Effect Equilibrium Approach in Calculating the Economic Range of a Freeway Industrial Zone

This research aims to develop a valid method to examine the relationship between transportation infrastructure and economic growth through the measurement of the economic boundary of a freeway industrial zone in developing countries. By comparing the similarities of a freeway industrial zone with an electromagnetic field, the Boit-Schwander law in electromagnetism is applied to create an electromagnetic model, which can calculate the attractive effect caused by a freeway on its influential area. When the attractive effect is equal to the traffic impedance, the economic range of the industrial zone can be determined by the effective equilibrium approach. An empirical analysis of the Ha-Shuang freeway demonstrates this approach is valid and practical

Parallel 2-Opt Local Search on GPU

To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities

A spatial filter and two linear PZT arrays based composite structure imaging method

Aerospace structures make increasing use of composite materials which can generate inner damage easily by outer impact. Thus, the damage and impact monitoring of composite structures is an important research topic of structural health monitoring (SHM) technology. Among existing SHM methods, piezoelectric transducer (PZT) array and Lamb wave based structural imaging method has become an effective approach to monitor the damage and impact. However, the anisotropic feature of the composite structures makes it difficult to achieve accurate damage and impact localization which are dependent on Lamb wave group velocity. In recent years, a linear PZT array and spatial filter based damage imaging method has been developed. But this method is only applied to damage monitoring at the current stage and it also needs the Lamb wave group velocity to fulfill the damage localization. In this paper, a spatial filter and two linear PZT arrays based structural imaging method for composite structures is proposed. With this method, an acoustic source angle-time image for each linear PZT array can be obtained by using the spatial filter technique. Then, it is transformed to an acoustic source probability-angle image of the linear PZT array. Based on the probability-angle image, the angle of the acoustic source relative to the linear PZT array can be estimated accurately. By fusing the two probability-angle images of the two linear PZT arrays, the acoustic source can be localized accurately without using the Lamb wave group velocity. Damage and impact can be both considered to be acoustic source on composite structure. Thus, they can be localized easily and accurately by using the proposed structural imaging method. This method is validated on a carbon fiber composite laminate plate, including damage imaging and impact imaging. The imaging and localization results are in good agreement with the actual damage and impact positions, and the maximum localization error is no more than 1 cm

A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network

In this paper, we employ Probabilistic Neural Network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater than 90%. Compared with other approaches, our algorithm is an accurate artificial intelligence approach which is fast in execution and easy in implementation.Comment: 6 pages, 3 figures, 2 table

Impact of Sucrose Addition on the Physiochemical Properties and Volatile Compounds of “Shuangyou” Red Wines

“Shuangyou,” a Vitis amurensis Rupr. variety, is widely cultivated in northeastern and western China. Its berries have high acidity and low sugar content. In this study, different proportions of sucrose were added to the must samples during fermentation to investigate the effect of sugar on the physicochemical properties and volatile compounds of “Shuangyou” wines. The addition of sucrose significantly improved yeast growth and alcohol production, altered the color qualities, and slightly decreased titratable acidity during fermentation. The highest tested proportion of added sucrose resulted in the highest maximum yeast counts and final ethanol concentrations. Moreover, 37 volatile compounds (esters, alcohols, fatty acids, ketones, and aldehydes) were identified and quantified by solid-phase microextraction with gas chromatography-mass spectrometry. The concentrations of these compounds were correlated with the addition of sucrose. Furthermore, the addition of 100 g/L sucrose was sufficient for improving the concentrations of the aromatic compounds. The increase in ester, alcohol, and fatty acid concentration led to a positive OAVs impact (odor activity value > 1) at the end of fermentation