Wind power prediction is an important basis for the monitoring and information management of wind farms. Ultra-short-term wind power prediction is often used to balance load and optimize scheduling and requires high prediction accuracy. Due to the complex environment of wind farm and many uncertainties of wind speed, the wind power time series signals are often non-stationary and random. Recurrent Neural Network (RNN) is suitable for time series tasks, but the non-periodic and non-stationary time series signals will increase the difficulty of network learning. To overcome the interference of non-stationary signal in the prediction task and improve the prediction accuracy of wind power, an ultra-short-term wind power prediction method combining empirical model decomposition and multi-branch neural network was proposed. Firstly, the original wind power time series signal was decomposed by Empirical Mode Decomposition (EMD) to reconstruct the data tensor. Then, the convolution layer and Gated Recurrent Unit (GRU) layer were used to extract the local features and trend features respectively. Finally, the prediction results were obtained through feature fusion and full connection layer. Experimental results on the dataset of a wind farm in Inner Mongolia show that compared with AutoRegressive Integrated Moving Average (ARIMA) model, the proposed method improves the prediction accuracy by nearly 30%, which verifies the effectiveness of the proposed method.

Brightness inconsistency and obvious boundary affect the reconstruction results of multi-temporal remote sensing images. In order to solve the problem, an improved thick cloud removal algorithm for multi-temporal remote sensing image was proposed by combining total variation model and Poisson equation. Firstly, the brightness correction coefficient was calculated by the brightness information of the common area of multi-temporal remote sensing images in order to correct the brightness of the images, so as to reduce the effect of brightness differences on cloud removal results. Then, multi-temporal images after brightness correction were reconstructed based on selective multi-source total variation model, and the fusion results' spatial smoothnesses and their similarities with the original images were improved. Finally, the local areas of the reconstruction image were optimized by using Poisson equation. The experimental results show that this method can effectively solve the problems of brightness inconsistency and boundary.

When applying traditional image enhancement algorithm to low illumination images with uneven illumination distribution,it is easy to produce color distortion and over enhancement of bright areas. To resolve theses problems,an adaptive enhancement algorithm of low illumination image based on maximum difference image was proposed. Firstly,the concept of maximum difference image was proposed,and the initial illumination component was roughly estimated by the maximum difference image. Secondly,the method of alternating guided filtering was proposed,which was used to correct the initial illumination component,so as to realize the accurate estimation of illumination component. Finally,the Gamma transform was designed for image brightness adaptivity,which was able to adaptively adjust the Gamma transform parameters according to the acquired illumination components,thus,the influence of uneven illumination was eliminated while enhancing the image. Experimental results show that the enhanced image effectively eliminates the influence of uneven illumination distribution,the brightness,contrast,detail performance and color fidelity of the image are significantly improved,the average gradient increases by more than one time,and the information entropy increases by more than 14%. Because the proposed algorithm estimates the light component accurately,and the adaptive Gamma transform is optimized for low illumination images,so that the proposed algorithm has very effective enhancement effect for color images under weak light conditions like night.

To solve the problem that blockchain technology is difficult to be used in large-scale business scenarios due to storage constraints, a blockchain shard storage model based on threshold secret sharing was proposed. Firstly, the transaction data to be placed in blockchain was processed into shards by consensus nodes using improved Shamir's threshold secret sharing. Secondly, consensus nodes constructed different blocks based on data shards and distributed them to other nodes existing in the blockchain network for storage. Finally, when a node wanted to read transaction data, the node would request data from k of the n nodes with transaction data shards, and use Lagrange interpolation algorithm to recover the original transaction data. The experimental results show that the model not only guarantees the security, reliability and privacy of data to be placed in blockchain, but also effectively reduces the amount of data stored by each node to 1/(k-1), which is conducive to blockchain technology using in large-scale business scenarios.

In order to improve the utilization efficiency of deep information in text context, based on Hierarchical Attention Network (HAN) and Independent Recurrent Neural Network (IndRNN), a regional bullying semantic recognition model called HACBI (HAN_CNN_BiLSTM_IndRNN) was proposed. Firstly, the manually annotated regional bullying texts were mapped into a low-dimensional vector space by means of word embedding technology. Secondly, the local and global semantic information of bullying texts was extracted by using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), and internal structure information of text was captured by HAN. Finally, in order to avoid the loss of text hierarchy information and solve the gradient disappearance problem, IndRNN was introduced to enhance the description ability of model, which achieved the integration of information flow. Experimental results show that the Accuracy (Acc), Precision (P), Recall (R), F1 (F1-Measure) and AUC (Area Under Curve) values are 99.57%, 98.54%, 99.02%, 98.78% and 99.35% respectively of this model, which indicates that the effectiveness provided by HACBI is significantly improved compared to text classification models such as Support Vector Machine (SVM) and CNN.

