In order to realize accurate management of yaks during the process of yak breeding, it is necessary to recognize the identities of the yaks. Yak face recognition is a feasible method of yak identification. However, the existing yak face recognition algorithms based on neural networks have the problems such as too many features in the yak face dataset and long training time of neural networks. Therefore, based on the method of transfer learning and combined with the Visual Geometry Group (VGG) network and Convolutional Neural Network (CNN), a Parallel CNN (Parallel-CNN) algorithm was proposed to identify the facial information of yaks. Firstly, the existing VGG16 network was used to perform transfer learning to the yak face image data and extract the yaks' facial information features for the first time. Then, the dimensional transformation was performed to the extracted features at different levels, and the processed features were inputted into the parallel-CNN for the secondary feature extraction. Finally, two separated fully connected layers were used to classify the yak face images. Experimental results showed that Parallel-CNN was able to recognize yak faces with different angles, illuminations and poses. On the test dataset with 90 000 yak face images of 300 yaks, the recognition accuracy of the proposed algorithm reached 91.2%. The proposed algorithm can accurately recognize the identities of the yaks, and can help the yak farm to realize the intelligent management of the yaks.

Traditional 2D convolutional models suffer from loss of semantic information and lack of sequential feature expression ability in sentiment classification. Aiming at these problems, a hybrid model based on 1D Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) was proposed. Firstly, 2D convolution was replaced by 1D convolution to retain richer local semantic features. Then, a pooling layer was used to reduce data dimension and the output was put into the recurrent neural network layer to extract sequential information between the features. Finally, softmax layer was used to realize the sentiment classification. The experimental results on multiple standard English datasets show that the proposed model has 1-3 percentage points improvement in classification accuracy compared with traditional statistical method and end-to-end deep learning method. Analysis of each component of network verifies the value of introduction of 1D convolution and recurrent neural network for better classification accuracy.

To reduce the unnecessary overhead of data source authentication in static security mechanism and defend the On-off attack in trust threshold mechanism, an adaptive security mechanism based on trust was proposed in the Internet of Things (IoT). Firstly, the trust evaluation model was built according to node behavior in information interaction, further the measure method for total trust value of nodes was given. Then, for the nodes whose total trust values were higher than the trust threshold, the trust-based adaptive detection algorithm was used to detect the changes of the total trust values of these nodes in real time. Finally, the relay nodes determined whether to authenticate the received message according to the returned result of adaptive detection algorithm. The simulation results and analysis show that the proposed mechanism reduces the energy overhead of relay nodes, and plays a better role in defense against On-off attacks in IoT.

Since the new generation of flash-based SSD (Solid-State Drivers) use the DDR3 interface as its interface, SSD must communicate with memory controller correctly. FPGA (Field-Programmable Gate Array) was used to design the DDR3 protocol parsing logic. Firstly, the working principle of DDR3 was introduced to understand the controlling mechanism of memory controller. Next, the architecture of this interface parsing logic was designed, and the key technical points, including clock, writing leveling, delay controlling, interface synchronous controlling were designed by FPGA. Last, the validity and feasibility of the proposed design were proved by the modelsim simulation result and board level validation. In terms of performance, through the test of single data, continuous data and mixed read and write data, the bandwidth utilization of DDR3 interface is up to 77.81%. As the test result shows, the design of DDR3 parsing logic can improve the access performance of storage system.

In order to solve the problem that the population diversity preservation strategy only based on crowding distance of Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) cannot reflect the real crowding degree of individuals, an improved NSGA-Ⅱ algorithm based on the adaptive hybrid non-dominated individual sorting strategy (NSGA-Ⅱ ^h) was proposed. First, a novel loop-clustering individual sorting strategy was designed. Second, according to the Pareto layer-sorting information the NSGA-Ⅱ ^h algorithm adaptively chose one from the two individual sorting strategies based on classical crowding distance and loop-clustering. Finally, the diversity maintain mechanism could be improved especially during the late period of evolutionary optimization. The NSGA-Ⅱ ^h algorithm was compared with three classical algorithms including NSGA-Ⅱ, Multi-Objective Particle Swarm Optimization (MOPSO) and GDE3. The experiments on five multi-objective benchmark functions show that the NSGA-Ⅱ ^h algorithm can acquire 80% of optimal Inverted Generational Distance (IGD) values, and the corresponding two-tailed t-test results at a 0.05 level of significance are remarkable. The proposed algorithm can not only improve convergence of the original algorithm, but also enhance the distribution of Pareto optimal set.

