• 한국항행학회논문지
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• 제24권6호
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• pp.573-580
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• 2020

본 논문에서는 회로의 기본 구성요소인 인덕터와 커패시터를 LTCC 다층 기판에서 활용이 가능한 형태로 구성하여 각각의 특성을 분석하였다. 분석을 위해 사용된 인덕터와 커패시터는 유전율 7인 유전체 내부에 각각 사각형 나선 구조와 MIM 구조로 설계되었으며, 인덕터의 감은 수와 커패시터의 적층 수를 달리하여 제작하고 측정하였다. 측정된 결과는 커브피팅 방식을 이용하여 논문에서 제안한 등가회로의 각 소자 값을 추출하였고 추출된 결과를 토대로 제안한 등가회로의 타당성을 검증하였다. 분석된 인덕터와 커패시터는 라이브러리 형태로 구현하였으며 일립틱 타입의 5차 LPF 설계에 적용하여 그 활용성을 입증하였다. LPF는 실제 제작을 통해 측정되었으며, 결과적으로 통과 대역인 DC ~ 3.7 GHz 대역에서 삽입손실이 최대 1.0 dB, 반사손실이 19.2 dB, 저지 대역에서의 감쇄 값이 23.9 dB로 모든 항목에서 설계 목표치에 근접한 결과를 보였다.

• 한국해양공학회지
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• 제35권4호
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• 대한치과교정학회지
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• 제52권4호
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• pp.268-277
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• 2022

Objective: Poor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment. Methods: In total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs. Results: The predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments. Conclusions: The constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.

• 센서학회지
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• 제31권4호
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• pp.249-254
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• 2022

To achieve high-performance colloidal quantum dot light-emitting diodes (QD-LEDs), the use of a densely packed QD film is crucial to prevent the formation of leakage current pathways and increase in interface resistance. Spin coating is the most common method to deposit QDs; however, this method often produces pinholes that can act as short-circuit paths within devices. Since state-of-the-art QD-LEDs typically employ mono- or bi-layer QDs as an emissive layer because of their low conductivities, the use of a densely packed and pinhole-free QD film is essential. Herein, we introduce the Langmuir-Blodgett (LB) technique as a deposition method for the fabricate densely packed QD films in QD-LEDs. The LB technique successfully transfers a highly dense monolayer of QDs onto the substrate, and multilayer deposition is performed by repeating the transfer process. To validate the comparability of the LB technique with the standard QD-LED fabrication process, we fabricate and compare the performance of LB-based QD-LEDs to that of the spin-coating-based device. Owing to the non-destructiveness of the LB technique, the electroluminescence efficiency of the LB-based QD-LEDs is similar to that of the standard spin coating-based device. Thus, the LB technique is promising for use in optoelectronic applications.

• Archives of Plastic Surgery
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• 제49권5호
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• pp.617-632
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• 2022

Tracheoesophageal puncture (TEP) and voice prosthesis insertion following laryngectomy may fail to form an adequate seal. When spontaneous closure of the fistula tract does not occur after conservative measures, surgical closure is required. The purpose of this study was to summarize the available evidence on surgical methods for TEP site closure. A comprehensive search across PubMed, Web of Science, SCOPUS, and Cochrane was performed to identify studies describing surgical techniques, outcomes, and complications for TEP closure. We evaluated the rate of unsuccessful TEP closure after surgical management. A meta-analysis with a random-effect method was performed. Thirty-four studies reporting on 144 patients satisfied inclusion criteria. The overall incidence of an unsuccessful TEP surgical closure was 6% (95% confidence interval [CI] 1-13%). Subgroup analysis showed an unsuccessful TEP closure rate for silicone button of 8% (95% CI < 1-43%), 7% (95% CI < 1-34%) for dermal graft interposition, < 1% (95% CI < 1-37%) for radial forearm free flap, < 1% (95% CI < 1-52%) for ligation of the fistula, 17% (95% CI < 1-64%) for interposition of a deltopectoral flap, 9% (95% CI < 1-28%) for primary closure, and 2% (95% CI < 1-20%) for interposition of a sternocleidomastoid muscle flap. Critical assessment of the reconstructive modality should take into consideration previous history of surgery or radiotherapy. Nonirradiated fields and small defects may benefit from fistula excision and tracheal and esophageal multilayer closure. In cases of previous radiotherapy, local flaps or free tissue transfer yield high successful TEP closure rates. Depending on the defect size, sternocleidomastoid muscle flap or fasciocutaneous free flaps are optimal alternatives.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.171-176
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• 2021

