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We assembled and simulated putative models including plexin A4 resepctively complexed with RND1 and RhoD, to investigate the structural mechanisms underlying RND1 as an activator and RhoD as an attenuator in plexin signaling. The models suggested that RND1 binding is compatible with the dimerization of plexin A4 while RhoD binding is likely disruptive to the dimerization. The models also showed two distinct modes of interactions of RND1 and RhoD with the membrane: RND1 interacts with the membrane loosely and its long C-terminal tail serves as a flexible tether to the membrane, whereas RhoD interacts with the membrane in a specific manner using a positively-charged membrane interface, which is absent in RND1. Importantly, with RhoD binding at the RBD, the buttress interaction with the dimerization helix in the plexin dimer is much reduced compared to that in the RND1-bound dimer, suggesting that RhoD weakens the buttress interaction with the dimerization helix and potentially destabilizes the plexin dimer. In summary, RhoD binding destabilizes the RBD with respect to the GAP domain, destabilizing the buttress segment with respect to the dimerization interface, and ultimately leads to destabilization of the dimer interface. In contrast, by the same RBD-centered route, RND1 binding helps stabilize the plexin dimer.

In this paper, a machine learning detection method, namely, SVM-ICA, which aims to solve the fault identification of droppers in high-speed railway, was proposed based on time-frequency analysis. The proposed SVM-ICA method can be utilized to detect and locate the faulty droppers. In detail, the time-frequency statistical features of the senor data are firstly extracted and the significant features are selected to construct the training set. Secondly, based on the training set, the support vector machine (SVM) fault detection model is trained. Finally, the trained model is used for detecting faulty droppers, and further the position of the break string can be located by using the independent component analysis (ICA) method. Simulation results show that the proposed fault detection method of droppers based on time-frequency analysis can accurately identify if the string breaks, and additionally can locate the position where the failure occurred.

It is important to improve the integrity and accuracy of sonar image target detection, which is significant for underwater detection. In this paper, a variety of sonar image denoising algorithms and segmentation algorithms are studied, and a denoising algorithm based on fast curve transform is proposed. The image segmentation algorithm based on k-means clustering is studied, and the optimal clustering number screening and sonar image subsurface segmentation are realized. The sonar image fast segmentation algorithm based on ICM algorithm and the object contour detection of sonar image based on level set method are realized in Matlab. The results show that the proposed algorithm can improve the noise reduction effect of the sonar image under reverberation interference, and obtain a better image detection effect.

In order to meet the requirement of underwater ultrasonic phased array imaging system, a high-precision underwater ultrasonic phased array transmitting system is designed and implemented. The system adopts FPGA as the control core, and realizes the high precision phase controlled delay of 0.5ns. At the same time, a driving circuit is built with a high speed triode to realize phase control emission, level conversion and power amplification of the 16 way ultrasonic trigger signal. The system can achieve high-precision focusing emission and deflection emission by setting various phase controlled delay. The actual test results show that the system realizes triggering of ultrasonic phased array transducer, and the phase delay accuracy and low hardware complexity, driving ability, stable and reliable, can provide a good foundation for improving the imaging quality of phased array system.

As a class of narrow band structures such as dock, bridge and dam, chain-type networks are applied to monitor and track underwater targets. This paper proposes a coverage strategy for probabilistic positioning of mobile targets, which can balance the coverage and positioning performance of underwater sensor network. Based on careful analysis characteristic of network architectures, multiple relay nodes integrated with non-uniform deployment are designed in the consideration of the extensibility of underwater sensor network, which consists of common sense nodes, relay nodes and a base station. The probabilistic positioning model for mobile targets is devised with the support of node optimization coverage. Furthermore, the simulation experiments have been implemented in order to evaluate the proposed algorithm performance for targets probabilistic positioning.

Acoustic feedback suppression is an important issue in acoustic signal processing. Adaptive feedback suppression is an effective algorithm, which uses an adaptive filter to estimate the feedback path. However, the adaptive technique causes large estimation errors of the feedback path when external input signal is correlated with the receiver input signal. This paper proposes a novel feedback suppression method based on linear prediction and parallel gradient projection. The purposed algorithm generates a FIR filter by linearly predicting the output signals to obtain the decorrelation signal. Then a parallel gradient projection method is designed to estimate the echo path based on the decorrelation signal. Simulation results show that the performance of the purposed method is better than previous methods in convergence speed, stability and precision.

Aimed at the characteristics of infrared images for moving target, such as complicated background, low signal-to-noise ratio and low contrast, a new method for complicated background suppression is proposed based on Large Field (LF) and Small Field (SF) system in the paper. Firstly, the CCD sensors are used to obtain infrared video data for moving target. Then, after analyzing the visual motion detection mechanism of insect compound eye, the paper establishes background suppression model based on LF&SF. In this model, LF is used to suppress noise of edge detection while SF is used to do nonlinear enhancement of target features and estimation of target movement direction. Finally, the paper uses the primary motion detector to integrate LF with SF. Real scene experiment shows that this proposed method has obvious advantages in suppressing the cloud cover, grass lands and other noise and enhancing target.

Research highlights? We design a vision inspection system for surface defects of strongly reflected metal. ? We use wavelet transform to eliminate noise and Otsu method to segment images. ? Five spectral characteristics are collected and input into support vector machine. ? The proposed method can identify seven classes of metal surface defects effectively. This paper describes the designing and testing process of a vision system for strongly reflected metal's surface defects detection. In the authors' view, an automatic inspection system has the following stages: image acquisition, image pre-processing, feature extraction and classification. Thus, the system including four subsystems is designed, and image processing method and pattern recognition algorithm that perform specific functions are outlined. First, the study uses wavelet smoothing method to eliminate noise from the images. Then, the images are segmented by Otsu threshold. At last, five characteristics based on spectral measure of the binary images are collected and input into a support vector machine (SVM). Furthermore, kernel function selection and parameters settings which are used for SVM method are evaluated and discussed. Also, a very difficult detection case for the surface defects of strongly reflected metal is depicted in details. The classification results demonstrate that the proposed method can identify seven classes of metal surface defects effectively, and the results are summarized and interpreted.

Influenced by many factors, the variation process of city taxi quantity is nonlinear, grey and random. So, the single prediction model can only reflect part information of variation process, and its prediction accuracy is low. In order to improve prediction accuracy, the grey neural network method is put forward based on the analysis of city taxi quantity variation process. First, GM(1,1) model is used to predict the variation trend of city taxi quantity; then, the BP neural network is trained through the prediction errors of GM(1,1), and trained model is used to predict the nonlinear and uncertain variation of city taxi quantity; finally, two results are combined to calculate the final predicting result. Grey neural network was used to predict taxi quantity of Nanjing. The experimental results indicated that the grey neural network can improve city taxi quantity prediction accuracy, describe the variation rule of the city taxi quantity, and have widespread application prospect in the field of nonlinear prediction.

Social Networks have become embedded in our daily life so much that we no longer realize it. But researches on the structures of networks are correspondingly fewer than their statistical properties, however, the studies of structural features are necessary and significant. Here we present an approach to study the structures of social networks based on Role-to-role Connectivity Profiles (RCP). We conduct experiments in some classical social networks, we find that the social networks with different functions exhibit obvious different profiles. Different from the classification of complex networks, social networks are divided into two distinct classes with strict judgement basis rather than vague judgement. In addition, according to the RCP, we can find important nodes in social networks, which is more significant than those with high degree. Through the use of the algorithm in social networks, we can thoroughly understand a social network by the comprehensive network classifications rather than only relying on community detection and other global properties.

The rheological behaviour of xanthan gum was determined at various temperatures (20–100 °C) and concentrations (0·3–1·3%), and was found to be most adequately described by a power-law model. The effect of temperature on the viscosity followed an Arrhenius relationship, whereas the effect of concentration followed an exponential relationship. A number of models to predict the viscosity as a function of shear rate, concentration and temperature are presented. The models developed had an excellent fit and would be useful for manufacture and applications of xanthan gum.

Feature selection of text is one of the basic matters for intelligent classification of text. Textual feature generating algorithm adopts weighted textual vector space model generally at present. This model uses BP network evaluation function to calculate weight value of single feature and textual feature redundancy generated in this algorithm is high generally. For this problem, a textual feature generating algorithm based on clustering weighting is adopted. This new method conducts initial weighted treatment for feature candidate set first of all and then conducts further weighted treatment of features through semantic and information entropy and it removes redundancy features with features clustering at last. Experiment shows that the average classification accuracy rate of this algorithm is about 5% higher than that of traditional BP network algorithm.

