The existing Airport boundary intelligent video surveillance system is complicated to construct and costs a lot. This paper presents a design of economical Airport boundary intelligent video surveillance system accord...
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ISBN:
(纸本)9783037859100
The existing Airport boundary intelligent video surveillance system is complicated to construct and costs a lot. This paper presents a design of economical Airport boundary intelligent video surveillance system according to the principle of optimization system and the resources share, combined the motion detection technology of NVR with intelligence video analyze equipment. The design can greatly decrease the false alarm rate and reduced the number of intelligence video analysis equipment. Therefor, it has higher application value and practical significance.
With the extensive implementations of wireless sensor networks in many areas, it is imperative to have better management of the coverage and energy consumption of such networks. These networks consist of large number ...
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ISBN:
(纸本)9781424415014
With the extensive implementations of wireless sensor networks in many areas, it is imperative to have better management of the coverage and energy consumption of such networks. These networks consist of large number of sensor nodes and therefore a multi-agent system approach needs to be taken in order for a more accurate model. Three coordination algorithms are being put to the test in this paper: (i) fully distributed Q-learning which we refer to as independent learner (IL), (ii) Distributed Value Function (DVF) and (iii) an algorithm we developed which is a variation of the IL, Coordinated algorithm (COOrd). The results show that the IL and DVF algorithm performed for higher sensor node densities but at low sensor node densities, the three algorithms have similar performance.
In recent years, particle swarm optimization (PSO) and genetic algorithm (GA) have been applied to solve various real-world problems. However, the original PSO is based on single population whose learning patterns (in...
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ISBN:
(数字)9783319959573
ISBN:
(纸本)9783319959573;9783319959566
In recent years, particle swarm optimization (PSO) and genetic algorithm (GA) have been applied to solve various real-world problems. However, the original PSO is based on single population whose learning patterns (inertia weights, learning factors) has no potentials in evolution. All particles in the population interact and search according to a fixed pattern, which leads to the reduction of population diversity in the later iterations and premature convergence on complex and multi-modal problems. Therefore, a novel multi-population PSO with learning patterns evolved by GA is proposed to improve diversity and exploration capabilities of populations. Meanwhile, the local search of PSO particles which start in the same position also evolved by GA independently maintains exploitation ability inside each sub population. Experimental results show that the accuracy is comparable and our method improves the convergence speed.
The proceedings contain 42 papers. The topics discuss include: fuzzy-based algorithm for efficient vehicle license plate recognition in Iran’s transportation system;convergence of deep learning and edge computing usi...
ISBN:
(纸本)9798350350494
The proceedings contain 42 papers. The topics discuss include: fuzzy-based algorithm for efficient vehicle license plate recognition in Iran’s transportation system;convergence of deep learning and edge computing using model optimization;weighted features based classification of polarimetric SAR images;leveraging Swin transformer for local-to-global weakly supervised semantic segmentation;enhancing the generalization of synthetic image detection models through the exploration of features in deep detection models;NEM: nested ensemble model for scene recognition;developing a novel deep learning approach to diagnosis COVID-19 disease using lung CT-scan images;improved stereo depth estimation using smoothness and geometrical attention;and spectral-spatial anomaly detection in hyperspectral imagery based on dual-domain autoencoders.
Giving attention to the benefits of the passengers and agency, this paper adopts the true value of the coding method using the start time as the variable and uses the penalty function method to add a variety of constr...
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ISBN:
(纸本)9783037857106
Giving attention to the benefits of the passengers and agency, this paper adopts the true value of the coding method using the start time as the variable and uses the penalty function method to add a variety of constraints to the objective function when constructing the fitness function, which simplifies the calculation. Finally, the simulation results are obtained by using the improved Genetic Algorithm for solving the non- uniform grid schedule. Results show that the improved Genetic Algorithm can find the approximate best result in the huge search space of optimization, and greatly increased the computational efficiency.
The automation of the driving task will gain importance in future mobility solutions for private transport. However, the sufficient validation of automated driving functions poses enormous challenges for academia and ...
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ISBN:
(纸本)9783030395124;9783030395117
The automation of the driving task will gain importance in future mobility solutions for private transport. However, the sufficient validation of automated driving functions poses enormous challenges for academia and industry. This contribution proposes a failure behavior model for driver models for generating skid-scenarios on motorways. The model is based on results of the five-step-method provided by accident researchers. The failure behavior model is implemented using a neural network, which is trained utilizing a reinforcement learning algorithm. Hereby, the aim of the neuronal network is to maximize the vehicle's side slip angle to initiate skidding of the vehicle. Concluding, the failure behavior model is validated by reconstructing a real accident in a traffic simulation using the failure behavior model.
In order to overcome the problems of traditional English teaching aids, this paper proposes a novel design scheme of intelligent English teaching aids system based on artificial intelligence technology. Starting from ...
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Contact measurements are significant for surface metrology and can provide highly precise results. However, the point-by-point touch sampling process is less efficient, which seriously limits their applications in man...
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Any condition that poses a significant risk to one's life and affects the cardiovascular system is referred to as ''cardiovascular disease Because of the potentially lethal nature of heart conditions, scie...
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At present, most text summary methods based on deep learning are supervised methods that require large datas et. However, large-scale multi-document summary(MDS) datasets are difficult to obtain in practical applicati...
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ISBN:
(纸本)9798350310801
At present, most text summary methods based on deep learning are supervised methods that require large datas et. However, large-scale multi-document summary(MDS) datasets are difficult to obtain in practical applications. This paper proposes an unsupervised MDS extraction method based on graph structure. First, we use a pre-trained language model based on BERT for sentence ***, according to the semantic relation between sentences, the semantic relation graph of sentences is constructed. Then the centroid and maximum marginal correlation algorithm (MMR) were used to remove the redundancy of candidate summary. Finally,experiments are conducted on a Multi-news dataset to validate the effectiveness of the proposed *** experimental results show that the method proposed in this paper achieves better results in the unsupervised domain and is comparable with some supervised models. The method proposed in this paper has wide application prospects in the field of multi-document sentiment summarization.
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