Market-oriented culture is the base for enterprise to achieve sustainable competitive advantage,and dynamic capability is an important media of transfering resources into campetitive advantage.This research made a thorough description of the connotation of market-oriented culture,dynamic capability and organizational performance,and the measurement of each concept was also discussed in this paper.Through research we proposed an integtated model of organizational performance,which included an exogenous variable(market-oriented culture),and a mediating variable(dynamic capability),and a moderating variable(environment uncertainty) was taken in and analyzed in this model,and finally we gave some advice on the future.
Recent research on single image super-resolution(SISR) shows that deep convolutional neural networks(DCNNs) with attention mechanism present a better improvement. Each different attention mechanism has its distinct focus. Specifically, channel attention mechanism has the capacity to enhance the influence of critical channels by focusing on the expression of characteristics at different channel levels, and pixel attention mechanism has the ability to improve the quality of reconstructed images by paying attention to the expression of spatial pixel features. We believe that the combination of these two mechanisms is a way to further improve the quality of super-resolution image. In this paper, an enhanced multi-attention network(EMAN) is proposed, which contains advantages of two attention mechanisms. Besides, to improve the utilization of high-frequency information, a novel edge-based loss function is added to boost the learning of the edge region. Plenty of experiments show that the proposed multi-attention network achieves better accuracy and visual effect against single-attention methods.
As the rapid development of wireless communication, the scarcity of spectrum become more and more prominent. Cognitive radio is proposed to overcome the problem of low spectrum utilization brought by static spectrum allocation, Game theory is an effective model describe cognitive spectrum sharing. In this survey, some basic elements of modelling by Game theory are first discussed and several dynamic spetrum sharing algorithms are introduced.
In wireless sensor networks (WSN), sensor deployment is one of the main topics for enhancing the sensor's coverage rate. In this paper, by modifying updating equation of onlooker bee and scout bee of original artificial bee colony (ABC) algorithm, a sensor deployment algorithm based on the modified ABC algorithm is proposed. Some new parameters such as forgetting and neighbor factor for accelerating the convergence speed and probability of mutant for maximizing the coverage rate are introduced. Simulation results showed that comparing with the deployment method based on original ABC and particle swarm optimization (PSO) algorithm, the proposed approach can achieve a better performance in coverage rate and convergence speed while needing a less total moving distance of sensors.
Image super-resolution is the process to reconstruct an image with higher resolution from a series of images with lower resolution of the same scene. It has been widely used in remote sensing, medical imaging and military. Contourlet transform is a multiresolution analysis approach which reserve several advantages of wavelet transform while with better performance in the multi-directions. In this paper, contourlet transform is introduced into image superresolution. Low-frequency approximation is first carried out with the low-resolution image data, then the difference between original signal and its approximation is decomposed by directional filter banks and is used to estimate the high-frequency component. Experimental results showed that the proposed approach can improve image resolution while retain the detail information effectively.
Robotic sensor deployment is fundamental for the effectiveness of wireless robot sensor networks-a good deployment algorithm leads to good coverage and connectivity with low energy consumption for the whole network. Virtual force-based algorithms (VFAs) is one of the most popular approaches to this problem. In VFA, sensors are treated as points subject to repulsive and attractive forces exerted among them-sensors can move according to imaginary force generated in algorithms. In this paper, a virtual spring force-based algorithm with proper damping is proposed for the deployment of sensor nodes in a wireless sensor network (WSN). A new metric called Pair Correlation Diversion (PCD) is introduced to evaluate the uniformity of the sensor distribution. Numerical simulations showed that damping can affect the network coverage, energy consumption, convergence time and general topology in the deployment. Moreover, it was found that damping effect (imaginary friction force) has significant influence on algorithm outcomes. In addition, when working under approximate critical-damping condition, the proposed approach has the advantage of a higher coverage rate, better configurational uniformity and less energy consumption.
The effectiveness of wireless sensor networks (WSN) depends on the regional coverage provided by node deployment, which is one of the key topics in WSN. Virtual force-based algorithms (VFA) are popular approaches for this problem. In VFA, all nodes are seen as points subject to repulsive and attractive force exerted among them and can move according to the calculated force. In this paper, a sensor deployment algorithm for mobile WSN based on van der Waals force is proposed. Friction force is introduced into the equation of force, the relationship of adjacency of nodes is defined by Delaunay triangulation, and the force calculated produce acceleration for nodes to move. An evaluation metric called pair correlation function is introduced here to evaluate the uniformity of the node distribution. Simulation results and comparisons have showed that the proposed approach has higher coverage rate, more uniformity in configuration, and moderate convergence time compared to some other virtual force algorithms.
. In this paper, a video tracking approach based on particle filter is proposed. Texture information is used instead of color. In the proposed approach, gray cooccurrence matrices are used as the texture metric. Experimental results show that the proposed algorithm lead to better result than color feature-based particle filter-based video tracking algorithm and is an effective tool for complicated video tracking application.
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