• Nuclear Engineering and Technology
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• 제53권1호
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 한국정보통신학회논문지
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• 제25권11호
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• pp.1477-1485
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• 2021

최근 이미지 초해상도는 정수배율만 가능한 모델에만 집중적으로 연구되고 있다. 하지만 관심 객체 인식, 디스플레이 화질 개선 등 실제 초해상도 기술의 대표 적용 분야에서는 소수 배율을 포함하는 임의배율 확대 필요성이 대두되고 있다. 본 논문에서는 기존 정수배율 모델의 가중치를 활용하여 임의배율을 실행할 수 있는 모델을 제안한다. 이 모델은 정수배율에 의해 우수한 성능을 가진 초해상도 결과를 DCT 스펙트럼 도메인으로 변환하여 임의배율을 위한 공간을 확장한다. DCT 스펙트럼 도메인에 의한 확장으로 인해 발생하는 이미지의 고주파 정보 손실 문제를 줄이기 위해 고주파 스펙트럼 정보를 적절히 복원할 수 있는 모델인 고주파 집중 네트워크를 제안한다. 제안된 네트워크는 고주파 정보를 제대로 생성하기 위해서 RGB 채널간의 상관관계를 학습하는 레이어인 channel attention을 활용하고, 잔차 학습 구조를 통해 모델을 깊게 만들어 성능을 향상시켰다.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.11-18
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• 2022

Linking Ukraine's education system with the trends of global digitalization is mandatory to ensure the sustainable, long-term development of the country, as well as to increase the sustainability of the education system and the economy as a whole during the crisis period. Now the main problems of the education system in Ukraine are manifested in a complex context caused by Russian armed aggression. In the context of war, problems include differences in adaptation to online learning among educational institutions, limited access to education for vulnerable groups in the zone of active hostilities, the lack of digital educational resources suitable for online learning, and the lack of basic digital skills and competencies among students and teachers necessary to properly conduct online classes. Some of the problems of online learning were solved in the pandemic, but in the context of war Ukrainian society needs a new vision of education and continuous efforts of all social structures in the public and private environment. In the context of war, concerted action is needed to keep education on track and restore it in active zones, adapting to the needs of a dynamic society and an increasingly digitized economy. Among the urgent needs of the education system are a change in the teaching-learning paradigm, which is based on content presentation, memorization, and reproduction, and the adoption of a new, hybrid educational model that will encourage the development of necessary skills and abilities for students and learners in a digitized society and enable citizens close to war zones to learn.

• Smart Structures and Systems
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• 제32권2호
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• pp.101-109
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• 2023

In order to improve the measurement accuracy of the 6-dimensional accelerometer, the cross coupling compensation method of the accelerometer needs to be studied. In this paper, the non-linear error caused by cross coupling of piezoelectric six-dimensional accelerometer is compensated online. The cross coupling filter is obtained by analyzing the cross coupling principle of a piezoelectric six-dimensional accelerometer. Linear and non-linear fitting methods are designed. A two-level calibration hybrid compensation algorithm is proposed. An experimental prototype of a piezoelectric six-dimensional accelerometer is fabricated. Calibration and test experiments of accelerometer were carried out. The measured results show that the average non-linearity of the proposed algorithm is 2.2628% lower than that of the least square method, the solution time is 0.019382 seconds, and the proposed algorithm can realize the real-time measurement in six dimensions while improving the measurement accuracy. The proposed algorithm combines real-time and high precision. The research results provide theoretical and technical support for the calibration method and online compensation technology of the 6-dimensional accelerometer.

• Wind and Structures
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• 제35권4호
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• pp.269-285
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• 2022

The pressure-mitigating effects of a high-speed train passing through a tunnel with a partially reduced cross-section are investigated via the numerical approach. A compressible, three-dimensional RNG k-ε turbulence model and a hybrid mesh strategy are adopted to reproduce that event, which is validated by the moving model test. Three step-like tunnel forms and two additional transitions at the tunnel junction are proposed and their aerodynamic performance is compared and scrutinized with a constant cross-sectional tunnel as the benchmark. The results show that the tunnel step is unrelated to the pressure mitigation effects since the case of a double-step tunnel has no advantage in comparison to a single-step tunnel, but the excavated volume is an essential matter. The pressure peaks are reduced at different levels along with the increase of the excavated earth volume and the peaks are either fitted with power or logarithmic function relationships. In addition, the Arc and Oblique-transitions have very limited gaps, and their pressure curves are identical to each other, whereas the Rec-transition leads to relatively lower pressure peaks in C_Pmax, C_Pmin, and ΔC_P, with 5.2%, 4.0%, and 4.1% relieved compared with Oblique-transition. This study could provide guidance for the design of the novel railway tunnel.

• 한국전기전자재료학회논문지
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• 제36권4호
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• pp.303-325
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• 2023

Flexible fiber- or yarn-based one-dimensional (1-D) energy storage devices are essential for developing wearable electronics and have thus attracted considerable attention in various fields including ubiquitous healthcare (U-healthcare) systems and textile platforms. 1-D supercapacitors (SCs), in particular, are recognized as one of the most promising candidates to power wearable electronics due to their unique energy storage and high adaptability for the human body. They can be woven into textiles or effectively designed into diverse architectures for practical use in day-to-day life. This review summarizes recent important development and advances in fiber-based supercapacitors, concerning the active materials, fiber configuration, and applications. Active materials intended to enhance energy storage capability including carbon nanomaterials, metal oxides, and conductive polymers, are first discussed. With their loading methods for fiber electrodes, a summary of the four main types of fiber SCs (e.g., coil, supercoil, buckle, and hybrid structures) is then provided, followed by demonstrations of some practical applications including wearability and power supplies. Finally, the current challenges and perspectives in this field are made for future works.

