• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3740-3749
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• 2024

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSOLSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.

• ETRI Journal
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• v.36 no.1
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• pp.99-105
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• 2014

This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.45-58
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• 1996

This paper describes an a, pp.oach to recognizing composite features of prismatic parts. AAG (Attribute Adjacency Graph) is adopted as the basis of describing basic feature, but it is extended to enhance the expressive power of AAG by adding face type, angles between faces and normal vectors. Our a, pp.oach is called Extended AAG (EAAG). To simplify the recognition procedure, feature classification tree is built using the graph types of EEA and the number of EAD's. Algorithms to find open faces and dimensions of features are exemplified and used in decomposing composite feature. The processing sequence of recognized features is automatically determined during the decomposition process of composite features.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.119-125
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• 2021

As the electricity grid systems has been intelligent with the development of ICT technology, power consumption information of users connected to the grid is available to acquired and analyzed for the power utilities. In this paper, the energy theft problem is solved by feature selection methods, which is emerging as the main cause of economic loss in smart grid. The data preprocessing steps of the proposed system consists of five steps. In the feature selection step, features are selected using analysis of variance and mutual information (MI) based method, which are filtering-based feature selection methods. According to the simulation results, the performance of support vector machine classifier is higher than the case of using all the input features of the input data for the case of the MI based feature selection method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.129-136
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• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.192-194
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• 2005

Development of nonlinear loads and compensation instruments make PQ(Power Quality) problem into important issue. Few studies by signal processing and pattern classification as NN(Neural Network), Wavelet Transform, and Fuzzy present feature extraction. A lot of Input features make not always good result and they are difficult to make realtime system. Thus, The dimentionality reduction is indispensable process. PCA(Principal Component Analysis) reduces high-dimensional input features onto a lower-dimensional subspace effectively. It will be useful to apply to realtime system and NN.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.1-15
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• 2022

The MobileNetV3 is specially designed for mobile devices with limited memory and computing power. To reduce the network parameters and improve the network inference speed, a new lightweight network is proposed based on MobileNetV3. Firstly, to reduce the computation of residual blocks, a partial residual structure is designed by dividing the input feature maps into two parts. The designed partial residual structure is used to replace the residual block in MobileNetV3. Secondly, a dual-path feature extraction structure is designed to further reduce the computation of MobileNetV3. Different convolution kernel sizes are used in the two paths to extract feature maps with different sizes. Besides, a transition layer is also designed for fusing features to reduce the influence of the new structure on accuracy. The CIFAR-100 dataset and Image Net dataset are used to test the performance of the proposed partial residual structure. The ResNet based on the proposed partial residual structure has smaller parameters and FLOPs than the original ResNet. The performance of improved MobileNetV3 is tested on CIFAR-10, CIFAR-100 and ImageNet image classification task dataset. Comparing MobileNetV3, GhostNet and MobileNetV2, the improved MobileNetV3 has smaller parameters and FLOPs. Besides, the improved MobileNetV3 is also tested on CPU and Raspberry Pi. It is faster than other networks