For the Flexible Job-shop Scheduling Problem (FJSP) with machine flexibility and machine tool depreciation, in order to reduce the energy consumption in the production process, a mathematical model with the minimization of weighted sum of maximum completion time and total energy consumption as the scheduling objective was established, and an Improved Genetic Algorithm (IGA) was proposed. Firstly, according to strong randomness of Genetic Algorithm (GA), the principle of balanced dispersion of orthogonal test was introduced to generate initial population, which was used to improve the search performance in global range. Secondly, in order to overcome genetic conflict after crossover operation, the coding mode of three-dimensional real numbers and the arithmetic crossover of double individuals were used for chromosome crossover, which reduced the steps of conflict detection and improved the solving speed. Finally, the dynamic step length was adopted to perform genetic mutation in mutation operation stage, which guaranteed local search ability in global range. By testing on the 8 Brandimarte examples and comparing with 3 improved heuristic algorithms in recent years, the calculation results show that the proposed algorithm is effective and feasible to solve the FJSP.

In order to solve the problem of human aggressive behavior recognition, an aggressive behavior recognition method based on human joint points was proposed. Firstly, OpenPose was used to obtain the human joint point data of a single frame image, and nearest neighbor frame feature weighting method and piecewise polynomial regression were used to realize the completion of missing values caused by body self-occlusion and environmental factors. Then, the dynamic "safe distance" threshold was defined for each human body. If the true distance between the two people was less than the threshold, the behavior feature vector was constructed, including the human barycenter displacement between frames, the angular velocity of human joint rotation and the minimum attack distance during interaction. Finally, the improved LightGBM (Light Gradient Boosting Machine) algorithm, namely w-LightGBM (weight LightGBM), was used to realize the classification and recognition of aggressive behaviors. The public dataset UT-interaction was used to verify the proposed method, and the accuracy reached 95.45%. The results show that this method can effectively identify the aggressive behaviors from various angles.

In order to support the distributed transmission of a lot of tasks on the data exchange platform for civil aviation information, it needs to establish the efficient task scheduling algorithms and models. Based on the infrastructure and needs of the platform, after analyzing the existing task scheduling models and scheduling algorithms, a new task scheduling model was proposed to fulfill the data exchange on this platform. This model mapped the point-to-multipoint data transmission network to a Steiner tree problem with delay and bandwidth constraints, and an improved Genetic Algorithm (GA) was also proposed to solve the constrained Steiner tree problem. The results of comparative experiment with the maximum bandwidth allocation algorithm prove the validity and feasibility of the proposed model.

In this paper, two discrete Harmony Search (HS) algorithms were proposed to solve lot-streaming flow shop scheduling problem with setup time under both the wait and no-wait cases. The objective was to minimize the maximum completion time or makespan. Unlike the original HS algorithm, the proposed algorithm represented a harmony as a discrete job permutation and applied inver-over and reconstruction operators to generate a new mechanism. An efficient initialization scheme based on the modified Nawaz-Enscore-Ham (NEH) heuristic was presented to construct an initial population with a certain level of quality and diversity. A simple but effective local search was added to enhance the intensification capability. The computational results and comparisons show that the proposed algorithm is effective and efficient in finding better solutions for the problem considered.

To solve the problem of technology docking negotiation of technology innovation platform, the Agent-based multi-issue negotiation algorithm and strategy were analyzed and designed with reference to the technology of IntelliSense Agent. In the practical application environment of technology docking on technology innovation platform, the data of history technology docking proposals were fully used and the technology docking benefits of both the technology trading sides was fully considered, and then the Agent-based multi-issue negotiation algorithm was designed. Based on this algorithm, the technology docking strategy and the proposed solution in technology docking negotiation were designed and proposed. Thus, the optimality of comprehensive benefits was ensured and the "win-win" benefits situation was reached by the two trading sides in the technology docking negotiation. Through practical examples of technology docking on the technology innovation platform, the applicability, rationality, feasibility and effectiveness of this negotiation algorithm and negotiation strategy in its practical application environment were exemplified.

This paper concerned the output tracking problem for a class of high-order stochastic nonlinear systems. Based on the backstepping control by adding a power integrator, an adaptive smooth state-feedback dynamic surface controller was proposed. The derivative of the designed adaption law was continuous by making use of the Sigmoid function. “Explosion of complexity” phenomenon in the adding a power integrator method design was eliminated by introducing a filter at each step of the recursive procedure and employing the dynamic surface control. The stability analysis was carried out by choosing an appropriate conol Lyapunov function. And its results show that the output can be regulated to the small neighborhood of the reference signal in probability. The results of a simulation example demonstrate the effectiveness of the proposed adaptive smooth state-feedback dynamic surface controller.

Concerning the random prediction results of the neural network model, a compact wavelet neural network was constructed. The method transferred the wavelet function into the hidden layer of the Back-Propagation (BP) network and made use of a random certain state command to obtain the definite prediction results. Compared with the wavelet neural network realized by programming and BP network, this method is suitable for mass data training and has such advantages as strong adaptability and robustness for data samples, especially has better adaptability for high frequency stochastic time series, and has characteristics of determined predicted results, powerful practicability and so on. It can obviously improve the training speed, prediction accuracy and prediction efficiency of the model. Its efficiency has been proved by the gas emission prediction experiment of wavelet packet transformation and wavelet neural network.

In order to enhance the organization and management efficiency of multi-source heterogeneous data in field data collection process for mobile Geographic Information System (GIS), based on the analysis of the limitations of the existing methods and structural features of the data, a new kind of fusion model based on EXchangeable Image File (EXIF) was proposed in which the digital image was used as carrier to fuse the information of spatial location and general attributes. In the proposed model, associated spatial information and general attributes were embedded into digital image to achieve the purpose of being highly integrated. A field data collection system was designed and developed based on the model, and applications of this system in the fields of agricultural resources management and decision analysis verify the validity and actual result of the model.

Under the cloud computing environment, it has become a significant problem to decrease the power consumption while the makespan is shortened in the process of scheduling resource. Thus, this paper made span and power consumption as the optimization objectives and established power-aware resource scheduling model, then improved the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) by adopting special initialization and the learning algorithm, to solve the problem of power-aware scheduling. Consequently, the simulation results prove that the proposed scheduling algorithm not only shortens the makespan, but also decreases the power consumption effectively.

 According to the Rate distortion optimization of EBCOT, and combined with the Maxshift method, an improved region-of-interest(ROI) method was proposed. By shifting the corresponding distortion-rate slopes instead of the corresponding ROT coefficients in Maxshift, this method avoided the coding efficiency defects caused by increased bit-plans in Maxshift. It could reconstruct the arbitrarily shaped ROI image with higher quality without receiving the entire image and improve the efficiency of coding. Besides, the new method enabled the flexible adjustment of compression quality in ROI and background. Experiments indicate its efficiency.