Abstract:In recent years, the vigorous development of “Internet ＋ Government Services” has promoted the deep integration of the Internet and the government public service system, and the construction of digital government has achieved initial results. How to achieve standardized, efficient, safe management, sharing and application of Internet ＋ big data in government affairs, and promote the solution of the “Three Integrations and Five Crossings Issues”, is the key to building a higher quality digital government and promoting the modernization of national governance system and capacity. This paper first summarizes the development of government data management at home and abroad. Secondly, it expounds the collection technology, storage technology, fusion technology, computing technology, application technology and security technology in the full life cycle of Internet ＋ government big data management, and summarizes the evolution of mainstream data management technology. Finally, this paper discusses some urgent problems in Internet ＋ government data management, and the development trend of Internet ＋ government data management technologies.

Abstract:Cross-domain collaboration of government services is a new governance model, which has been spawned by the combination of digital transformation of government and cross-domain governance. This model is aimed at achieving the value goal of governance of government services. However, due to the different specific business and functions of each government department, each department has an independent data management system, and each information system has diverse storage, complex data formats and different business processes. As a result, sharing and utilizing the heterogeneous data between departments in a safe and reliable way has become a challenging research problem. Traditional government data sharing usually adopts a centralized sharing mode, which is prone to a series of issues such as data privacy leakage, departmental authority problems, and single point of failure. To address this issue, this paper proposes a government data sharing scheme that combines attribute-based encryption and blockchain. Firstly, an access control policy is formulated by the data owner to restrict the attributes of data requesters. Subsequently, fine-grained access control as well as key update in secure data sharing is achieved by using subset overlay technology, which is combined with linear secret sharing to achieve complete hiding of the access policy. The inter planetary file system distributed network is used to store the ciphertext after symmetric encryption to relieve the storage pressure of the blockchain system. Finally, the hash of the retrieved data ciphertext is re-encrypted using the Keccak algorithm to achieve data integrity verification. Security analysis and experimental analysis show that the proposed scheme can meet the requirements of secure sharing of government data in terms of security and efficiency, and thus realize the secure and traceable sharing of government data.

Abstract:The construction of smart cities can effectively improve urban governance and operation capacity and break the urban development dilemma. To explore how to provide intelligent services for urban management through spatio-temporal big data in the physical-digital space intersection, this paper proposes a transparent fusion framework for multi-source spatio-temporal big data based on the analysis of the semantic relationships of multi-source, multi-dimensional, and heterogeneous spatio-temporal big data.To achieve this goal, a concept of “city information unit” is further proposed as the basis for building the data organization of physical-digital spatial integration. In particular, the multi-source, multi-dimensional,and heterogeneous spatio-temporal big data are first actively aggregated, semantically resolved, and then the geographic knowledge is constructed spatiotemporally; based on the unique data code, the data information is mapped to the city information unit; Next, in this paper, data matching model and association model are found, and a transparent data fusion framework is constructed. Combined with multi-source heterogeneous data element matching technology, a transparent spatio-temporal data fusion rule base is constructed. Finally, with the support of various fusion methods, the transparent fusion of urban entity and spatio-temporal multi-source spatio-temporal data is realized. With the help of urban information unit and data coding, we realize the dynamic integration system of urban entity and spatio-temporal big data, so as to provide users with intelligent information services.

Abstract:Government data resources are characterized by a wide range of sources, diverse types, large data volumes and unclear data distribution, which can lead to lacks of unified management, high efficiency in data access and use, and ability to continuously promote the data release value. In order to solve the above problems and realize the correlative fusion of data from different sources and different types, this paper adopts the fusion of multi-source heterogeneous data to provide technical means for the exchange and sharing of government data. This paper proposes the concept of information channel and channel coupling, and provides a multisource heterogeneous information channel coupling method, which includes three module for constructing the Initial channel coupling knowledge graph, realizing the channel data coupling based on the channel coupling knowledge graph, and realizing the knowledge updating based on the channel coupling knowledge graph, respectively. In the proposed method, the technologies of atlas construction, knowledge extraction, knowledge fusion, knowledge processing and knowledge updating are integrated together, and a top-down sorting retrieval model is proposed to accelerate the speed and accuracy of knowledge fusion and data retrieval.

