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In this study, inspired by ultrasonic-assisted soldering technique, we proposed the soldering method for GAF with ultrasonic assistance. Sn solder spot were firstly prepared on GAF terminates ultrasonically, and then remelted to realize soldering of GAF. As a result, GAF was successfully soldered with ultrasonic assistance. The optimal ultrasonic vibration time should be 10 seconds for the preparation of Sn solder spot. Under the action of ultrasonic waves, Sn solder was driven to not only wet and spread on GAF surface, but also squeeze into GAF interlayers, which allowed the Sn solder spots to form a mechanically interlocked tight bonding with GAF. Furthermore, a compact interface of GAF/amorphous carbon (a-C) layer/Sn was formed. Significantly, GAF soldered joints exhibited high tensile resistance and excellent electrical property closed to original GAF. All the findings can promote the application and development of GAF in the field of electronic devices.

Learning network representations of Web services plays a critical role in the service ecosystem and facilitates many downstream tasks, e.g., service composition, service recommendation, service clustering, and service classification, etc. However, the performance of most of the existing approaches is limited by the sparse and non-interaction relationships between services. Considering these shortcomings, by proposing a balance theory based weighted signed graph convolutional network, we explore a dedicated signed service link prediction method to expand accurate links in service relation networks. Concretely, we first define the positive and negative links based on historical prior knowledge concerning services, and then construct a signed service relation network. Furthermore, on the basis of quantifying the influence of different neighbor nodes, we employ balance theory to correctly aggregate and propagate the information across layers through a weighted signed graph convolutional network. Finally, we splice all service embeddings in pairs, and a multi-layer perceptron classifier is used to predict the links between services. Comparative experiments with six baselines demonstrate that our method significantly outperforms the state-of-the-art link prediction models.

Compared with microwave synthetic aperture radar (SAR), terahertz (THz) SAR has advantages of higher frequency, higher resolution, higher frame rate and so on. Meanwhile, THz SAR is able to penetrate through cloud, smoke and dust with low attenuation, which leads to the applications of high-resolution imaging and target recognition in complex battlefield environment. In this paper, the results of carborne 5-GHz THz SAR imaging test are demonstrated. Besides, we have verified the design of broadband THz SAR imaging system and the key technique of high-frequency vibration compensation. The resolution of THz SAR is about 5 cm×5 cm, which provides a foundation for the research of high resolution THz SAR imaging.

Federated edge intelligence (FEI) is an emerging framework that implements federated learning (FL)-based learning solutions in an edge networking and computing system. It has attracted significant interest due to its potential to enable machine learning (ML)-based smart services and applications in the next-generation wireless systems. Despite its potential, the environmental impact of implementing energy-consuming ML-based solutions in a large networking system has been considered as one of the major challenges for the sustainability of future digital networking systems. This paper proposes energy-efficient FEI (EE-FEI), an energy-efficient framework, that jointly optimizes multiple key parameters to minimize the overall energy consumption of an FEI-supported Internet of Things (IoT) network. We establish models to quantify the relationship between the total energy consumption of FEI and the key parameters including the number of edge servers, the number of local model training rounds, and the number of global coordination rounds. We formulate the energy consumption minimization problem and prove its approximation problem is biconvex. Alternate Convex Search (ACS) algorithm for solving the key parameters to minimize the energy consumption of an FEI system has been used. Finally, we evaluate our theoretical results using a hardware prototype. Numerical results have shown that EE-FEI can significantly reduce the energy consumption of FEI systems by 49.8%.

Variable-flux memory machine is a novel permanent magnet machine, whose back electro-motive force can be manipulated using online magnetization current. The variable-flux principle contributes to wide-speed high-efficiency operation, especially the flux-weakening loss is reduced at high speed. This paper will propose a comprehensive control system for a hybrid-magnet variable-flux memory machine in the washing machine application. This application exhibits huge difference between the rated speed of 500 rpm and the maximum speed of 14,000 rpm. The proposed control system features a three-mode control with specific control approaches, i.e., maximum-torque-per-ampere control and flux-weakening control in a single magnetization state, efficiency-based magnetization state switch control, and feedforward-based magnetization current control between different magnetization states. Verified by experiments, the proposed control system provides the comprehensive control solution for the variable flux memory machine in the operation cycles.

With the development of electric vehicles (EVs) and increased energy interaction, there is increasing need for accurate assessment of the integrated reliability of the energy Internet, which includes the power distribution network and the urban road network. This study examined the relevant concepts of power distribution network and urban road network reliability and selected important evaluation indicators. Game theory was used to analyze the power distribution and urban road networks. The network information was then combined with the charge-discharge characteristics and behavior characteristics of the EVs for an integrated reliability analysis. Finally, the changes of the integrated reliability index of the urban road network and power distribution network were assessed after optimizing the driving behavior of EVs, to illustrate the accuracy and practicability of the proposed method.

Whale optimization algorithm is a new type of swarm intelligence optimization algorithm. Aiming at the problems of low optimization accuracy and easy falling into local optima in the basic whale optimization algorithm, an improved whale optimization algorithm that combines the whale algorithm and the hybrid leapfrog search strategy is proposed. The local search mechanism of the hybrid frog leaping algorithm is used to update the position of the whale to improve the accuracy of convergence; when the individual position of the population is updated, an attenuation factor is introduced to disturb the update position of the individual whale, thereby increasing the diversity of the population and adjusting the search scale of the algorithm . Simulation experiment results show that the overall optimization performance of the improved algorithm is better than the original whale algorithm, particle swarm and gray wolf optimization algorithm.

