010

In this paper, we study the structure of neural network for predicting defect size of steam generator tube. After extracting the features from the eddy current testing (ECT) signals, multi-layer neural networks are used to predict the defect size. In order to maximize the prediction performance for the defect size, we should carefully choose the structure of neural networks, especially the number of neurons in the hidden layer. In this paper, it is shown that, for the prediction of defect size, the number of neurons in the hidden layer can be efficiently determined by using cross-validation.

• Journal of the Korean Graphic Arts Communication Society
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• v.15 no.1
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• pp.31-40
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• 1997

The objectives of color reproduction in printing, photography, and digital hard-copy is an important problem. The Color is obsorved differently from illumination an obsorvation condition, and varied according to individual taste. Generally, the color reproduction system is designed with colorimetric color reproduction method. But the color gamut of the color reproduction system is different each other and the one device has nonlinear relationalship between the other. By these reason, to predict the reproduced color based on linear color transform method is difficult. Some methods of non-linear color transform by neural network was proposed. These method was theoretical useful and valid to transform from CIE color to device color. But more studies were needed to realize the non-linear color transform system. In this paper, we described a method to realize the non-linear color transform system by neural network. The optimum structure of the non-linear color transform system was found out. The structure of descrived system has four layer( input, output and two hidden layers.) Input and output layer have 3 units, and a hidden layer has 27 units. We trained 216 color-samples, and estimated the realized color transform system by 1115 color-samples. The average color difference between original color samples and transformed color samples was 2.54.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.803-809
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• 2007

This study suggests a computational model to inquire the roles of phonological information and orthography information in the process of visual word recognition among the courses of language information processing, and the representation types of the mental lexicon. The model that this study is presenting here was designed as a feed forward network structure which is comprised of input layer which uses two Korean syllables as its input value, hidden layer, and output layer which express meanings. As the result of the study, the computational model showed the phonological and orthographic neighborhood effect among language phenomena which are shown in Korean word recognition, and showed proofs which implies that the mental lexicon is represented as phonological information in the process of Korean word recognition.