Abstract:With the continuous improvement of digital construction of government affairs, the need for realizing “knowledge-based, personalized and intelligent” government services based on knowledge graph is gradually being awakened. At present, the application of knowledge graph in the government domain is often oriented to a single scenario, making it difficult to establish connections between different scenarios with respect to government knowledge. The search, management and approval efficiency of government services based on traditional databases is still not high. To expand the scope of government services and improve the efficiency of search, management, examination and approval, this paper proposes a set of top-down mapping methods to construct a multi-layer government knowledge graph. Specifically, this method first construct the conceptual model of government knowledge from the perspective of government service, and then obtain the government knowledge, data preprocessing and knowledge fusion according to the conceptual model; finally, forming a multi-layer government knowledge graph with top-down relationships of concept, business service, social service and information sharing. With the visual display of Neo4j and the deployed services, paper validate the proposed method by taking the search of real estate approval, the examination and approval of occupied forest land and the social service for public complaints as examples, which proves that the method is efficient and feasible. It not only provides knowledge graph support and association for different government scenarios, but also helps to realize the fusion and sharing of multi-source government data, thus providing a reference for the subsequent construction of government knowledge graph.

Abstract:Subject to the friction and other uncertain factors, the multi-manipulator force & position hybrid controlling method is still a challenging issue. To investigate this problem, this paper proposes a hybrid control method based on fuzzy adaptive robust sliding mode algorithm. Firstly, the multi-manipulator system model is constructed with consideration of both multi-manipulator and the object dynamics equations. Secondly, the fuzzy algorithm is combined with the adaptive sliding mode algorithm to compensate for uncertain factors and unknown nonlinear terms, so as to improve the system reliability. Simulation results showed that, both controlling accuracy and responding time of the system can be improved by the proposed method.

Abstract:Arrhythmia classification is a hot topic in physiological signal analysis. Arrhythmias are very common in clinical practice, and they are accompanied by abnormal patterns and rhythms in the heartbeat of the electrocardiogram signal. Correct and timely detection of arrhythmias and accurate early warning of cardiovascular diseases are of particular importance in the early stage of clinical diagnosis. However, the lack of real time diagnosis of electrocardiogram may delay the best time for patient treatment. Implementing heart rate disorder classification algorithms at edge-side smart terminals such as wearable devices enable real-time analysis and processing of electrocardiogram signals. In addition, they improve the flexibility and safety of the devices as well. By far, the field programmable gate array devices have been widely used in physiological signal processing as a form of edge computing due to its capability of real-time pipeline operation. Whereas, the field programmable gate array implementation needs a long development cycle, has high cost and is difficult to debug. To address these problems, the new high-level synthesis tool Vivado HLS from Xilinx is used to implement the arrhythmia five classification algorithm based on the MIT-BIH dataset. By using a Xilinx Zynq field programmable gate array, an average classification accuracy of 99.12% on the electrocardiogram signal test set is achieved. Moreover, an average of 3.185 ms required to classify a single heartbeat is realized, which leads to a speedup of more than 5.64 times compared to a single ARM core on the pure PS side.

Abstract:In radiation oncology, it is usually difficult and time-consuming to manually profile the targets in the head and neck. Therefore, it is very necessary to develop an automatic medical image segmentation method, which not only saves time and energy, but also avoids the subjective variations among different physicians. In this work, we used positron emission computed tomography and computed tomography image data to segment head and neck tumors, and realized more accurate segmentation by using the complementary information between them. The network was developed based on the U-Net architecture, and an inception module was added into the encoder module. In addition, dense modules and spatial attention are added to the decoder to improve the network performance. Experimental results show that our method outperforms the other U-Net networks. Quantitatively, the dice similarity coefficient, recall rate and Jaccard similarity coefficient are found to be 0.782, 0.846 and 0.675, respectively. Compared with the original U-Net, these results corresponds to an improvement by 6.8%, 13.4% and 9.8%, respectively. The 95% Hausdorff distance is found to be 5.661, which is 1.616 smaller than the original U-Net. In conclusion, this study demonstrates that the inception spatial-attention dense U-Net model can effectively improve the segmentation accuracy on the head and neck tumor PET-CT images.