Aiming at the problem of the existing drift detection methods in balancing the detection delay， false positives， false negatives， and spatiotemporal efficiency， a new stage transition threshold parameter was proposed， and a multi-stage weighting mechanism including “stable stage-warning stage-drift stage” was introduced in the concept drift detection to weight the instances in stages， and the mechanism was applied to the double sliding window. Then a Multi-Stage weighted Drift Detection Method （MSDDM） based on Hoeffding inequality was proposed. On artificial datasets， MSDDM detected abrupt and gradual concept drift faster than Fast Hoeffding Drift Detection Method （FHDDM）， Drift Detection Method based on Hoeffding’s bound （HDDM） and other drift detection methods， while maintained a low false detection rate and a false alarm rate. At the same time， MSDDM had the highest classification accuracy in most cases compared with other methods on real-world datasets. Experimental results show that MSDDM can detect concept drift in data streams with high drift detection performance and great spatiotemporal efficiency.

The computational costs of traditional deep neural network-based person search algorithms are very high， so that these algorithms are difficult to deploy on devices with limited hardware resources and budgets because of high cost and low speed. Aiming at the above problems， a person detection and person re-identification algorithm based on the high-performance inference chip Sophon SC5+ was proposed to optimize the efficiency of deep learning from the algorithm end to the hardware end in a top-down approach. Firstly， by using the lightweight Ghost module to replace the backbone network of YOLOv5s， the parameters and computational cost of the model were greatly reduced. Secondly， Convolutional Block Attention Module （CBAM） attention mechanism was integrated to enhance the feature learning capability and improve the detection precision of the algorithm. Thirdly， the central loss constraint and Non-local attention mechanism were added to the person re-identification module， and the central constrained triple loss and the additional interval cross-entropy loss were combined to optimize the model and improve the performance of the person re-identification algorithm. Finally， based on Sophon SC+， person detection model and person re-identification model were quantized and the final inference model was generated. Experimental results on Market-1501 and DukeMTMC-ReID datasets show that， the mean Average Precisions （mAPs） of the person detection and person re-identification algorithms were improved by at least 43.8 and 25.7 percentage points compared with YOLOv4-tiny， Attribute-Complementary Re-ID Net （ACRN）， Singular Vector Decomposition Net （SVDNet） and other mainstream algorithms. After the implementation of int8 quantization based on Sophon SC5+ chip， although the proposed algorithm has the mAP decreased by 1.7 percentage points， it has the model size reduced by 74.4%. It can be seen that the proposed algorithm can be used in large-scale， city-level person search systems.

Non-Intrusive Load Monitoring （NILM） technology provides technical support for demand side management， and non-intrusive load identification is the key link in the process of load monitoring. The long-term sampling process of load data cannot be carried out in real time and high frequency， and the time sequence of the obtained load data is lost. At the same time， the defect of insufficient representation of low-level signal features occurs in Convolution Neural Network （CNN）. In view of the above two problems， a CNN based non-intrusive load identification algorithm with upsampling pyramid structure was proposed. In the proposed algorithm， with direct orientation to the collected load current signals， the time sequence of the data was compensated by the relevant information in the time dimension of the upsampling network expanded data， and the high-level and low-level features of load signals were extracted by the bidirectional pyramid one-dimensional convolution， so that the load characteristics were fully utilized. As a result， the purpose of identifying unknown load signals can be achieved. Experimental results show that the recognition accuracy of non-intrusive load identification algorithm based on CNN with upsampling pyramid structure can reach 95.21%， indicating that the proposed algorithm has a good generalization ability， and can effectively realize load identification.

As a kind of pure location routing algorithm in Wireless Multimedia Sensor Network (WMSN), Two Phase Geographic Greedy Forwarding (TPGF) helps to select the next-hop node which is of nearest distance to the destination from neighbor ones. In some cases, the distance between the next-hop node and the destination node could be farther than that of the current node and the destination node; At the same time, by numbering the nodes, TPGF solves the problem of hole and satisfies the Quality of Service (QoS) requirements. In line with the strategy of selecting the next-hop node farther than the current node, action-angle variables and DATF (Direction-Angle Greedy Forwarding) algorithm were introduced to guarantee and optimize the process of selecting the bound nodes. The simulation result indicates that DATF algorithm shows better performance than TPGF in both energy consumption and end-to-end transmission delay and also has a significant effect on solving the problem of hole.

Concerning the trust degree rank question of which attribute weights are unknown and data are multidimensional, a trust degree rank method based on linear programming theory was proposed. Firstly, the proposed method used linear programming model to make sure the trust degree rank ceiling-floor vector of nodes under the condition of one-dimensional data and unknown attribute weights, and then built the optimal coordinating rank model under multidimensional timing, and then converted the model to the classical assignment problem, and used the Hungary algorithm to solve the model, at last the trust degree rank of each node was obtained. The example analysis shows that, when the optimal coordinating rank model distance parameter q takes low value, the extreme data are not sensitive to the model, the proposed model can prevent evaluation error caused by a few mutation data; when q takes high value, the extreme data are sensitive to the model, the proposed model can identify the potential dishonest evaluation objects of a swing trust degree.

Considering the non-linearity of TCP model, uncertainty of Round Trip Time (RTT) and fluctuation of network load, an Active Queue Management (AQM) scheme based on Variable Structure Controller (VSC) using single neuron adaptive learning was proposed. The nonlinear VSC was used to guarantee the swiftness and robustness of queue response at router. However, the jitter of VSC would cause the queue fluctuation and performance degradation. Therefore, a single neuron was introduced to adjust the parameters of the VSC in order to alleviate the effect of jitter and modeling uncertainty. The proposed scheme can reduce the jitter and enhance the robustness for AQM control system greatly. Finally, the simulation results show the effectiveness of the proposed algorithm through NS-2 simulator.

The Symbian platform and current models of resource sharing on mobile devices were introduced firstly. Next, a new P2P model of resource sharing on Symbian devices was put forward by analyzing the existing mobile P2P protocol JXME. And then the corresponding P2P protocol JXTA-Symbian for Symbian platform was constructed through a series of modifications and adjustments on reference protocol JXME, which included the improvements of relay and the re-implementation of Symbian-End protocol. Finally, this paper discussed and analyzed the implementation architecture of JXTA-Symbian, especially the implementation of Symbian-End protocol, in detail.