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基于改进Kinky Inference的输出调节自适应无拖曳控制
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作者 孙笑云 沈强 吴树范 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2024年第5期1604-1613,共10页
在空间引力波探测任务中,航天器内部检验质量因存在载荷硬件噪声、环境噪声及微推力器耦合噪声等复杂干扰,影响其无拖曳控制精度,难以实现超净、超稳控制需求。提出一种基于惰性适配Lipschitz常数Kinky Inference (LACKI)的航天器自适... 在空间引力波探测任务中,航天器内部检验质量因存在载荷硬件噪声、环境噪声及微推力器耦合噪声等复杂干扰,影响其无拖曳控制精度,难以实现超净、超稳控制需求。提出一种基于惰性适配Lipschitz常数Kinky Inference (LACKI)的航天器自适应无拖曳控制方法,运用监督学习规则实现先验知识不足、样本数据存在损坏时外界干扰的逼近和抑制,及基于输出调节的模型参考自适应控制(MRAC)方法实现检验质量精确的无拖曳控制。数值仿真验证了无拖曳控制中敏感轴平动和转动自由度的状态响应性能及LACKI规则针对外界干扰的估计效果,通过与常规线性控制方法的对比,验证了所提方法对于提高无拖曳控制精度的有效性。 展开更多
关键词 监督学习 LIPSCHITZ估计 模型参考自适应控制 无拖曳控制 输出调节 Kinky inference
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A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures
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作者 Akshay Kumar Gaurav Tiwari 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第3期840-859,共20页
Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating du... Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully. 展开更多
关键词 Bayesian inference Multi-model inference Statistical uncertainty Global sensitivity analysis(GSA) Borgonovo’s indices Limited data
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Plasma current tomography for HL-2A based on Bayesian inference
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作者 刘自结 王天博 +5 位作者 吴木泉 罗正平 王硕 孙腾飞 肖炳甲 李建刚 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第5期165-173,共9页
An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to rec... An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile.Two different Bayesian probability priors are tried,namely the Conditional Auto Regressive(CAR)prior and the Advanced Squared Exponential(ASE)kernel prior.Compared to the CAR prior,the ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters,which can make the shape of the current profile more flexible in space.The results indicate that the ASE prior couples more information,reduces the probability of unreasonable solutions,and achieves higher reconstruction accuracy. 展开更多
关键词 plasma current tomography Bayesian inference machine learning Gaussian distribution
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Variational Neural Inference Enhanced Text Semantic Communication System
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作者 Zhang Xi Zhang Yiqian +1 位作者 Li Congduan Ma Xiao 《China Communications》 SCIE CSCD 2024年第7期50-64,共15页
Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,etc.While efforts have been di... Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,etc.While efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and decoder.However,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart devices.Furthermore,existing work tends to prioritize explicit semantics,neglecting the potential of implicit semantics.This paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder structures.We propose a novel semantic communication system with variational neural inference for text transmission.Specifically,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received text.This information is then utilized to assist in the decoding process.The simulation results show a significant enhancement in system performance and improved robustness. 展开更多
关键词 deep learning semantic communication variational neural inference
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Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme
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作者 Fangrui Xiu Zengan Deng 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2024年第5期121-132,共12页
The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kineti... The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes. 展开更多
关键词 Langmuir turbulence physical-informed neural network parameter inference
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Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations
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作者 Shuo Zhao Peng Cui +1 位作者 Jing Shen Haibo Liu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期239-249,共11页
Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully superv... Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results. 展开更多
关键词 label inference salient object detection weak supervision
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Distributed process monitoring based on Kantorovich distancemultiblock variational autoencoder and Bayesian inference
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作者 Zongyu Yao Qingchao Jiang Xingsheng Gu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第9期311-323,共13页
Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring sche... Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder(KD-MBVAE)is introduced.Firstly,given the high consistency of relevant variables within each sub-block during the change process,the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory.Subsequently,the variational autoencoder(VAE)model was separately established,and corresponding T^(2)statistics were calculated.To improve fault sensitivity further,a novel statistic,derived from Kantorovich distance,is introduced by analyzing model residuals from the perspective of probability distribution.The thresholds of both statistics were determined by kernel density estimation.Finally,monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference.Additionally,a novel approach for fault diagnosis is introduced.The feasibility and efficiency of the introduced scheme are verified through two cases. 展开更多
关键词 Chemical processes SAFETY Kantorovich distance Neural networks Process monitoring Bayesian inference
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Multi-modal knowledge graph inference via media convergence and logic rule
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作者 Feng Lin Dongmei Li +5 位作者 Wenbin Zhang Dongsheng Shi Yuanzhou Jiao Qianzhong Chen Yiying Lin Wentao Zhu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期211-221,共11页
Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the intro... Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features. 展开更多
关键词 logic rule media convergence multi-modal knowledge graph inference representation learning
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A dynamic algorithm for trust inference based on double DQN in the internet of things
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作者 Xiaodong Zhuang Xiangrong Tong 《Digital Communications and Networks》 SCIE CSCD 2024年第4期1024-1034,共11页
The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge t... The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability. 展开更多
关键词 Internet of things Information security Reinforcement learning Trust propagation Trust inference
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Parallel Inference for Real-Time Machine Learning Applications
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作者 Sultan Al Bayyat Ammar Alomran +3 位作者 Mohsen Alshatti Ahmed Almousa Rayyan Almousa Yasir Alguwaifli 《Journal of Computer and Communications》 2024年第1期139-146,共8页
Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes... Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware. 展开更多
关键词 Machine Learning Models Computational Efficiency Parallel Computing Systems Random Forest inference Hyperparameter Tuning Python Frameworks (TensorFlow PyTorch Scikit-Learn) High-Performance Computing
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A Preliminary Analysis on Inferences in Discourse Comprehension
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作者 冯政 《海外英语》 2011年第6X期355-357,共3页
By analysing the nature of inference and discourse comprehension as well as the role and classification of inference, it is concluded that inference is a productive mode of thinking that decides from something known o... By analysing the nature of inference and discourse comprehension as well as the role and classification of inference, it is concluded that inference is a productive mode of thinking that decides from something known or assumed, and the inference in discourse works out the underlying propositions, necessary or elaborative, and the unsaid speaker's meaning. To derive a good inference, one has to make use of world knowledge and share some experiences with the speaker. 展开更多
关键词 ANALYSIS inferENCE DISCOURSE COMPREHENSION
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Relevance-driven Pragmatic Inferences
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作者 王瑞彪 《海外英语》 2013年第20期138-139,共2页
Relevance theory, an inferential approach to pragmatics, claims that the hearer is expected to pick out the input of optimal relevance from a mass of alternative inputs produced by the speaker in order to interpret th... Relevance theory, an inferential approach to pragmatics, claims that the hearer is expected to pick out the input of optimal relevance from a mass of alternative inputs produced by the speaker in order to interpret the speaker's intentions. The degree of the relevance of an input can be assessed in terms of cognitive effects and the processing effort. The input of optimal relevance is the one yielding the greatest positive cognitive effect and requiring the least processing effort. This paper attempts to assess the degrees of the relevance of a mass of alternative inputs produced by an imaginary speaker from the perspective of her corresponding hearer in terms of cognitive effects and the processing effort with a view to justifying the feasibility of the principle of relevance in pragmatic inferences. 展开更多
关键词 OPTIMAL RELEVANCE INPUT COGNITIVE EFFECTS processi
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Exact Distribution of Difference of Two Sample Proportions and Its Inferences 被引量:1
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作者 Keshab R. Dahal Mohamed Amezziane 《Open Journal of Statistics》 2020年第3期363-374,共12页
Comparing two population proportions using confidence interval could be misleading in many cases, such </span><span style="font-family:Verdana;">as</span><span style="font-family:Ve... Comparing two population proportions using confidence interval could be misleading in many cases, such </span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> the sample size </span><span style="font-family:Verdana;">being</span><span style="font-family:Verdana;"> small and the test </span><span style="font-family:Verdana;">being</span><span style="font-family:Verdana;"> based on normal approximation. In this case, the only </span><span style="font-family:Verdana;">one</span><span style="font-family:Verdana;"> option that we have is to collect a large sample. Unfortunately, the large sample might not be possible. One example is a person suffering from a rare disease. The