In the method of combining Object detection Network (ObjectNet) and scene recognition network, the object features extracted by the ObjectNet and the scene features extracted by the scene network are inconsistent in dimensionality and property, and there is redundant information in the object features that affects the scene judgment, resulting in low recognition accuracy of scenes. To solve this problem, an improved indoor scene recognition method combined with object detection was proposed. First, the Class Conversion Matrix (CCM) was introduced into the ObjectNet to convert the object features output by ObjectNet, so that the dimension of the object features was consistent with that of the scene features, as a result, the information loss caused by inconsistency of the feature dimensions was reduced. Then, the Context Gating (CG) mechanism was used to suppress the redundant information in the features, reducing the weight of irrelevant information, and increasing the contribution of object features in scene recognition. The recognition accuracy of the proposed method on MIT Indoor67 dataset reaches 90.28%, which is 0.77 percentage points higher than that of Spatial-layout-maintained Object Semantics Features (SOSF) method; and the recognition accuracy of the proposed method on SUN397 dataset is 81.15%, which is 1.49 percentage points higher than that of Hierarchy of Alternating Specialists (HoAS) method. Experimental results show that the proposed method improves the accuracy of indoor scene recognition.

By analyzing the characteristics of hot words in network news, a hot new word discovery method was proposed for detection of network hot news. Firstly, the Frequent Pattern tree (FP-tree) algorithm was improved to extract the frequent word strings as the hot new word candidates. A lot of useless information in the news data was reduced by deleting the infrequent 1-word strings from news data and cutting news data based on infrequent 1, 2-infrequent word strings, so as to greatly decrease the complexity of FP-tree. Secondly, the multivariant Pointwise Mutual Information (PMI)was formed by expanding the binary PMI, and the Time PMI (TPMI) was formed by introducing the time features of hot words. TPMI was used to judge the internal cohesion degree and timeliness of hot new word candidates, so as to remove the unqualified candidates. Finally, the branch entropy was used to determine the boundary of new words for selecting new hot words. The dataset formed by 7 222 news headlines collected from Baidu network news was used for the experiments. When the events reported at least 8 times in half a month were selected as hot news, and the adjustment coefficient of time feature was set 2, TPMI correctly recognized 51 hot words, missed 2 hot words because they were hot for a long time and 2 less-hot words because they occurred insufficiently; the multivariant PMI without time features correctly recognized all 55 hot words, but incorrectly recognized 97 non-hot words. It can be seen from the analysis that the time and space cost is reduced by decreasing the complexity of FP-tree, and experimental results show that the recognition rate of hot new words is improved by introducing time feature during the hot new word judgement.

Concerning of the security and efficiency problems of the agricultural product traceability system, based on the decentralization security feature of blockchain, an efficient solution based on consortium blockchain was proposed. Firstly, through Inter-Planetary File System (IPFS), the agricultural product data was hashed, so as to reduce the data size of single transactions in the block, and the initial guarantee of data was achieved by using the irreversible principle of IPFS data. Secondly, the consortium blockchain model for data verification was established, and Practical Byzantine Fault Tolerant (PBFT) algorithm was used as consensus algorithm for blockchain data verification to reduce the consensus time of the whole network. Finally, according to the number of participating nodes, block size and network bandwidth in the simulation experiment, the time curve of the verification transaction was fitted, and then the blockchain transaction efficiency under different bandwidths was calculated; by using tens of thousands of actual situations of the agricultural product traceability system with the participation of sensors, the blockchain double-chain structure was compared to obtain the analysis results. Experimental results show that under the condition of less than 1000 verification nodes, the maximum consensus time of blockchain is 32 min, and the consortium blockchain system can support 350000-400000 sensor data, which can be applied to large-scale and multi-data agricultural product traceability.

To solve the problems of the cumbersome process in medical record authorization, the low efficiency in record sharing and the difficulty in identity authentication in current medical systems, a method of asymmetric encryption technology combining with blockchain technology was proposed to make medical records cross-domain sharing traceable, data tamper-resistant and identity authentication simplified by applying charatistics of asymmetric encryption technology like high safety and simple cooperation to the peer-to-peer network constructed by blockchain technology. Firstly, based on the anti-tampering of blockchain technology and with asymmetric encryption technology combined, file synchronization contract and authorization contract were designed, in which the distributed storage advantages secure the privacy of user's medical information. Secondly, cross-domain acquisition contracts were designed to validate the identity of both parties and improve authentication efficiency, so that non-legitimate users can be securely filtered without third-party notary agency. The experimental and analysis results show that the proposed scheme has obvious advantages in data guard against theft, multi-party authentication and data access control compared with the traditional scheme of using cloud computing method to solve medical record sharing problem. The proposed method provides a good application demonstration for solving the security problems in the data sharing process across medical institutions and a reference for cross-domain identity verification in the process of sharing data by using decentralization and auditability of blockchain technology.

At present, the researches of ensemble clustering mainly focus on the optimization of ensemble strategy, while the measurement and optimization of the quality of basic clustering are rarely studied. On the basis of information entropy theory, a quality measurement index of basic clustering was proposed, and a three-way screening method for basic clustering was constructed based on three-way decision. Firstly, α, β were reset as the thresholds of three-way decision of basic clustering screening. Secondly, the average cluster quality of each basic clustering was calculated and was used as the quality measurement index of each basic clustering. Finally, the three-way decision was implemented. For one three-way screening, its decision strategy is:1) deleting the basic clustering if the quality measurement index of the basic clustering is less than the threshold β; 2) keeping the basic clustering if the quality measurement index of the basic clustering is greater than or equals to the threshold α; 3) recalculating the quality of a basic clustering and if the quality measurement index of the basic clustering is greater than β and less than α or equals to β. For the third option, the decision process continues until there is no deletion of basic clustering or reaching the times of iteration. The comparative experiments show that the three-way screening method of basic clustering can effectively improve the ensemble clustering effects.

The imperialist competitive algorithm is a swarm intelligence optimization algorithm with strong local search ability, but excessive local search will lead to the loss of diversity and fall into local optimum. Aiming at this problem, an Imperialist Competitive Algorithm based on Multiple Search Strategy (MSSICA) was proposed. The country was defined as a feasible solution and the kingdoms were defined as four mechanisms of combinatorial artificial chromosome with different characteristics. The block mechanism was used to retain the dominant solution fragment during search and differentiated mechanisms of combinatorial artificial chromosome was used for different empires to search the effective and feasible solution information of different solution spaces. When it come to the local optimum, the multiple search strategy was used to inject a uniformly distributed feasible solution to replace a less advantageous solution to enhance the diversity. Experimental results show that the multiple search strategy can effectively improve diversity of the imperialist competitive algorithm and improve the quality and stability of the solution.

Aiming at the issue of low efficiency existing in resource scheduling of cloud computing, a new resource scheduling algorithm of cloud computing based on Quality of Service (QoS) was proposed. Firstly, the quality function and convergence factor were used in Ant Colony Optimization (ACO) algorithm to ensure the efficiency of pheromone updating and the feedback factor was set to improve the selection of probability. Secondly, the local search efficiency of Shuffled Frog Leading Algorithm (SFLA) was improved by setting crossover factor and mutation factor in the SFLA. Finally, the local search and global search of the SFLA were introduced for updating in each iteration of ACO algorithm, which improved the efficiency of algorithm. The simulation experimental results of cloud computing show that, compared with the basic ACO algorithm, SFLA, Improved Particle Swarm Optimization (IPSO) algorithm and Improved Artificial Bee Colony algorithm (IABC), the proposed algorithm has advantages in four indexes of QoS:the least completion time, the lowest cost of consumption, the highest satisfaction and the lowest abnormal value. The proposed algorithm can be effectively used in resource scheduling of cloud computing.

The traditional lightning data forecasting methods often use single optimal machine learning algorithm to forecast, not considering the spatial and temporal variations of meteorological data. For this phenomenon, an ensemble learning based multi-machine learning model was put forward. Firstly, attribute reduction was conducted for meteorological data to reduce dimension; secondly, multiple heterogeneous machine learning classifiers were trained on data set and optimal base classifier was screened based on predictive quality; finally, the final classifier was generated after weighted training for optimal base classifier by using ensemble strategy. The experimental results show that, compared with the traditional single optimal algorithm, the prediction accuracy of the proposed model is increased by 9.5% on average.

The Dynamic Time Warping (DTW) algorithm cannot keep high classification accuracy while improving the computation speed. In order to solve the problem, a Coarse-Granularity based Dynamic Time Warping (CG-DTW) algorithm based on the idea of naive granular computing was proposed. First of all, the better temporal granularities were obtained by computing temporal variance features, and the original series were replaced by granularity features. Then, the relatively optimal corresponding temporal granularity was obtained by executing DTW with dynamically adjusting intergranular elasticity of granularities compared. Finally, the DTW distance was calculated in the case of the corresponding optimal granularity. During this progress, an early termination strategy of lower bound function was introduced for further improving the CG-DTW algorithm efficiency. The experimental results show that, the proposed algorithm was better than classical algorithm in running rate with increasing by about 21.4%, and better than dimension reduction strategy algorithm in accuracy with increasing by about 32.3 percentage points.Especially for the long time sequences classification, CG-DTW takes consideration into both high computing speed and better classification accuracy. In actual applications, CG-DTW can adapt to long time sequences classification with uncertain length.

Focusing on the issues of failing to pinpoint the edge information accurately and lacking texture detail of image by using integer order differential or 0-1-order fractional differential mask operators in digital image processing, a new 1-2-order edge detection operator based on Laplacian operator was proposed. Deduced from the definition of Riemann-Liouville (R-L),the 1-2-order fractional differential had the advantage in enhancing high-frequency signal and reinforcing medium frequency signal. The simulation results demonstrate that the proposed operator can take an higher recognition rate on the subjective recognition, and it's better at extracting the edge information, especially for the image with rich texture detail in the smooth region with little change of gray scale. Objectively, the integrated location error rate is 7.41% which is less than that of integer order differential operators (a minimum of 10.36%) and 0-1-order differential operator (a minimum of 9.97%). Quantitative indicators show the new fractional operator can effectively improve the positioning accuracy of the edge, and the proposed operator is particularly suitable for edge detection with high frequency information.

Private data is easy to suffer from the attacks about data confidentiality, integrity and freshness. To resolve this problem, a secure data aggregation algorithm based on homomorphic Hash function was proposed, called HPDA (High-Efficiency Privacy Preserving Data Aggregation) algorithm. Firstly, it used homomorphic encryption scheme to provide data privacy-preserving. Secondly, it adopted homomorphic Hash function to verify the integrity and freshness of aggregated data. Finally, it reduced the communication overhead of the system by improved ID transmission mechanism. The theoretical analyses and experimental simulation results show that HPDA can effectively preserve data confidentiality, check data integrity, satisfy data freshness, and bring low communication overhead.

As an improvement of IP multicast technology, Application Layer Multicast (ALM) has many advantages such as unlimited network architecture, rich resource and high data transfer rate. Considering node performance and end to end delay, a fast and efficient method was proposed to establish application layer multicast tree, it was named Ant Colony Algorithm based Tree-ring Application Layer Multicast Model (ACOTRM). The available studies only gave a topology cursory and had no complete and clear description of the concrete construction process. In view of this, a complete ALM hierarchical tree-ring concrete construction process was put forward including several key steps, such as clustering division, connection in cluster ring, generation of feasible solution and maintenance of the model in survival time. In addition, in order to optimize the ALM state tree, each node was set with a specific priority. The simulation results show that the proposed model provides lower average delay and higher average data delivery ratio, which increases the system stability and forwarding efficiency at the same time.

Concerning the Internet news tracking, the study put forward a dynamic model for event tracking with reference to the time information. The dynamic model introduced the time factor into the traditional vector model to get the time similarity of the same characteristic words between the document and the event,and then applied the time similarity to calculate the similarity of the document and the event.If a document was related to the event,the new characteristic words in the document would be added to the event term set,and the weight and time information of characteristic words in the event term set should be re-adjusted. The experiment was evaluated by Detection Error Tradeoff (DET), and the results show that the dynamic model for event tracking improves the system performance effectively, and its minimum normalized cost of tracking loss is reduced by about 9%.

