For multi-target route optimization with constraint conditions, the mathematical model for logistics distribution route optimization is built to accelerate response speed of logistics enterprises to customers, improve...
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For multi-target route optimization with constraint conditions, the mathematical model for logistics distribution route optimization is built to accelerate response speed of logistics enterprises to customers, improve service quality, and strengthen the satisfaction of customers, and a new algorithm with the combination of genetic and ant colony algorithms is proposed to solve the selection issues of such logistics route. Initial pheromone is formed with genetic algorithm, based on which the optimal solution is rapidly sought with ant colony algorithm, and complementary advantages are achieved between above two algorithms. Application examples and simulations are available for calculation, and the results show that such algorithm is practical and effective to optimize logistics distribution route.
Let $$G=(V, E)$$ be a graph. Denote $$d_G(u, v)$$ the distance between two vertices $$u$$ and $$v$$ in $$G$$ . An $$L(2, 1)$$ -labeling of $$G$$ is a function $$f: V \rightarrow \{0,1,\cdots \}$$ such that for any two...
Let $$G=(V, E)$$ be a graph. Denote $$d_G(u, v)$$ the distance between two vertices $$u$$ and $$v$$ in $$G$$ . An $$L(2, 1)$$ -labeling of $$G$$ is a function $$f: V \rightarrow \{0,1,\cdots \}$$ such that for any two vertices $$u$$ and $$v$$ , $$|f(u)-f(v)| \ge 2$$ if $$d_G(u, v) = 1$$ and $$|f(u)-f(v)| \ge 1$$ if $$d_G(u, v) = 2$$ . The span of $$f$$ is the difference between the largest and the smallest number in $$f(V)$$ . The $$\lambda $$ -number of $$G$$ , denoted $$\lambda (G)$$ , is the minimum span over all $$L(2,1 )$$ -labelings of $$G$$ . In this article, we confirm Conjecture 6.1 stated in X. Li et al. (J Comb Optim 25:716–736, 2013) in the case when (i) $$\ell $$ is even, or (ii) $$\ell \ge 5$$ is odd and $$0 \le r \le 8$$ .
This paper presents a new similarity measure and matching scheme for content-based image retrieval (CBIR), based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. The...
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ISBN:
(纸本)9781479900152
This paper presents a new similarity measure and matching scheme for content-based image retrieval (CBIR), based on modeling positive and negative hypotheses and testing a query image against these two hypotheses. The paper proposes to calculate first a universal image model (UIM), which is built based on a large set of images. The derived UIM is then used as a reference for the calculation of adapted models for each image class, which is done by a Bayesian adaptation of the GMM. The image class models (ICM) are therefore based on adapted versions of the background mixture components. Querying is based on the likelihood ratio between the values of these two hypotheses. A parameter adaptation technique is also introduced based on the background hypothesis. In addition, the paper discussed an acceleration technique based on ranking the closest Gaussian components of the background model and using their corresponding components in the positive classes. The experimental results show that the proposed approach improves the robust and evident performance.
The study provides a design scheme for the mountain landslide monitoring System based on wireless sensor networks. Different sensors are mainly used to collect data about the water depth in the soil and the sloping an...
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The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients wit...
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The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinicians effectively distinguish acute angina patients from those with other causes of chest pain. Key to our new approach is (1) an intelligent prospective clinical decision support framework for primary and secondary care clinicians, (2) learning from missing/impartial clinical data using Bernoulli mixture models and Expectation Maximisation (EM) techniques, (3) utilisation of state-of-the-art feature section, patternrecognition and data mining techniques for the development of intelligent risk prediction models for cardiovascular patients. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating clinical risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. A comparative analysis case study of machine learning methods was carried out for predicting RACPC clinical outcomes using real patient data acquired from Raigmore Hospital in Inverness, UK. The proposed framework was also validated using the University of Cleveland's Heart Disease dataset which contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Experiments with the Cleveland database (based on 18 clinical features of 270 patients) were concentrated on attempting to distinguish the presence of heart disease (values 1, 2, 3, 4) from absence (value 0). The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. As part of these case studies, various online RACPC risk assessment prototypes
This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of ...
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The bag of visual words model (BoW) and its variants have demonstrate their effectiveness for visual applications and have been widely used by researchers. The BoW model first extracts local features and generates the...
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Due to the semantic gap, the low-level features are not able to semantically represent images well. Besides, traditional semantic related image representation may not be able to cope with large inter class variations ...
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The dynamics of a face are often implemented as a system of connected particles with various forces acting upon them. Animation of such a system requires the approximation of velocity and position of each particle thr...
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In this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes ...
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