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Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection
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作者 郑湾 LIU Lerong +5 位作者 Lü Hanlin WANG Yuhang LI Feihu ZHANG Yixuan 陈艳军 WANG Yifeng 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第2期487-496,共10页
We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,wh... We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection. 展开更多
关键词 visual detection ionic responsiveness fluorescent hydrogels double network hydrogels mechanical property
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Supramolecular polymer-based gel fracturing fluid with a double network applied in ultra-deep hydraulic fracturing
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作者 Yong-Ping Huang Yong Hu +5 位作者 Chang-Long Liu Yi-Ning Wu Chen-Wei Zou Li-Yuan Zhang Ming-Wei Zhao Cai-Li Dai 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1875-1888,共14页
A gel based on polyacrylamide,exhibiting delayed crosslinking characteristics,emerges as the preferred solution for mitigating degradation under conditions of high temperature and extended shear in ultralong wellbores... A gel based on polyacrylamide,exhibiting delayed crosslinking characteristics,emerges as the preferred solution for mitigating degradation under conditions of high temperature and extended shear in ultralong wellbores.High viscosity/viscoelasticity of the fracturing fluid was required to maintain excellent proppant suspension properties before gelling.Taking into account both the cost and the potential damage to reservoirs,polymers with lower concentrations and molecular weights are generally preferred.In this work,the supramolecular action was integrated into the polymer,resulting in significant increases in the viscosity and viscoelasticity of the synthesized supramolecular polymer system.The double network gel,which is formed by the combination of the supramolecular polymer system and a small quantity of Zr-crosslinker,effectively resists temperature while minimizing permeability damage to the reservoir.The results indicate that the supramolecular polymer system with a molecular weight of(268—380)×10^(4)g/mol can achieve the same viscosity and viscoelasticity at 0.4 wt%due to the supramolecular interaction between polymers,compared to the 0.6 wt%traditional polymer(hydrolyzed polyacrylamide,molecular weight of 1078×10^(4)g/mol).The supramolecular polymer system possessed excellent proppant suspension properties with a 0.55 cm/min sedimentation rate at 0.4 wt%,whereas the0.6 wt%traditional polymer had a rate of 0.57 cm/min.In comparison to the traditional gel with a Zrcrosslinker concentration of 0.6 wt%and an elastic modulus of 7.77 Pa,the double network gel with a higher elastic modulus(9.00 Pa)could be formed only at 0.1 wt%Zr-crosslinker,which greatly reduced the amount of residue of the fluid after gel-breaking.The viscosity of the double network gel was66 m Pa s after 2 h shearing,whereas the traditional gel only reached 27 m Pa s. 展开更多
关键词 Ultra-deep reservoir Gel fracturing fluid double network Supramolecular polymer system Proppant suspension property
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Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning
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作者 Yameng Yin Lieping Zhang +3 位作者 Xiaoxu Shi Yilin Wang Jiansheng Peng Jianchu Zou 《Computers, Materials & Continua》 SCIE EI 2024年第11期2769-2790,共22页
By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning... By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation. 展开更多
关键词 double Deep Q network path planning average Q-value estimation reward redistribution mechanism reward-prioritized experience selection method
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基于Dueling Double DQN的交通信号控制方法
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作者 叶宝林 陈栋 +2 位作者 刘春元 陈滨 吴维敏 《计算机测量与控制》 2024年第7期154-161,共8页
为了提高交叉口通行效率缓解交通拥堵,深入挖掘交通状态信息中所包含的深层次隐含特征信息,提出了一种基于Dueling Double DQN(D3QN)的单交叉口交通信号控制方法;构建了一个基于深度强化学习Double DQN(DDQN)的交通信号控制模型,对动作... 为了提高交叉口通行效率缓解交通拥堵,深入挖掘交通状态信息中所包含的深层次隐含特征信息,提出了一种基于Dueling Double DQN(D3QN)的单交叉口交通信号控制方法;构建了一个基于深度强化学习Double DQN(DDQN)的交通信号控制模型,对动作-价值函数的估计值和目标值迭代运算过程进行了优化,克服基于深度强化学习DQN的交通信号控制模型存在收敛速度慢的问题;设计了一个新的Dueling Network解耦交通状态和相位动作的价值,增强Double DQN(DDQN)提取深层次特征信息的能力;基于微观仿真平台SUMO搭建了一个单交叉口模拟仿真框架和环境,开展仿真测试;仿真测试结果表明,与传统交通信号控制方法和基于深度强化学习DQN的交通信号控制方法相比,所提方法能够有效减少车辆平均等待时间、车辆平均排队长度和车辆平均停车次数,明显提升交叉口通行效率。 展开更多
关键词 交通信号控制 深度强化学习 Dueling double DQN Dueling network
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Self-potential inversion based on Attention U-Net deep learning network
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作者 GUO You-jun CUI Yi-an +3 位作者 CHEN Hang XIE Jing ZHANG Chi LIU Jian-xin 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第9期3156-3167,共12页
Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention an... Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring. 展开更多
关键词 SELF-POTENTIAL attention mechanism u-net deep learning network INVERSION landfill
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Transcriptome and morphological analyses of double flower formation in Dianthus chinensis
