Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

Innovative Landslide Susceptibility Mapping Portrayed by CA-AQD and K-Means Clustering Algorithms

This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

Modeling the Adhesion Bonding Strength in Injection Overmolding of Polypropylene Parts

In this work, the bonding strength of overmolded polypropylene is investigated and modeled. A T-joint specimen was designed to replicate the bonding between a base and an overmolded stem made of the same polymer: a previously molded plaque was used for the base, and the stem was directly overmolded. The effect of melt temperature, holding pressure, and localized heating was investigated following the design of experiments approach. Both the melt and base temperature positively affect the welding strength. On the contrary, the holding pressure negatively contributed, as the crystallization temperature significantly increases with pressure. Then, the bonding strength of the specimens was predicted using a non-isothermal healing model. Moreover, the quadratic distance of diffusion (based on the self-diffusion model) was calculated and correlated with the bonding strength prediction. The non-isothermal healing model well predicts the bonding strength when the reptation time is calculated within the first 0.09 s of the interface temperature evolution. The prediction error ranges from 1% to 35% for the specimens overmolded at high and low melt and base temperatures, respectively.

Verification of post-processed seasonal predictions

<p>Traditionally, verification of (ensemble) model predictions is done by comparing them to deterministic observations, e.g. with scores like the Continuous Ranked Probability Score (CRPS). While these approaches allow uncertain predictions basing on ensemble forecasts, it is open how to verify them against observations with non-parametric uncertainties.</p><p>This contribution focuses on statistically post-processed seasonal predictions of the Winter North Atlantic Oscillation (WNAO). The post-processing procedure creates in a first step for a dynamical ensemble prediction and for a statistical prediction basing on predictors two separate probability density functions (pdf). Afterwards these two distributions are combined to create a new statistical-dynamical prediction, which has been proven to be advantageous compared to the purely dynamical prediction. It will be demonstrated how this combination and with it the improvement of the prediction can be achieved before the focus will be set on the evaluation of those predictions at the hand of uncertain observations. Two new scores basing on the Earth Mover's Distance (EMD) and the Integrated Quadratic Distance (IQD) will be introduced and compared before it is shown how they can be used to effectively evaluate probabilistic predictions with uncertain observations. </p><p>Furthermore, a common approach (e.g. for correlation measures) is to compare predictions with observations over a longer time period. In this contribution a paradigm shift away from this approach towards comparing predictions for each single time step (like years) will be presented. This view give new insights into the performance of the predictions and allows to come to new understandings of the reasons for advantages or disadvantages of specific predictions. </p>

Some Notes on Maximum Entropy Utility

The maximum entropy principle is effective in solving decision problems, especially when it is not possible to obtain sufficient information to induce a decision. Among others, the concept of maximum entropy is successfully used to obtain the maximum entropy utility which assigns cardinal utilities to ordered prospects (consequences). In some cases, however, the maximum entropy principle fails to produce a satisfactory result representing a set of partial preferences properly. Such a case occurs when incorporating ordered utility increments or uncertain probability to the well-known maximum entropy formulation. To overcome such a shortcoming, we propose a distance-based solution, so-called the centralized utility increments which are obtained by minimizing the expected quadratic distance to the set of vertices that varies upon partial preferences. Therefore, the proposed method seeks to determine utility increments that are adjusted to the center of the vertices. Other partial preferences about the prospects and their corresponding centralized utility increments are derived and compared to the maximum entropy utility.

Automated Real-Time Vision Quality Inspection Monitoring System

The requirement of product quality inspection in industries for product standardized leads to a development of the quality inspection system. The problem is related to a manual inspection that is done by a human as an inspector. This paper presents an automated real-time vision quality inspection monitoring system as a problem solver to a manual inspection that is tedious and time-consuming task as well as reducing cost especially in small and medium enterprise industries (SME). For the proposed system, soft drink is used as the test product for quality inspection. The system uses computer-network to inspect two quality inspections which are color concentration and water level. The analysis includes pre-processing, color concentration using the histogram and quadratic distance and level inspection using coordinate vertical and horizontal reference levels. The similarities of both experimental and simulation results are obtained for both parameters which are 100% accuracy using 205 samples.