ALBERT

Description: With the rapid development of renewable energy, power supply structure is changing. However, thermal power is still dominant. With the background in low carbon economy, reasonable adjustment and optimization of the power supply structure is the trend of future development in the power industry. It is also a reliable guarantee of a fast, healthy and stable development of national economy. In this paper, the sustainable development of renewable energy sources is analyzed from the perspective of power supply. Through the research on the development of power supply structure, we find that regional power supply structure development mode conforms to dynamic characteristics and there must exist a Markov chain in the final equilibrium state. Combined with the characteristics of no aftereffect and small samples, this paper applies a Markov model to the power supply structure prediction. The optimization model is established to ensure that the model can fit the historical data as much as possible. Taking actual data of a certain area of Ningxia Province as an example, the models proposed in this paper are applied to the practice and results verify the validity and robustness of the model, which can provide decision basis for enterprise managers.

Description: A novel microfluidic flow rate detection method based on surface plasmon resonance (SPR) temperature imaging is proposed. The measurement is performed by space-resolved SPR imaging of the flow induced temperature variations. Theoretical simulations and analysis were performed to demonstrate a proof of concept using this approach. Experiments were implemented and results showed that water flow rates within a wide range of tens to hundreds of μL/min could be detected. The flow rate sensor is resistant to disturbances and can be easily integrated into microfluidic lab-on-chip systems.

Description: In the background of exhaustion of the traditional fossil energy sources, developing renewable energy has become a strategic choice for China to achieve energy sustainable utilization and energy security. The coordination between renewable energy generation and the traditional power grid is a problem that needs to be solved in the development of the power grid. The three sectors of power generation, transmission, distribution, and scheduling are considered comprehensively in this paper and an evaluation index system for the development of renewable energy and traditional power grid is designed. The traditional method of Technique for Order Preference by Similarity to Ideal Solution （TOPSIS） is improved using the idea of matter element extension, and mathematical model of comprehensive evaluation is constructed. Combined with the development index data of a regional power grid and renewable energy sources in Ningxia province, this paper applied the evaluation model to empirical research. The results show that the model meets the real situation of development of the regional power grid and renewable energy generation and has certain reference and promotion significance.

Description: To increase the responsivity is one of the vital issues for a photodetector. By employing ZnO as a representative material of ultraviolet photodetectors and Si as a representative material of visible photodetectors, an impact ionization process, in which additional carriers can be generated in an insulating layer at a relatively large electric field, has been employed to increase the responsivity of a semiconductor photodetector. It is found that the responsivity of the photodetectors can be enhanced by tens of times via this impact ionization process. The results reported in this paper provide a general route to enhance the responsivity of a photodetector, thus may represent a step towards high-performance photodetectors.

Description: In most applications of optical computed tomography (OpCT), limited-view problems are often encountered, which can be solved to a certain extent with typical OpCT reconstructive algorithms. The concept of entropy first emerged in information theory has been introduced into OpCT algorithms, such as maximum entropy (ME) algorithms and cross entropy (CE) algorithms, which have demonstrated their superiority over traditional OpCT algorithms, yet have their own limitations. A fused entropy (FE) algorithm, which follows an optimized criterion combining self-adaptively ME with CE, is proposed and investigated by comparisons with ME, CE and some traditional OpCT algorithms. Reconstructed results of several physical models show this FE algorithm has a good convergence and can achieve better precision than other algorithms, which verifies the feasibility of FE as an approach of optimizing computation, not only for OpCT, but also for other image processing applications.

