With the development of social networks, social recommendation algorithms are widely used. Existing recommendation algorithms often only introduce one kind of social relationship into the recommendation system, but in reality there are multiple social relationships between users. Based on the multi-subnet composite complex network model and the shared user characteristic matrix, this paper proposes a matrix decomposition recommendation algorithm based on the multi-relational social network. Through the analysis of experimental results on the Epinions data set, the accuracy evaluation indexes MAE, RMSE and NMAE increased by 34%, 27% and 7% respectively. This proves that the matrix factorization recommendation algorithm of multi-relational social networks can effectively improve the accuracy of recommendation.

Based on the natural phenomena of shepherd dogs and sheep, this paper studies the formation problem of a class of first-order multi-agent systems with cooperation competition topology. For each agent, a distributed formation control algorithm based on leader-follower method is designed to realize the formation movement of cooperative competitive interaction network. A sufficient condition is derived by using the structure balanced independent strongly connected components (SBiSCCs) to ensure that all agents move in cooperative competitive formation in a distributed manner. Finally, the theoretical results are verified by numerical simulation. The results show that the algorithm can ensure that a group of agents can achieve the desired formation and realize the cooperative competitive formation movement.

In this paper, we mainly investigated the estimation of networked control systems (NCSs) with packet losses. Firstly, we introduced the classical Kalman filter estimator and the covariance matrix. When the measurement equation is with noise, we give the optimal estimator. The optimal estimator is strictly calculated by the recursive method. Moreover, according to whether the packet loss process can be observed, we discussed the estimation problem. Finally, for the application, a simple sub-optimal approximation estimator was developed. It will be helpful to study the NCSs with packet losses in large finite horizon, and provides the research direction for further analyzing the problem of NCSs.

In this paper, we use generative adversarial network (GAN) to improve semantic segmentation of images. The model is composed of a semantic segmentation network and a discriminant network, where the segmentation network responses for generating semantic segmentation result while the discriminant network responses for detecting the difference between the generated result and the labels on the global structure level and improving the segmentation effect. In order to extract context information, we adopt the spatial pyramid pooling module in the segmentation network, which could perform pooling operation on multiple levels of sub-regions. Meanwhile, in order to solve the problem of a large number of manual annotations needed in the semantic segmentation data set, we use the discriminant network to generate pseudo labels and realize semi-supervision in the training of the segmentation network. The model has been tested using PASCAL VOC2012 dataset, and the results show that supervised and semi-supervised approaches proposed in this paper are superior to the existing methods.

This paper proposes a new laser complex chaotic system and a new point multiplication function projection synchronization method. Firstly, a new laser complex chaotic system is constructed on the basis of the 4D laser real chaotic system. Based on the conventional dynamic analysis method and MATLAB simulation software, the basic dynamic characteristics of the system such as dissipation, equilibrium point, Lyapunov exponent spectrum, phase diagram and bifurcation diagram are studied. The results show that the new chaotic system is rich in dynamics and has bow-tie chaotic and hyperchaotic attractors under certain parameters. Secondly, according to the vector dot product operation, a new point multiplication function projection synchronization method is defined. Based on the sliding mode control method, the bow-tie laser complex chaotic system can realize point multiplication function projection synchronization according to the function scaling factor. Numerical simulations verify the correctness and validity of the theoretical analysis.

Based on the 2017 Hangzhou electricity consumption data of industrial enterprises, this paper aims to establish correlation networks to filter out the central enterprises and their connections in the city. First, we exclude the seasonal effect on electricity consumption and calculate the pure correlation of these enterprises by using the conditional Pearson correlation model. Next, we adjust the correlation coefficient thresholds by making the node degree distribution approximately satisfy the power-law distributions. Then, the empirical analysis on network properties is carried out, including the heavy-head distribution, the assortative mixing, and the small-world property. Based on these properties, we propose an approach for identifying central enterprises in the electricity consumption correlation network. The study shows that the central enterprises are closely related and can promote the connection between other non-central enterprises. Decision-makers can regulate the central enterprises detected by our approach in order to affect the overall development of the city.

This paper studies the dynamic encirclement problem, in which traffic and patrol polices are reassigned with suspect escape information update. After the suspect decides the escape direction at a node, the reassignment decision is made based on the suspect escape information. We divide the edges of the network and reassign polices among vertices of the new network. We assume the polices and the suspect have the same speed. The model with different speeds can be easily developed by revising the corresponding parts. A linear integer optimization reassignment model is developed, in which the optimization model of vertex cut is used to narrow the gap of potential encirclement. Based on the assumption about the escape rule of the suspect, a numerical example is provided to show the effectiveness of the model.

