Aiming at the optimal choice of sales channel structure in the closed-loop supply chain, considering the uncertainty of market demand and quality level of recycled products, four average gross profit models for the closed-loop supply chain system with four sales channel structures under the government differentially weighted subsidy were constructed with the objective of maximizing the gross profit. Firstly, Fuzzy Chance Constrained Programming (FCCP) method was used to transform the fuzzy constraints into clear corresponding expressions equivalently. Then, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) were used to solve numerical examples of the model comparatively. Finally, sensitivity analysis was performed on the parameters. The results show that the maximum difference ratio of the above two algorithms is 0.018%, indicating that both algorithms do not fall into the local optimal solution, which verifies the validity of the algorithms and the confidence of the models. Enterprises can formulate optimal recycling, production and sales strategies according to different confidence levels of the potential demands, choose the optimal channel structure and increase the gross profit gradually.

In order to solve the problems of poor denoising effect and low computational efficiency in image denoising, a double blind denoising algorithm based on Denoising Convolutional Neural Network (DnCNN) and Conditional Generative Adversarial Net (CGAN) was proposed. Firstly, the improved DnCNN model was used as the CGAN generator to capture the noise distribution of the noisy image. Secondly, the noisy image after eliminating the noise distribution and the tag were sent to the discriminator to distinguish the noise reduction image. Thirdly, the results of discrimination were used to optimize the hidden layer parameters of the whole model. Finally, a balance between the generator and the discriminator was achieved in the game, and the generator's residual capture ability was optimal. Experimental results show that on Set12 dataset, when the noise levels are 15, 25, 50 respectively:compared with the DnCNN algorithm, the proposed algorithm has the Peak Signal-to-Noise Ratio (PSNR) increased by 1.388 dB, 1.725 dB and 1.639 dB respectively based on the error evaluation index between pixel points. Compared with the existing algorithms such as Block Matching 3D (BM3D), Weighted Nuclear Norm Minimization (WNNM), DnCNN, Cascade of Shrinkage Fields (CSF) and ConSensus neural NETwork (CSNET), the proposed algorithm has the index value of Structural SIMilarity (SSIM) improved by 0.000 2 to 0.104 1 on average based on the evaluation index of structural similarity. The above experimental results verify the superiority of the proposed algorithm.

In order to solve the problem that the clustering centers cannot be determined automatically by Density Peaks Clustering (DPC) algorithm, and the clustering center points and the non-clustering center points are not obvious enough in the decision graph, Comparative density Peaks Clustering algorithm with Automatic determination of clustering center (ACPC) was designed. Firstly, the distance parameter was replaced by the distance comparison quantity, so that the potential clustering centers were more obvious in the decision graph. Then, the 2D interval estimation method was used to perform the automatic selection of clustering centers, so as to realize the automation of clustering process. Experimental results show that the ACPC algorithm has better clustering effect on four synthetic datasets; and the comparison of the Accuracy indicator on real datasets shows that on the dataset Iris, the clustering accuracy of ACPC can reach 94%, which is 27.3% higher than that of the traditional DPC algorithm, and the problem of selecting clustering centers interactively is solved by ACPC.

Concerning the phenomenon that the queues in the entrance lanes of the intersections are imbalanced or overflowed during the peak hours, an incremental split selection method based on video queue length management was proposed. Firstly, the queueing state at the end of the red time and the queueing length level at the end of the green time were judged. Then， the increment or decrement of the green time of each phase was calculated. Finally, the dynamic balance between the green time of each phase and the queue length of each entrance lane was realized with the purpose of balancing the queue lengths of the entrance lanes. The experimental results show that, the proposed optimization method can effectively balance the queue lengths of the entrance lanes, reducing the traffic delay and traffic congestion at the intersection. When the split does not match the queue length, the optimization method can quickly adjust the split to adapt to the change of the queue length.

