| 1. |
- Chen, Zhibin, et al.
(författare)
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Summary of the 3rd International Workshop on Gas-Dynamic Trap based Fusion Neutron Source (GDT-FNS)
- 2022
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 0029-5515 .- 1741-4326. ; 62:6
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Tidskriftsartikel (refereegranskat)abstract
- The 3rd International Workshop on Gas-Dynamic Trap-based Fusion Neutron Source (GDT-FNS) was held through the hybrid mode on 13-14 September 2021 in Hefei, China, jointly organized by the Hefei Institutes of Physical Science (HFIPS), Chinese Academy of Sciences (CAS), and the Budker Institute of Nuclear Physics (BINP), Russian Academy of Sciences (RAS). It followed the 1st GDT-FNS Workshop held in November 2018 in Hefei, China, and the 2nd taking place in November 2019 in Novosibirsk, Russian Federation. With the financial support from CAS and China Association for Science and Technology (CAST), this workshop was attended by more than 80 participants representing 20 institutes and universities from seven countries, with oral presentations broadcast via the Zoom conferencing system. Twenty-two presentations were made with topics covering design and key technologies, simulation and experiments, steady-state operation, status of the ALIANCE project, multi applications of neutron sources, and other concepts (Tokamaks, Mirrors, FRC, Plasma Focus, etc). The workshop consensus was made including the establishment of the ALIANCE International Working Group. The next GDT-FNS workshop is planned to be held in May 2022 in Novosibirsk.
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| 2. |
- Liu, Shichao, et al.
(författare)
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一种β-内酰胺类抗生素的酶热检测方法
- 2019
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Ingår i: Journal of Shanxi University (Natural Science Edition). ; :2021-02
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Tidskriftsartikel (refereegranskat)abstract
- The penicillinase thermistor biosensor（Penicillinase sensor） was developed for the rapid monitoring of blood penem antibiotics concentration and rapid identification of extraneous penicillinase in milk on site.However, the wide application of the penicillinase thermistor biosensor was limited due to its intrinsic poor activity to hydrolyze cephem and carbapenem antibiotics.The recently identified carbapenemase New Delhi metallo-beta-lactamase 1（NDM-1） is able to hydrolyze all commercially available β-lactam antibiotics in high efficacy.We coupled the NDM-1 and the enzymatic thermistor biosensor to develop a NDM-1 thermistor biosensor（NDM-1 sensor） by the installment of the enzymatic thermistor with an enzyme column loaded with NDM-1 conjugated CPG beads.The NDM-1 sensor shows high response activity to Piperacillin（PIP）,Ceftriaxone（CTRX）, and Meropenem（MEM）, and the response activity of the NDM-1 sensor to these three β-lactam antibiotics are all Zn2+ dependent.Moreover, the response activity of the NDM-1 sensor to Penicillin G（P）, PIP, Cefazolin（CZO）, CTRX, Cefepime（FEP） and MEM all linearly correlated with antibiotic concentration from 31.25 to 1 000 mg/L.Within pH from 6.0 to 8.0, the optimal response activity of the NDM-1 sensor to P,PIP, CZO, CTRX and FEP are found at pH 6.5, while the optimal response activity of the NDM-1 sensor to MEM is found at pH8.0.These data indicate that the featured activity of NDM-1 was well maintained after conjugation on CPG beads, and NDM-1 sensor is capable to quantitate three classes of β-lactam antibiotics including penem, cephem and carbapenem within a wide concentration range.
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| 3. |
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| 4. |
- Cheng, Haibo, et al.
(författare)
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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM
- 2020
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:19
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Tidskriftsartikel (refereegranskat)abstract
- Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.
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| 5. |
- Cheng, Haibo, et al.
(författare)
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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data
- 2023
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Ingår i: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.
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| 6. |
- Jie, Xu, et al.
(författare)
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Energy efficient downlink MIMO transmission with linear precoding
- 2013
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Ingår i: Science China Information Sciences. - : Springer Science and Business Media LLC. - 1674-733X .- 1869-1919. ; 56:2, s. 022309-
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Tidskriftsartikel (refereegranskat)abstract
- Energy efficiency (EE) is becoming increasingly important for wireless cellular networks. This paper addresses EE optimization problems in downlink multiuser MIMO systems with linear precoding. Referring to different active transmit/receive antenna sets and transmission schemes as different modes, we propose a joint bandwidth/power optimization and mode switching scheme to maximize EE. With a specific mode, we prove that the optimal bandwidth and transmit power is either full transmit power or full bandwidth. After deriving the optimal bandwidth and transmit power, we further propose mode switching to select the mode with optimal EE. Since the optimal mode switching, i.e. exhaustive search, is too complex to implement, an alternative heuristic method is developed to decrease the complexity through reducing the search size and avoiding the EE calculation during each search. Through simulations, we demonstrate that the proposed methods can significantly improve EE and the performance is similar to the optimal exhaustive search.
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| 7. |
- Li, Shichao, et al.
(författare)
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Increasing Efficiency of a Wireless Energy Transfer System by Spatial Translational Transformation
- 2018
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Ingår i: IEEE transactions on power electronics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0885-8993 .- 1941-0107. ; 33:4, s. 3325-3332
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Tidskriftsartikel (refereegranskat)abstract
- A magnetic translational projector (MTP) designed by transformation optics is applied to improve energy transfer efficiency in a wireless power transfer (WPT) system. Our numerical simulation results showtheMTP can greatly enhance energy transfer efficiency (e.g., nearly two orders, compared to the case without our MTP) in the WPT system, which is much larger than that of a previous method (i.e., using magnetic super-lens). A 3-D reduced MTPcomposed of layered isotropicmagnetic materials is designed, whose performance is verified by our 3-D numerical simulation in 10 MHz. The influence of loss in metamaterial on the performance of the proposed MTP is also studied, which shows that the MTP can still enhance energy transfer efficiency when loss exists. Further simulation is also carried out to show that the function of the MTP is not sensitive to large perturbation. Finally, detailed experimental suggestion for implementing the simplified MTP, which is composed of layered medium is given and then verified by our numerical simulation.
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