Evaluation of Ocean Forecasting in the East China Sea

The accuracy of the initial condition of a global ocean forecasting system and its prediction skill was evaluated against in situ temperature, salinity and satellite salinity observations during the winter of 2015 and the summer of 2016 for the East China Sea. The ocean forecasting system demonstrates better skill for the Yangtze River estuary and the East China Sea during winter time than during summer time. During winter time, the root-mean-square error (RMSE) of the initial fields of the system for salinity is 1.90 psu, and the correlation is 0.56. The model has a salty bias of 0.29 psu. The salinity RMSE reduces with increasing distance from the coast. In contrast, the RMSE for temperature is 0.76°C, and the correlation is as high as 0.95. There is no bias between model temperature and observation. During summer time, the accuracy and forecast skill of the global ocean forecasting system are very poor. The RMSE for salinity is 3.14 psu, and the correlation is 0.28. The model has a salty bias of 0.95 psu. The RMSE for temperature is 7.22°C, and the model has a warm bias as high as 5.52°C

Research on Gear Tooth Longitudinal Modification of Locomotive Traction Gear with Shaft Structure

By the contact analysis of the traction gears with shafts of high-speed and heavy-load locomotive, the deformation and load distribution of the Front Guide Surface in starting operation are obtained. According the results, the ideal longitudinal modification curve is fitted out. The gear parametric models were completed, and the load distribution and the contact stress of the gears are compared before and after tooth longitudinal modification in ANSYS. It shows the modification methods and the amount of the modification are reasonable

Validation and Application of SMAP SSS Observation in Chinese Coastal Seas

Using sea surface salinity (SSS) from the Soil Moisture Active Passive (SMAP) mission from September 2015 to August 2016, the spatial distribution and seasonal variation in SSS in the Chinese coastal seas were investigated. First, in situ salinity observation over Chinese East Sea was used to validate SMAP observation. Then, the SSS signature of the Yangtze River fresh water was analyzed using SMAP data and the river discharge data. The SSS around the Yangtze River estuary in the Chinese East Sea, the Bohai Sea and the Yellow Sea is significantly lower than that of the open ocean. The SSS of Chinese coastal seas shows significant seasonal variation, and the seasonal variation in the adjacent waters of the Yangtze River estuary is the most obvious, followed by that of the Pearl River estuary. The minimum value of SSS appears in summer while maximum in winter. The root-mean-squared difference of daily SSS between SMAP observation and in situ observation is around 3 psu in both summer and winter, which is much lower than the annual range of SSS variation. The path of fresh water from SMAP and in situ observation is consistent during summer time

Effect of Ag nanopowders on microstructure, hardness and elastic modulus of Sn-Bi solders

This paper presents the microstructure, hardness and elastic modulus of Sn58Bi, Sn57Bi1Ag and Ag nanopowders reinforced Sn58Bi composite solders. Microstructural observations reveal that the Ag nanopowders reinforced Sn58Bi composite solders have smaller grains of Ag3Sn and a more uniform Ag3Sn distribution in comparison with those of Sn57Bi1Ag solder. Nanoindentation test results show that the addition of Ag nanopowders has greatly enhanced the mechanical properties of Sn58Bi solder, i.e., it exhibits 13-30% increase in hardness and 10-22% increase in modulus of the composite solder. Besides, hardness and elastic modulus of solder are dependent on the size, distribution and the quantity of the second-phase

Nanocomposites of Carbon Nanotube (CNTs)/CuO with High Sensitivity to Organic Volatiles at Room Temperature

AbstractIn order to enhance the sensitivity of carbon nanotube based chemical sensors at room temperature operation, CNTs/CuO nanocomposite was prepared under hydrothermal reaction condition. The resulted-product was characterized with TEM (transmission electron microscopy), XRD (X-ray diffraction) and so on. A chemical prototype sensor was constructed based on CNTs/CuO nanocomposite and an interdigital electrode on flexible polymer substrate. The gas-sensing behavior of the sensor to some typical organic volatiles was investigated at room temperature operation. The results indicated that the carbon nanotube was dispersed well in CuO matrix, the CuO was uniformly coated on the surface of carbon nanotube, and the tubular structure of carbon nanotube was clearly observed. From morphology of TEM images, it can also be observed that a good interfacial adhesion between CNT and CuO matrix was formed, which maybe due to the results of strong interaction between CNTs with carboxyl groups and CuO containing some hydroxy groups. The CNTs/CuO nanocomposite showed dramatically enhanced sensitivity to some typical organic volatiles. This study would provide a simple, low-cost and general approach to functionalize the carbon nanotube. It is also in favor of developing chemical sensors with high sensitivity or catalysts with high activity to organic volatiles at low temperature

OpenAUC: Towards AUC-Oriented Open-Set Recognition

Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some test samples belong to unknown classes (open-set). To fix this issue, Open-Set Recognition (OSR), whose goal is to make correct predictions on both close-set samples and open-set samples, has attracted rising attention. In this direction, the vast majority of literature focuses on the pattern of open-set samples. However, how to evaluate model performance in this challenging task is still unsolved. In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction. (2) Novelty detection AUC, which measures the ranking performance between close-set and open-set samples, ignores the close-set performance. To fix these issues, we propose a novel metric named OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise formulation that evaluates open-set performance and close-set performance in a coupling manner. Further analysis shows that OpenAUC is free from the aforementioned inconsistency properties. Finally, an end-to-end learning method is proposed to minimize the OpenAUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness