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您的检索式：作者名="Guangdi Feng"
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| 1 | Microbial alteration of natural gas in Xinglongtai field of the Bohai Bay Basin, China显示文摘Xinglongtai field has been an important petroleum-producing area of Liaohe Depression for 30 years. Oil exploration and production were the focus of this field, but the gas was ignored. This study examined twenty five gas samples with the purpose of determining the gas genetic types and their geochemical characteristics. Molecular components, stable carbon isotopic compositions and light hydrocarbons were also measured, and they proved that microbial activity has attacked some of the gas components which resulted in unusual carbon isotopic distributions. Propane seems to be selectively attacked during the initial stage of microbial alteration, with abnormally lower con-tent compared to that of butane as well as anomalously heavy carbon isotope. As a consequence, the carbon isotopic distribution among the gas components is partially reverse, as δ13C1<δ13C2<δ13C3>δ13C4. Besides, n-alkanes of C3+ gas components are preferentially attacked during the process of microbial alteration. This is manifested that n-alkanes are more enriched in 13C than corresponding iso-alkanes. As a result, the concentrations of n-alkanes be-come very low, which may be misleading in indentifying the gas genetic types. As to four gas samples, light hydro-carbon compositions display evidence for microbial alteration. The sequence of hexane isomers varies obviously with high content of 2,3-DMC4, which indicates that they have been in the fourth level of extensively bacterial al-teration. So the geochemical characteristics can be affected by microbial alteration, and recognition of microbial alteration in gas accumulations is very important for interpreting the natural gas genetic types. | YANG Weiwei LIU Guangdi GONG Yaojin FENG Yuan | 2012 | Chinese Journal Of Geochemistry2012，31，1： | 4 |
| 2 | Retinomorphic hardware for in-sensor computing显示文摘Rapid developments in the Internet of Things and Artificial Intelligence trigger higher requirements for image perception and learning of external environments through visual systems.However,limited by von Neumann's bottleneck,the physical separation of sense,memory,and processing units in a conventional personal computer-based vision system tend to consume a significant amount of energy,time latency,and additional hardware costs.By integrating computational tasks of multiple functionalities into the sensors themselves,the emerging bio-inspired neuromorphic visual systems provide an opportunity to overcome these limitations.With high speed,ultralow power and strong adaptability,it is highly desirable to develop a neuromorphic vision system that is based on highly precise in-sensor computing devices,namely retinomorphic devices.We here present a timely review of retinomorphic devices for visual in-sensor computing.We begin with several types of physical mechanisms of photoelectric sensors that can be constructed for artificial vision.The potential applications of retinomorphic hardware are,thereafter,thoroughly summarized.We also highlight the possible strategies to existing challenges and give a brief perspective of retinomorphic architecture for in-sensor computing. | Guangdi Feng Xiaoxu Zhang Bobo Tian Chungang Duan | 2023 | InfoMat2023，5，9： | 1 |
| 3 | Associative learning of a three-terminal memristor network for digits recognition显示文摘Imitating the associative intelligence of the biological brain is attractive but is poorly achieved in hardware because the complex tunable connection in neural networks is difficult to reproduce. We develop a circuit composed of a three-terminal memristor network to reproduce the biological conditioning process artificially. The synaptic weight between co-firing neurons is strengthened simultaneously by generating a feedback signal from the integrate-and-fire neuron to the gate of the synaptic memristor. The network allows the multi-associative capacity of recalling more than one digit in one circuit. Both single and multi-associative learning for recalling digital images are achieved. Furthermore, all 10 digital images from “0” to “9” are successfully recalled in an associative network with such paralleling circuits. Assisted by this associative layer, a typical classification network effectively improves the recognition rate for fragmentary digital images.Our work sheds light on brain-inspired artificial associative memory and provides a strategy for applications,such as object recognition with partial features and similar scenes. | Yiming REN Bobo TIAN Mengge YAN Guangdi FENG Bin GAO Fangyu YUE Hui PENG Xiaodong TANG Qiuxiang ZHU Junhao CHU Chungang DUAN | 2023 | Science China(Information Sciences)2023，66，2： | 0 |
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