main purpose of this journal is to derive a closed formula for the exact distribution of the difference between two independent sample proportions, and use it to perform related inferences such as a confidence interval, regardless of the sample sizes and compare with the existing Wald, Agresti-Caffo </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> Score. In this journal, we have derived a closed formula for the exact distribution of the difference between two independent sample proportions. This distribution doesn’t need any </span><span style="font-family:Verdana;">requirements,</span><span style="font-family:Verdana;"> and can be used to perform inferences such </span><span style="font-family:Verdana;">as:</span><span style="font-family:Verdana;"> a hypothesis test for two population proportions, regardless of the nature of the distribution and the sample sizes. We claim </span><span style="font-family:Verdana;">that</span><span style="font-family:Verdana;"> exact distribution has the </span><span style="font-family:Verdana;">least</span><span style="font-family:Verdana;"> confidence width among Wald, Agresti-Caffo </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> Score, so it is suitable for inferences of the difference between the population proportion regardless of sample size. 展开更多
关键词 Statistical inferences Exact Distribution Difference of Sample Proportions
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A Theoretical Model of L2 Readers' Causal Inferences in Narrative Comprehension
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作者 Correspondence: Yu Yi China Foreign Trade Guangzhou Exhibition CorP.,11 7, LiuHua Road, Guangzhou, PR. China 510014 《现代外语》 CSSCI 北大核心 1999年第4期397-398,共2页
Withincreasingawarenessoftheimportanceofreadingprocessitself,researchers(Fletcher&Bloom,1988;VandenBroek,1990;Horiba,1990,1993,1996,etc)showsteadyinterestintheinvestigationofinferences,particularly,thecausalinfere... Withincreasingawarenessoftheimportanceofreadingprocessitself,researchers(Fletcher&Bloom,1988;VandenBroek,1990;Horiba,1990,1993,1996,etc)showsteadyinterestintheinvestigationofinferences,particularly,thecausalinferencesinnarrativecomprehensioninrecentd... 展开更多
关键词 NARRATIVE COMPREHENSION CAUSAL inferences language PROFICIENCY story schema
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Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference 被引量:3
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作者 Xue‑Jun Jiang Wen Zhou Jie Hou 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第2期58-75,共18页
Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research objec... Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge graph.The subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert experience.In this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query.The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously.Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction.Additionally,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically.Furthermore,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system.Finally,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective. 展开更多
关键词 CRDM Knowledge graph Fault diagnosis Bayesian inference RBT3-TextCNN Web interface
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Ensemble Bayesian method for parameter distribution inference:application to reactor physics 被引量:1
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作者 Jia‑Qin Zeng Hai‑Xiang Zhang +1 位作者 He‑Lin Gong Ying‑Ting Luo 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第12期216-228,共13页
The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model ... The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering. 展开更多
关键词 Model parameters Bayesian inference Frequency distribution Ensemble Bayesian method KL divergence
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Interpreting forest diversity-productivity relationships:volume values,disturbance histories and alternative inferences
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作者 Douglas Sheil Frans Bongers 《Forest Ecosystems》 SCIE CSCD 2020年第1期64-75,共12页
Understanding the relationship between stand-level tree diversity and productivity has the potential to inform the science and management of forests.History shows that plant diversity-productivity relationships are ch... Understanding the relationship between stand-level tree diversity and productivity has the potential to inform the science and management of forests.History shows that plant diversity-productivity relationships are challenging to interpret—and this remains true for the study of forests using non-experimental field data.Here we highlight pitfalls regarding the analyses and interpretation of such studies.We examine three themes:1)the nature and measurement of ecological productivity and related values;2)the role of stand history and disturbance in explaining forest characteristics;and 3)the interpretation of any relationship.We show that volume production and true productivity are distinct,and neither is a demonstrated proxy for economic values.Many stand characteristics,including diversity,volume growth and productivity,vary intrinsically with succession and stand history.We should be characterising these relationships rather than ignoring or eliminating them.Failure to do so may lead to misleading conclusions.To illustrate,we examine the study which prompted our concerns—Liang et al.(Science 354:aaf8957,2016)—which developed a sophisticated global analysis to infer a worldwide positive effect of biodiversity(tree species richness)on“forest productivity”(stand level wood volume production).Existing data should be able to address many of our concerns.Critical evaluations will improve understanding. 展开更多
关键词 CAUSATION Correlation DIVERSITY inference PRODUCTIVITY Richness Tree-growth Wood-density
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Bayesian inference of the crust–core transition density via the neutron-star radius and neutron-skin thickness data 被引量:4
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作者 Wen-Jie Xie Zi-Wei Ma Jun-Hua Guo 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第6期125-133,共9页
In this work,we perform a Bayesian inference of the crust-core transition density ρ_(t) of neutron stars based on the neutron-star radius and neutron-skin thickness data using a thermodynamical method.Uniform and Gau... In this work,we perform a Bayesian inference of the crust-core transition density ρ_(t) of neutron stars based on the neutron-star radius and neutron-skin thickness data using a thermodynamical method.Uniform and Gaussian distributions for the ρ_(t) prior were adopted in the Bayesian approach.It has a larger probability of having values higher than 0.1 fm^(−3) for ρ_(t) as the uniform prior and neutron-star radius data were used.This was found to be controlled by the curvature K_(sym) of the nuclear symmetry energy.This phenomenon did not occur if K_(sym) was not extremely negative,namely,K_(sym)>−200 MeV.The value ofρ_(t) obtained was 0.075_(−0.01)^(+0.005) fm^(−3) at a confidence level of 68%when both the neutron-star radius and neutron-skin thickness data were considered.Strong anti-correlations were observed between ρ_(t),slope L,and curvature of the nuclear symmetry energy.The dependence of the three L-K_(sym) correlations predicted in the literature on crust-core density and pressure was quantitatively investigated.The most probable value of 0.08 fm^(−3) for ρ_(t) was obtained from the L-K_(sym) relationship proposed by Holt et al.while larger values were preferred for the other two relationships. 展开更多
关键词 Crust–core transition density of neutron stars Neutron-star radius Neutron-skin thickness Bayesian inference approach L–K_(sym)
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Intention Estimation of Adversarial Spatial Target Based on Fuzzy Inference 被引量:2
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作者 Wenjia Xiang Xiaoyu Li +4 位作者 Zirui He Chenjing Su Wangchi Cheng Chao Lu Shan Yang 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3627-3639,共13页
Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making... Estimating the intention of space objects plays an important role in air-craft design,aviation safety,military and otherfields,and is an important refer-ence basis for air situation analysis and command decision-making.This paper studies an intention estimation method based on fuzzy theory,combining prob-ability to calculate the intention between two objects.This method takes a space object as the origin of coordinates,observes the target’s distance,speed,relative heading angle,altitude difference,steering trend and etc.,then introduces the spe-cific calculation methods of these parameters.Through calculation,values are input into the fuzzy inference model,andfinally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability.Ver-ified by simulation experiment,the target intention inferred by this method is roughly the same as the actual behavior of the target,which proves that the meth-od for identifying the target intention is effective. 展开更多
关键词 Intension estimation motion parameters calculation fuzzy inference fuzzy rule table historical weighted probability
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Development of a Topic Providing System with Inferences of Behaviors from Daily Life
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作者 Seiji Suzuki Go Tanaka +5 位作者 Chiaki Doi Tomohiro Nakagawa Hiroshi Inamura Ken Ohta Tadanori Mizuno Hiroshi Mineno 《Computer Technology and Application》 2013年第3期144-152,共9页
Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support t... Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support their face-to-face communication using information technologies. We have developed a topic-providing system that can infer behaviors from daily life and provides users with information about their conversation partner, including that on his hometown, hobbies and life logs when face-to-face communication is initiated. The life logs are details about a user's life, and are generated using a Bayesian network on the basis of sensor data provided by our system. This system enables users to access other users' information of behaviors from the accumulated life logs and it utilizes this infbrmation to generate topics for conversation. We evaluated the accuracy with which proposal system inferred behaviors to confirm whether exact life log generation is possible. And we also evaluated the proposed system by administering a questionnaire to confirm whether the proposed system can support face-to-face communication. 展开更多
关键词 Face-to-face communication support inference of behaviors Bayesian network life log sensor network.
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