Aiming at the low efficiency of test case automatic generation technology, an IMproved Bacterial Foraging Optimization Algorithm (IM-BFOA) was proposed with introduction of Knet map. Firstly, Kent map was used to increase the diversity of the initial population and global search of bacteria. Secondly, the step size of chemotaxis stage in the algorithm was adaptively designed to make it more rational in the process of bacterial chemotaxis. Finally, a fitness function was constructed according to the program under test to accelerate the optimization of test data. The experimental results show that compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm and standard Bacterial Foraging Optimization Algorithm (BFOA), the proposed algorithm is the best in terms of iterations number and running time with the guarantee of coverage and has high efficiency of test case generation.

Aiming at the throughput of high-speed railway Multiple Input Multiple Output (MIMO) system has not been fully improved, an adaptive beam transmission scheme based on antenna grouping was proposed. Firstly, the train position information was predicted by the Base Station (BS), and the beamforming technology was introduced into high-speed railway environment to establish a high-speed Massive MIMO three-dimensional model. Secondly, it was verified that in BS antenna grouping situation, the throughput of a sub-beam and its corresponding number of transmit antennas satisfied nonlinear relationship and the number change of sub-beam antennas did not affect the throughput of other beams. Finally, based on the above, an adaptive beamforming scheme based on antenna grouping was used to adjust the number of beams required and the number of transmit antennas required by the sub-beams when the train runed at different locations to ensure optimal system throughput at all the locations. The computer simulation results show that compared with the traditional single-beam, dual-beam and eight-beam schemes, the proposed scheme achieves 87.9%, 62.3%, and 50.6% improvement respectively in system throughput when the distance between the train and the BS is less than 125 m, achieves a similar system throughput of single beamforming when the distance is more than 125 m. The experimental results show that the proposed scheme has best system throughput whether the train is far away from or close to the BS, and is better adapted to high-speed railway environment.

An improved low-illumination image enhancement algorithm was proposed to solve the problems that the overall intensity of low-illumination color image is low, the color in the enhanced image is easy to be distorted, and some enhanced image details are drowned in the pixels with low gray value. Firstly, an image to be processed was converted to the Hue Saturation Intensity (HSI) color space, and the nonlinear global intensity correction was carried out for the intensity component. Then, an intensity enhancement model based on multi-scale guided gradient domain filtering was put forward to enhance the corrected intensity component, and the intensity correction was further performed to avoid color distortion. Finally, the image was converted back into Red Green Blue (RGB) color space. Experimental results show that the enhanced images have the intensity increased by more than 90.0% on average, and the sharpness increased by more than 123.8% on average, which are mainly due to the better intensity smoothing and enhancement ability of multi-scale gradient domain guided filtering. At the same time, due to the reduction of color distortion, the detail performance of enhanced images increases by more than 18.2% on average. The proposed low-illumination image enhancement algorithm is suitable for enhancing color images under night and other weak light source conditions, because of using intensity enhancement model based on multi-scale gradient domain guided filtering and histogram adaptive intensity correction algorithm.

Existing optimizations have low precision and slow speed for reconfiguration of distribution network. In order to improve the safety and reliability of distribution network with Distributed Generation (DG), a simplified particle swarm optimization with adaptive inertial weight and full information was proposed based on leap-frog grouping. Firstly, from the viewpoints of reducing the active power loss of the network, increasing the voltage stability, and balancing the load of the feeder, a multi-objective mathematical model for distribution network was established. Secondly, through the Pareto dominance principle, the multi-objective was converted into several single objects with the same dimension, the same attribute and the same order of magnitude according to the standardized satisfaction of fuzzy membership function to make up for the disadvantages subjectivity and disunited dimension of weight method. Finally, in order to avoid random initialization to produce a large number of infeasible solutions, a kind of multi-objective reconfiguration strategy of distribution network with DG-combining Ant Colony Optimization (ACO) algorithm with random spanning tree and improved particle swarm optimization was designed. Through the IEEE33 node distribution system simulation, the experimental results show that the proposed reconfiguration strategy has a decrease of 41.0% in search efficiency compared to Particle Swarm Optimization (PSO) algorithm. Compared to before reconfiguration, the active power loss of the network is decreased by 41.47%, the voltage stability is decreased by 57.0%, and the load of the feeder is improved by 31.25%. The reconfiguration strategy effectively improves the optimizing accuracy and speeds up the optimization, therefore, improves the safety and reliability of distribution network operation.

In the Energy Balanced Clustering algorithm for Self-energized wireless sensor network (EBCS), a node has no threshold limit of energy in the cluster-head election, which leads to that a node with low energy might be elected as the cluster-head; and the cluster-head node can only hold one round, which leads to that the node with rich energy can not continue to be reappointed. Meanwhile, the EBCS has no consideration about the election mechanism after the death node was resurrected based on the self-energized characteristic. In order to solve the problems, a new Clustering routing algorithm based on Cluster-head Optimization for Self-energized wireless sensor network (CCOS) was proposed. Firstly, the energy threshold of cluster-head election was optimized, which limited the election of the node with incompetent energy for the cluster-head. Secondly, the cluster-head reappointment mechanism was introduced and improved, which made that the cluster-head can decide whether to be the cluster-head in the next round with its own level of energy harvesting. What's more, a threshold sensitive node resurrection mechanism was proposed, soft and hard resurrection thresholds were set to let the death node resurrected when its harvesting energy reached the corresponding energy threshold. The experimental results show that, under different energy harvesting scenes, compared with EBCS, the number of available nodes of the proposed CCOS in the current network is increased by about 8% and its success ratio of data transmission is increased by about 5%. The proposed CCOS can make more rational use of renewable energy and is helpful to the deployment of self-energized sensor network.

In view of the problems that upper limit of radiated peak power is low for continuous wave laser radar, which limits the maximum measurement range in the application of range and speed measurement, a waveform of modulated signal based on Golomb series was proposed, and the feasibility of simultaneously measuring target's range and speed in road environments by the method was studied. Firstly, the problem of low transmitted signal peak power that exists in continuous wave modulating method was analyzed by using a quasi-continuous, i.e., Pseudo random Noise (PN) code modulation as an example. Characteristics of Golomb series were discussed, and a modulation method based on Golomb series was proposed for raising the peak power of transmitted pulse. Then, a method for analyzing spectrum of Doppler signal modulated by Golomb series was discussed, as well as a data accumulation method for locating signal delay time, such that range and speed could be measured simultaneously. Finally, within the range of Doppler frequency that is generated by moving road targets in road environment, simulations were performed to verify the correctness of the proposed method. The experimental results show that Fast Fourier Transform (FFT) can be used for obtaining the frequency of Doppler signal even when the sampling frequency provided by the pulse series is much lower than the Nyquist frequency, thus largely increasing the peak power of single pulse under the condition that average transmission power keeps unchanged. Furthermore, data accumulation method can be used for locating laser pulse flight time by exploiting the non-equal interval property of Golomb series, ensuring both target range measurement and speed measurement from the same signal.

The haze removal of remote sensing image may easily result in ground object distortion. In order to solve the problem, an improved haze removal algorithm was proposed on the basis of the traditional additive haze pollution model, which was called high fidelity haze removal method based on estimation for Haze Thickness Map (HTM). Firstly, the HTM was obtained by using the traditional additive haze removal algorithm, and the mean value of the cloudless areas was subtracted from the whole HTM to ensure the haze thickness of the cloudless areas closed to zero. Then, the haze thickness of blue ground objects was estimated alone in degraded images. Finally, the cloudless image was obtained by subtracting the finally optimized haze thickness map of different bands from the degraded image. The experiments were carried out for multiple optical remote sensing images with different resolution. The experimental results show that, the proposed method can effectively solve the serious distortion problem of blue ground objects, improve the haze removal effect of degrade images, and promote the data fidelity ability of cloudless areas.

In order to solve the problems of copyright registration, rampant piracy and copyright disputes faced by digital intellectual property under Internet ecology, a Digital Copyright Identifier (DCI) control model of digital works without trusted third party was proposed. Firstly, the Peer-to-Peer (P2P) architecture based on the concept of de-centralization of blockchain was constructed. The blockchain replaced the traditional database as the core of storage mechanism. Through the creation of transactions, construction of blocks, legitimacy validation and link of blocks a digital work blockchain transaction information storage structure was built, guaranteeing the copyright information not be tampered and traceable. Secondly, the digital distribution protocol based on smart contract was proposed, three types of contracts include copyright registration, inquiry and transfer were designed, and the transactions were generated by automatically executing the preset instructions to ensure the transparency and high efficiency of models. Theoretical analysis and simulation show that the probability of forged block attack is close to zero in the digital work blockchain network, compared with the traditional copyright authentication mechanism based on trusted third party, the model has better architectural security. The experimental results show that the model simplifies the threshold of digital copyright registration, enhances the authority of copyright certification and has better real-time and robustness.

In the cluster-based routing algorithm of Wireless Sensor Network (WSN), "energy hole" phenomenon was resulted from energy consumption imbalance between sensors. For this problem, a hybrid multi-hop routing algorithm of effective energy-hole avoidance was put forward on the basis of the research of the flat and hierarchical routing protocols. Firstly, the concept of hotspot area was introduced to divide the monitoring area, and then in clustering stage, the amount of data outside the hotspot area was reduced by using uneven clustering algorithm which could integrate data within the clusters. Secondly, energy consumption was cut down in the hotspot area during clustering stage by no clustering. Finally, in inter-cluster communication phase, the Particle Swarm Optimization (PSO) algorithm was addressed to seek optimal transmission path which could simultaneously meet the minimization of the maximum next hop distance between two nodes in the routing path and the minimization of the maximum hop count, so the minimization of whole network energy consumption was realized. Theoretical analysis and experimental results show that, compared with the Reinforcement-Learning-based Lifetime Optimal routing protocol (RLLO) and Multi-Layer routing protocol through Fuzzy logic based Clustering mechanism (MLFC) algorithm, the proposed algorithm shows better performance in energy efficiency and energy consumption uniformity, and the network lifetime is raised by 20.1% and 40.5%, which can avoid the "energy hole" effectively.

Due to the threats of Cross-Site Scripting (XSS) attack in Online Social Network (OSN), a approach combined classifiers and improved n-gram model was proposed to detect the malicious OSN webpages infected with XSS code. Firstly, similarity-based features and difference-based features were extracted to build classifiers and the improved n-gram model. After that, the classifiers and model were combined to detect malicious webpages in OSN. The experimental results show that compared with the traditional classifier detection methods, the proposed approach is more effective and the false positive rate is about 5%.

According to the IEEE 802.15.4 slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithm, a network analysis model using analysis method of two-dimensional Markov chain was proposed. Not only the sleep mode of IEEE 802.15.4 agreement but also the condition where the backoff window reached the maximum value before the Number of Backoff (NB) were especially considered in the model. On this basis, combined with M/G/1/K queuing theory, the throughput expression was derived, and the packet arrival rate effect on the throughput was analyzed under unsaturated network. Using the simulation platform Network Simulator Version2 (NS2), the experimental results show that the theoretical analysis fits well with the simulation result, and the network throughput is described accurately. Then the effectiveness of the analytical model is validated.

Current software protection technologies generally achieve the software protection through improving the code and applying encryption scheme. To address the problem of whether the static authorized anti-attack capability of software code and the strength of the software encryption can sufficiently resist attack, the authors proposed a software protection game model based on divided-storage strategy. The strategy of divided-storage was used by the model to divide secret key into many segments, so multiple verified functions that were used to inspect and resist the cracker's attack were received. After being hidden in the program, the program was protected by multiple different verified functions during the running of the software. The model was analyzed and demonstrated from the perspective of game theory, also applied to the instances of software registration code verification. The security of the software code had been improved. The experimental results and analysis show that the proposed model is correct and effective.

With regard to the problem that recognition software does not work when the Quick Response (QR) code image is contaminated, damaged or obscured, a QR code recognition method based on sparse representation was proposed. Forty categories QR code images were used as research subjects and each category has 13 images. Three images were randomly selected from each category and thus a total of 120 images were got as the training sample and the remaining 400 as test sample. Sparse representation dictionary was composed of all training samples. The test samples were a sparse linear combination of the training samples and the coefficients were sparse. The projection of each test sample in the dictionary was calculated, so category with the smallest residual was classification category. Finally, comparison and analysis were done between the recognition results of the proposed method and the QR code recognition software PsQREdit. The experimental results show that, the proposed method is able to correctly identify for partially contaminated, damaged and obscured image, and it has good robustness. It is a new effective means for the recognition of QR code.

Large clusters have been developed to multicore era, and multicore architecture makes new demands on parallel computation. Message Passing Interface (MPI） is the most commonly used parallel programming model, and collective communications is an important part of the MPI standard. Efficient collective communications algorithm plays a vital role in improving the performance of parallel computation. This paper first analyzed the architecture features of KD60 and communication hierarchy characteristics under multicore architecture, and then introduced the implementation of collective communications algorithm in MPICH2 and pointed out its deficiencies. At last, this article took broadcasting as an example, using an improved algorithm based on CMP architecture，which changes the communication mode of the original algorithm. At the same time, this paper optimized the algorithm according to the architecture characteristics of KD60. The experimental results show that the improved algorithm improves the performance of broadcast in MPI.

For the trivial desgning and the complexity of the user permission management system, the user permission management component model was put forward. One user permission management component generating system was also developed. This system implemented the management and reuse of user permission component, and is applied to the development of concreate information system.