As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.

Broadband wireless networks are rapidly expanding over the world. To provide wireless broadband services for Shanghai Expo 2010, pre-research is launched and a test-bed has been developed, in which WiMAX and Wi-Fi mesh are involved. The test-bed shows that Wi-Fi mesh integrated with WiMAX is highly suitable for large-scale activities like the Olympic Games and the World Expo.

The aim of this paper is to propose effective attentional regions for fine-grained visual recognition. Based on the Spatial Transformers' capability of spatial manipulation within networks, we propose an extension model, the Attention-Guided Spatial Transformer Networks (AG-STNs). This model can guide the Spatial Transformers with hard-coded attentional regions at first. Then such guidance can be turned off, and the network model will adjust the region learning in terms of the location and scale. Such adjustment is conditioned to the classification loss so that it is actually optimized for better recognition results. With this model, we are able to successfully capture detailed attentional information. Also, the AG-STNs are able to capture attentional information in multiple levels, and different levels of attentional information are complementary to each other in our experiments. A fusion of them brings better results.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.

With the development of power market demand response capability, load aggregators play a more important role in the coordination between power grid and users. They have a wealth of user side business data resources related to user demand, load management and equipment operation. By building a business model of business data resource utilization and innovating the content and mode of intelligent power service, it can guide the friendly interaction between power supply, power grid and load, effectively improve the flexibility of power grid regulation, speed up demand response and refine load management. In view of the current situation of insufficient utilization of business resources, low user participation and imperfect business model, this paper analyzes the process of home appliance enterprises participating in peak shaving and valley filling (PSVF) as load aggregators, and expounds the relationship between the participants in the power market; a business service model of smart home appliance participating in PSVF based on cloud platform is put forward; the market value created by home appliance business resources for each participant under the joint action of market-oriented means, information technology and power consumption technology is discussed, and typical business scenarios are listed; taking Haier business resource analysis as an example, the feasibility of the proposed business model in innovating the content and value realization of intelligent power consumption services is proved.

The IEEE 802.11 standard has been extensively deployed all over the world. Many studies have been put on its performance, especially throughput. Most research focused on the analysis of saturated throughput, but non-saturated situation is more usual in practice. By extending a saturation throughput model, a concise and novel model is proposed in this paper, which can be used to analyze both saturated and non-saturated conditions. Moreover, the model can also deal with the heterogeneous condition, which allows stations to have different traffic. Different access mechanisms and packet payloads are used in simulation to validate it, and the results show that the model is accurate.

This paper introduces and uses spider monkey optimization (SMO) for synthesis sparse linear arrays, which are composed of a uniformly spaced core subarray and an extended sparse subarray. The amplitudes of all the elements and the locations of elements in the extended sparse subarray are optimized by the SMO algorithm to reduce the side lobe levels of the whole array, under a set of practical constraints. To show the efficiency of SMO, different examples are presented and solved. Simulation results of the sparse arrays designed by SMO are compared with published results to verify the effectiveness of the SMO method.