In order to improve the efficiency of dam safety operation and maintenance, the dam defect object detection models can help to assist inspectors in defect detection. There is variability of the geometric shapes of dam defects, and the Single Shot MultiBox Detector (SSD) model using traditional convolution methods for feature extraction cannot adapt to the geometric transformation of defects. Focusing on the above problem, a DeFormable convolution Single Shot multi-box Detector (DFSSD) was proposed. Firstly, in the backbone network of the original SSD:Visual Geometry Group (VGG16), the standard convolution was replaced by the deformable convolution, which was used to deal with the geometric transformation of defects, and the model's spatial information modeling ability was increased by learning the convolution offset. Secondly, according to the sizes of different features, the ratio of the prior bounding box was improved to prompt the detection accuracy of the model to the bar feature and the model's generalization ability. Finally, in order to solve the problem of unbalanced positive and negative samples in the training set, an improved Non-Maximum Suppression (NMS) algorithm was adopted to optimize the learning effect. Experimental results show that the average detection accuracy of DFSSD is improved by 5.98% compared to the benchmark model SSD on dam defect images. By comparing with Faster Region-based Convolutional Neural Network (Faster R-CNN) and SSD models, it can be seen that DFSSD model has a better effect in improving the detection accuracy of dam defect objects.

Focusing on the issue that knowledge is not equal in task assignment of product development, a task assignment model of product development based on bilateral matching of task and designer was proposed. Firstly, the matching between task and designer was transformed into the similarity of knowledge between task and designer from the perspective of knowledge quantification, then the order value matrix was established and transformed into the satisfaction degree matrix of task to designer matching. Secondly, according to the degree of preference of the designer to the task under different task attributes, the order value matrix of designer satisfaction to task was obtained. Thirdly, based on the principle of maximum satisfaction between the two parties, a multi-objective optimization model based on knowledge similarity and designer preference was constructured. The method of weighted sums based on membership function was used to change the multi-objective optimization model into linear programming model, then the obtained model would be solved by Matlab programming. Finally, taking a crankshaft linkage mechanism produced by one enterprise as an example, the matching result between four tasks and seven designer was obtained to determine the final assignment plan. By comparing with task assignment methods of product development based on clustering analysis and bilateral matching, the apparent difference between the knowledge similarity and the designer preference between the designer 3 and 7 indicated that the proposed method can assign tasks more efficiently.

To realize reasonable allocation and scheduling of mobile user task requests under cloud and fog collaboration, a task assignment algorithm based on cloud-fog collaboration model, named IGA (Improved Genetic Algorithm), was proposed. Firstly, individuals were coded in the way of mixed coding, and initial population was generated randomly. Secondly, the objective function was set as the cost of service providers. Then select, cross, and mutate were used to produce new qualified individuals. Finally, the request type in a chromosome was assigned to the corresponding resource node and iteration counter was updated until the iteration was completed. The simulation results show that compared with traditional cloud model, cloud-frog collaboration model reduces the time delay by nearly 30 seconds, reduces Service Level Objective (SLO) violation rate by nearly 10%, and reduces the cost of service providers.

In order to improve the rapidity and tracking accuracy of Extended Kalman Filter (EKF), an improved EKF for attitude estimation of quadrotor was proposed by introducing a dynamic step gradient descent algorithm with acceleration restraint. Gradient descent algorithm was used to carry out nonlinear observation in the Kalman measurement update, eliminate the linearity error caused by the linearization of the standard extended Kalman algorithm and improve the accuracy and rapidity of the algorithm. The gradient step of gradient descent algorithm was dynamically processed to be proportional to the angular velocity of the quadrotor, thus enhancing the dynamic performance of the quadrotor. The motion acceleration generated during strong maneuverability was restrained to remove the adverse effect to attitude calculation and improve tracking accuracy of quadrotor's attitude estimation. To verify the feasibility and effectiveness of proposed algorithm, a quadrotor experimental platform was set up based on STM32 microcontroller. The experimental results show that the proposed algorithm has higher estimation accuracy, better dynamic performance and anti-interference characteristics under strong maneuverability and high-speed motion, and can ensure the stable flight of the quadrotor.

The real-time time-series data of multiple water parameters are acquired via the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of pollution events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively evaluate the abnormal event detection to reduce the detection deviation, a Temproal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, it could analyze the time-series data of multiple parameters with BP (Back Propagation) model to determine the possible outliers, respectively. M-TAEDA algorithm could detect the potential pollution events through Bayesian sequential analysis to estimate the probability of an abnormal event. Finally, it can make decision through the multiple event probability fusion in the water supply systems. The experimental results indicate that the proposed M-TAEDA algorithm can get the 90% accuracy with BP model and improve the rate of detection about 40% and reduce the false alarm rate about 45% compared with the temporal abnormal event detection of Single-Variate Temproal Abnormal Event Detection Algorithm (S-TAEDA).