Die-to-wafer (D2W) hybrid bonding in the multilayer semiconductor manufacturing process is one of wafer direct bonding, and various studies are being conducted around the world. A noteworthy point in the current die-to-wafer process is that a lot of voids occur on the bonding surface of the die during bonding. In this study, as a suggested method for removing voids generated during the D2W hybrid bonding process, a flexible mechanism for implementing convex for die bonding to be applied to the bond head is proposed. In addition, modeling of flexible mechanisms, analysis/design/control/evaluation of static/dynamics properties are performed. The proposed system was controlled by capacitive sensor (lion precision, CPL 290), piezo actuator (P-888,91), and dSpace. This flexure mechanism implemented a working range of 200 ㎛, resolution(3σ) of 7.276nm, Inposition(3σ) of 3.503nm, settling time(2%) of 500.133ms by applying a reverse bridge type mechanism and leaf spring guide, and at the same time realized a maximum step difference of 6 ㎛ between die edge and center. The results of this study are applied to the D2W hybrid bonding process and are expected to bring about an effect of increasing semiconductor yield through void removal. In addition, it is expected that it can be utilized as a system that meets the convex variable amount required for each device by adjusting the elongation amount of the piezo actuator coupled to the flexible mechanism in a precise unit.

• 지역과문화
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• 제8권1호
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• pp.25-53
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• 2021

이 연구는 시대적 맥락에 따라 문화다양성의 해석과 실천이 달라졌다는 전제 아래, 문화다양성의 지역화에 필요한 정책 방향을 모색하는 데 목적이 있다. 첫째, 이 연구에서는 정치투쟁의 결과로 나타난 문화다양성 개념의 해석과 정책실천의 중층성을 분석하여 시대적 맥락에 따라 문화다양성을 해석하고 실천하는 자세가 필요함을 밝혔다. 둘째, 전라북도를 사례로 사회적 소수자가 지역에서 경험한 차별과 문화정책 관계자의 인식을 분석하여 지역에서도 문화다양성이 중층적으로 해석되고 실천됨을 확인하였다. 셋째, 정부 정책의 한계에 대응하여 지역에서 문화다양성 정책을 수립하는데 고려해야 할 방향을 제안하였다. 제안한 내용은 정책의 탈중앙화와 지역차원의 정책 발굴, '분배의 정치'에서 '인정의 정치'로 전환, 분리가 아닌 접촉면을 늘리는 상호문화주의적 접근이다.

• Geomechanics and Engineering
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• 제28권5호
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• pp.505-520
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• 2022

In recent years, geotextile-encased gravel columns (usually called stone columns) have become a popular method to increasing soil shear strength, decreasing the settlement, acceleration of the rate of consolidation, reducing the liquefaction potential and increasing the bearing capacity of foundations. The behavior of improved loose base-soil with gravel columns under shear loading and the shear stress-horizontal displacement curves got from large scale direct shear test are of great importance in understanding the performance of this method. In the present study, by performing 36 large-scale direct shear tests on sandy base-soil with different fine-content of zero to 30% in both not improved and improved with gravel columns, the effect of the presence of gravel columns in the loose soils were investigated. The results were used to predict the shear stress-horizontal displacement curve of these samples using support vector machines (SVM). Variables such as the non-plastic fine content of base-soil (FC), the area replacement ratio of the gravel column (Arr), the geotextile encasement and the normal stress on the sample were effective factors in the shear stress-horizontal displacement curve of the samples. The training and testing data of the model showed higher power of SVM compared to multilayer perceptron (MLP) neural network in predicting shear stress-horizontal displacement curve. After ensuring the accuracy of the model evaluation, by introducing different samples to the model, the effect of different variables on the maximum shear stress of the samples was investigated. The results showed that by adding a gravel column and increasing the Arr, the friction angle (ϕ) and cohesion (c) of the samples increase. This increase is less in base-soil with more FC, and in a proportion of the same Arr, with increasing FC, internal friction angle and cohesion decreases.

• Advances in nano research
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• 제12권1호
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• pp.81-99
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• 2022

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE. The outcome reveals that MEE layer, temperature change, GNP weight function, and GNP distribution patterns GNP weight function have significant influence on the critical temperature and voltage of cylindrical shell made from GNP nanocomposites core with MEE face sheet on outer of the shell.