Equipment failure often brings some defects on the surface of batteries in the battery production process. The traditional artificial detection has weakness on the timeliness and durability. But there has not been any efficient automatic detection means for the ordinary battery by now. Concerning the distribution and morphological characteristics of the defects, a new automatic optical detection method based on computer vision was proposed. The proposed method used Canny operator and virtual granule collision method with the minimum value searching method to determine the area to be detected based on the battery anode surface morphology features. Considering the sharpness of the defect, Harris corner points were used to mark the defects as mark points. False mark points were filtered by the degree of aggregation of the points. The defect region would be extracted at last according to the location of mark points. The experimental results illustrate the detection success rate of the proposed method is over 90% and the method can work more efficiently than the popular wavelet analytical method. The study achievement provides a reference for product quality automatic detection on battery production.

This paper represented the information of each vertex in ontology graph as a vector. According to its structure of ontology graph, the vertices were divided into k parts. It chose vertices from each part, and chose the ranking loss function. It used k-partite ranking learning algorithm to get the optimization ranking function, mapped each vertex of ontology structure graph into a real number, and then calculated the relative similarities of concepts by comparing the difference between real numbers. The experimental results show that the method for calculating the relative similarity between the concepts of ontology is effective.

To overcome the deficiency in sharing and reusing design knowledge in manufacturing industry, a strategy based on functional ontology was proposed for design knowledge-sharing. The existing product structure can be mapped to its product functions by using functional ontology, and the design principles can be represented by functional decomposition tree. First, the basic framework of functional ontology was introduced. Then, the role of the functional decomposition tree to product design was analyzed. Finally, a knowledge-sharing platform for household appliances was designed and developed based on functional ontology. It was used to verify the proposed strategy. The experimental results show that the proposed strategy can effectively realize information retrieval, sharing and reuse of design knowledge in manufacturing industry. It can also shorten product development cycles.

Nowadays, Radio Frequency IDentification (RFID) security protocols based on Hash function have poor mobility and cannot satisfy the requirements of some fields. Aiming to boost up the mobility of RFID security protocols, a Mobile RFID Security Protocol was presented and GNY logic was used to prove its correctness. Analysis shows that presented protocol has satisfied mobility and security, and the protocol can be used in transmitting freight and field operation of war time, etc.

The design approach of a Linux-based embedded MPEG-4 Video Server (EMP4VS) with MPEG-4 Hardware Encoder was studied. The implementation of an EMP4VS was presented from the aspects of hardware architecture, software design, control/video data flow, the QoS mechanism, and client software. The extension of EMP4VS B/S architecture with middleware server was also discussed.

The retrieval efficiency of the component library includes not only the retrieval efficiency of the component itself, but also the efficiency of the understanding of component. General component retrieval system provides descriptive information on understanding the component simply, but seldom provides information from the aspect of reusing actually. Data Mining technologies provide a feasible approach for effective decision support. In this paper, the application of decision-tree-based classification method was discussed. It could excavate the cryptic knowledge in component user feedback information, and enable the relevant personnel in the component library to understand the component from the aspect of using the component actually. DM solutions improved possibility and quality of reusing. In the end, the feasibility and validity of this method were verified through experiments.

An algorithm based on random Dijkstra algorithm and applying genetic algorithm to solve SPDRGS(Shortest Path problem on Dynamic Route Guidance System) was proposed. By applying random Dijkstra algorithm, the algorithm cleared out the biggest obstruction between the genetic algorithm and SPDRGS, which is how to get the initial generation of GA(Genetic algorithm). According to DRGS’s (Dynamic Route Guidance System) demand for time complexity and network constraint condition of route guidance algorithms, this algorithm can quickly find the excellent path and does not need any network constraint condition, which also can solve the problems on continuously and discrete dynamic networks. So the algorithm proposed can satisfy the demand of DRGS.

Workflow engine is the core component of workflow system, architecture of which is vital to the whole system. This paper addressed the architecture of J2EE-based workflow engine, which was consisted of six components: parser, process manager, executor, task assigner, event server, time server and client interface. By the comparisons with other similar research works, the advantages of this approach in terms of applicability, expansibility, interview mode and transaction management were illustrated.