• 대한화학회지
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• 제53권3호
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• pp.257-265
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• 2009

$Cu^{+}$ 및 $Cu^{2+}$와 proline의 결합형태에 따른 구조 및 금속 친화도를 DFT(Density Functional Theory) 방법으로 조사하였다. 금속-proline의 결합과 여러 결합형태에 따른 에너지 순서는 $Cu^{+}$-Proline및 $Cu^{2+}$-proline 착화합물에서 서로 매우 상이함을 알았다. $Cu^{+}$-Proline의 경우, 바닥상태의 구조는 $Cu^{+}$가 중성 proline의 카르보닐 산소 및 이미노기 질소에 배위된 두 자리 배위를 하며, 이에 비해 $Cu^{2+}$-Proline 의 바닥상태의 구조는 zwitter이온 형태 proline의 카르복시기의 두 산소 사이에 chelation을 형성하는 구조임을 확인하였다. 가장 안정한 $Cu^{+}$-Proline 착화합물에서 proline의 금속 이온 친화도는 6-311++G(d,p) 수준에서 76.0 kcal/mol로 계산되었으며, proline의 $Cu^{2+}$ 이온 친화도는 258.5 kcal/mol로 나타났다.

• 마이크로전자및패키징학회지
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• 제24권4호
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• pp.79-84
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• 2017

태양전지와 같은 광전소자의 특성 및 신뢰성 유지하기 위해서는 수분과 산소 등으로 부터 소자 내부가 보호되어야 한다. 본 연구는 여러 연성(flexible) 플라스틱 기판위에 유 무기 복합 보호막을 스프레이코팅 방법으로 형성하여 공정조건(노즐 위치, 박막 두께, 기판 구성)에 따른 소자의 보호특성을 연구하였다. 사용된 복합 보호막 재료로서 PVA (polyvinyl alcohol)와 SA(sodium alginate) 혼합 유기 물질(P.S)에 $Al_2O_3$($P.S+Al_2O_3$)과 $SiO_2$($P.S+SiO_2$) 나노 분말을 혼합하여 유 무기 복합 보호막 용액을 합성하였다. 플라스틱 기판 위에 코팅한 보호막의 두께가 $5{\mu}m$에서 91%의 투과율을 나타내었으며 $78{\mu}m$에서 $178{\mu}m$로 두께가 증가할 경우 광 투과율은 81.6%에서 73.6%으로 감소하였다. 또한 합성한 $P.S+Al_2O_3$ 복합재료를 사용하여 PEN(polyethylene naphthalate), PC(polycarbonate) 단일 플라스틱 기판과 Acrylate film과 PC 이중막(Acrylate film/PC double layer) 구조와 $Al_2O_3$ 무기박막과 PEN 이중막($Al_2O_3$ film/PEN double layer) 구조의 기판 위에 $P.S+Al_2O_3$ 용액을 사용하여 수분투과도(water vapor transmission rate, WVTR)와 표면형상 등을 측정하여 최적의 보호막 구조를 확인하였다. 즉, $Al_2O_3$ film/PEN 이중막 기판위에 형성한 보호막의 수분투과 값은 $0.004gm/m^2-day$로 가장 우수한 내 투습 특성을 나타내었다.

• 청정기술
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• 제20권3호
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• pp.306-313
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• 2014

나노다공성 $TiO_2$ 필름은 주로 염료감응형 태양전지의 작동전극으로 사용된다. 지금까지 염료감응형 태양전지의 광전환효율을 높이기 위해 $TiO_2$ 나노구조체에 대한 다양한 연구가 시도되어왔다. 본 연구에서는 수열합성법을 이용하여 FTO glass 위에 루타일 $TiO_2$ 나노로드를 수직적으로 성장시켰고 그 위에 아나타제 $TiO_2$ 필름을 재 합성하였다. 이 새로운 방법은 아나타제 $TiO_2$ 합성시 요구되는 시드층 합성단계를 피할 수 있었다. 밀집한 아나타제 $TiO_2$ 층은 전자생성층으로써 고안되었고 시드층 대신 합성된 루타일 $TiO_2$ 나노로드는 생성된 전자들이 FTO glass로 이동하는 통로역할을 하게 되었다. 전자이동률을 증진시키기 위해 루타일 나노로드에 $TiCl_4$ 수용액을 이용하여 표면 처리하였고 열처리 후 표면 위에 얇은 아나타제 $TiO_2$ 필름을 형성시켰다. 합성된 루타일-아나타제 $TiO_2$ 구조체의 두께는 $4.5-5.0{\mu}m$이고 셀 테스트 결과 3.94%의 광전환효율을 얻게 되었다. 이는 루타일 $TiO_2$ 나노로드 전극과 비교했을 때 광전환효율이 상당히 향상되는 것을 확인할 수 있었다.