Focused on the issues of low accuracy, high energy consumption of target tracking network and short life cycle of network in Wireless Sensor Network (WSN), the moving target tracking technology based on dynamic clustering was proposed. Firstly, a Two-Ring Dynamic Clustering (TRDC) structure and the corresponding TRDC updating methods were proposed; secondly, based on centroid localization, considering energy of node, the Centroid Localization based on Power-Level (CLPL) algorithm was proposed; finally, in order to further reduce the energy consumption of the network, the CLPL algorithm was improved, and the random localization algorithm was proposed. The simulation results indicate that compared with static cluster, the life cycle of network increased by 22.73%; compared with acyclic cluster, the loss rate decreased by 40.79%; there was a little difference from Received Signal Strength Indicator (RSSI) algorithm in accuracy. The proposed technology can effectively ensure tracking accuracy and reduce energy consumption and loss rate.

In the process of converter blowing state recognition based on flame image recognition, flame color texture information is underutilized and state recognition rate still needs to be improved in the existing methods. To deal with this problem, a new converter blowing recognition method based on feature of flame color texture complexity was proposed. Firstly, the flame image was transformed into HSI color space, and was nonuniformly quantified; secondly, the co-occurrence matrix of H component and S component was computed in order to fuse color information of flame images; thirdly, the feature descriptor of flame texture complexity was calculated using color co-occurrence matrix; finally, the Canberra distance was used as similarity criteria to classify and identify blowing state. The experimental results show that in the premise of real-time requirements, the recognition rate of the proposed method is increased by 28.33% and 3.33% respectively, compared with the methods of Gray-level co-occurrence matrix and gray differential statistics.

Considering the problem that Extended Kalman Filter (EKF) does better in linear system for real-time 3D mapping and largerly affected by errors to linearize nonlinear systems, Iterated Extended Kalman Filter (IEKF) based on depth data of Kinect was proposed. This method used IEKF to achieve camera trajectory prediction applied to Microsoft Kinect RGB-D(Red-Green-blue-Depth) data, after that Iterative Closest Point (ICP) algorithm was employed to perform fine registration on depth image to generate the 3D point cloud map. The experimental results show that compared with the traditional EKF algorithm, the IEKF generates less error than EKF, and gets the more smooth 3D point cloud map. The method realizes the 3D map-building, and it is more practical.

Plane Wave Pseudopotential (PWP) Density Functional Theory (DFT) calculation is the most widely used method for material calculation. The projector calculation plays an important part in PWP-DFT calculation for the self-consistent iteration solution, while it often becomes a hinder to the speed-up of software. Therefore, according to the features of Graphic Processing Unit (GPU), a speed-up algorithm was proposed: 1) using a new parallel mechanism to solve the potential energy of nonlocal projector, 2) redesigning the distribution structure of data, 3) reducing the use of computer memory, 4) Proposing a solution to the related data problems of the algorithm. Eventually got 18-57 times acceleration, and reached the 12 seconds per step of the molecular dynamics simulation. In this paper, the testing time of running this model on GPU platform was analysed in detail, meanwhile the calculation bottleneck of the implementation of this method into GPU clusters was discussed

A new algorithm based on cyclic spectrum was proposed for classification of communication signals in multipath channel, which solved the problems of fewer identification types, difficults table feature parameters extraction and low recognition rate. Firstly, the features face and projective planes of cyclic spectrum, square cyclic spectrum and the fourth power cyclic spectrum were extracted. Secondly, correlation coefficients of features face and projective planes were used as the characteristic parameters. At last, the suitable decision threshold was chosen and seven signals of BPSK, QPSK, 2FSK, 4FSK, MSK, 16QAM and OFDM were identified automatically. The experimental results show that the characteristic parameters have great ability for multipath interference and high recognition rate is acquired at last. When the Signal-to-Noise Ratio (SNR) is higher than 2dB, its overall recognition rate is up to 94%. Compared with the existing algorithms, the simulation results prove that the algorithm is superior.

Among all of the signal detection algorithms in multiple-input multiple-output systems, the capability of sphere decoding algorithm is most close to the capability of maximum-likelihood algorithm. But the calculation complexity of the sphere decoding algorithm is still very high. To decrease the calculation complexity of sphere algorithm, a new sphere decoding algorithm was proposed. The new algorithm was combined by an improved fast sphere decoding algorithm and the Minimum Mean Square Error (MMSE) algorithm. The improved fast sphere decoding algorithm can increase the decreasing rate of sphere radius via multiplying the contraction process of sphere radius by a constant parameter, so that it can reduce the number of signal points in search process to decrease calculation complexity. Meanwhile, the MMSE algorithm can reduce the interference that caused by noise, so that it can decrease the calculation complexity caused by the process of searching noise points. The channel matrix of the MMSE algorithm was applied to the improved fast sphere decoding algorithm, so these two algorithms can be combined with each other efficiently, and the combined algorithm can further reduce the calculation complexity. The simulation results show that, when Signal-to-Noise Ratio (SNR) is less than 10dB, the proposed algorithm improves average performance by 9% compared with original sphere decoding algorithm. Multiple-Input Multiple-Output (MIMO); signal detection; calculation complexity; Sphere Decoding (SD) algorithm; Minimum Mean Square Error (MMSE) algorithm