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作者 Xiaoni Zhang Shengnan Lin +6 位作者 Quanshu Wu Qijian Wang Chunmei Shi Manzhu Bao Mohammed Bendahmane Xiaopeng Fu Zhiqiang Wu 《Horticultural Plant Journal》 SCIE CAS CSCD 2024年第1期181-193,共13页
The double flower developmental process is regulated via a complex transcriptional regulatory network.To understand this highly dynamic and complex developmental process of Dianthus spp.,we performed a comparative ana... The double flower developmental process is regulated via a complex transcriptional regulatory network.To understand this highly dynamic and complex developmental process of Dianthus spp.,we performed a comparative analysis of floral morphology and transcriptome dynamics in simple flowers and double flowers.We found that the primordium of double flowers of‘X’was larger in size compared to that of simple flowers of‘L’in Dianthus chinensis.RNA-seq and Weighted Gene Co-expression Network Analysis(WGCNA)during flower development,identified stage-specific gene network modules.Expression analysis by RNA-seq indicated that a group of genes related to floral meristem identity,primordia position and polarity were highly expressed in double flowers genotypes compared to simple flowers genotypes,suggesting their roles in double-petal formation.A total of 21 DEGs related to petal number were identified between simple and double flowers.The experiments of in situ hybridization revealed that DcaAP2L,DcaLFY and DcaUFO genes were expressed in the intra-sepal boundary and petal boundary.We proposed a potential transcriptional regulatory network for simple and double flower development.This study provides novel insights into the molecular mechanism underlying double flower formation in Dianthus spp. 展开更多
关键词 Dianthus spp. Petal boundary Floral morphology double flower Co-expression network
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Prediction model of surrounding rock deformation in doublecontinuous-arch tunnel based on the ABC-WNN
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作者 Yahui Zhang 《Railway Sciences》 2024年第6期717-730,共14页
Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization... Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects. 展开更多
关键词 double arch tunnel Deformation prediction Artificial bee colonies Surrounding rock Wavelet neural network
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Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model
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作者 Weiyan Liu 《Open Journal of Geology》 CAS 2024年第4期578-593,共16页
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou... Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis. 展开更多
关键词 Deep Learning Convolutional Neural networks (CNN) Seismic Fault Identification u-net 3D Model Geological Exploration
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基于改进Double U-Net的秀丽隐杆线虫显微图像端泡分割
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作者 杜自豪 刘俊 《计算机与数字工程》 2023年第4期927-932,共6页
针对秀丽隐杆线虫显微图像噪声较多,端泡区域像素与虫体及周围环境相似,标准Double U-Net分割效果较差的问题。该研究提出一种改进的Double U-Net网络的秀丽隐杆线虫端泡分割方法,在网络中引入密集连接,并采用改进的损失函数,解决了传... 针对秀丽隐杆线虫显微图像噪声较多,端泡区域像素与虫体及周围环境相似,标准Double U-Net分割效果较差的问题。该研究提出一种改进的Double U-Net网络的秀丽隐杆线虫端泡分割方法,在网络中引入密集连接,并采用改进的损失函数,解决了传统网络无法进行精确分割的问题。实验表明:改进后算法对线虫端泡的分割,Dice Coefficien、准确率和召回率达到了90.12%、87.45%和91.53%。 展开更多
关键词 秀丽隐杆线虫 分割 double u-net 密集连接 混合损失函数
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Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach
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作者 Xingtang Wu Mingkun Yang +3 位作者 Wenbo Lian Min Zhou Hongwei Wang Hairong Dong 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第10期2014-2025,共12页
High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading del... High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded. 展开更多
关键词 Delay propagation double layer network high speed rail network max-plus algebra
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Unlocking the potentials of gel conformance for water shutoff in fractured reservoirs: Favorable attributes of the double network gel for enhancing oil recovery
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作者 Qian-Hui Wu Ji-Jiang Ge +1 位作者 Lei Ding Gui-Cai Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2023年第2期1005-1017,共13页
The double-network prepared with an in-situ monomer gel and a fast-crosslinked Cr(III) gel is introduced to develop a thixotropic and high-strength gel (THSG), which is found to have many advantages over the tradition... The double-network prepared with an in-situ monomer gel and a fast-crosslinked Cr(III) gel is introduced to develop a thixotropic and high-strength gel (THSG), which is found to have many advantages over the traditional gels. The THSG gel demonstrates remarkable thermal stability, and no syneresis is observed after 12 months with high salinity brine (95,500 mg/L). Moreover, the SEM and XRD results indicate that the gel is intercalated into the lamellar structures of Na-MMT, where the gel can form a uniform and compact structure. In addition, the THSG gel has an excellent swelling behavior, even in the high salinity brine. In the slim tube experiments, the THSG gel exhibits high rupture pressure and improves blocking capacity after being ruptured. The core flooding results show that a layer of gel filter cake is formed on the face of the fracture, which may be promoted by a high matrix permeability, a small aperture fracture, and a high injection rate. After the gel treatment, the fracture can be completely blocked by the THSG gel. It is found that a high incremental oil recovery (65.3%) can be achieved when the fracture was completely blocked, compared to 40.2% if the gel is ruptured. Although the swelling of ruptured gel can improve oil recovery, part of the injected brine may be channeled through the gel-filled fractures, resulting in a decrease in the sweep efficiency. Therefore, the improved blocking ability by gel swelling (e.g., in fresh water) may be less efficient to contribute to an enhancement of oil recovery. It is also found that the pressure gradient and residual resistance factor to water (Frrw) are higher if the matrix is less permeable, indicating that the fractured reservoir with lower matrix permeability may require a higher gel strength for treatment. The findings of this study may provide novel insights on designing robust double network gels for water shutoff in fractured reservoirs. 展开更多
关键词 double network structure Gel swelling Rupture pressure Fractured core Oil recovery factor
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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks
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作者 Nada M.Elfatih Elmustafa Sayed Ali +2 位作者 Maha Abdelhaq Raed Alsaqour Rashid A.Saeed 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期329-342,共14页
In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to ... In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput. 展开更多
关键词 Cognitive radio spectrum sensing energy detection double threshold neural network machine learning OPTIMIZATION quality of service
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A Double Network Hydrogel with High Mechanical Strength and Shape Memory Properties 被引量:3
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作者 Lei Zhu Chun-ming Xiong +3 位作者 Xiao-fen Tang Li-jun Wang Kang Peng Hai-yang Yang 《Chinese Journal of Chemical Physics》 SCIE CAS CSCD 2018年第3期350-358,368,共10页
Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into t... Double network(DN)hydrogels as one kind of tough gels have attracted extensive at-tention for their potential applications in biomedical and load-bearing fields.Herein,we import more functions like shape memory into the conventional tough DN hydro-gel system.We synthesize the PEG-PDAC/P(AAm-co-AAc)DN hydrogels,of which the first network is a well-defined PEG(polyethylene glycol)network loaded with PDAC(poly(acryloyloxyethyltrimethyl ammonium chloride))strands,while the second network is formed by copolymerizing AAm(acrylamide)with AAc(acrylic acid)and cross-linker MBAA(N;N′-methylenebisacrylamide).The PEG-PDAC/P(AAm-co-AAc)DN gels exhibits high mechanical strength.The fracture stress and toughness of the DN gels reach up to 0.9 MPa and 3.8 MJ/m^3,respectively.Compared with the conventional double network hydrogels with neutral polymers as the soft and ductile second network,the PEG-PDAC/P(AAm-co-AAc)DN hydrogels use P(AAm-co-AAc),a weak polyelectrolyte,as the second network.The AAc units serve as the coordination points with Fe^3+ions and physically crosslink the second network,which realizes the shape memory property activated by the reducing ability of ascorbic acid.Our results indicate that the high mechanical strength and shape memory properties,probably the two most important characters related to the potential application of the hydrogels,can be introduced simultaneously into the DN hydrogels if the functional monomer has been integrated into the network of DN hydrogels smartly. 展开更多
关键词 double network HYDROGEL WEAK POLYELECTROLYTE High mechanical strength Shape MEMORY properties
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Model-constrained and data-driven double-supervision acoustic impedance inversion
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作者 Dong-Feng Zhao Na-Xia Yang +2 位作者 Jin-Liang Xiong Guo-Fa Li Shu-Wen Guo 《Petroleum Science》 SCIE EI CSCD 2023年第5期2809-2821,共13页
Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geoph... Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method. 展开更多
关键词 Acoustic impedance inversion Model constraints double supervision BiLSTM neural network Reservoir structure characterization
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Simultaneous Determination of Gold and Platinum by Double Artificial Neural Network Analysis with Flow-injection Chemiluminescence 被引量:1
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作者 Ming Yang LIU Hai Tao ZHANG Jun Feng LI Shu Gui CHEN Hong Yan WANG 《Chinese Chemical Letters》 SCIE CAS CSCD 2006年第10期1343-1346,共4页
A highly sensitive double artificial neural network (DANN) analysis with flow-injection chemiluminescence (FI-CL) has been developed to simultaneously determine the trace amounts of the gold and platinum in simula... A highly sensitive double artificial neural network (DANN) analysis with flow-injection chemiluminescence (FI-CL) has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process. 展开更多
关键词 double artificial neural networks FLOW-INJECTION CHEMILUMINESCENCE simultaneous determination gold and platinum.
