Heuristic modelling of traffic accident characteristics

Due to the complex structure of observation based traffic accident data and the absence of an analytic model to define their characteristics, different models are required. Accident characteristics have been modeled for different road segments with two different methods: evolutionary data clustering method and resilient neural networks. In the first method, observation data was clustered using an evolutionary search-based clustering algorithm. The first method is based on determining whether observation based test data have the conditions of a possible death or injury accident based on the distance to the cluster centers obtained. The second one is a regression method that predicts whether an accident will cause death or injury according to observation based traffic data in test road segments by using resilient neural networks. Experiment results show that data analysis methods are very effective in determining the existence of the conditions that may cause accidents resulting in death or injury.No sponso

When Good Policies Go Bad: Controlling Risks Posed By Flawed Incentive-Based Compensation

The recent Wells Fargo scandal revealed the harm that can result from flawed incentive-based compensation arrangements. Large financial institutions have both a legal and an ethical obligation to ensure that any incentive-based compensation arrangements that are in place will not encourage risky or fraudulent employee behavior. The continued existence of inappropriate and poorly structured arrangements demonstrates that existing regulations are inadequate to ensure compliance and protect consumers. Regulations should include increased penalties and should more evenly distribute the burden of oversight and compliance between the public and private sectors. In addition to regulatory reform, the government should prosecute culpable high-level executives more aggressively. Arguably, white-collar criminals are in a position to be more effectively deterred by the threat of incarceration than other types of criminals

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others

Bezier Search Differential Evolution Algorithm for numerical function optimization A comparative study with CRMLSP, MVO, WA, SHADE and LSHADE

Differential Evolution Algorithm (DE) is a commonly used stochastic search method for solving real-valued numerical optimization problems. Unfortunately, DE's problem solving success is very sensitive to the internal parameters of the artificial numerical genetic operators (i.e., mutation and crossover operators) used. Although several mutation and crossover methods have been developed for DE, there is not still an analytical method that can be used to select the most efficient mutation and crossover method while solving a problem with DE. Therefore, selection and parameter tuning processes of artificial numerical genetic operators used by DE are based on a trial-and-error process which is time consuming. The development of modern DE versions has been focused on developing fast, structurally simple and efficient genetic operators that are not sensitive to the initial values of their internal parameters. Problem solving successes of the Universal Differential Algorithms (uDE) are not sensitive to the structure and internal parameters of the related artificial numerical genetic operators used, unlike DE. In this paper a new uDE, Bezier Search Differential Evolution Algorithm, BeSD, has been proposed. BeSD's mutation and crossover operators are structurally simple, fast, unique and produce highly efficient trial patterns. BeSD utilizes a partially elitist unique mutation operator and a unique crossover operator. In this paper, the experiments were performed by using the 30 benchmark problems of CEC2014 with Dim=30, and one 3D viewshed problem as a real world application. The problem solving success of BeSD was statistically compared with five top-methods of CEC2014, i.e., CRMLSP, MVO, WA, SHADE and LSHADE by using Wilcoxon Signed Rank test. Statistical results exposed that BeSD's problem solving success is better than those of the comparison methods in general

Contrast stretching based pansharpening by using weighted differential evolution algorithm

Pansharpening techniques were developed to generate a super-resolution multispectral pansharpened image, PI, with the combination of a multispectral image carrying high-resolution spectral information with a panchromatic image carrying high resolution spatial information. For using energy resources and communication bandwidth efficiently, Earth Observation Satellites acquire multispectral images with lower spatial resolution when compared to panchromatic images. Contrast Stretching alters the range and statistical distribution of pixel values of an image to facilitate perception of image features and can be used to match histograms of distinct images. The Contrast Stretching Based Pansharpening method, CSP, has been introduced in this paper. CSP considers the pansharpening as a resealing-based pixel-level image fusion problem in spatial domain. CSP uses the contrast stretching to generate two modified-multispectral images and one modified-panchromatic image, which are used to compute pansharpened image. The Weighted Differential Evolution Algorithm has been used to optimize the numerical values of internal parameters of CSP. The successes of CSP and 17 different pansharpening methods have been statistically compared by using three Test Image sets with different characteristics. Ten different image quality measures have been used in accuracy assessment analysis of the PIs generated by the related methods used in the Experiments. Statistical analysis exposed that CSP generates more pleasing PIs both quantitatively and qualitatively compared to the comparison methods employed in this paper