The sensitivity of complex integrated circuits to single-event effects is investigated. Sensitivity depends not only on the cross section of physical modules but also on the behavior of data patterns running on the sy...The sensitivity of complex integrated circuits to single-event effects is investigated. Sensitivity depends not only on the cross section of physical modules but also on the behavior of data patterns running on the system.A method dividing the main functional modules is proposed. The intrinsic cross section and the duty cycles of different sensitive modules are obtained during the execution of data patterns. A method for extracting the duty cycle is presented and a set of test patterns with different duty cycles are implemented experimentally. By combining the intrinsic cross section and the duty cycle of different sensitive modules, a universal method to predict SEE sensitivities of different test patterns is proposed, which is verified by experiments based on the target circuit of a microprocessor. Experimental results show that the deviation between prediction and experiment is less than 20%.展开更多
Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve...Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.展开更多
Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of informatio...Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that help</span><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span><span style="font-family:Verdana;"> to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services </span><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span><span style="font-family:Verdana;"> depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Na<span style="white-space:nowrap;">ï</span>ve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree al展开更多
文摘The sensitivity of complex integrated circuits to single-event effects is investigated. Sensitivity depends not only on the cross section of physical modules but also on the behavior of data patterns running on the system.A method dividing the main functional modules is proposed. The intrinsic cross section and the duty cycles of different sensitive modules are obtained during the execution of data patterns. A method for extracting the duty cycle is presented and a set of test patterns with different duty cycles are implemented experimentally. By combining the intrinsic cross section and the duty cycle of different sensitive modules, a universal method to predict SEE sensitivities of different test patterns is proposed, which is verified by experiments based on the target circuit of a microprocessor. Experimental results show that the deviation between prediction and experiment is less than 20%.
基金funded by the National Basic Research Program of China (No. 2015CB251201)the NSFC-Shandong Joint Fund for Marine Science Research Centers (No. U1606401)+3 种基金the Scientific and Technological Innovation Project financially supported by Qingdao National Laboratory for Marine Science and Technology (No. 2016ASKJ13)the Major National Science and Technology Programs (No. 016ZX05024-001-002)the Natural Science Foundation of Hainan (No. ZDYF2016215)Key Science and Technology Foundation of Sanya (Nos. 2017PT13, 2017PT2014)
文摘Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.
文摘Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that help</span><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span><span style="font-family:Verdana;"> to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services </span><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span><span style="font-family:Verdana;"> depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Na<span style="white-space:nowrap;">ï</span>ve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree al