Description: Farmland abandonment has important impacts on biodiversity and ecosystem recovery, as well as food security and rural sustainable development. Due to rapid urbanization and industrialization, farmland abandonment has become an increasingly important problem in many countries, particularly in China. To promote sustainable land-use management and environmental sustainability, it is important to understand the socioeconomic causes and spatial patterns of farmland abandonment. In this study, we explored the dynamic mechanisms of farmland abandonment in Jiangxi province of China using a spatially explicit economical model. The results show that the variables associated with the agricultural products yield are significantly correlated with farmland abandonment. The increasing opportunity cost of farming labor is the main factor in farmland abandonment in conjunction with a rural labor shortage due to rural-to-urban population migration and regional industrialization. Farmlands are more likely to be abandoned in areas located far from the villages and towns due to higher transportation costs. Additionally, farmers with more land but lower net income are more likely to abandon poor-quality farmland. Our results support the hypothesis that farmland abandonment takes place in locations in which the costs of cultivation are high and the potential crop yield is low. In addition, our study also demonstrates that a spatially explicit economic model is necessary to distinguish between the main driving forces of farmland abandonment. Policy implications are also provided for potential future policy decisions.

Description: The changes of spatial pattern in energy consumption have an impact on global climate change. Based on the spatial autocorrelation analysis and the auto-regression model of spatial statistics, this study has explored the spatial disparities and driving forces in energy consumption changes in China. The results show that the global spatial autocorrelation of energy consumption change in China is significant during the period 1990–2010, and the trend of spatial clustering of energy consumption change is weakened. The regions with higher energy consumption change are significantly distributed in the developed coastal areas in China, while those with lower energy consumption change are significantly distributed in the less developed western regions in China. Energy consumption change in China is mainly caused by transportation industry and non-labor intensive industry. Rapid economic development and higher industrialization rate are the main causes for faster changes in energy consumption in China. The results also indicate that spatial autoregressive model can reveal more influencing factors of energy consumption changes in China, in contrast with standard linear model. At last, this study has put forward the corresponding measures or policies for dealing with the growing trend of energy consumption in China.

Description: Entropy, Vol. 20, Pages 569: Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems Entropy doi: 10.3390/e20080569 Authors: Peng Wang Ge Li Yong Peng Rusheng Ju Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by using random finite set. Based on the proposed system model and measurement model, the key principles and formula derivation of the proposed algorithm are detailed. Then, the implementation of the algorithm is presented by using sequential Monte Carlo based Probability Hypothesis Density (PHD) filter and simulated tempering based importance sampling. Finally, the experiments of systematic errors estimation of multiple sensors are provided to prove the main advantages of the proposed algorithm. The sensitivity analysis is carried out to further study the mechanism of the algorithm. The experimental results verify the superiority of the proposed algorithm.

Description: Materials, Vol. 11, Pages 1591: Strain Rate Sensitivity of Tensile Properties in Ti-6.6Al-3.3Mo-1.8Zr-0.29Si Alloy: Experiments and Constitutive Modeling Materials doi: 10.3390/ma11091591 Authors: Jun Zhang Yang Wang Bin Zhang Hanjun Huang Junhong Chen Peng Wang The complex deformation usually involves wide strain-rate change. However, few efforts have been devoted to investigate the effect of strain rate history on the tensile behavior of α + β titanium alloy. In present paper, tensile tests of Ti-6.6Al-3.3Mo-1.8Zr-0.29Si alloy were carried out under both constant and variable strain-rate conditions within the region from 10−3~500 s−1. A single stress pulse experimental technique was utilized to conduct the recovery tests. The strain-rate history effect was examined. It is found that the flow stress is independent on the strain rate history, though the alloy exhibits obvious positive strain rate sensitivity. The Taylor-Quinney coefficient of the plastic work converted to heat is proved as 0.9 at high strain rates. The cavitation fracture mechanism is revealed by microstructural observation over the full range explored. In basis of the experimental results and other pulished literatures, empirical Khan-Huang-Liang constitutive model was suitably modified to account for the strain-rate dependent behavior. Good agreement is achieved between the modeling prediction results and experimental data.

Description: Sensors, Vol. 18, Pages 2932: A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series Sensors doi: 10.3390/s18092932 Authors: Huihui Qiao Taiyong Wang Peng Wang Shibin Qiao Lan Zhang Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment.