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Most traditional soft sensor modeling requires the labeled training samples that contain both subsidiary and key variables. However, key variables are difficult to be obtained online due to lack of detection information or high measurement cost. In this paper, a novel semi-supervised learning algorithm, called cotraining-style kernel extreme learning machine, is proposed to exploit unlabeled training...
We consider the problem of link prediction in dynamic networks under the condition of a set of snapshots of the networks. To address the nonlinear transitional patterns in network structures, we propose an approach that incorporates the historical linkage and neighboring information into the restricted Boltzmann machine (RBM) model by adding temporal and neighboring connections between the hidden...
Building a human-computer interactive parachute simulator is an efficient way to avoid the high risk and high cost of field parachute training. In this paper, a novel dynamic recognition and simulation approach of parachute training is developed. Firstly we process the skeletal data acquired by Kinect and enforce the indication of the trainees' parachute posture, where principle component analysis...
The dynamic and system reliability of driving system in battery electric vehicles (BEVs) highly depend on the fault diagnosis technology. In this paper, we provided a new data compression approach and validated it on a method based on neural network (NN) to detect both failures' types and degree in drive system. In time-/frequency domain several statistical features were extracted from signals acquired...
The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network,...
Composites are widely used in aviation, aerospace and other fields because of their high specific strength, high specific stiffness and easy molding. However, in the process of using the concentrated stress, heavy shocks may form different degrees of damage. Especially, the internal delamination will reduce the stability and safety of the structure. Based on the analysis of damage location and damage...
The state monitoring issue of the induced draft fan in a thermal power plant by employing the gravitational searching algorithm optimized BP neural network is investigated in this paper. A new method to estimate the air quantity of the induced draft air of a thermal power plant is proposed based on the historical operation data extracted from the supervisory information system (SIS). In order to predict...
This paper presents an intelligent operation and maintenance training system for power grid operation staff of substation based on augmented reality (AR), whose functions, characteristics, software configuration and implementation are introduced in details. As the training and examination system for operation and maintenance personnel, the system was composed of intelligent training system and intelligent...
Monitoring of dynamic industrial process has been increasingly important due to more and more strict safety and reliability requirements. Popular methods like time lagged arrangement-based and subspace-based approaches exhibit good performance in fault detection, however, they suffer from difficulty in accurately isolating faulty variables and diagnosing fault types. To alleviate this difficulty,...
Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir is very large, the collinearity problem may exist in ESN. To overcome this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The proposed ALESN can...
Bearings are widely used in industrial equipment, and their fault detections have tight relationship with the safety production and economic operation. Its running state can influence the performance of the whole machine directly, and the fault of bearing is one of the main factors that lead to the fault of machinery equipment during the running process. Instead of using traditional measures based...
On the basis of general pivot method for paraphrase extraction which might introduce much noise in extracted paraphrases, this paper proposes a syntactic knowledge-enhanced method to extract higher-quality paraphrases to further improve the quality of statistical machine translation. Firstly, the syntactic knowledge is acquired and added to paraphrase extraction algorithm as constraints to obtain...
A new methodology for image synthesis based on two cooperative training ConvNets is proposed. Two generative ConvNets and unsupervised joint learning are designed to effectively reflect the characteristics of real scenery and image pattern. Every ConvNet is directly derived from the discriminate ConvNet and has the potential to learn from big unlabeled data, either by contrastive divergence. One ConvNet...
Joint sparse representation (JSR) models have been widely applied into the field of hyperspectral image (HSI) classification. However, most of JSR-based models adopt the Frobenius norm to measure the reconstruction error, which ignores the structural information of the small patch. In this paper, we propose a nuclear-norm joint sparse representation (NuJSR) model for hyperspectral image classification...
Fault detection method using k nearest neighbor rule has shown its advantages in dealing with nonlinear, multi-mode, and nonGaussian distributed data. Once a fault is detected in industrial processes, recognizing fault variables becomes the crucial task subsequently. Recently, the method of fault variables recognition using k nearest neighbor reconstruction (FVR-kNN) has been reported. However, the...
In this paper, because of infinite-dimensional feature and complex nonlinearities of the distributed parameter systems, a new data-driven modeling method has been proposed. The temporal-spatial output of the system is measured at a finite number of spatial locations. At the same time it is assumed that the input of the system is a temporal variable. Firstly, Karhunen-Loève(KL) decomposition is used...
The correct analysis of power system transient stability is of great significance to the safe and stable operation of power system and the construction of smart grid. Based on the basic theory of Extreme Learning Machine (ELM), this paper studies the transient stability of power system. Firstly, the simulation model is built to simulate power system for obtaining data sets. Then, the implementation...
Based on the method of Skeletonization, the concept of influence factor is introduced in this paper. A method for trimming the fat from a Back Propagation (BP) neural network is proposed by modifying weight and influence factor alternately, and node with the least influence factor was deleted. This method is applied to modeling superheated steam temperature system of plant station. Simulation results...
It is difficult to establish accurate mathematical models to describe the range extender electric vehicles due to the non-stationary, non-linear and interconnection of the monitoring signal sources resulted from the massive moving parts and complex architecture in range-extender. And the support vector machine (SVM) and other algorithms would lead to the destruction of the natural structure and the...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
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