• Nuclear Engineering and Technology
• /
• v.53 no.1
• /
• pp.148-163
• /
• 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
• /
• v.29 no.1
• /
• pp.77-91
• /
• 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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.11
• /
• pp.1477-1485
• /
• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.11-18
• /
• 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
• /
• v.32 no.2
• /
• pp.101-109
• /
• 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
• /
• v.35 no.4
• /
• pp.269-285
• /
• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.36 no.4
• /
• pp.303-325
• /
• 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.

• Journal of the Korean Chemical Society
• /
• v.53 no.3
• /
• pp.257-265
• /
• 2009

The structures and metal affinities of the binding configurations of $Cu^{+}$ and $Cu^{2+}$ to proline have been investigated using the hybrid three-parameter Density Functional Theory(DFT/B3LYP). We found that the metal-proline bonding and the energy ordering of several conformers were very different in $Cu^{+}$-proline and $Cu^{2+}$-proline. For $Cu^{+}$-proline, the ground state structure was found to have a bidentated coordination in which $Cu^{+}$ was coordinated to the carbonyl oxygen and imino group nitrogen of neutral proline. On the contrary, the ground state structure of $Cu^{2+}$-proline involves chelation between the two oxygens of the carboxylate group in a zwitterionic proline. The metal ion affinity of proline of the most stable $Cu^{+}$-proline complex was calculated as 76.0 kcal/mol at 6-311++G(d,p) level, whereas the $Cu^{2+}$ ion affinity of proline was calculated as 258.5 kcal/mol.

• Journal of the Microelectronics and Packaging Society
• /
• v.24 no.4
• /
• pp.79-84
• /
• 2017

The solar cells should be protected from the moisture and oxygen in order to sustain the properties and reliability of the devices. In this research, we prepared the protection films on the flexible plastic substrates by spray coating method using organic-inorganic hybrid solutions. The protection characteristics were studied depending on the various process conditions (nozzle distance, thicknesses of the coatings, film structures). The organic-inorganic solutions for the protection film layer were synthesized by addition of $Al_2O_3$ ($P.S+Al_2O_3$) and $SiO_2$ ($P.S+SiO_2$) nano-powders into PVA (polyvinyl alcohol) and SA (sodium alginate) (P.S) organic solution. The optical transmittances of the protection film with the thicknesses of $5{\mu}m$ showed 91%. The optical transmittance decreased from 81.6% to 73.6% with the film thickness increased from $78{\mu}m$ to $178{\mu}m$. In addition, the protective films were prepared on the PEN (polyethylene naphthalate), PC (polycarbonate) single plastic substrates as well as the Acrylate film coated on PC substrate (Acrylate film/PC double layer), and $Al_2O_3$ film coated on PEN substrate ($Al_2O_3$ film/PEN double layer) using the $P.S+Al_2O_3$ organic-inorganic hybrid solutions. The optimum protection film structure was studied by means of the measurements of water vapor transmittance rate (WVTR) and surface morphology. The protective film on PEN/$Al_2O_3$ double layer substrate showed the best water protective property, indicating the WVTR value of $0.004gm/m^2-day$.

• Clean Technology
• /
• v.20 no.3
• /
• pp.306-313
• /
• 2014

Nanoporous $TiO_2$ films are commonly used as working electrodes in dye-sensitized solar cells (DSSCs). So far, there have been attempts to synthesize films with various $TiO_2$ nanostructures to increase the power-conversion efficiency. In this work, vertically aligned rutile $TiO_2$ nanorods were grown on fluorinated tin oxide (FTO) glass by hydrothermal synthesis, followed by deposition of an anatase $TiO_2$ film. This new method of anatase $TiO_2$ growth avoided the use of a seed layer that is usually required in hydrothermal synthesis of $TiO_2$ electrodes. The dense anatase $TiO_2$ layer was designed to behave as the electron-generating layer, while the less dense rutile nanorods acted as electron-transfer pathwaysto the FTO glass. In order to facilitate the electron transfer, the rutile phase nanorods were treated with a $TiCl_4$ solution so that the nanorods were coated with the anatase $TiO_2$ film after heat treatment. Compared to the electrode consisting of only rutile $TiO_2$, the power-conversion efficiency of the rutile-anatase hybrid $TiO_2$ electrode was found to be much higher. The total thickness of the rutile-anatase hybrid $TiO_2$ structures were around $4.5-5.0{\mu}m$, and the highest power efficiency of the cell assembled with the structured $TiO_2$ electrode was around 3.94%.