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In view of the preconscious behavior of pedestrian and walking speed differences, a lattice gas model of bidirection pedestrian flow is established in this paper. According to the characteristics of pedestrian following behavior and preconscious dynamic change in different walking conditions, bi-direction pedestrian behavior model based on dynamic preconsciousness is constructed to study the bias...
Gear as a critical component of transportation, its operating status has a direct impact on traffic safety. Under the high speed and heavy load, the spalling is one of the main failure mechanisms of the gear teeth. This study focuses on the effect of spalling defects in different positions on the time-varying mesh stiffness (TVMS). By studying the distribution of meshing force in meshing process,...
Aiming at the problem of transient saturation of P class current transformer (TA) in power system, this paper simulates the working condition of TA in 330/110kV short circuit system. The transient saturation characteristics of TA are discussed and demonstrated in the form of exploratory test at transient currents up to 48kA. For the application level of transient saturation, the maloperation of relay...
Lithium ion battery as a kind of new energy is a promising energy storage medium for electric and hybrid electric vehicles with their characteristics of lightness and high energy density. However, some accidents about battery fire or explosion remind us to focus on their reliability and safety issues. Thus, life prediction as the essential part should be considered during the design phrase of batteries...
A temperature sensor with two cascaded fiber Fabry-Perot interferometers (FPIs) is constructed and demonstrated, which works on envelope-based demodulation and exhibits a high temperature sensitivity. The sensor is fabricated by splicing a short segment of large mode area (LMA) fiber to a short segment of capillary tube fused with a section of single-mode fiber to form a cascaded FPI structure. Experimental...
Aiming to improve positioning precision of the INS/GPS integrated navigation system during GPS outages, a novel neural network learning algorithm based on model predictive filter (MPFNN) for INS errors compensation is proposed. MPFNN is applied to establish a highly accurate mapping relationship when GPS works well and to predict INS errors during GPS outages. Different from traditional algorithm,...
Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems. Residual learning is an efficient method to help neural networks converge easier and faster. In this paper, we propose several types of residual LSTM methods for our acoustic modeling. Our experiments indicate that, compared with classic LSTM, our architecture...
Wearable devices such as smartwatches do not have enough power and computation capability to process computationally intensive tasks. One viable solution is to offload these tasks to the connected smartphone. Existing Android smartphones allocate CPU resources to a task according to its performance requirement, which is determined by the context of the task. However, due to lack of context information,...
The optimization of spare parts inventory for equipment support system is becoming a dominant support strategy, especially in the defense industry. Tremendous researches have been made to achieve optimal support performance of the supply system. However, the lack of statistical data brings limitations to these optimization models which are grounded on probability theory. And, the spare parts inventory...
This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle (HFV) models by using neural network (NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbance-observer-based-control (DOBC) algorithm with...
To make full use of the data information and improve the classification performance, a new evidential neural network classifier is proposed and a novel implementation of multiple classifier systems based on the new evidential neural network classifier is presented in this paper. The ambiguous data contained in the training data is considered as a new class — compound class and the training data is...
In this paper, a least squares based on two-step update identification algorithm is established for the Wiener system by introducing a relaxation factor, which controls the relative importance of the two estimation parts. In addition, the convergence performance of the proposed LS-TSU algorithm is then analyzed. It is shown by a numerical example that if the weighting factor is appropriately chosen,...
With the tremendous advances made by Convolutional Neural Networks (ConvNets) on object recognition, we can now easily obtain adequately reliable machine-labeled annotations easily from predictions by off-the-shelf ConvNets. In this work, we present an abstraction memory based framework for few-shot learning, building upon machine-labeled image annotations. Our method takes large-scale machine-annotated...
In this paper, we present a novel and general network structure towards accelerating the inference process of convolutional neural networks, which is more complicated in network structure yet with less inference complexity. The core idea is to equip each original convolutional layer with another low-cost collaborative layer (LCCL), and the element-wise multiplication of the ReLU outputs of these two...
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Our baselines address three issues: the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification (re-ID) accuracy and assessing the effectiveness of different...
An improved KNN text classification algorithm based on Simhash has been proposed by introducing Simhash and the average Hamming distance of adjacent texts as a unit, which solves the problems caused by data imbalance and the large computational overhead in the traditional KNN text classification algorithms. Experimental results demonstrate that the proposed algorithm performs a higher precision, a...
Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate...
A single frequency erbium-doped fiber laser with an on-chip high-Q silicon nitride (Si3N4) microring cavity is demonstrated. The laser linear cavity is composed of a microring with a Q of up to 2.5×105 and a free spectral range (FSR) of 2 nm, a 12 cm erbium-doped fiber and a fiber Bragg grating (FBG). The linewidth of 8 kHz is achieved.
Simulation of a permanent magnet synchronous motor (PMSM) servo system which was based on active disturbance rejection controller (ADRC) was presented in this paper. A new Monte-Carlo method for parameter tuning was invented, which was based on different parameter values and their corresponding dynamic performance. What's more, according to the simulation results of the servo system, the ADRC controller...
This paper is aimed to study the fluctuation of the instantaneous availability (IA) for the two-unit series repairable system. The system is based on the renewal model with two states (up and down) under exponential distribution. By Laplace transformation, the analytic solution can be worked out. According to the fluctuation theories, the fluctuation of IA is analysed. Some figures are provided to...
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