Correlation between the BYK's balance index and the appearance of visually assessed achromatic automotive finishes

In the present investigation, the effectiveness of “Structure Balance Index” in correctly predicting the appearance of visually assessed achromatic automotive finishes was evaluated. For visual evaluation, the pair comparison method utilizing 16 observers assessing in a light cabinet, having a 45/0° viewing geometry, was used. The statistical results show that the observer repeatability and reproducibility were acceptable. However, evaluation of the Balance index (B) illustrates that the tolerance regions of green, yellow and red in the Balance chart of the presently prepared achromatic samples differed from the BYK's originally defined regions when compared to visual assessments. This means that this index cannot be extended to all lightness levels. Furthermore, such acceptability regions of green, yellow and red are not symmetrical enough for different lightness to promote the balance index for general use. Instead, this index can be regarded as a secondary fine-tuning corrector of Wd and LW parameters.     

An LO architecture with novel wide locking range,quadrature output RILFDs and ILROs for cognitive radio applications

In this paper, a wide locking range, quadrature output ring type injection locked frequency divider (ILFD) is presented for division ratios of 3 and 4. This ILFD proposes a novel injection scheme that shapes the injection signal to a proper form and provides a convenient situation for divider locking. Furthermore, two new wide locking range, low power consumption, injection locked ring oscillators (ILROs) are proposed for quadrature generation in local oscillator architectures. A novel cognitive radio quadrature local oscillator (LO) architecture is presented by utilizing the proposed ILFDs and ILROs to verify the effectiveness of the proposed circuits. Moreover, a new technique is implemented on the LO architecture to widen the frequency range without consuming any extra power. Because of using a single LC tank, this architecture is very compact. Also, it has the benefit of low power consumption and low output phase noise.     

Self-learning based image decomposition for blind periodic noise estimation: a dual-domain optimization approach

Periodic noise reduction is a fundamental problem in image processing, which severely affects the visual quality and subsequent application of the data. Most of the conventional approaches are only dedicated to either the frequency or spatial domain. In this research, we propose a dual-domain approach by converting the periodic noise reduction task into an image decomposition problem. We introduced a bio-inspired computational model to separate the original image from the noise pattern without having any a priori knowledge about its structure or statistics. From the filtering perspective, the proposed method filters out only a portion of the noisy frequencies. Some considerations have to be taken into account for computational resources (computing time and memory space) which permits reducing computation complexity without sacrificing the quality of the image reconstruction. In addition, the separator size provided in the decomposition algorithm does not depend on the image size. Experiments on both synthetic and non-synthetic noisy images have been carried out to validate the effectiveness and efficiency of the proposed algorithm. The simulation results demonstrate the effectiveness of the proposed method both qualitatively and quantitatively.

     

Optimizing Multiple-Pollutant Waste Load Allocation in Rivers: An Interval Parameter Game Theoretic Model

In this paper, a new methodology is developed for optimal multiple-pollutant waste load allocation (MPWLA) in rivers considering the main existing uncertainties. An interval optimization method is used to solve the MPWLA problem. Different possible scenarios for treatment of pollution loads are defined and corresponding treatment costs are taken into account in an interval parameter optimization model. A QUAL2Kw-based water quality simulation model is developed and calibrated to estimate the concentration of the water quality variables along the river. Two non-cooperative and cooperative multiple-pollutant scenario-based models are proposed for determining waste load allocation policies in rivers. Finally, a new fuzzy interval solution concept for cooperative games, namely, Fuzzy Boundary Interval Variable Least Core (FIVLC), is developed for reallocating the total fuzzy benefit obtained from discharge permit trading among waste load dischargers. The results of applying the proposed methodology to the Zarjub River in Iran illustrate its effectiveness and applicability in multiple-pollutant waste load allocation in rivers.     

Cross-linked poly(N-alkyl-4-vinylpyridinium) iodides as new eco-friendly inhibitors for corrosion study of St-37 steel in 1 M H2SO4

Iranian Polymer Journal - Cross-linked poly (N-alkyl-4-vinylpyridinium) iodides abbreviated as P4-VPMI, P4-VPOI and P4-VPDI, were synthesized from quaternization of poly(4-vinyl pyridine)...     

Pectic acid–graphene oxide nanocomposite as an adsorbent in vortex-assisted dispersive solid-phase extraction for preconcentration of copper ion followed by flame atomic absorption spectrometry

Polymer Bulletin - An efficient, green, novel and rapid vortex-assisted dispersive solid-phase extraction (VADSPE) technique was used for the preconcentration and determination of trace levels of...     

Numerical analysis of the process chain for the production of PM components with integrated information storage

The powder metallurgical production offers a number of advantages compared to other manufacturing technologies. A few examples are the high level of material utilization, the production of net-shape parts and an extensive amount of alloying options. This paper describes the production process to integrate component relevant information in the component to increase its functional range. The analysis of the production process is done by means of numerical simulation. Therefore, the process chain, consisting of die pressing and sintering, is modeled based on a finite-element-analysis.     

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

Due to deregulation of electricity industry, accurate load forecasting and predicting the future electricity demand play an important role in the regional and national power system strategy management. Electricity load forecasting is a challenging task because electric load has complex and nonlinear relationships with several factors. In this paper, two hybrid models are developed for short-term load forecasting (STLF). These models use “ant colony optimization (ACO)” and “combination of genetic algorithm (GA) and ACO (GA-ACO)” for feature selection and multi-layer perceptron (MLP) for hourly load prediction. Weather and climatic conditions, month, season, day of the week, and time of the day are considered as load-influencing factors in this study. Using load time-series of a regional power system, the performance of ACO?+?MLP and GA-ACO?+?MLP hybrid models is compared with principal component analysis (PCA)?+?MLP hybrid model and also with the case of no-feature selection (NFS) when using MLP and radial basis function (RBF) neural models. Experimental results and the performance comparison with similar recent researches in this field show that the proposed GA-ACO?+?MLP hybrid model performs better in load prediction of 24-h ahead in terms of mean absolute percentage error (MAPE).     

Estimating the Depth of Anesthesia During the Induction by a Novel Adaptive Neuro-Fuzzy Inference System: A Case Study

Neural Processing Letters - This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase....