This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and...An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However,most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information.Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps.The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix.展开更多
Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Seman...Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.展开更多
Background:Salvia miltiorrhiza Bunge,commonly known as“Danshen”in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions ...Background:Salvia miltiorrhiza Bunge,commonly known as“Danshen”in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72%by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.展开更多
Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectr...Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.展开更多
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the National Key R&D Program of China(Grant No.2020YFB2007202)the National Natural Science Foundation of China(Grant No.52005323)+1 种基金the National Postdoctoral Program for Innovative Talents(Grant No.BX20200210)the China Postdoctoral Science Foundation(Grant No.2019M660086)。
文摘An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However,most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information.Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps.The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix.
基金Supported by the National Natural Science Foundation of China (No. 61072110)the Industrial Tackling Project of Shaanxi Province (2010K06-20)the Natural Science Foundation of Shaanxi Province (SJ08F15)
文摘Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.
基金the National Key R&D Program of China(2024YFC3506800,2024YFC3506805)the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2023E002,CI2021B009)+2 种基金the Quality and Technical Service Platform for the Traditional Chinese Medicine Whole Industry Chain(2022-230-221)the China Agricultural Research System of MOF and MARA(CARS-21)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ16-YQ-040,ZZXT2023012)。
文摘Background:Salvia miltiorrhiza Bunge,commonly known as“Danshen”in China due to the distinctive red color of its roots,is one of the most widely used traditional Chinese medicines.It is cultivated in various regions across China,and environmental differences among these regions can affect the secondary metabolites of plants,thereby influencing the quality of S.miltiorrhiza.In recent years,increasing demand for S.miltiorrhiza has exacerbated the problem of origin fraud.Therefore,ensuring the authenticity of its geographical origin is crucial for the sustainable development of the industry.Objective:The red coloration of S.miltiorrhiza is closely associated with the content of its primary active compounds,particularly tanshinones.Therefore,both its internal chemical composition and external color characteristics serve as key indicators for quality assessment.This study utilized hyperspectral imaging technology to evaluate its potential in classifying the geographical origin of S.miltiorrhiza.Methods:Spectral data reflecting the internal chemical properties of S.miltiorrhiza were integrated with color information representing its external features through 3 levels of data fusion.These fused datasets were then combined with deep learning algorithms to achieve accurate origin classification.Results:The results demonstrated that the Transformer model combined with soft-voting decision-level fusion achieved the highest classification accuracy of 98.72%by integrating image color and short-wave infrared spectral data.Conclusion:This study demonstrates that integrating hyperspectral imaging spectral data with color information provides a reliable and innovative approach for verifying the authenticity and traceability of S.miltiorrhiza.
文摘Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared （IR） images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression （NVIE） spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images＇ supplementary role for visible facial expression recognition.
