The influence of vehicle aerodynamic and control response characteristics on driver-vehicle performance

The effects of changes in understeer, control sensitivity, and location of the lateral aerodynamic center of pressure (c.p.) of a typical passenger car on the driver's opinion and on the performance of the driver-vehicle system were studied in a moving-base driving simulator. Twelve subjects with no prior experience on the simulator and no special driving skills performed regulation tasks in the presence of both random and step wind gusts

Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming

Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we outline a new methodology to be carried out by predicting rainfall with Genetic Programming (GP). This is the first time in the literature that GP is used within the context of rainfall derivatives. We have created a new tailored GP to this problem domain and we compare the performance of the GP and MCRP on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform MCRP, which acts as a benchmark. Results indicate that in general GP significantly outperforms MCRP, which is the dominant approach in the literature

Does firing a CEO pay off?

We examine whether involuntary CEO replacements pay off by improving firm prospects. We find CEO successors’ acquisition investments to be associated with significantly higher shareholder gains relative to their predecessors and the average CEO. This improvement in post-turnover acquisition performance appears to be a function of board independence, hedge fund ownership, and the new CEO’s relative experience. CEO successors also create sizeable shareholder value by reversing prior investments through asset disposals and discontinuing operations and by employing more efficient investment strategies. Our evidence suggests that firing a CEO pays off

Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms

Bidirectional dc/dc power converters with current limitation based on nonlinear control design

A new nonlinear controller for bidirectional dc/dc power converters that guarantees output voltage regulation with an inherent current limitation is proposed in this paper. In contrast to traditional single or cascaded PI controllers with a saturation unit that can lead to integrator windup and instability, the proposed controller is based on a rigorous nonlinear mathematical analysis and, using Lyapunov stability theory, it is proven that the current of the converter is always limited without the need of additional saturation units or limiters. The proposed concept introduces a virtual resistance at the input of the converter and a controllable voltage that can take both positive and negative values leading to bidirectional power flow capability. The dynamics of this voltage are proven to remain bounded and with a suitable choice of the voltage bound and the virtual resistance, the upper limit for the converter current is guaranteed at all times, even during transients. Simulation results for a bidirectional converter equipped with the proposed controller are presented to verify the current-limiting capability and the desired voltage regulation

Feature extraction and identification techniques for the alignment of perturbation simulations with power plant measurements

In this work, a methodology is proposed for the comparison of the measured and simulated neutron noise signals in nuclear power plants, with the simulation sets having been generated by the CORE SIM+ diffusion-based reactor noise simulator. More specifically, the method relies on the computation of the Cross-Power Spectral Density of the detector signals and the subsequent comparison with their simulated counterparts, which involves specific frequency values corresponding to the signals’ high energy content. The different simulated perturbations considered are (i) axially traveling perturbations, (ii) fuel assembly vibrations, (iii) core barrel vibrations, and finally (iv) generic “absorber of variable strength” types. The reactor core used for the current study is a German 4-loop pre-Konvoi Pressurized Water Reactor

Connecting wearable textile transmission lines: all-textile fabrication solutions and design techniques

A new method for connecting transmission lines is presented without using rigid connectors in order to implement a fully textile interconnecting system appropriate for signal transmission in wearable applications. This method is applied to textile striplines and named ‘complementary overlap’. The proposed method is examined from 1 to 6 GHz covering the frequency bands of the target applications: ISM (WLAN, Bluetooth, ZigBee etc.) and L-band (GPS)

Design, Analysis, and Measurements of an Antenna Structure for 2.4 GHz Wireless Applications

This paper reports measured results of a multielement antenna implementation, we constructed, that performs at 2.4 GHz ISM band. Particular emphasis was given to the scattering parameters and validation characterization of this antenna structure. The constructed multielement antenna that was studied in both azimuth and elevation planes consists of a number of printed dipoles with integrated baluns. Due to its multielement construction, the proposed antenna structure is suitable for applications that require multielements nature such as MIMO, channel sounder, and digital beamforming

Structure of nanoparticles embedded in micellar polycrystals

We investigate by scattering techniques the structure of water-based soft composite materials comprising a crystal made of Pluronic block-copolymer micelles arranged in a face-centered cubic lattice and a small amount (at most 2% by volume) of silica nanoparticles, of size comparable to that of the micelles. The copolymer is thermosensitive: it is hydrophilic and fully dissolved in water at low temperature (T ~ 0{\deg}C), and self-assembles into micelles at room temperature, where the block-copolymer is amphiphilic. We use contrast matching small-angle neuron scattering experiments to probe independently the structure of the nanoparticles and that of the polymer. We find that the nanoparticles do not perturb the crystalline order. In addition, a structure peak is measured for the silica nanoparticles dispersed in the polycrystalline samples. This implies that the samples are spatially heterogeneous and comprise, without macroscopic phase separation, silica-poor and silica-rich regions. We show that the nanoparticle concentration in the silica-rich regions is about tenfold the average concentration. These regions are grain boundaries between crystallites, where nanoparticles concentrate, as shown by static light scattering and by light microscopy imaging of the samples. We show that the temperature rate at which the sample is prepared strongly influence the segregation of the nanoparticles in the grain-boundaries.Comment: accepted for publication in Langmui