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A new control problem of roll-pitch seeker about the disturbance coupling is investigated in this work. Previous control strategies of roll-pitch seeker rarely considered the effects of the dynamic coupling disturbances from the missile angular motion, and because of the polar coordinate structure, the disturbance rejection strategies of yaw-pitch seeker cannot be utilized directly on roll-pitch seeker. Thus this article presents a new control strategy to improve the disturbance rejection ability of roll-pitch seeker. To analyze the disturbance coupling problem, the roll-pitch seeker model is derived with consideration of the missile body angular motion and torque disturbances. The motion equation of roll-pitch seeker shows that part of the dynamic coupling disturbance can be eliminated by certain choices of inertial parameters. The rest of disturbance is reduced by the stabilization loops using a differentiator-based disturbance compensation controller. Particularly, a missile body angular acceleration estimation method is designed for the compensator based on an adaptive differentiator. In addition, comparisons with other control strategies are carried out via numerical simulations, and the results confirm the effectiveness of the proposed method.

In the literature, a lot of work focused on studying intermittent control problems via feedback control strategy. No study on the intermittent control problems via sliding mode control method has been reported so far. This paper studies the problem of fast synchronization between two complex dynamical networks via aperiodically intermittent control and sliding mode control. In order to achieve fast synchronization of complex dynamical networks by using aperiodically intermittent sliding mode controller, new differential inequalities are derived firstly. After that, some sufficient finite-time synchronization criteria and finite-time achieving slide mode surface are obtained based on finite-time stability theory, aperiodically intermittent sliding mode control technique and constructing Lyapunov function. Finally, an example is provided to verify the effectiveness of the proposed theoretical methods.

Online product recommendation and market segmentation are directly related to the consumer experience and the healthy development of the e-commerce market. In order to improve the selection of network products and the effect of market segmentation, this study introduced a wireless sensing system based on the recommendation of traditional network products and combined the operational conditions of the network commodity market to construct a personalized recommendation e-commerce system. Meanwhile, a series of representative WSN congestion control strategies are designed by dynamically adjusting the network topology, routing algorithm, channel allocation protocol, and channel resource utilization, and the system structural components and functions are implemented. The research shows that the proposed algorithm and system have certain practical effects and can provide theoretical reference for subsequent related research.

Thermal infrared (TIR) object tracking is one of the most challenging tasks in computer vision. This paper proposes a robust TIR tracker based on the continuous correlation filters and adaptive feature fusion (RCCF-TIR). Firstly, the Efficient Convolution Operators (ECO) framework is selected to build the new tracker. Secondly, an optimized feature set for TIR tracking is adopted in the framework. Finally, a new strategy of feature fusion based on average peak-to-correlation energy (APCE) is employed. Experiments on the VOT-TIR2016 (Visual Object Tracking-TIR2016) and PTB-TIR (A Thermal Infrared Pedestrian Tracking Benchmark) dataset are carried out and the results indicate that the proposed RCCF-TIR tracker combines good accuracy and robustness, performs better than the state-of-the-art trackers and has the ability to handle various challenges.

At present, the one-stage detector based on the lightweight model can achieve real-time speed, but the detection performance is challenging. To enhance the discriminability and robustness of the model extraction features and improve the detector’s detection performance for small objects, we propose two modules in this work. First, we propose a receptive field enhancement method, referred to as adaptive receptive field fusion (ARFF). It enhances the model’s feature representation ability by adaptively learning the fusion weights of different receptive field branches in the receptive field module. Then, we propose an enhanced up-sampling (EU) module to reduce the information loss caused by up-sampling on the feature map. Finally, we assemble ARFF and EU modules on top of YOLO v3 to build a real-time, high-precision and lightweight object detection system referred to as the ARFF-EU network. We achieve a state-of-the-art speed and accuracy trade-off on both the Pascal VOC and MS COCO data sets, reporting 83.6% AP at 37.5 FPS and 42.5% AP at 33.7 FPS, respectively. The experimental results show that our proposed ARFF and EU modules improve the detection performance of the ARFF-EU network and achieve the development of advanced, very deep detectors while maintaining real-time speed.

Gravity and magnetic surveys are widely used in geology exploration because of its advantages, such as efficient and economy, green and environment-friendly, widely coverage and strong horizontal resolution. In order to well study in the geology exploration, it is required to comprehensively combine the different scales (different scales data) and different dimensions (satellite data, aeronautical data, ground data, ocean data, well data, etc.) of gravity and magnetic data that were observed in different periods, however, the comprehensive application of the multi-dimensional and multi-scale gravity and magnetic data still stays in the initial stage. In this paper, we do research on the key point of the fusion of potential field data (gravity and magnetic data): the way to fuse the different scales and different dimensions of potential field data into a benchmark and the same surface. Based on this research, we propose a scheme to fuse the multi-dimensional and multi-scale gravity and magnetic data. The synthetic models show that this fusion scheme is able to fuse the multi-dimensional and multi-scale gravity and magnetic data with great fusion results and small errors, in addition, the most important is that the fusion data conform to the characteristics of the potential field data and can meet the needs of data processing in the following steps. One of case studies in China has been accomplished to fuse aeronautical and ground gravity data that are different scales by using this fusion scheme. The fusion scheme we proposed in this study can be used in the fusion of the multi-dimensional (aeronautical, ground and ocean) and multi-scale gravity and magnetic data, which is good for interpretation and popularization.

Three-dimensional (3D) digital images are helpful in studying the physical properties of a medium. Moreover, reconstructing a 3D image from a two-dimensional (2D) image via computer-mathematical modeling is an important process. Based on the relationship between cascaded grids, an ingenious cascaded polymorphic method is proposed in this work to reconstruct a 3D image from a 2D image. By using the cascaded polymorphic method, a patch can be copied directly into a reconstruction grid by matching the patterns between cascaded grids; the polymorphism of the templates correspond to the grid size. To improve the reconstruction performance further, the non-edge region expansion strategy for a layer and the non-edge region replication strategy for adjacent layers were also proposed. A core sample and building material were selected as the test materials, and several effective measurements were used as quantitative means. To display the reconstruction effect intuitively, 2D reconstruction experiments were also performed along with the 3D experiment. The results show that the reconstruction speed is significantly improved using the cascaded polymorphic method, and the morphological features of the 3D reconstruction are clearly promoted.

There are two main contributions in this paper: Firstly, by analyzing the frequency domain features and Mel domain features, we can identify footstep events and non-footstep events. Secondly, we compared the two footstep sound signals of the same person in frequency domain under different experimental conditions, finding that almost all of their peak frequencies and trough frequencies in the main frequency band are respectively corresponding one-to-one. However for the two different people, even under the same experimental conditions, it is difficult to have the same peak frequencies and trough frequencies in the main frequency band of their footstep sound signals. Therefore, this feature of footstep sound signals can be used to identify different people.

The purpose of semantic segmentation is to classify the pixels within the target contour. Edge detection is another major basic vision task in machine vision. Today’s most effective semantic segmentation models and contour edge detection models are isolated networks. The edge of the output of the semantic segmentation model is coarse and cannot be directly used. And the output of the edge detection network cannot output the classification information of the pixels inside the contour. In view of the above shortcomings of the existing network, we propose a semantic segmentation model based on edge constraint optimization, so that the output of the semantic segmentation model has more delicate edge information, and the network directly outputs accurate contour edge graphs. The edge information output by the network can be directly used for tasks such as corner detection and center point detection. Experiments show that the mIOU statistics obtained by our model on the validation set of PASCAL VOC2012 can reach 83.9%. At the same time, more detailed edge details can be obtained. This algorithm has high engineering and theoretical research value.

This paper presents a solar tracking system based on ARM, which mainly adopts the solar trajectory tracking mode, supplemented by the photoelectric sensor tracking mode according to the weather condition, and control the motor rotation to achieve all-time accurate solar tracking. The experimental results show that, the system performance is reliable, and all-weather tracking accuracy is maintained within 0.15 degrees. Therefore, the system can be widely used in solar photovoltaic power generation and lighting devices with solar energy utilization ratio improving greatly.

