• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.2-157
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• 2001

This paper describes the synergism of Artificial Neural Network and Fuzzy Logic based approach to improve the reliability of transformer differential protection, the conventional transformer differential protection commonly used a harmonic restraint principle to prevent a tripping from inrush current during initial transformer´s energization but such a principle can not performs the best optimization on tripping time. Furthermore, in some cases there may be false operation such as during CT saturation, high DC offset or harmonic containing in the line. Therefore an artificial neural network and fuzzy logic has been proposed to improve reliability of the transformer protection relay. By using EMTP-ATP the power transformer is modeled, all currents flowing ...

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.370-375
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• 2006

This paper suggests the method for estimating a partial discharge (PD) location using divide of the inside transformer as a grid. The PD location is found swiftly and economically compared with the typical method detecting a PD. The reason is that the location of PD is detected in the section. The estimation of PD location is trained using the Neural Network. JavaNNS(Java Neural Network Simulator) and SNNS(Stuttgart Neural Network Simulator) are used for searching the location of PD. The simulation procedure is following, The transformer is assumed that the case is a regular hexahedron. The sensor is installed in a proper location. A section of PD location is set as a target, and training set is studied with several PD locations in the inside of the transformer. As a result of training process, the learning capability of neural network is excellent. The PD location is detected by division of internal structure of transformer and application of neural network.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.458-476
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• 2024

To address the issue of class imbalance in network traffic data, which affects the network intrusion detection performance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGAN are used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encode traffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network model combining a bidirectional gated current unit and deep neural network is proposed, which is used to extract long-dependence features. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detection accuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusion detection performance, thereby improving the communication security of network data.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1103-1111
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• 1999

STACOM will be utilized to control substation voltage in the near future. Although STACOM shows good voltage regulation performance owing to its rapid and continuous response, it needs additional reactive power compensation device to keep control margin for emergency such as fault. ULTC transformer is one of good candidates. This paper presents a Kohonen Neural Network (KNN) based coordination control scheme of ULTC transformer and STACOM. In this paper, the objective function of the coordination control is minimization of both STACOM output and the number of switchings of ULTC transformer while maintaining substation voltage magnitude to the predefined constant value. This coordination, control is performed based on reactive load trend of the substation and KNN which offers optimal tap position in view of STACOM output minimization. The input variables of KNN are active and reactive power of the substation, current tap position, and current STACOM output. The KNN is trained by effective Iterative Condensed Nearest Neighbor (ICNN) rule. This coordination control applied to IEEE 14 bus system and shows satisfactory results.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.12-22
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• 2023

The development of neural networks is evolving towards the adoption of transformer structures with attention modules. Hence, active research focused on extending the concept of lightweight neural network algorithms and hardware acceleration is being conducted for the transition from conventional convolutional neural networks to transformer-based networks. We present a survey of state-of-the-art research on lightweight neural network algorithms and hardware architectures to reduce memory usage and accelerate both inference and training. To describe the corresponding trends, we review recent studies on token pruning, quantization, and architecture tuning for the vision transformer. In addition, we present a hardware architecture that incorporates lightweight algorithms into artificial intelligence processors to accelerate processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2626-2641
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• 2024

Images taken in low lamination condition suffer from low contrast and loss of information. Low lumination image enhancement algorithms are required to improve the quality and broaden the applications of such images. In this study, we proposed a new Low lumination image enhancement architecture consisting of a transformer-based curve learning and an encoder-decoder-based texture enhancer. Considering the high effectiveness of curve matching, we constructed a transformer-based network to estimate the learnable curve for pixel mapping. Curve estimation requires global relationships that can be extracted through the transformer framework. To further improve the texture detail, we introduced an encoder-decoder network to extract local features and suppress the noise. Experiments on LOL and SID datasets showed that the proposed method not only has competitive performance compared to state-of-the-art techniques but also has great efficiency.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.47-56
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• 2016

In transformer cooling systems, oil temperature is controlled through the use of a blower and oil pump. For this paper, set-point algorithms, a reset algorithm and control algorithms of the cooling system were developed by neural networks and fuzzy logics. The oil inlet temperature was set by a $2{\times}2{\times}1$ neural network, and the oil temperature difference was set by a $2{\times}3{\times}1$ neural network. Inputs used for these neural networks were the transformer operating ratio and the air inlet temperature. The inlet set temperature was reset by a fuzzy logic based on the transformer operating ratio and the oil outlet temperature. A blower was used to control the inlet oil temperature while the oil pump was used to control the oil temperature difference by fuzzy logics. In order to analysis the performance of these algorithms, the initial start-up test and the step change test were performed by using the dynamic model of a transformer cooling system. Test results showed that algorithms developed for this study were effective in controlling the oil temperature of a transformer cooling system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.8
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• pp.515-522
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• 2010

In order to improve the efficiency of a transformer cooling system, the intelligent algorithm was developed. The intelligent algorithm is composed of a setpoint algorithm and a control algorithm. The setpoint algorithm was developed by the neural network, and the control algorithm was developed by the fuzzy logic. These algorithms were used for the control of a blower and an oil pump of the transformer cooling system. In order to analyse performances of these algorithms, the dynamic model of a transformer cooling system was used. Based on various performance tests, energy savings and stable controls of a transformer cooling system were observed. Therefore, control algorithms developed for this study may be effectively used for the control of a transformer cooling system.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.266-273
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• 2024

With the rising interaction between robots and humans, detecting collisions has become increasingly vital for ensuring safety. In this paper, we propose a novel approach for detecting collisions without using force torque sensors or tactile sensors, utilizing a Transformer-based neural network architecture. The proposed collision detection approach comprises a torque estimator network that predicts the joint torque in a free-motion state using Synchronous time-step encoding, and a collision discriminator network that predicts collisions by leveraging the difference between estimated and actual torques. The collision discriminator finally creates a binary tensor that predicts collisions frame by frame. In simulations, the proposed network exhibited enhanced collision detection performance relative to the other kinds of networks both in terms of prediction speed and accuracy. This underscores the benefits of using Transformer networks for collision detection tasks, where rapid decision-making is essential.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.5
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• pp.43-50
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• 2003

This presents a fault detecting method for a power transformer based upon a neural network. To maintain a normal relay operating conditions, external winding faults of a power transformer and magnetic inrush have been tested under consideration of the EMTP/ATP software and internal faults of power transformer have been tested by the EMTP/BCTRAN software. The neural network has been evaluated by the proposed fault. Input variables of the neural network for the proposed model can be obtained from fundamental currents, restraining and operating currents. This algorithm uses back-propagation and the ratio of a restraining current and an operating current as relay input parameters. The ratio may enhance the fault detection since the restraining currents increase rapidly at external faults. The proposed detecting method has been applied to the practical relay systems for transformer protection. As a result, the proposed detecting method based on the neural network has been shown to have better characteristics.