• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.61-67
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• 2023

In order to optimize the pulse electroforming copper process, a double hidden layer BP (Back Propagation) neural network is constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties is accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process is realized. The predicted results are verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by "3-4-3-2" structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.32%. In the parameter range, the microhardness of copper layer is between 100.3~205.6MPa and the tensile strength is between 112~485MPa.When the microhardness and tensile strength are optimal,the corresponding process conditions are as follows: current density is 2A-dm-2, pulse frequency is 2KHz and pulse duty cycle is 10%.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.1
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• pp.152-159
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• 2002

Neural networks are used to predict the sense of touch of polyurethane coated fabrics. In this study, we used the multi layer perceptron (MLP) neural networks in Neural Connection. The learning algorithm for neural networks is back-propagation algorithm. We used 29 polyurethane coated fabrics to train the neural networks and 4 samples to test the neural networks. Input variables are 17 mechanical properties measured with KES-FB system, and output variable is the sense of touch of polyurethane coated fabrics. The influence of MLF function, the number of hidden layers, and the number of hidden nodes on the prediction accuracy is investigated. The results were as follows: MLP function, the number of hidden layer and the number of hidden nodes have some influence on the prediction accuracy. In this work, tangent function, the architecture of the double hidden layers and the 24-12-hidden nodes has the best prediction accuracy with the lowest RMS error. Using the neural networks to predict the sense of touch of polyurethane coated fabrics has hotter prediction accuracy than regression approach used in our previous study.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.88-94
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• 2001

In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore, there is a need to use a precompensator for rejecting the undesired noise. In this paper, the expanded PID controller with a precompensator is constructed. The precompensator and PID controller are designed by a neural network with two-hidden layer and these coefficients are changed automatically to be a desired response of system when the response characteristic is changed under a condition.

• 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.

• Archives of Craniofacial Surgery
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• v.22 no.4
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• pp.183-192
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• 2021

Background: The purse-string suture (PSS) is a simple and rapid wound closure method that results in minimal scarring. It has been used to treat circular or oval skin defects caused by tumor excision or trauma. However, due to obscurity, it is not widely used, especially for the head and neck. This study aimed to modify the PSS to obtain predictable and acceptable results. Methods: A total of 45 sites in 39 patients with various types of skin and soft tissue defects in the head and neck were treated with PSS. We used PDS II (2-0 to 5-0), which is an absorbable suture. Minimal dissection of the subcutaneous layer was performed. The suture knot was hidden by placing it in the dissection layer. Depending on the characteristics of the skin and soft tissue defects, additional surgical interventions such as side-to-side advancement sutures, double PSS, or split-thickness skin graft were applied. Results: All wounds healed completely without any serious complications. Large defects up to 45 mm in diameter were successfully reconstructed using only PSS. Postoperative radiating folds were almost flattened after approximately 1-2 months. Conclusion: PSS is simple, rapid, and relatively free from surgical design. Owing to the circumferential advancement of the surrounding tissue, PSS always results in a smaller scar than the initial lesion and less distortion of the body structures around the wound in the completely healed defect. If the operator can predict the process of healing and immediate radiating folds, PSS could be a favorable option for round skin defects in the head and neck.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1022-1030
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• 2006

The location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet in spite of many researches. Among various location tracking systems, we choose the RFID system due to its wide applications. However, the sensed RSSI signal is too sensitive to the direction of a RFID reader antenna, the orientation of a RFID tag, the human interference, and the propagation media situation. So, the existing location tracking method in spite of using the particle filter is not working well. To overcome this shortcoming, we suggest a robust location tracking method with a double layered structure, where the first layer coarsely estimates a tag's location in the block level using a regression technique or the SVM classifier and the second layer precisely computes the tag's location, velocity and direction using the particle filter technique. Its layered structure improves the location tracking performance by restricting the moving degree of hidden variables. Many extensive experiments show that the proposed location tracking method is so precise and robust to be a good choice for implementing the location estimation of a person or an object in the ubiquitous computing. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complicate workplace.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.