Aiming at the problems of detail information loss and low segmentation accuracy in the segmentation of day and night ground-based cloud images， a segmentation network called CloudResNet-UNetwork （CloudRes-UNet） for day and night ground-based cloud images based on improved Res-UNet （Residual network-UNetwork） was proposed， in which the overall network structure of encoder-decoder was adopted. Firstly， ResNet50 was used by the encoder to extract features to enhance the feature extraction ability. Then， a Multi-Stage feature extraction （Multi-Stage） module was designed， which combined three techniques of group convolution， dilated convolution and channel shuffle to obtain high-intensity semantic information. Secondly， Efficient Channel Attention Network （ECA?Net） module was added to focus on the important information in the channel dimension， strengthen the attention to the cloud region in the ground-based cloud image， and improve the segmentation accuracy. Finally， bilinear interpolation was used by the decoder to upsample the features， which improved the clarity of the segmented image and reduced the loss of object and position information. The experimental results show that， compared with the state-of-the-art ground-based cloud image segmentation network Cloud-UNetwork （Cloud-UNet） based on deep learning， the segmentation accuracy of CloudRes-UNet on the day and night ground-based cloud image segmentation dataset is increased by 1.5 percentage points， and the Mean Intersection over Union （MIoU） is increased by 1.4 percentage points， which indicates that CloudRes-UNet obtains cloud information more accurately. It has positive significance for weather forecast， climate research， photovoltaic power generation and so on.

For the channel access and resource allocation problem in the p-persistent Mobile Ad hoc NETwork （MANET）， an adaptive channel access and resource allocation algorithm with low complexity was proposed. Firstly， considering the characteristics of MANET， the optimization problem was formulated to maximize the channel utility of each node. Secondly， the formulated problem was then transformed into a Markov decision process and the state， action， as well as the reward function were defined. Finally， the network parameters were trained based on policy gradient to optimize the competition probability， priority growth factor， and the number of communication nodes. Simulation experiment results indicate that the proposed algorithm can significantly improve the performance of p-persistent CSMA （Carrier Sense Multiple Access） protocol. Compared with the scheme with fixed competition probability and predefined p-value， the proposed algorithm can improve the channel utility by 45% and 17%， respectively. The proposed algorithm can also achieve higher channel utility compared to the scheme with fixed number of communication nodes when the total number of nodes is less than 35. Most importantly， with the increase of packet arrival rate， the proposed algorithm can fully utilize the channel resource to reduce the idle period of time slot.

To enhance the integrity and security of cloud data storage， a data storage scheme based on hybrid algorithm blockchain and a decentralized framework integrating identity authentication and privacy protection were proposed in Wireless Sensor Network （WSN）. Firstly， the collected information was transmitted to the base station by the cluster heads， and all the key parameters were recorded on the distributed blockchain and transmitted to the cloud storage by the base station. Then， in order to obtain a higher security level， the 160-bit key of Elliptic Curve Cryptography （ECC） and the 128-bit key of Advanced Encryption Standard （AES） were combined， and the key pairs were exchanged between the cloud storage layers. The proposed blockchain is based on a hybrid algorithm and combined with an identity verification scheme， which can well ensure the secure storage of cloud data， thus achieving excellent security. In addition， malicious nodes were able to be directly removed from the blockchain and also their authentication was able to be revoked through the base stations. And this operation is convenient and fast. Simulation results show that compared with schemes of decentralized Blockchain Information Management （BIM） scheme， secure localization algorithm based on trust and Decentralized Blockchain Evaluation （DBE） and Key Derivation Encryption and Data Analysis （KDE-DA） management scheme， the proposed scheme has some advantages in delay， throughput and computational overhead.

Aiming at the problem that the key entity information in the police field is difficult to recognize， a neural network model based on BERT （Bidirectional Encoder Representations from Transformers）， namely BERT-BiLSTM-Attention-CRF， was proposed to recognize and extract related named entities， in the meantime， the corresponding entity annotation specifications were designed for different cases. In the model ，the BERT pre-trained word vectors were used to replace the word vectors trained by the traditional methods such as Skip-gram and Continuous Bag of Words （CBOW）， improving the representation ability of the word vector and solving the problem of word boundary division in Chinese corpus trained by the character vectors. And the attention mechanism was used to improve the architecture of classical Named Entity Recognition （NER） model BiLSTM-CRF. BERT-BiLSTM-Attention-CRF model has an accuracy of 91% on the test set， which is 7% higher than that of CRF++ Baseline， and 4% higher than that of BiLSTM-CRF model. The F1 values of the entities are all higher than 0.87.

Tumor in brain Magnetic Resonance Imaging (MRI) images is often difficult to be segmented accurately due to noise, gray inhomogeneity, complex structrue, fuzzy and discontinuous boundaries. For the purpose of getting precise segmentation with less position bias, a new method based on Fuzzy C-Means (FCM) clustering and morphological multi-scale modification was proposed. Firstly, a control parameter was introduced to distinguish noise points, edge points and regional interior points in neighborhood, and the function relationship between pixels and the sizes of structure elements was established by combining with spatial information. Then, different pixels were modified with different-sized structure elements using morphological closing operation. Thus most local minimums caused by irregular details and noises were removed, while region contours positions corresponding to the target area were largely unchanged. Finally, FCM clustering algorithm was employed to implement segmentation on the basis of multi-scale modified image, which avoids the local optimization, misclassification and region contours position bias, while remaining accurate positioning of contour area. Compared with the standard FCM, Kernel FCM (KFCM), Genetic FCM (GFCM), Fuzzy Local Information C-Means (FLICM) and expert hand sketch, the experimental results show that the suggested method can achieve more accurate segmentation result, owing to its lower over-segmentation and under-segmentation, as well as higher similarity index compared with the standard segmentation.

In order to discover the special behaviors of Sub Complex System (SCS) under intervention, the authors proposed the concept of contrast inequality, proposed and implemented the algorithm for mining the segmentwise intervention; by imposing perturbance intervention on SCS, the authors proposed and implemented the causality discovery algorithm. The experiments on the real data show that segmentwise intervention algorithm discovers new intervention rules, and the causality discovery algorithm discovers the causality relations in the air pollution data set, and both are difficultly discovered by traditional methods.

The customer meaning for outlier explanation is rarely provided in the current studies. The outliers usually contain important information, and for many applications, the explanations are as important to the user as the outliers. A new definition of outlier customer meaning was given, and a new outlier customer meaning analysis algorithm named DSCM was put forward based on distance sum. The algorithm gave an explanation of every outlier, which improved the user’s understanding of the data. Then the algorithm was applied to quality management, and the results show that the algorithm is effective and practical, and more easy to use.

Embedded Safety-Critical Systems (ESCS) are those systems whose failure could result in loss of life, significant property damage, or damage to the environment. Because of the nature of ESCS, designing the applications for ESCS is harder than that for distributed real-time embedded systems. In this paper, a multilevel embedded safety-critical middleware called Adaptive Safety-Critical Middleware (ASCM) was described. ASCM provided related services to ease the development of embedded safety-critical applications. Multi-layer end-to-end adaptive QoS management technology was also presented to satisfy the dynamic and unpredictable mission requirements of ESCS.