To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposes a novel approach based on the Local Region Similarity Difference (LRSD). Then the regions where the true targets and suspected target areas exist in are extracted. To further eliminate the false target regions, we process the LRSD image with pipeline filter and then the true target is finally obtained. Experimental results on some real-life infrared (IR) images with different types of targets verify that the proposed method could effectively prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate.

Roll-pitch gimballed infrared seeker has wide field of view but suffers from the rapid roll motion of the zenith-pass problem. For estimating the line-of-sight (LOS) rate with the roll-pitch seeker, we propose a coordinate transformation method to compensate for the impact of rapid roll motion. A precise LOS rate dynamics model is derived on the transformed coordinate system. And based on the model, we design a novel LOS rate estimator with the Improved Iterated Cubature Kalman Filter algorithm (IICKF). The proposed IICKF is derived from reducing the dimensionality of the optimization problem of the ICKF, which inherits the estimation accuracy but decreases the computational complexity of the algorithm. For validating, Monte Carlo simulations are performed at last. The simulation results show that the proposed LOS rate estimator outperform the standard EKF, CKF, ICKF, and can well cope with the rapid roll motion of the zenith-pass problem in roll-pitch gimballed infrared seeker system.

Long-term robust tracking remains a challenging problem in visual object tracking. Based on the popular tracking-by-detection framework, we propose an approach named real-time long-term correlation tracking by single-shot multibox detection (RLCT-SSD), in which tracking and detection work in parallel and cooperate together. Our algorithm consists of two parts: a tracking module is expected to track in real time and accurately, whereas a detection module is responsible for verifying the tracking results at intervals, and redetecting if necessary. The detector updates the tracking module when the tracking result is unreliable. We introduce a motion model on the basis of fast discriminative scale space tracker to design our tracking module. The detection module is based on the single-shot multibox detector algorithm. To further reduce the computational cost, we use the ShuffleNet as our base network of detection. The experimental results on OTB2013 and OTB2015 demonstrate that our RLCT-SSD performs favorably against most state-of-the-art trackers and achieves long-term accurate tracking running in real time.

If we use computer to simulate simple form of three-dimensional dynamic process that is virtual or very difficult to achieve in reality directly in the two-dimensional space, we will reduce the development difficulty of simulation process. This paper introduces the simulation methods and key technologies by some relevant examples in “Audio and Video Electronic Textbook of Mechanical Drawing.” The examples include the formation process of gyration curved surface, the process of an object being cut by a plane, the decomposition process of a combined object. The Flash animation software is mainly used in the simulation process, and AutoCAD software and Photoshop software are the tools used for preparing materials. The results show that simple form of three-dimensional dynamic process can be simulated very realistically in the two-dimensional space, not using the tools of three-dimensional animation.

According to the needs of dim and small targetdetection, a new detection algorithm based on wavelet transform and image fusion is put forward in the paper. In this algorithm, firstly, the original image is decomposed and reconstructed by wavelet and it is separated into a low frequency image and some high frequency images., secondly, the low frequency image is removed and the high frequency images are fused into a new image , finally, the fused image is segmented with threshold and the target is detected and signed. After the flight test validation, this algorithm has better accuracy and stability.

This paper proposes a scheme for nondestructive detection of blockboard. According to intensity differences of the x-ray through the blockboard, a lot of valuable target information such as bond quality, uniformity degree, and situations of filler were detected. First of all, x-ray nondestructive system was used to detect blockboard and the blockboard image was obtained. And then some image pre-processing methods just as function imadjust and value filter are employed to enhance the valuable target information in blockboard image. Then, mathematical morphology double-gradient algorithm was used in edge detection of blockboard image. Experimental results demonstrate that the method performs better in noise- suppression and edge detection than conventional edge detection operations. This study provides a new tool for nondestructive detection of blockboard.