In order to solve the problems of high economic costs, large amount of carbon emissions and insufficient attention to social benefits in the closed-loop logistics network for fresh foods, a multi-objective closed-loop logistics network model for fresh foods under uncertain conditions was established by considering the uncertainty of return quantity and aiming at the minimum economic costs, the minimum carbon emissions and the maximum social benefits. Firstly, the improved Genetic Algorithm (GA) was used to solve the model. Then, the feasibility of the model was verified by combining the operation and management data of a fresh food enterprise in Shanghai. Finally, the results of improved GA was compared to the results of Particle Swarm Optimization (PSO) algorithm to verify the effectiveness of the algorithm， and to highlight the superiority of the improved GA in solving multi-objective complex constraint problems. The example results show that the satisfaction degree of multi-objective optimization is 0.92, which is higher than that of single-objective optimization, demonstrating the effectiveness of the proposed model.

Portrait inpainting was widely used in the photo editing based on image rendering and computational photography. A lot of factors including the variety in clothing, different body types such as tall, short, fat and thin size, the high freedom degree of human body pose, bring difficulties to portrait inpainting. Therefore, an efficient portrait inpainting method based on Generating Adversarial Network (GAN) was proposed. The algorithm consists two stages. During the first stage, the image was roughly inpainted based on an encoder-decoder network, and then the body pose information in the image was estimated. During the second stage, the portrait was accurately inpainted based on the pose information and GAN. Besides, the key points of the portrait pose were connected by using portrait pose information to form the pose framework and perform the dilation operation, and the portrait pose mask was obtained. Thereby, a portrait pose loss function was constructed for network training. The experimental results show that: compared with the Contextual Attention inpainting method, the proposed method has the SSIM （Structural SIMilarity index） increased by one percentage point. The method, by adding the portrait pose information into the portrait inpainting process, effectively constrains the solution space range of portrait data in the zone to be inpainted, and strengthens the network's attention to the portrait pose information.

Concerning the high frequency logistics distribution of fresh products due to the products' perishability and vulnerability, as well as the uncertainty of demand and return, a multi-period closed-loop logistics network for fresh products with fuzzy variables was constructed to achieve the multi-decision arrangement of minimum system cost, optimal facility location and optimal delivery route. In order to solve the Fuzzy Mixed Integer Linear Programming (FMILP) model corresponding to the system, firstly, the amounts of demand and return were defined as triangular fuzzy parameters; secondly, the fuzzy constraints were transformed into crisp formula by using fuzzy chance constrained programming method; finally, the optimal solution of case was obtained by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The experimental results show that multi-period closed-loop system performs better than single-period system in the aspect of multi-decision programming, meanwhile, the confidence levels of triangular fuzzy parameters have significant influence on the optimal operation of enterprise, thus providing a reference for relevant decision makers.

Focusing on the detection problem of false data that conform to a certain pattern or rule, a false trend time series detection method based on randomness analysis was proposed. Based on the analysis of time series composition, firstly the simple forgery method and complex forgery method of false trend time series were explored, and decomposed into two parts:false trendness and false randomness. Then the false trend of time series was extracted by the approximation of base function, the false random of time series was analyzed with the randomness theory. Finally, monobit frequency and frequency within a block were adopted to test whether the false random part has randomness, which established a detection method of false time series with a certain trend feature. The simulation results show that proposed method can decompose the false time series and extract the false trend part effectively, meanwhile realize the detection of simple and complex forged data. It also supports the authenticity analysis for the numerical data obtained by means of observation or monitoring equipment, which improves the discrimination range of false data with average detection accuracy of 74.7%.

Concerning the problem of concept drift and noise in data stream, a new kind of incremental learning data stream ensemble classification algorithm was proposed. Firstly, a noise filtering mechanism was introduced to filter the noise. Then, a hypothesis testing method was introduced to detect the concept drift, and an incremental C4.5 decision tree was used as the base classifier to construct the weighted ensemble model. Finally, the incremental learning examples were realized, and the classification model was updated dynamically. The experimental results show that, the detection accuracy of the proposed ensemble classifier for concept drift reaches 95%-97%, and its noise immunity in data steam stays above 90%. The proposed algorithm has higher classification accuracy and better performance in the accuracy of detecting concept drift and noise immunity.

Aiming at the problem that strong and weak targets can not be detected at the same time in the same single frequency band, according to the differences of beamforming output DC response of every sub-covariance matrix, a target detection method based on sub-matrix beamforming output DC response weighting was proposed. Firstly, the eigen-analysis technique was used to decompose the covariance matrix of the linear array received data. Secondly, the corresponding sub-covariance matrix for every eigenvector was obtained by conjugate multiplication, the sub-matrices were beam-formed, and the difference of beamforming output DC response formed by each sub-matrix was utilized to form the weighting factor. Finally, the weighting factor was used to weight the output of each sub-matrix beamforming to obtain the final result, and the proportion of the weak target sub-matrix beamforming output in the final result was improved, and the unknown targets were effectively detected in the same single frequency band. The results of theoretical analysis, numerical simulation and measured data processing show that under the simulation conditions, compared with the conventional beamforming and conventional subspace reconstruction method, the proposed method increases the proportion of the weak target sub-matrix beamforming output in the final result from 0.09% to 45.36%, which reduces the influence of background noise and strong target on unknown target detection, reduces the difference of output DC response between targets, and improves the detection performance of unknown targets in the same single frequency band.

Aiming at the problems of effective feature extraction of speech signal and influence of noise in speaker recognition, a novel method called Mel Frequency Cepstral Coefficients based on S-transform (SMFCC) was proposed for speech feature extraction. The speech features were obtained which were based on traditional Mel Frequency Cepstral Coefficients (MFCC), employed the properties of two-dimensional Time-Frequency (TF) multiresolution in S-transform and effective denoising of two-dimensional TF matrix with Singular Value Decomposition (SVD) algorithm, and combined with other related statistic methods. Based on the TIMIT corpus, the extracted features were compared with the current features by the experiment. The Equal Error Rate (EER) and Minimum Detection Cost Function (MinDCF) of SMFCC were smaller than those of Linear Prediction Cepstral Coefficient (LPCC), MFCC, and LMFCC; especially, the EER and MinDCF08 of SMFCC were decreased by 3.6% and 17.9% respectively compared to MFCC.The experimental results show that the proposed method can eliminate the noise in the speech signal effectively and improve local speech signal feature resolution.

Concerning the low accuracy of identifying relevant Chinese events, a correlation recognition algorithm for Chinese news events based on Grey Relational Analysis (GRA) was proposed, which is a multiple factor analysis method. Firstly, three factors that affect the event correlation, including co-occurrence of triggers, shared nouns between events and the similarity of the event sentences, were proposed through analyzing the characteristics of Chinese news events. Secondly, the three factors were quantified and the influence weights of them were calculated. Finally, GRA was used to combine the three factors, and the GRA model between events was established to realize event correlation recognition. The experimental results show that the three factors for event correlation recognition are effective, and compared with the method only using one influence factor, the proposed algorithm improves the accuracy of event correlation recognition.

Aiming at the problem that traditional localization algorithm has a slow convergence speed, combining with the characteristics of perfect immunity to time in UWB (Ultra Wide-Band) communication, a novel Davidon-Fletcher-Powell (DFP) algorithm based on Armijo step size was proposed to locate the target node on TDOA (Time Difference Of Arrival) location model. Taylor series expansion algorithm was further introduced to acquire final location at the initial position, achieving the precise location of UWB communication system. The experimental results show that the proposed algorithm not only decreases the demand of localization optimization algorithm to initial position, but also improves the average localization precision 7 times than the steepest decent method with precise measure time. The proposed localization algorithm has better performance on localization accuracy and efficiency.

