Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about their effectiveness in conveying meaningful information. In this paper, we present a user study that evaluates the perception of uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and two-dimensional data. The techniques evaluated are traditional errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. The study uses generated data that was designed to represent the systematic and random uncertainty components. Twenty-seven users performed two types of search tasks and two types of counting tasks on 1D and 2D datasets. The search tasks involved finding data points that were least or most uncertain. The counting tasks involved counting data features or uncertainty features. A 4 times 4 full-factorial ANOVA indicated a significant interaction between the techniques used and the type of tasks assigned for both datasets indicating that differences in performance between the four techniques depended on the type of task performed. Several one-way ANOVAs were computed to explore the simple main effects. Bonferronni's correction was used to control for the family-wise error rate for alpha-inflation. Although we did not find a consistent order among the four techniques for all the tasks, there are several findings from the study that we think are useful for uncertainty visualization design. We found a significant difference in user performance between searching for locations of high and searching for locations of low uncertainty. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. The efficiency of most of these techniques were highly dependent on the tasks performed. We believe that these findings can be used in future uncertainty visualization design. In addition, the framework developed in this user study presents a structured approach to evaluate uncertainty visualization techniques, as well as provides a basis for future research in uncertainty visualization.

/pdf/a-user-study-to-compare-four-uncertainty-visualization-48aq9sxeox.pdf

A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets

Mapping and navigation for mobile robots, human-robot interaction, cognitive systems, and systems integration .

IEEE transactions on systems, man and cybernetics

The fuzzy c-means (FCM) algorithm is a widely applied clustering technique, but the implicit assumption that each attribute of the object data has equal importance affects the clustering performance. At present, attribute weighted fuzzy clustering has became a very active area of research, and numerous approaches that develop numerical weights have been combined into fuzzy clustering. In this paper, interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability. Moreover, a genetic heuristic strategy for attribute weight searching is proposed to guide the alternating optimization (AO) of WFCM, and improved attribute weights in interval-constrained ranges and reasonable data partition can be obtained simultaneously. The experimental results demonstrate that the proposed algorithm is superior in clustering performance. It reveals that the interval weighted clustering can act as an optimization operator on the basis of the traditional numerical weighted clustering, and the effects of interval weight perturbation on clustering performance can be decreased.

An interval weighed fuzzy c-means clustering by genetically guided alternating optimization

Variable-rate technology (VRT) may reduce input costs, increase crop productivity and quality, and help to protect the environment. The present study was conducted to evaluate the performance of a variable-rate fertilizer applicator for rice (Oryza sativa L.). Three replications were conducted, each of which was divided into four plots. Field performance of the system was assessed at different nitrogen levels (N1 to N4, i.e., 75, 125, 175, 225 kg ha−1), growth stages (tillering, panicle initiation, heading), and heights (40, 60, 80, 100 cm) of the sensor from the crop canopy. Fertilizer rate was at minimum 12.59 kg ha−1 at 10 rpm of drive-shaft rotational speed and at maximum 50.41 kg ha−1 at 40 rpm. The system response time was within the range of 3.53 to 4.93 s, with overall error ranging between 0.83% to 4.92%. Across different growth stages, when fertilizer rate was increased from N1 to N4, NDVI increased from 0.49 to 0.69. Hence, drive-shaft rotational speed is decreased from 25 to 7 rpm to shift the application rate from 30.83 to 9.15 kg ha−1. There was a 45% reduction in total fertilizer rate applied by the system, with respect to the recommended rate.

/pdf/performance-assessment-of-a-sensor-based-variable-rate-real-iejkquu2.pdf

Performance Assessment of a Sensor-Based Variable-Rate Real-Time Fertilizer Applicator for Rice Crop

In this paper, a fuzzy c-means clustering algorithm based on interval-valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval-valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process AHP method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient-based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human-computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well-known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c-means clustering algorithm.

A Human-Computer Cooperation Fuzzy c-Means Clustering with Interval-Valued Weights

Generally the spatial fuzzy clustering algorithm has not differentiated between the various attributes imbalance and the multi-dimensional data description complexity and that isn’t favor to the complex spatial data relation correct description. In response to this problem, this paper uses a spatially weighted fuzzy dynamic clustering algorithm and realizes the dynamic process visualization of the algorithm. The research combines the weight of AHP with spatial fuzzy clustering and then uses statistical probability F-distribution to determine the optimal threshold in order to enhance the intelligence of spatial fuzzy clustering algorithm. The use of Star coordinate can achieve the spatial fuzzy clustering algorithm visualization. Tests show that compared with based on fuzzy equivalent relation transitive closure method, the clustering is higher than the not weighted fuzzy clustering algorithm.

Research on Spatially Weighted Fuzzy Dynamic Clustering Algorithm and Spatial Data Mining Visualization

Variable-Rate Fertilization Decision-Making System Based on Visualization Toolkit and Spatial Fuzzy Clustering

Soil fertility is a comprehensive reflection of soil properties, and its variation has been a central issue in the field of soil science. Most of current research on Variation of the soil fertility are most for a single time and place, and using expertise, simple mathematical model approach that can not effectively simulate the temporal and spatial variation of soil nutrients law. Therefore, this paper presents a spatial fuzzy clustering mining algorithms based on visualization to investigate the law of spatial and temporal evolution of soil fertility mining.

/pdf/research-and-application-of-space-time-evolution-of-soil-4wlneqwpfj.pdf

Research and Application of Space-Time Evolution of Soil Fertility Data Mining Based on Visualization

This paper selected the raw data of the corn output of Dehui City, Jilin Province from 1990 to 2000, through data cleansing, data conversion and data integration technologies obtains time series data set, choosing the appropriate time series methods ARIMA(Autoregressive Integrated Moving Average) to confirm the corn output in a time series prediction model. The experimental results show that comparing the actual output and the prediction achieved by the model of corn output from 2001 to 2003, the error is very small; the relative error can be controlled within 5%. This proves that the ARIMA(2,2,1) model can fairly predict the developing trend of the corn output in this region, and the result of the prediction can provide very important theory evidence for the agricultural production management department to make decisions.

The corn output in a time series prediction model

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Research on Spatially Weighted Fuzzy Dynamic Clustering Algorithm and Spatial Data Mining Visualization

Variable-Rate Fertilization Decision-Making System Based on Visualization Toolkit and Spatial Fuzzy Clustering

Research and Application of Space-Time Evolution of Soil Fertility Data Mining Based on Visualization

The corn output in a time series prediction model