The outbreak of COVID19 has turned many areas into disaster areas. In order to provide timely relief to the disaster areas, accurate supply of post-disaster emergency resources has become the primary factor to ensure the safety of the people in the disaster areas. In this paper, SEIR was used to predict the number of infected people in each disaster area at the decision-making moment, and then the weight of urgency degree and material demand in the disaster area were calculated. Based on the degree of urgency, a multi-objective optimization model of emergency resource scheduling was constructed to maximize the satisfaction of the victims, minimize the total cost and consider the fairness of distribution. A multi-objective artificial bee colony algorithm is proposed. Aiming at the disadvantages of artificial bee colony algorithm such as precocity, the dynamic parameter and Pareto solution set are used to define the new bee colony location updating formula, and the teaching optimization is used to disturb the bee colony location, so as to avoid the algorithm falling into local extremum. The simulation results show that the proposed model and algorithm can effectively solve the problem of optimal allocation of emergency resources at multiple disaster points under epidemic events, and the improved algorithm has better performance.

It is a common operation mode of shuttle-based storage and retrieval systems (SBS/RS) to combine storage and outbound tasks to form a compound operation. Optimizing a reasonable compound operation path is of great significance for improving operation efficiency and reducing operation costs. In order to improve the order picking efficiency of SBS/RSs, the optimization of the system's compound operation path is attributed to a task assignment problem, and an optimization model is established with the goal of minimizing the total time to complete a batch of tasks. An improved discrete particle swarm optimization (IDPSO) algorithm is designed. The position and velocity of particles and the equation of motion are redefined. Cycle crossover and exchange mutation are introduced into the addition of velocity to achieve fast convergence of the algorithm, while maintaining the diversity of the particle swarm through a repulsion operator, reducing the possibility of falling into a local optimum. The simulation results show that the performance of the algorithm is better than genetic algorithm, which effectively shortens the time of compound operations and improves the picking efficiency.

The vector of p. vivax malaria, Anopheles sinensis, is still prevalent in China, but the study on transmission dynamics of p. vivax malaria is still limited. We took p. vivax malaria in Anhui province as an example, and estimated the mosquito biting rate, p. vivax malaria transmission rate and their seasonality. We further analyzed the factors affecting the seasonality in the transmission rate. The SIR-SI(Susceptible Infected Recovered-Susceptible Infected) human-mosquito coupled model was adopted for the transmission dynamics of p. vivax and the mosquito biting rate and the transmission rate over time were estimated. A multiple linear regression model was established to analyze the effects of temperature and rainfall on mosquito biting rate and p. vivax malaria transmission rate. Our results showed that: The mosquito biting rate and transmission rate of p. vivax malaria have significant annual seasonality (seasonal amplitude of 42.4%); The transmission rate of p. vivax malaria was significantly affected by temperature (p=7.23e11) and rainfall (p=0.004);The number of the Infected of p. vivax malaria not only had one year cyclic pattern, but also showed multi-year cyclic pattern due to long-term change in the number of the Susceptible.

In order to improve the traffic efficiency and environmental benefits of intersections, a new Lagrange multiplier method is proposed to optimize the traffic signal timing at intersections by improving penalty parameters. The mathematical model of vehicle delay and exhaust emission is established by weight coefficient, and optimized by improved Lagrange multiplier method. The results are compared with those of two typical intelligent algorithms, and verified by VISSIM (Verkehr in stadten simulation) microscopic traffic visualization simulation software. The experimental results show that the optimized signal timing can reduce vehicle delay by 19.89% and exhaust emissions by 2.379%. It can be seen that vehicle delay at intersections is greatly optimized and exhaust emissions can be reduced at the same time.

In order to further improve the comprehensive performance of road intersections, a typical intersection of Nanjing Road and Jiangxi Road in Qingdao is selected, an optimization model with a left-turn waiting area is established. The improved fast non-dominant genetic algorithm is used to optimize both vehicle delays and motor vehicle CO emissions at intersections. A model with VISSIM software is built to verify the effectiveness of the algorithm. The results show that the improved algorithm search efficiency increased by 57.4%, after multi-objective optimization of timing, the average delay of vehicles dropped by 11.7%, CO emissions decreased by 13.5%, average queue length decreased by 11.3%, and HC and NOx emissions both decreased by 2.7 %. The algorithm effectively improves the traffic capacity and environmental benefits of the intersection.