Directing against the huge amounts of calculation for traditional target tracking, a fast target tracking algorithm based on image correlation matching is proposed in this paper. Firstly, for the continuity of movement target in the real-time image sequences, the image matching algorithm based on wavelet transform is used to match the first frame image to determine the best match point, then the best match point is used as initial point and combined with prediction characteristics of Kalman filter to predict the possible position of the target on the next frame and optimal matching point of the next frame. Eventually, it realized a fast and accurate target tracking algorithm. The experimental results show that the proposed matching algorithm can provide favorable tracking performance and improve the speed and accuracy of target tracking, which meets the real-time request of system.

The image includes much useful information, so it’s very important in intelligence traffic system. In this paper, one method of object recognition and location based on binocular stereo vision is introduced. Through dealing with two images what are captured from difference points in the ca, the system can analyze the object in images and compute its location, with the real-time and accuracy requirements.

Along with the rapid development of Chinese economy, the spatial distribution pattern and specialization level of commodity exchange markets changes greatly. According to analysis on large scale commodity exchange markets (LSCEM) of China from 2000 to 2005 under multi-spatial scales, the authors draw the following conclusions: the spatial concentration degree of LSCEM which develops based on particular geographical environments and natural resources is related with market types, which developed based upon the particular geographical environments and natural resources possess relatively high and steady degree of spatial concentration which developed ground on the high tech and advanced industries have relatively low degree of spatial concentration at zonal level and relatively high degree of spatial concentration at provincial level; which developed found on the popular consumption demand are provided with rather low degree of spatial concentration at various of spatial levels. Meanwhile, LSCEM incline to centralize in the eastern coastal areas of China and big cities. Their specialization indexes are high at the provincial level, however, drop on the whole. The statistical regression analysis shows that the urbanization level, Engel coefficient and market development degree have fairly notable impact on the specialization of LSCEM, but the industrial concentration level, private economy development level and the net income of urban residents have no significant effect on the specialization of LSCEM, and the trade competitiveness index has extremely little impact on it.

Entities in the real world evolve along the time-line from the start time point to the future in multi-dimensions space. The spatio-temporal process composed of meta-spatio-temporal processes, is bought out and used to describe this change process. The traditional association rule mining is extended to spatio- temporal processes, generating spatio-temporal process association rule mining which is used to extract association knowledge among spatio-temporal process data. Change processes of the western Pacific Ocean warm pool and rainfall of southeast area of China are the typical spatio-temporal processes, and relation between them is a remote relation. Based on spatio- temporal process association rule mining, we obtain some interesting association rules between change processes of them. Finally, it is concluded that spatio-temporal association rule mining can extract valuable association knowledge from spatio- temporal processes, and change trend of one entity or phenomenon can be forecasted through varying trend of others based on those association rules.

Members of the epidermal growth factor receptor family (EGFR/ERBB1, ERBB2/HER2, ERBB3/HER3 and ERBB4/HER4) are key targets for inhibition in cancer therapy. Critical for activation is the formation of an asymmetric dimer by the intracellular kinase domains, in which the carboxy-terminal lobe (C lobe) of one kinase domain induces an active conformation in the other. The cytoplasmic protein MIG6 (mitogen-induced gene 6; also known as ERRFI1) interacts with and inhibits the kinase domains of EGFR and ERBB2 (refs 3-5). Crystal structures of complexes between the EGFR kinase domain and a fragment of MIG6 show that a approximately 25-residue epitope (segment 1) from MIG6 binds to the distal surface of the C lobe of the kinase domain. Biochemical and cell-based analyses confirm that this interaction contributes to EGFR inhibition by blocking the formation of the activating dimer interface. A longer MIG6 peptide that is extended C terminal to segment 1 has increased potency as an inhibitor of the activated EGFR kinase domain, while retaining a critical dependence on segment 1. We show that signalling by EGFR molecules that contain constitutively active kinase domains still requires formation of the asymmetric dimer, underscoring the importance of dimer interface blockage in MIG6-mediated inhibition.