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Application of Grinding to Waste Material Processing: An Overview
Qiwu Zhang,Junya Kano,Fumio Saito 한국자원공학회 2010 한국자원공학회지 Vol.47 No.5
This paper introduces several waste material processing by means of fine grinding followed by chemical and/or physical operation or reaction: The first example consists of grinding the dust produced in a power station using a heavy oil as a fuel, following water rinsing to extract V from the electric precipitation (EP) dust. The second is to extract indium (In) from ITO scrap produced by a sand-blasting using Al2O3 powder, followed by grinding by a planetary mill in air. The third is to extract rare earths (Y, Eu, La, Ce and Tb) from fluorescent lamp wastes which contain Y, Eu, La, Ce and Tb-compounds. Leaching the fluorescent powder using 1N HCl solution at room temperature after grinding of the powder in air using a planetary ball mill was conducted. The fourth example is to dechlorinate polyvinyl-chloride (PVC) and defluorination of PVDF. The final example is to recover the energy in biomass in form of hydrogen by solid-state reaction of cellulose with hydroxide additives with aid of MC treatment.
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( Long Zhang ),( Wei Huang ),( Qiwu Wu ),( Wenjing Cao ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.10
In this paper, we investigate the differential game theoretic approach for distributed dynamic cooperative power control in cognitive radio ad hoc networks (CRANETs). First, a payoff function is defined by taking into consideration the tradeoff between the stock of accumulated power interference to the primary networks and the dynamic regulation of the transmit power of secondary users (SUs). Specifically, the payoff function not only reflects the tradeoff between the requirement for quickly finding the stable available spectrum opportunities and the need for better channel conditions, but also reveals the impact of the differentiated types of data traffic on the demand of transmission quality. Then the dynamic power control problem is modeled as a differential game model. Moreover, we convert the differential game model into a dynamic programming problem to obtain a set of optimal strategies of SUs under the condition of the grand coalition. A distributed dynamic cooperative power control algorithm is developed to dynamically adjust the transmit power of SUs under grand coalition. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithm for efficient power control in CRANETs.
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Tang Qiwu,Deng Zaofu,Tan Shengguo,Song Guo,Zhang Hai,Ge Lingrui 한국미생물·생명공학회 2022 Journal of microbiology and biotechnology Vol.32 No.9
Japanese encephalitis virus (JEV), the causative agent of Japanese encephalitis (JE), is an importantly zoonotic, vector-borne virus widely prevalent in Asia. Although JE has been well controlled in China, its prevalence remains a huge threat to the pig industry as well as human health. Herein, we report on our molecular and serological investigations of JEV among pigs from different regions in Hunan Province of China from 2019 to 2021. Collectively, 19.27% (583/3026, 95% Confidential Interval (CI) 17.86-20.68) of sampled pigs were positive for JEV IgG antibody as revealed by indirect enzyme-linked immunosorbent assay, and the seroprevalence of JEV among pigs was significantly associated with the development stage and breeding scale (p < 0.01). Meanwhile, 10.99% (42/382, 95% CI 7.86-14.13) of tissue samples of pigs with suspected clinical symptoms of JE and 23.44% (15/64, 95% CI 13.06-33.82) of mosquito batches were JEV-positive via reverse polymerase chain reaction. In addition, the complete E gene sequences of 14 JEV strains identified in this study were amplified and sequenced. Phylogenetic analysis showed that all 14 JEV strains belonged to genotype I-b and displayed a distinct genetic relationship to the present JEV vaccine strain (SA14-14-2). In conclusion, our results revealed not only the severe prevalence of JEV in Hunan Province, but also that JEV I-b might be the predominant genotype in Hunan Province, suggesting therefore that effective measures for JE control are urgently needed.
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Few-shot transfer learning with attention for intelligent fault diagnosis of bearing
Yao Hu,Qingyu Xiong,Qiwu Zhu,Zhengyi Yang,Zhiyuan Zhang,Dan Wu,Zihui Wu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
The bearing is one of the key components in modern industrial equipment. In the past few years, many studies have been carried out on bearing diagnosis through datadriven methods. However, there are two practical problems. First, under actual working conditions, the lack of fault samples is a major factor that hinders the application of these methods in industrial environments. Second, there is a lack of full utilization of a priori knowledge in the current stage of methods using relational networks for fault diagnosis. It is manifested by the incompleteness of the relational network structure. To address these problems, we present a new diagnosis method based on few-shot learning, which is suitable for the environment where the data is scarce. In this method, we train the model with the data generated by the artificial damaged bearings instead of the data from the real bearing. We experimentally validate the performance improvement of the complete relational network structure. It is able to perform the few-shot learning task better. In addition, we also reduce the global feature discrepancy by introducing an attention mechanism to improve the performance of the model. And the impact of the number of layers of the attention mechanism on the model is also discussed in detail. In this paper, our model performs better under the same experimental conditions compared with other transfer learning models.
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