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基于Double Deep Q Network的无人机隐蔽接敌策略 被引量:9
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作者 何金 丁勇 高振龙 《电光与控制》 CSCD 北大核心 2020年第7期52-57,共6页
基于深度强化学习的连续状态空间无人机隐蔽接敌问题,提出了基于马尔可夫决策过程的隐蔽接敌双深度Q网络(DDQN)方法。利用DDQN生成目标值函数的方法解决了传统DQN的过拟合问题;采用按优先级随机抽样的方法获取训练样本,加速了神经网络... 基于深度强化学习的连续状态空间无人机隐蔽接敌问题,提出了基于马尔可夫决策过程的隐蔽接敌双深度Q网络(DDQN)方法。利用DDQN生成目标值函数的方法解决了传统DQN的过拟合问题;采用按优先级随机抽样的方法获取训练样本,加速了神经网络的训练速度;设定贪婪系数按照指数下降的方法,解决了传统强化学习的“探索利用窘境”;在势函数奖赏函数设计中引入角度因子,使其更加符合实际作战情况。仿真实验结果表明,DDQN具有较好的收敛性,能有效生成隐蔽接敌策略。 展开更多
关键词 隐蔽接敌策略 空战决策 马尔可夫决策过程 双神经网络结构 DDQN算法
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Analysis on Connectivity of Inter-Orbit-Links in a MEO/LEO Double-Layer Satellite Network 被引量:3
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作者 王振永 李集林 +1 位作者 郭庆 顾学迈 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2006年第4期340-345,共6页
As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network (DLSN) with MEO satellites and LEO satellites is proposed. The Inter-Orb... As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network (DLSN) with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links (IOLs) between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle. 展开更多
关键词 double-layer satellite network MEO/LEO inter-orbit-links CONNECTIVITY
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A new algorithm for diameter of double loop network
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作者 刘焕平 胡铭曾 杨义先 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2000年第2期32-38,共7页
Presents a new algorithm for diameter of double loop network(DLN) by which two new classes of infinite families of tight DLN of the known twelve infinie families of tight DLNs, under 3.3 in [1] including eleven are co... Presents a new algorithm for diameter of double loop network(DLN) by which two new classes of infinite families of tight DLN of the known twelve infinie families of tight DLNs, under 3.3 in [1] including eleven are constructed. 展开更多
关键词 double LOOP network DIAMETER TIGHT DLN
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Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks
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作者 JiangXU XishanXIE ZhongXU 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2003年第5期404-406,共3页
A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) ar... A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workplace voltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three important technical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratio of the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surface alloying. The processing parameters and technical target are then used as a training set for an artificial neural network. The model is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and the calculated results are in good agreement with the experimental ones. 展开更多
关键词 double glow Artificial neural network Multi-element alloying
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A Routing Algorithm for Distributed Optimal Double Loop Computer Networks
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作者 Li Layuan(Department of Electrical Engineering and Computer Science.Wuhan University of Water Transportation, Wuhan 430063, P. R. China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1994年第1期37-43,共7页
A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The pr... A routing algorithm for distributed optimal double loop computer networks is proposed and analyzed. In this paper, the routing algorithm rule is described, and the procedures realizing the algorithm are given. The proposed algorithm is shown to be optimal and robust for optimal double loop. In the absence of failures,the algorithm can send a packet along the shortest path to destination; when there are failures,the packet can bypasss failed nodes and links. 展开更多
关键词 Computer networks double loop Routing algorithm
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