Matthew Arnold: A Life <i>by Park Honan</i>s New York, McGraw Hill, 1981. 496 pp. $19.95

Spear, Jeffrey L.

doi:10.1177/004057368203900332

Cited by 7 publications

(9 citation statements)

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“…The comparative results are presented in box-and-whisker plots (see Figure 2). The legend and explanation of the box-and-whisker plots refer to the work by M. E. Spear [33]. In every box plot, a range bar represents the interquartile range of the data set, which indicates the degree of data dispersion and the algorithm robustness to some extent.…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Xin

Sheng

et al. 2019

Sensors

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Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

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Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Xin

Sheng

et al. 2019

Sensors

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show abstract

“…Figure 4 presents the comparative results in the form of box-and-whisker plots. The legend refers to the explanation by Spear [37]. In every plot, a blue range bar is drawn to represent the interquartile range of the data set.…”

Section: Resultsmentioning

confidence: 99%

Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

Xin

Zhong

et al. 2019

Sensors

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Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.

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“…Tufte suggests that “If the nature of the data suggests the shape of the graphic follow the suggestion” [ 55 ]. Therefore, in the opinion of the authors, the vertical display of box plots [ 3 ] should be favoured to the horizontal counterpart of range parts [ 7 ], and other schematic plots such as violin plots [ 4 ] should be displayed vertically.…”

Section: Discussionmentioning

confidence: 99%

“…When the goal is to evaluate many features simultaneously, four approaches are of particular interest: the Box-Whisker diagram (box plot) [ 3 ], the violin plot [ 4 ], the bean plot [ 5 ] and the ridgeline plot [ 6 ]. Since the box plot and it’s counterpart the range bar [ 7 ], together with its extension, the notched box plot [ 8 ], are nearly unable to visualize multimodality [ 3 ] they are disregarded in this work. On the other hand, the violin plot, as suggested by the name, was specifically intended to identify multimodality by exposing the waist between two modes of distribution.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the fine structure of distributions

2020

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One aim of data mining is the identification of interesting structures in data. For better analytical results, the basic properties of an empirical distribution, such as skewness and eventual clipping, i.e. hard limits in value ranges, need to be assessed. Of particular interest is the question of whether the data originate from one process or contain subsets related to different states of the data producing process. Data visualization tools should deliver a clear picture of the univariate probability density distribution (PDF) for each feature. Visualization tools for PDFs typically use kernel density estimates and include both the classical histogram, as well as the modern tools like ridgeline plots, bean plots and violin plots. If density estimation parameters remain in a default setting, conventional methods pose several problems when visualizing the PDF of uniform, multimodal, skewed distributions and distributions with clipped data, For that reason, a new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed. The MD plot does not require adjusting any parameters of density estimation, which is what may make the use of this plot compelling particularly to nonexperts. The visualization tools in question are evaluated against statistical tests with regard to typical challenges of explorative distribution analysis. The results of the evaluation are presented using bimodal Gaussian, skewed distributions and several features with already published PDFs. In an exploratory data analysis of 12 features describing quarterly financial statements, when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs. In sum, the MD plot outperforms the above mentioned methods.

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Matthew Arnold: A Life by Park Honans New York, McGraw Hill, 1981. 496 pp. $19.95

Cited by 7 publications

References 0 publications

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

Analyzing the fine structure of distributions

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