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Anomaly detection aims to capture the discriminative features with limited training samples. However, when some anomalies share common compositional patterns with the normal training data, the model likely reconstructs the anomalies well, leading to the miss detection of anomalies. To mitigate this drawback, this paper proposed a novel contrastive memory network, which used the contrast learning framework to separate the sample features based on the autoencoder, and then designed a memory network to suppress the normal features similar to anomaly. This method proposed a two-stage framework for detecting abnormal events. In the first stage, the method used contrastive learning to increase the difference between normal features and abnormal features, and gained representation to be the augment memory and suppression memory of memory network. In the second stage, the model used augment memory to record multi-time normal behavior features, and used suppression memory to constrain the expression of pseudo anomaly items in the augment memory. The AUC value reached 83. 26% on UCF Crime datasets and 87.53% on Shanghai Tech datasets, which were 1. 14% and 2.43% higher than the existing methods. The results demonstrate that this method can efficiently predict the temporal localization of anomaly events. [ABSTRACT FROM AUTHOR]

Mild cognitive decline is the early stage of Alzheimer's disease, and the feature extraction and classification of mild cognitive decline using EEG signals is an important method for diagnosing mild cognitive decline. In the automatic detection technology of mild cognitive decline based on electroencephalogram artificial intelligence, the existing research only extracted a certain feature in the electroencephalogram signal or simply concatenates multiple features, which caused these methods to fail to well consider the correlation between different features and it would cause the problem of dimensional disaster. This paper proposed a convolutional neural network based automatic detection algorithm for resting state electroencephalogram data of mild cognitive decline. By extracting the power spectrum and brain network features of the electroencephalogram, it fused the two features by matrix operation, and designed a convolutional neural network to classify the fused features. This method achieves a high accuracy rate on the data set collected by a hospital in Shanghai. In addition, by inputting different subsets of the feature set, this method found the few groups of features that contribute the most to mild cognitive decline, thereby it also had a certain interpretability. Experiments on the dataset of this paper, it p roves the advantages of the power brain network for the automatic diagnosis of mild cognitive decline. [ABSTRACT FROM AUTHOR]

This paper proposed the mathematical model with the goal of minimizing transportation costs and vehicle fixed costs which based on the urban waste classification collection and transportation problem, considering the vehicle loading capacity constraints, hard time window constraints, and the impact of loading rate on cost. Dynamic fusion of an improved ant colony algorithm that considered the time fit factor and the vehicle capacity utilization factor and chaotic electromagnetic field optimization algorithm, combined with 2-opt and two-point exchange local search method, this paper proposed a novel hybrid ant colony algorithm with improved ant colony algorithm as the external framework and chaotic electromagnetic field optimization algorithm as the internal module to solve the urban waste classification collection and transportation problem. According to the idea of complementary advantages between algorithms, using the advantages of the two algorithms to make up for the defects of a single algorithm, it successfully applied to this problem. Finally, it used the vehicle routing problem standard test set and data from Yangpu district, Shanghai as examples to test and compare, verifies the correctness of the model and the effectiveness and optimization capabilities of the algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the problems of uneven population distribution and different target scales affecting the crowd counting in public places, this paper proposed a novel crowd counting model based on image field division. Firstly, this model divided the image scene into two parts : the near and far field of vision area. For the near field of vision area, it used the YOLO based network for p edestrian d etection and added scene constraints to avoid repeated counting in the near and far field of vision. For the far field of vision area, it used the improved MobileNets to extract the population density distribution characteristics, and introduced the super-resolution reconstruction module to improve the quality of the population density map . Finally, it obtained the number of the c rowd in the whole image by calculating the sum of the two. This paper tested the proposed model on Shanghai Tech and Mall d atasets. The results show that the model has a significant improvement in accuracy and robustness and prove the model is feasible. [ABSTRACT FROM AUTHOR]

This paper developed a hybrid convolution neural network for perceptual crowd counting, which could accurately predict density maps in extremely crowded scenes . It consisted of merely two components: the front-end was a dilated convolutional neural network to extract two-dimensional features ;the back-end deployed a fractionally stride convolution to lower the loss of image information caused by down-sampling. This paper designed the model structure based on the dataset Shanghai Tech, then in an attempt to acknowledge and analyze the performance of the algorithm, and afterwards made use of the evaluation indicators of the regression problem, the average absolute error ( MAE) and the mean-square error ( MSE) as the criteria. Additionally, testing the method on Shanghai Tech ( MAE= 100. 8), UCF CC_50 ( MAE = 305. 3) and WorldExpo' 10 datasets while the experiment results reveal that the proposed model can effectively reduce MAE and MSE when compared with previous methods . [ABSTRACT FROM AUTHOR]

In view of the uncertainty of demand and return for perishable products in different periods, this paper proposed a multi-period model of closed-loop logistics network for perishable products and the corresponding mixed integer linear programming ( MILP) model, so as to achieve the decision of the minimum total system cost, the best facility location and vehicle route. In order to avoid the influence of uncertain parameters effectively, it converted some defined constraints in the model to robust corresponding expressions by using cardinality constrained robust approach. Through the case of the fruit and vegetable agricultural enterprises of Shanghai, this paper verified the validity of model by genetic algorithm. The result shows that compared with the single period system, the multi-period system has the advantages of dynamic performance and lower system cost. Meanwhile, it verifies the feasibility and effectiveness of the robust model through the analysis of the variation of uncertain budget parameters. It provides a reference for the construction of multi-period closed-loop logistics network and the reduction of system cost under uncertain environment. [ABSTRACT FROM AUTHOR]

As for ihe problem of coordinating optimization between ihe automatic guided vehicles (AGV) and ihe double-trolley quay cranes ( QG) in ihe automated container terminals ( ACT) , this paper was based on ihe actual layoul and loading and unloading technology of Shanghai Yangshan Port Fourth project. Il took ihe lolai lask completion time as objective function considering ihe condition of dual cycle. Il established and used a double-trolley quay crane and AGV model with time window constraints to calculate ihe practical example by using ihe genetic algorithm. The sensitivity analysis verifies ihe validity of ihe model and ihe algorithm, and tests ihe validity of ihe genetic algorithm parameter setting. The results show that this method can help improve ihe operating efficiency of automated container terminals, reduce ihe container al port time and improve ihe terminal competitiveness. [ABSTRACT FROM AUTHOR]