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This paper provides some reasons for the slow development of shared manufacturing in China. The concept, research status, and significance of shared manufacturing are introduced. The paper first gives the sharing mode of China's sharing manufacturing platform and basic organizational architecture of manufacturing capacity sharing. The remarkable characteristics of sharing manufacturing are presented in many different ways, especially sharing content. The main sharing content in manufacturing is then analyzed and classified in detail through examples. The differences between traditional sharing manufacturing and modern sharing manufacturing were compared and analyzed, as well as the advantages and disadvantages of Chinese sharing manufacturing compared with foreign sharing manufacturing. The detailed sharing process of distributed maintenance management platform was given and gained a good effect. Finally, the methods to improve the efficiency of shared manufacturing are presented, which help to understand the shared manufacturing in the manufacturing industry in China currently. [ABSTRACT FROM AUTHOR]

A group consensus reaching process (CRP) based on dynamic expert reliability is proposed in this paper. The method is designed to support uncertain multi-attribute group decision making in situations where experts in a group use distributed preference relations (DPRs) to express their preferences when making a decision. In the method, it is assumed that a predefined consensus requirement can be specified and must be satisfied before consensus-based solutions are generated. Consensus measures of DPRs are constructed to ensure consensus convergence and used to check whether the predefined consensus requirement at a specific level is satisfied. If the requirement is not satisfied, expert reliability is first defined and calculated in terms of data depicted by the experts, and then used to design an expert reliability based feedback mechanism composed of identification and suggestion rules to help identify the DPRs hindering CRP. Additionally, experts update their DPRs to accelerate convergence to CRP. Arguably, it is the first attempt to introduce expert reliability in consensus convergence. Once the predefined consensus requirement is satisfied, experts' preferences are aggregated to generate a consensus-based solution. The problem of selecting an appropriate supplier in a high-end equipment manufacturing enterprise located in Changzhou, Jiangsu Province, China is analyzed by the proposed method to demonstrate its applicability and validity. [ABSTRACT FROM AUTHOR]

This paper proposes an ensemble model based on distribution deep learning network. The ensemble model is composed of deep belief network (DBN) for reconstructing original data, and the bidirectional long short-term memory (BLSTM) method is used for prediction due to its good results in big data applications. The dynamic weighting strategies are proposed and applied to the sub models of the ensemble by a weighted least square method. The weight update with variable training sets and the predictions for each ensemble are obtained from the distributed computing engine Apache Spark. The performance of the proposed model is evaluated on wind data on the wind farm of the Hexi Corridor in China. The simulation results show that the dynamic ensemble algorithm performs well, which is a very valuable result for the forecasting of big data time series. Furthermore, the results are successfully compared with back propagation neural Network (BPNN), LSTM, BLSTM, and stacked LSTMs with memory between batches (SBLSTM), improving the accuracy of prediction. [ABSTRACT FROM AUTHOR]

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs. [ABSTRACT FROM AUTHOR]

The rapid updates of the resources and media in the big data age provide new opportunities for oversea Chinese education. It is an urgent task to effectively use the big data to boost the development of oversea Chinese education. However, very few studies are conducted in this area. Map-Reduce is a programming model of cloud computing used for the parallel computing of the large-scale data sets and this model enables programmers to run their own programs in the distributed system. In this paper we proposed a personalized overseas Chinese education model based on Map-Reduce mechanism, which can analyze the behavioral habits and personal preferences of users from a large pool of Chinese educational resources. In this way, the customer needs can be accurately grasped and their favorite resources are recommended from huge amounts of resources. The proposed model has a good application prospect for overseas Chinese education. [ABSTRACT FROM AUTHOR]

Nowadays, the financial securities investment and transaction are processed digitally. The securities companies have accumulated a large amount of financial data. Given that, quantitative analysis has become a common method for securities investors. However, as the scale of data continues to grow, data storing and processing have increasingly become a big challenge to financial quantitative analysts. First, most existing stand‐alone quantitative analysis systems are hard to accelerate data processing in a distributed way. Second, existing distributed computing frameworks such as Apache Spark demands financial quantitative analysts with expertise knowledge on distributed computing. Third, transition from traditional stand‐alone financial quantitative analysis (FQA) system to such distributed computing system often introduces learning efforts and development overheads. To solve these problems, we propose Alchemy, a distributed processing platform tailored for FQA with high‐level programming interface. Alchemy offers distributed computing service and provides a pipeline‐based programming model which shares many similarities with the traditional stand‐alone systems, reducing learning efforts for financial quantitative analysts. The performance of Alchemy is evaluated in both experimental environment and a real‐world production environment in Huatai Securites which is a leading financial company in China. The results show that Alchemy is able to achieve up to 300 times speedup compared against existing financial quantitative analysis systems. [ABSTRACT FROM AUTHOR]

The application of wireless sensor network can achieve real-time monitoring of the underwater environment. Nodes must be effectively deployed in the targeted monitoring area to gain full coverage of the resources in a monitored area of water. However, the variability of flowing water is likely to cause a delay in the transmission of the resource data. Node mobility also requires the use of a more advanced algorithm in the coverage control method of the WSN. What type of methods or measures should be used to deploy mobile sensor nodes to effectively meet the requirement of water resource monitoring tasks and realize the full coverage of water resources, thereby improving the monitoring quality? Moreover, how can the coverage capacity of mobile sensor nodes be improved in water resource monitoring? These are pressing research issues to resolve. This study proposes the deployment of an algorithm for an improved sensor network k-coverage based on probabilistic sensing; maximum weight matching is introduced to realize a centralized allocation strategy, which can effectively reduce the energy consumed in node allocation. The coverage results obtained in a simulation were higher than those of the event probability and the energy-efficient coverage algorithms. The energy consumed during sensor node movement was lower than that with the latter two approaches, indicating that the network node coverage algorithm based on probabilistic sensing that is proposed in this study can effectively meet the requirements for the application of WSNs in water resource monitoring. [ABSTRACT FROM AUTHOR]

Blockchain technology is a core, underlying technology with promising application prospects in the banking industry. On one hand, the banking industry in China is facing the impact of interest rate liberalization and profit decline caused by the narrowing interest-rate spread. On the other hand, it is also affected by economic transformation, Internet development, and financial innovations. Hence, the banking industry requires urgent transformation and is seeking new growth avenues. As such, blockchains could revolutionize the underlying technology of the payment clearing and credit information systems in banks, thus upgrading and transforming them. Blockchain applications also promote the formation of 'multi-center, weakly intermediated' scenarios, which will enhance the efficiency of the banking industry. However, despite the permissionless and self-governing nature of blockchains, the regulation and actual implementation of a decentralized system are problems that remain to be resolved. Therefore, we propose the urgent establishment of a 'regulatory sandbox' and the development of industry standards. [ABSTRACT FROM AUTHOR]