In order to reduce the test application time and guarantee high test data compression rate, a selection sequence of parallel folding counter was proposed. Selection test sequences were generated by recording group number and in-group number which represented folding index based on the analysis of parallel folding computing theory, so as to avoid generating useless and redundant test sequences. The experimental results on ISCAS benchmark circuits demonstrate the average test compression rate of the proposed scheme is 94.48%, and the average test application time is 15.31% of the similar scheme.

For reducing the communication overhead of traditional data aggregation method in wireless sensor networks, the authors proposed a probabilistic transmissionbased data aggregation scheme for Wireless Sensor Network (WSN). Due to limited number of nodes in the cluster and the fact that aggregation error is unavoidable, probabilistic transmission was adopted to reduce the number of innercluster transmissions and lower the communication overhead with tolerable error. Besides, Dixon criterion was adopted to eliminate the gross error in the small sample to provide high reliability of innercluster aggregation. The experimental results show that the probabilistic transmission can lower the innercluster transmissions effectively with tolerable error, the communication overhead of proposed scheme is about 27.5% that of traditional data aggregation schemes. The aggregation error of probabilistic transmission and all nodes transmission are at the same level and both are acceptable for wireless sensor networks.

To analyze and estimate the parameters of Frequency-Hopping (FH) signals effectively, an adaptive time-frequency analytical method was proposed according to the characteristics of FH signal spectrogram and Wigner-Ville Didtribution (WVD). The width of window function for spectrogram was selected based on entropy measure so as to obtain optimal spectrogram representation of FH signal. The theoretical analysis and simulation results show that the spectrum analytical method based on entropy measure could give accurate estimation of the hopping cycle of FH signal under the white Gaussian noise environment of greater than 0dB. Compared with Smoothed Pseudo WVD (SPWVD) and its adaptive method, the proposed method can effectively reduce the variance and improve the accuracy of parameter estimation at low Signal-to-Noise Ratio (SNR). While for long observation signal, it owns faster operation speed.

To suppress the cross-term and increase aggregation of time-frequency signal components, a new time-frequency analysis method for frequency-hopping signals was proposed based on adjusted window for kernel function design of Smoothed Pseudo Wigner Ville Distribution (SPWVD). According to the auto-term energy and cross-term energy ratios in the time-frequency plane, the shape of kernel function of SPWVD was adjusted through changing spread factors of window function to obtain excellent time frequency representation in fixed-kernel width. Compared with the fixed window function, using the proposed time frequency representation, time frequency parameters of frequency hopping signals can be estimated efficiently and good noise immunity can be got.

Concerning the filtering problem of trees, buildings or other ground objects in field terrain reverse engineering, the disadvantages of conventional angle-limitation algorithm were analyzed, which accumulated errors or used a single threshold and could not meet the requirement of wavy terrain. Therefore, a self-adaptive angle-limitation algorithm based on scanning line was put forward. This method worked through limiting the angle of scanning center, reference point (known ground point) and the point to be sorted, which was adaptive with the wavy terrain. Then the modified point cloud was optimized with a curve fitting method by moving window. The experimental results prove that, the proposed algorithm has a sound control of the macro-terrain, and it can filter the wavy terrain point cloud much better.

Images acquired in bad weather have poor contrasts and colors. This paper proposed a simple method to remove haze based on dark channel priority. After acquiring the transmission, getting the difference between the dark channel and dark value of nearest eight pixels, the pixel of minimal difference was redefined as new dark channel. Besides, the air light was automatically estimated from the histogram of the dark channel. At last, the clear image could be recovered based on physical model. The experimental results show that the method can sharp the edge and improve the quality of the degraded image.

To the questions of adverse effect of unusual data and difficulties in defining the number of clusters, a method of Restricted Minimum Variance Hierarchical Cluster(RMVHC) was proposed, including varying initial data linearly, inspection standard analysis and main factors analysis etc. Through clustering to the sample data of investigation, excavating and analysing various characteristics existing in customers actually, cluster course and relatively rational grouping result were directly perceived through the senses.