Advanced Materials Research Vol. 974

Abstract: This paper proposes a new marshaling method for assembling an outgoing train. In the proposed method, each set of freight cars that have the same destination make a group, and the desirable group layout constitutes the best outgoing train. The incoming freight cars are classified into several ``sub-tracks'' searching better assignment in order to reduce the transfer distance of locomotive. Classifications and marshaling plans based on the transfer distance of a locomotive are obtained autonomously by a reinforcement learning system. Then, the number of sub-tracks utilized in the classification is determined by the learning system in order to yield generalization capability.

Abstract: This paper demonstrates the achievable performance enhancement in a multi-user network using optical unique code sequences. The study is conducted in a four-user Metropolitan Area Network (MAN) with a transmission rate of 10 Gbps. This paper investigates the feasibility of implementing Differential Phase Shift Keying (DPSK) and Differential Quadrature Phase Shift Keying (DQPSK) technique to replace conventional techniques such as On-Off Keying (OOK) and Amplitude Shift Keying (ASK). The performance of the integrated formulation of optical unique code sequenceswith DPSK and DQPSKtechnique is evaluated by determining the Bit Error Rate (BER) for various configurations and transmission distances up to 100 km.

Abstract: A chance-constrained vehicle scheduling model for fresh agriculture products pickup with uncertain demands is proposed in this paper. The uncertain measure that vehicle loading will not exceed capacity constraint is presented in the model because of the uncertainty of demands. Based on uncertainty theory, when the demands are some special uncertain variables with uncertainty distribution such as linear, zigzag and normal uncertain distribution etc., the model can be transformed to a deterministic form and solved by genetic algorithm. When the demands are general uncertain variables, a hybrid genetic algorithm with uncertain simulation is presented to obtain the optimal solution. At last, to illustrate the effective of the model and algorithm, and to analyze the impact of parameters on model solution, an experiment is provided.

Abstract: The aim of this paper is to review various techniques smeared in the last decade for the elevation of low Specific Absorption Rate (SAR) of cellular phone using auxiliary antenna elements. Considering health hazards of EM radiation from mobile phone, it is discernible that a mobile phone antenna must have low SAR characteristics which can be achieved by imposing SAR reduction methods. There are some abridgments for every reduction technique which restrain the extensive use of available mobile phone considering bandwidth, efficiency, size, cost and easy implementation. Among all of SAR reduction methods metamaterial technique may be more satisfying considering all aspects. Along with, we can consider SAR affecting parameters and make optimization for better response.

Abstract: The progressive failure study of the slope is a challenging problem. There exist a lot of problems at present in this area, it’s necessary to do some summaries. This paper did some analysis and discussion from four aspects: limit equilibrium analysis of the slope progressive failure; test analysis of the slope progressive failure, numerical simulation of the slope progressive failure and limit equilibrium analysis on the basis of finite element, and provided some reference for slope progressive failure study.

Abstract: The objective of this research is to reduce the defective rate from bending defects in media disks of hard disk drives by finding an optimal machine setting in the assembly process. The Six Sigma method was applied to find out the factors which statistically affected the bending value and to obtain the optimal setting of those factors. It was found that a minimal bending value was achieved with the setting of the clamp screw torque at 3.25 in-lb, the screw bit height at 3.00 mm., and the vertical force on the disk clamp and the motor at 2.50 lbs. With this optimal setting, the process capability index C_pk increased from 0.69 to 1.39, the mean bending value decreased from 5.12% to 3.43%, and the defective rate reduced from 32,219 ppm to 39 ppm.

Abstract: Efficiency evaluation is a critical step for effective supply chain management. The evaluation system should be constructed based on realistic structure of supply chain. In practical supply chain, the members can be divided into two types, replaceable and irreplaceable. However, the current studies do not consider such situations. In order to consider such situation, a two-stage DEA model is going to be extended and proposed. Frozen seafood supply chains of Thailand are presented as an application to show an efficacy and applicability of the proposed model. As a result, eleven inefficient chains have been found by using the proposed model while only six and seven inefficient chains have been found by using traditional models. We can ague that the proposed model can identify an efficiency of the realistic supply chain more sensitively than traditional models.

Abstract: Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the technique to decompose crude oil prices. Then we use Dynamic Artificial Neural Network (DAN2) and Back Propagation (BP) Neural Network separately as the technique to predict the deposed time series, and finally integrate the predictions produced by DAN2 or BP by Adaptive Linear Neural Network (ALNN) as the final result of predictions. EEMD has been proved as a very useful method to decompose the nonlinear and non-stationary time series, and DAN2, different from traditional artificial neural networks, also has obvious advantages over traditional ones. In this paper, EEMD and DAN2 are used to predict crude oil prices at the first time。 All in all, we build four models-EEMD-DAN2-ALNN, EMD-BP-ALNN, EEMD-BP-ALNN and EMD-DAN2-ALNN to test which technique, EMD or EEMD, could do better job in decomposition of crude oil prices in this kind of hybrid model and whetherDAN2 could outshine BP when used in this hybrid model. Experimental results of four hybrid models indicate EEMD-DAN2-ALNN could gives the most precise predictions of crude oil prices, and DAN2 has a better performance than traditional neural networks-BP,when used in this hybrid model and EEMD could do a better job than EMD in decomposition of crude oil prices to yield precise predictions of crude oil prices in this model.

Abstract: As a member of the city life lines, water towers are still widely used as an outdoor device to supply water. Study on the seismic performance of the water tower is of great significance because earthquakes often happen in China. Finite element methods, such as the ANSYS, are widely used as traditional structural dynamic analysis methods, but the calculation efficiency of the ANSYS is very low. In this paper, the transfer matrix method of multibody system (MS-TMM) was introduced to analyze the seismic performance of a water tower. Based on the example as a water tower, the calculation speed of the MS-TMM is much faster than the calculation speed of the ANSYS and the responses obtained by the MS-TMM are nearly equivalent to the responses obtained by the ANSYS. Evidently, the MS-TMM can satisfy the requirements of calculation efficiency and calculation accuracy in computing the responses of water towers during earthquakes.

Abstract: Nanotechnology has taken the world of science by a storm and construction industry is no exception. The most important aspect of construction industry that can be influenced by nanotechnology is cement and concrete. Recent research on application of carbon nanotubes (CNT), both single-walled and multi-walled, shows significant increase in mechanical properties of concrete. Other properties of concrete e.g. durability, permeability, cement hydration etc. can be conveniently influenced with the help of Alkali-Silicate Reaction (ASR) studies, nanoScale Silica Fume, integration of nanoParticles in cement-synthesis and a lot other methods. And as a matter of fact, the future of cement based construction industry seems to be shaped by nanotechnology as even developing countries like Bangladesh are coming forward now-a-days to harness the potential of this rapid growing field.