• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.6
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• pp.592-597
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• 2005

The push pull forward converter is suitable in a low output voltage, a high output current applications with wide input voltage ranges. All magnetic components including output inductor, transformer and input filter can be integrated into single EI/EE core. The integrated push pull forward converter is considered through the comparison of efficiency according to the circuit parameters. The Nicera company's 5M FEE18/8/10C and NC-2H FEI32/8/20 cores are used for the transformer. The integrated push pull forward converter ratings are of $36\~72V$ input and 3.3V/30A output. In case that NC-2H FEI32/8/20 core used in the converter, the efficiency is measured up to $83.5\%$ at the switching frequency 200 kHz and the 11A load. The efficiencies of $76.4\%$ at a full load and $82.95\%$ at a half load are measured.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.115-119
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• 2023

The natural language processing models used in current artificial intelligence are huge, causing various difficulties in processing and analyzing data in real time. In order to solve these difficulties, we proposed a method to improve the efficiency of processing by using less memory and checked the performance of the proposed model. The technique applied in this paper to evaluate the performance of the proposed model is to divide the large corpus by adjusting the number of attention heads and embedding size of the BERT[1] model to be small, and the results are calculated by averaging the output values of each forward. In this process, a random offset was assigned to the sentences at every epoch to provide diversity in the input data. The model was then fine-tuned for classification. We found that the split processing model was about 12% less accurate than the unsplit model, but the number of parameters in the model was reduced by 56%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Journal of information and communication convergence engineering
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• v.1 no.3
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• pp.163-168
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• 2003

This paper presents a cascade multilevel PWM rectifier without the isolation transformers for energy build-up at each inverter modules. The features and advantages of the proposed PWM rectifier can be summarized as follows; I) It realizes the high power high voltage AC/DC power conversion, 2) It uses no transformer which is bulky and heavy, 3) It has hybrid structure so that switching devices can be effectively utilized, 4) It produces high quality AC current even in high power high voltage applications, 5) The input power factor remains unity by simple modulation index control. The multilevel rectifier is analyzed by using the circuit DQ transformation whereby the characteristics and control equations are obtained. Finally, it will be shown that the system simulation reveals the validity of analyses.

• Journal of the Korean Institute of Telematics and Electronics
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• v.5 no.1
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• pp.13-25
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• 1968

A detailed analysis of the transistor wide-band feedback amplifiers using the hybrid-$\pi$ equivalent circuit has been made. It is considered both for the low freqnency and for the high frequency. The expressions of the gain, bandwidth. input impedance and output impedance have been presented. It is shown that a series feedback amplifier should be driven from the voltage source and should drive into the low resistance load, and a shunt feedback amplifier should be driven from the current source and should drive into the high resistance load. It is also shown that these stages can be coupled without use of the buffer stage or coupling transformer.

• Progress in Superconductivity and Cryogenics
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• v.3 no.1
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• pp.16-21
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• 2001

Bubble behavior is studied with an electrode system which consists of coaxial spiral coil-to-cylindrical electrode with an insulation barrier and spacers and is immersed in liquid nitrogen for simulation of insulation environments in high temperature superconducting(HTS) coils The results show that the bubble behavior Is affected severely by electric field: (1) under low applied voltage bubbles rise by buoyancy, but at higher applied voltage they are trapped in a lower electric field region below the coil electrode, and (2) the trapped bubble flows along the downside of coil electrode if no obstruction is in a groove between coil turns. but it splashes out of the groove after its growing if the obstruction such as spacer-exists.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.233-233
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• 2009

Piezoelectric Transformer (PT) was emerged in device and material industry. PT has some advantages such as low profile and mechanical energy transfer with little electromagnetic interface (EMI). But, It is known that the maximum PT efficiency can be obtained when it operates near the resonant frequency of the PT. Also PT's resonant frequency moves according to the load conditions Therefore, As the operating frequency moves further from the resonant frequency, the PT efficiency decreases dramatically due to the increase of the circulating current. This paper proposes analyzes modeling of PT convert and propose a guide-line to adaptive control