In the Unmanned Aerial Vehicle （UAV）-assisted and Non-Orthogonal Multiple Access （NOMA）-enabled data collection system， the total energy efficiency of all sensors is maximized by jointly optimizing the three-dimensional placement design of the UAVs and the power allocation of sensors under the ground-air probabilistic channel model and the quality-of-service requirements. To solve the original mixed-integer non-convex programming problem， an energy efficiency optimization mechanism was proposed based on convex optimization theory， deep learning theory and Harris Hawk Optimization （HHO） algorithm. Under any given three-dimensional placement of the UAVs， first， the power allocation sub-problem was equivalently transformed into a convex optimization problem. Then， based on the optimal power allocation strategy， the Deep Neural Network （DNN） was applied to construct the mapping from the positions of the sensors to the three-dimensional placement of the UAVs， and the HHO algorithm was further utilized to train the model parameters corresponding to the optimal mapping offline. The trained mechanism only involved several algebraic operations and needed to solve a single convex optimization problem. Simulation experimental results show that compared with the travesal search mechanism based on particle swarm optimization algorithm， the proposed mechanism reduces the average operation time by 5 orders of magnitude while sacrificing only about 4.73% total energy efficiency in the case of 12 sensors.

The ultra-lightweight block cipher PFP based on Feistel structure is suitable for extremely resource-constrained environments such as internet of things terminal devices. Up to now， the best impossible differential cryptanalysis of PFP is to use 7-round impossible differential distinguishers to attack the 9-round PFP， which can recover 36-bit master key. The structure of PFP was studied in order to evaluate the ability for resisting impossible differential cryptanalysis more accurately. Firstly， by analyzing the differential distribution characteristics of S-box in the round function， two groups of differences with probability 1 were found. Secondly， combined with the characteristics of the permutation layer， a set of 7-round impossible differential distinguishers containing 16 impossible differences was constructed. Finally， based on the constructed 7-round impossible differential distinguishers， 40-bit master key was recovered by performing impossible differential cryptanalysis on the 9-round PFP， and an impossible differential cryptanalysis method for 10-round PFP was proposed to recover 52-bit master key. The results show that the proposed method has great improvement in terms of the number of distinguishers， the number of cryptanalysis rounds， and the number of bits of the recovered key.

To address the problems that many existing studies ignore the correlation between interlocutors’ emotions and sentiments， a sentiment boosting model for emotion recognition in conversation text was proposed， namely Sentiment Boosting Graph Neural network （SBGN）. Firstly， themes and dialogue intent were integrated into the text， and the reconstructed text features were extracted by fine-tuning the pre-trained language model. Secondly， a symmetric learning structure for emotion analysis was given， with the reconstructed features fed into a Graph Neural Network （GNN） emotion analysis model and a Bi-directional Long Short-Term Memory （Bi-LSTM） sentiment classification model. Finally， by fusing emotion analysis and sentiment classification models， a new loss function was constructed with sentiment classification loss function as a penalty， and the optimal penalty factor was adjusted and obtained by learning. Experimental results on public dataset DailyDialog show that SBGN model improves 16.62 percentage points compared with Dialogue Graph Convolutional Network （DialogueGCN） model， and improves 14.81 percentage points compared with the state-of-art model Directed Acyclic Graph-Emotion Recognition from Conversation （DAG-ERC） in micro-average F1. It can be seen that SBGN model can effectively improve the performance of emotion analysis in dialogue system.

Most of the existing drowsiness recognition algorithms are based on machine learning or deep learning， without considering the relationship between the sequence of human eye closed state and drowsiness. In order to solve the problem， a drowsiness recognition algorithm based on human eye state was proposed. Firstly， a human eye segmentation and area calculation model was proposed. Based on 68 feature points of the face， the eye area was segmented according to the extremely large polygon formed by the feature points of human eye， and the total number of eye pixels was used to represent the size of the eye area. Secondly， the area of the human eye in the maximum state was calculated， and the key frame selection algorithm was used to select 4 frames representing the eye opening state the most， and the eye opening threshold was calculated based on the areas of human eye in these 4 frames and in the maximum state. Therefore， the eye closure degree score model was constructed to determine the closed state of the human eye. Finally， according the eye closure degree score sequence of the input video， a drowsiness recognition model was constructed based on continuous multi-frame sequence analysis. The drowsiness state recognition was conducted on the two commonly used international datasets such as Yawning Detection Dataset （YawDD） and NTHU-DDD dataset.Experimental results show that， the recognition accuracy of the proposed algorithm is more than 80% on the two datasets， especially on the YawDD， the proposed algorithm has the recognition accuracy above 94%. The proposed algorithm can be applied to driver status detection during driving， learner status analysis in class and so on.

The Iterative Closest Point (ICP) algorithm requires two point clouds to have a good initialization to start, otherwise the algorithm may easily get trapped into local optimum. In order to solve the problem, a novel translation domain estimating based global point cloud registration algorithm was proposed. The translation domain was estimated according to axis-aligned bounding box of calculating the defuzzification principal point clouds of data and model point clouds. With the estimated translation domain and [-π, π] ³ rotation domain, an improved globally optimal ICP was used to register for global searching. The proposed algorithm could estimate translation domain adaptively and register globally according to the point clouds for registration. The process of registration did not need to calculate the feature information of point clouds and was efficient for any initialization with less setting parameters. The experimental results show that the proposed algorithm can get accurate registration results of global optimization automatically, and also improve the efficiency of global registration.

A new image retrieval method based on enhanced micro-structure and context-sensitive similarity was proposed to overcome the shortcoming of high dimension of combined image feature and intangible combined weights. A new local pattern map was firstly used to create filter map, and then enhanced micro-structure descriptor was extracted based on color co-occurrence relationship. The descriptor combined several features with the same dimension as single color feature. Based on the extracted descriptor, normal distance between image pairs was calculated and sorted. Combined with the iterative context-sensitive similarity, the initial sorted image series were re-ranked. With setting the value of iteration times as 50 and considering the top 24 images in the retrieved image set, the comparative experiments with Multi-Texton Histogram (MTH) and Micro-Structure Descriptor (MSD) show that the retrieval precisions of the proposed algorithm respectively are increased by 13.14% and 7.09% on Corel-5000 image set and increased by 11.03% and 6.8% on Corel-10000 image set. By combining several features and using context information while keeping dimension unchanged, the new method can enhance the precision effectively.

Particle Filter based on Particle Swarm Optimization (PSO-PF) algorithm is not precise and easily trapped in local optimum, which can hardly satisfy the requirement of fault diagnosis of temperature control system in power plant. To solve these problems, a new particle swarm optimization particle filter named NPSO-PF suitable for fault diagnosis was proposed. This algorithm introduced the cognition rule of individuals to groups to optimize the method for updating particles and improved the speed update strategy. As a result, the superior particle velocity can mutate with a small probability and improve the search ability. Meanwhile, due to the random initialization of on inferior particle, the diversity of samples is ensured. The simulation results show that NPSO-PF improves the precision and robustness compared with PSO-PF, and it is suitable for fault diagnosis of temperature control system.

An improved coding particle swarm optimization algorithm for the job shop scheduling problem was presented, and an effective modeling based on Petri nets was put forward. Analysis and comparison were done on the existing job shop scheduling problem algorithm based on the artificial intelligence algorithm. And the simulation study results show the algorithm is feasible and effective.

Traditional gray projection image stabilizing algorithm just works under horizontal and vertical movement; but it is unable to deal with either scaling or rotation of the matched images. Due to the limitation, a gray projection image stabilizing algorithm based on log-polar image transform was introduced. When log-polar image transform was used in scaling or rotation image, the scaling or rotation movement in Descartes reference frame was represented by horizontal and vertical movement in log-polar reference frame. Accordingly, gray projection algorithm could be used in scaling or rotation image.