As the source of the haze data, the reliability of the haze monitoring sites is very important to the reliability of the big data. Due to the lack of effective evaluation method for the haze monitoring points, the monitoring data is not reliable enough. In order to solve the problem that the perceived data was not reliable, a kind of perceptual source trust evaluation and selection model was proposed based on the data trigger detection method. When the perceived data arrived, the K-Means clustering algorithm and the statistical results were firstly used to calculate the benchmark data, then the trust degree of data was calculated by using the current perceived data, the benchmark data and the threshold values. Secondly, according to the location of the perceptual source, neighbor relationship was determined. The current perceived data and the data of the neighbors were compared, according to the absolute value of the difference and the value of the threshold, the neighbor recommendation trust degree was calculated. Finally, the comprehensive trust degree was calculated by using the truest degree of perceived data, the historical trust degree and the recommendation trust degree of the neighbor. The initial value of the historical trust was set as the number of monitoring items, and then updated by the comprehensive trust. Theoretical analysis and simulation results prove that the proposed method can effectively evaluate the perceived source, avoid the abnormal data, and reduce post processing overhead.

Multiple Directed Acyclic Graphic (DAG) scheduling algorithms are supposed to take many factors into account, such as execution time, communication overhead, cost and fairness of all DAG. Therefore, in order to increase fairness and reduce cost, a new scheduling strategy CAFS (Communication Aware Fair Scheduling), based on CA-DAG (Communication Aware-DAG), was proposed. Also, a BD (Backward Difference) rule was introduced to optimize finish time of all DAGs. CAFS is consisted of two parts: the pre-scheduling part adopts CACO (Communication Aware Cost Optimization) to optimize the total cost, and utilizes the classical fairness algorithm to decide the sequence for scheduling. Based on the sequence the second part schedules all the DAGs using BD rule to reduce the finish time. The simulation results show that CAFS can reduce the finish time without increasing cost and guarantee the fairness, and the average execution time has been reduced by 19.82%.

The mouse behaviors cannot be accurately recognized by the existing computer-based automatic analysis system, and the ground truth is generally achieved from experts’ annotation on a massive number of video images. However, to some extent, subjective misjudgments are unavoidable. To solve these problems, a human computation-based mouse behavior recognition method was proposed in this paper. Because of the superiority of human visual perception, and the decentralization and cooperation of the internet, human brains were treated as processors in a distributed system. Firstly, every mouse behavior frames were distributed to on-line individuals randomly, and each behavior frame was classified by a large number of on-line individuals. Secondly, all the effective classifications from the on-line individuals were collected, analyzed and processed by computer system, realizing the final mouse behavior classification based on these frame sequences. The experimental results show that the proposed method is effective to improve the correct recognition rate of mouse behaviors with limited cost.

To resolve the problem of the complex 3D tunnel modeling generated by the changes of the tunnel sections at different rock formations during the project, the 3D tunnel modeling based on the characteristic sections was proposed. Through establishing the characteristic section model library, the 3D modeling of the changed tunnel sections was realized and the efficiency of the modeling of the complex tunnel sections was improved. Following the illustration of the data collection method and an analysis of the characteristic sections with detailed coordinates, the smoothing algorithm (smoothing the tunnel axis at the corner by an arc) of the changed tunnel sections at the corner was proposed. For the ordinary tunnel sections, the triangulation was applied in the 3D modeling; for the complex sections with simple quadrilateral structure, the 3D modeling was realized by using the Bézier surface method and the surface splicing techniques which has been validated through experiments.

Concerning the specific application environment of embedded microprocessor, the branch prediction technology was researched in this paper, and a new scheme of branch prediction was proposed. Compatible with cache design, jump direction and destination address of branch prediction happened on extended instruction bus. The unexecuted instruction and address pointer were saved for possible recovery after misprediction, which reduced misprediction penalty, simultaneously guaranteed the instruction flow to execute correctly. The study shows this scheme is of little hardware spending, high prediction efficiency and low misprediction penalty.

Concerning the inequality of cluster-head distribution and node energy consumption in Wireless Sensor Network (WSN) cluster routing protocol, a node-energy load-balance clustering algorithm was proposed. Group according to the node energy, dynamically adjust the group number to the node energy reduction, conduct cluster-head election in the group according to the energy focus, and further balance the node energy consumption using cluster-head rotate and multi-hop routing between clusters. The simulation results show that this protocol effectively balances the energy consumption among network nodes and achieves an obvious improvement in network stable period.