The probability hypothesis density filter has attracted increasing interest since Mahler first introduced it in *** paper proposes an improved merging algorithm for the Gaussian mixture probability hypothesis density ...
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ISBN:
(纸本)9781509046584
The probability hypothesis density filter has attracted increasing interest since Mahler first introduced it in *** paper proposes an improved merging algorithm for the Gaussian mixture probability hypothesis density filter,which can track closely proximity *** proposed algorithm utilizes not only the Gaussian components' means and covariance,but their weights as a new criterion to improve the conventional pruning algorithm's estimate *** results demonstrate that this improved algorithm is more robust and easier to implement than the formal one.
Image Segmentation plays an important role in image processing as it is at the foundation of many high-level computer vision tasks, such as scene understanding and object recognition. In this paper, an adaptive growin...
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ISBN:
(纸本)9781509024018
Image Segmentation plays an important role in image processing as it is at the foundation of many high-level computer vision tasks, such as scene understanding and object recognition. In this paper, an adaptive growing and merging algorithm is proposed to segment an image accurately. First, mean shift is applied to produce superpixels, and then superpixels grow according to their lab histograms and textures under the constraint of the edge's intensity. In adaptive region merging, we use the proposed dissimilarity measures, which are based on colors, textures, region sizes and multi-scale contour maps with non-constant weights that are adaptive to the region features. Furthermore, we take account of the contact rate of two adjacent regions to avoid over-merging. We also exploit the saliency map to maintain the main objects when the number of regions is small. The simulations on Berkeley segmentation database show that our proposed method outperforms state-of-the-art methods.
A noncrossing forest is a forest drawn on n points numbered in counterclockwise order on a circle in such a way that its edges are rectilinear and do not cross. Utilizing analytic combinatorics, Flajolet and Noy obtai...
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A noncrossing forest is a forest drawn on n points numbered in counterclockwise order on a circle in such a way that its edges are rectilinear and do not cross. Utilizing analytic combinatorics, Flajolet and Noy obtained the number of noncrossing forests with n vertices and k components. In this paper, we will give a new representation for noncrossing forests. Based on such respresentation, we establish a decomposition algorithm and a merging algorithm, which leads to a bijection between labeled noncrossing forests and sets of rooted matches. As an application, we derive a new formula, which is a refinement of the formula given by Flajolet and Noy.
Structure learning aims to uncover the underlying dependencies or relationships between variables in a network. Traditional methods rely on statistical inference to reveal causal relationships or conditional dependenc...
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Structure learning aims to uncover the underlying dependencies or relationships between variables in a network. Traditional methods rely on statistical inference to reveal causal relationships or conditional dependencies. However, there is a growing need for innovative approaches that enhance network merging and improve both the accuracy and scalability of structure learning. In this study, we propose a novel method that improves network merging by combining multiple network structures based on their conditional dependencies, while leveraging domain knowledge to eliminate unrealistic dependencies, something that traditional methods often overlook. This innovative approach enhances both the accuracy and scalability of structure learning by prioritizing the most relevant dependencies and ensuring the structural integrity of the network. We evaluate this approach using environmental quality data collected from a commercial building in Winnipeg, Canada, to predict the likelihood of abnormal conditions. The results show our novel method outperforms the classical method by an average of 4.4% in accuracy. Furthermore, we improved the proposed method by integrating environmental data from neighboring locations, which enhanced the accuracy of anomaly detection by an average of 5.4%. In conclusion, the comparative analysis demonstrates that the Bayesian network model, incorporating our proposed structure learning method, performs better in anomaly detection compared to classical structure learning approaches.
The capabilities of automated vehicles have increased over the last years, and different driving strategies have shown promising results on a variety of scenarios. However, there are still many challenges to be solved...
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The capabilities of automated vehicles have increased over the last years, and different driving strategies have shown promising results on a variety of scenarios. However, there are still many challenges to be solved, and handling crowded roundabouts is one of them. This kind of scenario requires both safe and efficient maneuvers from the autonomous driving systems in order to maintain a proper traffic flow. This work presents a strategy to generate different speed profiles for a set of path candidates in order to obtain a merging maneuver according to current traffic scene. The proposed mechanism relies on the use of fictitious accelerations generated by leader and lag vehicles, incorporating by design comfort and safety bounds. The autonomous driving system proposed in this work was tested on realistic driving scenarios collected from public datasets and its performance was compared to human drivers on the same scenarios. The results showed in a variety of situations that the automated vehicle was capable of merging into roundabouts with tight merging gaps while maintaining both comfort and safety constraints.
We present an adaptive upstream (US) bandwidth allocation and merging algorithm for multi-tenant passive optical networks (MT-PONs), addressing the bandwidth starvation, access denial and huge delay issues in US traff...
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ISBN:
(纸本)9798350371635;9798350371628
We present an adaptive upstream (US) bandwidth allocation and merging algorithm for multi-tenant passive optical networks (MT-PONs), addressing the bandwidth starvation, access denial and huge delay issues in US traffic. This method consolidates the virtual dynamic bandwidth assignments (vDBAs) associated with each ONU and allocates the residual US bandwidth based on the shares of the tenants. The proposed approach significantly enhances bandwidth efficiency and reduces US delay in MT-PONs compared to the benchmarks by coordinating multiple vDBAs and managing adaptive traffic demand efficiently, contributing substantially to the advancement of MT-PONs beyond 5G.
Purpose–This study aims to evaluate the influence of connected and autonomous vehicle(CAV)merging algorithms on the driver behavior of human-driven vehicles on the ***/methodology/approach–Previous studies designed ...
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Purpose–This study aims to evaluate the influence of connected and autonomous vehicle(CAV)merging algorithms on the driver behavior of human-driven vehicles on the ***/methodology/approach–Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing,which lacks consideration of realistic driving behaviors in the merging *** study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging ***–Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle *** results revealed significant variation of the algorithm ***,the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp ***/value–To the best of the authors’knowledge,this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the *** achieve the research purpose,a novel multi-driver driving simulator was developed,which enables multi-drivers to simultaneously interact with each other during a virtual driving *** results are expected to have practical implications for further improvement of the CAV merging algorithm.
In this paper, a method used to generate continuous and complete radar simulation data was described. This new method based on previous radar simulation data, which extracted from different scales S-57 standard electr...
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ISBN:
(纸本)9783319111940;9783319111933
In this paper, a method used to generate continuous and complete radar simulation data was described. This new method based on previous radar simulation data, which extracted from different scales S-57 standard electronic navigation charts in navigational simulator. In this work, the clipping buffer in per radar simulation data was set and the boundary line was cut off by forward angle firstly. Then radar simulation data were sort according to scale and area range after deducting clipping buffer from the original chart. Subsequently the redundant data were deleted based on the original scale. Finally, the direct connection was proposed to wave the radar simulation data. Taken charts about waters around Dalian Port as an example, the radar simulation data generated meet the need in navigational simulator.
Recently, actor-critic structure based neural networks are widely used in many reinforcement learning tasks. It consists of two main parts: (i) an actor module which outputs the probability distribution of action, and...
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Recently, actor-critic structure based neural networks are widely used in many reinforcement learning tasks. It consists of two main parts: (i) an actor module which outputs the probability distribution of action, and (ii) a critic module which outputs the predicted value based on the current environment. Actor-critic structure based networks usually need expert demonstration to provide an appropriate pre-training for the actor module, but the demonstration data is often hard or even impossible to obtain. And most of them, such as those used in the maze and robot control tasks, suffer from a lack of proper pre-training and unstable error propagation from the critic module to the actor module, which would result in poor and unstable performance. Therefore, a specially designed module which is called relatively optimal historical information learning (ROHI) is proposed. The proposed ROHI module can record the historical explored information and obtain the relatively optimal information through a customized merging algorithm. Then, the relatively optimal historical information is used to assist in training the actor module during the main learning process. We introduce two complex experimental environments, including the complex maze problem and flipping game, to evaluate the effectiveness of the proposed module. The experimental results demonstrate that the extended models with ROHI can significantly improve the success rate of the original actor-critic structure based models and slightly decrease the number of iteration required to reach the stable phase of value-based networks.
A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow a...
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A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors' previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.
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