The local communication system of the metering automation system is mainly composed of concentrators, collectors, and smart meters. In addition to power metering, it also has functions such as power consumption information storage, user-side control, and power theft prevention. Attacks on smart meters and data concentrators may cause major power outages and losses. Taking CAN bus as an example, this paper proposes a communication protection scheme based on the national secret SM4-SM3 algorithm joint mechanism, which is applied to the local communication of the metering automation system. The software and hardware design of the scheme is carried out using the ZYNQ-7020 platform, and finally analyze and evaluate the results of experimental to verify the correctness of its function.

to understand and master the resource allocation of TCM hospitals in China from 2014 to 2018, analyze the main problems in resource allocation of TCM hospitals, and put forward suggestions for optimization measures. Methods: the basic allocation of institutions, actual beds, practicing (Assistant) doctors and registered nurses in 31 provinces of China from 2014 to 2018 were sorted out through the national statistical extract of traditional Chinese medicine. The equity of resource allocation in TCM hospitals was analyzed by using Gini coefficient and Lorenz curve according to population and geography, and Theil index was used to determine the main causes of unfair resource allocation Source. Results: the resource allocation of TCM hospitals increased rapidly, but the regional distribution was uneven. The fairness of population allocation was better than that of geographical distribution, and the unfairness mainly came from regional differences. Conclusion: the rational allocation of resources in TCM hospitals is a powerful guarantee to provide effective TCM medical services. In resource allocation, we should strengthen macro-control, optimize talent allocation and guide Reasonable flow, boost resource integration, and promote co-construction and sharing.

Objective: to analyze the distribution of pulmonary diseases in TCM and to find out the characteristics and rules in season, sex, age and region. Methods: In this paper, the thought and steps of FP-Growth algorithm were analyzed. Meanwhile, correlation analysis was conducted on 5229 pulmonary disease data. Results: In summer, the incidence of male pulmonary diseases is higher than that of female, young than middle-aged or old, and the incidence of cough is higher than that of other pulmonary diseases. Conclusion: According to the distribution law of pulmonary diseases, this paper puts forward the idea of treating pulmonary diseases according to three factors, such as time, region and person.

Blockchain has been recently extended into Internet-of-Things (IoT) networks to further improve security and reliability of data communication. Modern IoT networks need to support a wide range of services with various requirements in terms of data delivering security, reliability, and timeliness. In this paper, a fog computing-supported hierarchical blockchain network framework is proposed. In the first layer, the Inter-Planetary File System is used to store the message uploaded by IoT devices. In the second layer, a set of subchains that are referred as channels are designed. Fog nodes subscribed to the same channel maintain the corresponding distributed ledger, and each channel support a specific IoT service. The security, latency, and communication cost of the proposed framework are derived. Besides, tradeoffs among the above three performance metrics are analyzed. Finally, a real deployment scenario based on the actual base station locations in Dublin is considered to evaluate the proposed framework. Numerical results show that it is impossible to optimize all the performance metrics at the same time. By carefully selecting the parameters of different “channels”, the proposed framework can support highly diversified services without sacrificing security.

In this paper, we propose a deep learning base batch invoice recognition system, obtaining highly accurate recognition for value-added tax (VAT) invoice. Invoices are scanned in batch and imported into computer to derive raw images, with which our work proceeds in the following steps: 1) raw images are normalized; 2) we improve the character segmentation by the projection algorithm according to location information in template matching algorithm; 3) convolutional neural network (CNN) was further adopted for character recognition; 4) we introduce a module for judgment and manual modification, which estimates recognition considering the association of invoice contents, and then the wrong recognized invoice is modified to improve the system's fault tolerance; 5) the correct information is recorded. Empirical results show that the location algorithm based on template matching is most suitable for the invoice system among several tested algorithms. The application of deep learning technology to character recognition ensures the recognition accuracy. The performance of the entire system was also evaluated with random invoices. The proposed system, with the aid of the manual modification, can reach 100% recognition rate.

Digital technology is a kind of science and technology which is concomitant with computer. Digital technology will make data become the core element, further optimize resource allocation, and drive the production efficiency of the real economy. Based on the perspective of digital industrialization and industry digitization, this paper puts forward that digitization is an important direction for the revitalization of real economy, and emphatically analyzes the internal mechanism of digital revitalization of real economy. This paper puts forward the idea of revitalizing the real economy and promoting the deep integration of the digital economy and the real economy, and holds that the digital revitalization of the real economy is an important way to enhance national competitiveness and build China into a great modern socialist country.

Digital economy is an economic form in which data is directly or indirectly used to realize rapid optimal allocation and regeneration of resources and achieve high-quality economic development. At the technical level, it includes emerging technologies such as big data, cloud computing, the Internet of Things, blockchain, artificial intelligence and 5G communications. At the application level, "new retail", "new manufacturing" and so on are its typical representatives. Based on the perspective of industry integration, this paper discusses the mechanism of digital economy and real economy integration to promote high-quality economic development. At the same time, combining with China's specific national conditions, this paper puts forward the idea of digital economy and real economy deep integration to promote high-quality economic development. It is believed that developing digital economy is an inevitable choice to achieve high-quality economic development.