Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determini...Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.展开更多
Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network lev...Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.展开更多
The research on fault diagnosis based on multi-source information fusion technology aims to comprehensively integrate the diagnostic information of complex mechanical and electrical equipment,providing a scientific an...The research on fault diagnosis based on multi-source information fusion technology aims to comprehensively integrate the diagnostic information of complex mechanical and electrical equipment,providing a scientific and precise decision-making basis for decision-makers.However,in diagnostic practice,issues such as the impact of component replacement,rule combination explosion,and information redundancy have become research difficulties.To address these challenges,this paper innovatively combines equipment mechanisms with expert knowledge to build an optimized model that considers the impact of component replacement based on the traditional Belief Rule Base(BRB-h).Meanwhile,under the framework of traditional independent component analysis,this paper proposes an Independent Component Analysis(ICA)method that considers Expert knowledge(ICA-E).Furthermore,to quantify the impact of component replacement on equipment performance,this paper delves into the transparency and traceability of replacement impact factors and conducts a sensitivity analysis.Through empirical case studies,the advancement and practicability of this new method in the field of fault diagnosis are verified.展开更多
Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional h...Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.展开更多
Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-cons...Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.展开更多
Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutive...This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).展开更多
Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its ...Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.展开更多
In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troubleso...In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.展开更多
It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent...It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.展开更多
A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is t...A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.展开更多
Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decompo...Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.展开更多
To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive...To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive interference suppression scheme based on independent component analysis （ICA）. Taking into account statistical independence of subcarriers＇ signals of OFDM, the signal recovery mechanism is investigated to achieve the goal of blind equalization. The received OFDM signals can be considered as the mixed observation signals. The effect of CFO and multipath corresponds to the mixing matrix in the problem of blind source separation （BSS） framework. In this paper, the ICA- based OFDM system model is built, and the proposed ICA-based detector is exploited to extract source signals from the observation of a received mixture based on the assumption of statistical independence between the sources. The blind separation technique can increase spectral efficiency and provide robustness performance against erroneous parameter estimation problem. Theoretical analysis and simulation results show that compared with the conventional pilot-based scheme, the improved performance of OFDM systems is obtained by the proposed ICA-based detection technique.展开更多
A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target infor...A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.展开更多
Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT...Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.展开更多
Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers...Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.展开更多
The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills...The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.展开更多
Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of th...Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity(FC) in upper-limb amputees(ULAs). This observational study included 40 ULAs and 40 healthy control subjects;all participants underwent resting-state functional magnetic resonance imaging. Changes in intra-and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus(precuneus gyrus) within the dorsal attention network and left precentral(precentral gyrus) within the auditory network;but increased in the left Parietal_Inf(inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2(crus Ⅱ of cerebellum) and left Temporal_Mid(middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper(rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping;rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain.展开更多
A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introdu...A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.展开更多
Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent A...Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent Analysis （PCA） technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis （DICA） is proposed, which combines the strength＂ of unsupervised learning of ICA and supcrvised learning of Linear Discriminant Analysis （LDA）, and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called ＂discriminant independent faces＂ are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA＋LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.展开更多
基金supported by the National Natural Science Foundation of China(62076157 and 61703253)the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(20210033)the National Institutes of Health(R01MH123610 and R01EB006841).
文摘Functional networks(FNs)hold significant promise in understanding brain function.Independent component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN estimation.Here,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders.We extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI data.Based on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters.Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age increases.Furthermore,both small-scale and large-scale brain FN templates are provided as benchmarks for future studies.Taken together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.
基金supported by the National Natural Science Foundation of China,Nos.81871836(to MZ),82172554(to XH),and 81802249(to XH),81902301(to JW)the National Key R&D Program of China,Nos.2018YFC2001600(to JX)and 2018YFC2001604(to JX)+3 种基金Shanghai Rising Star Program,No.19QA1409000(to MZ)Shanghai Municipal Commission of Health and Family Planning,No.2018YQ02(to MZ)Shanghai Youth Top Talent Development PlanShanghai“Rising Stars of Medical Talent”Youth Development Program,No.RY411.19.01.10(to XH)。
文摘Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.
基金co-supported by the National Natural Science Foundation of China(Nos.62273354,61673387,62227814,62203461,62203365)Shaanxi Provincial Science and Technology Innovation Team,China(No.2022TD-24)+2 种基金China Postdoctoral Science Foundation(No.2023M742843)Young Talent Promotion Program of Shaanxi Association for Science and Technology,China(Nos.20220121,20230125)Natural Science Basic Research Program of Shaanxi,China(No.2022JQ-580).
文摘The research on fault diagnosis based on multi-source information fusion technology aims to comprehensively integrate the diagnostic information of complex mechanical and electrical equipment,providing a scientific and precise decision-making basis for decision-makers.However,in diagnostic practice,issues such as the impact of component replacement,rule combination explosion,and information redundancy have become research difficulties.To address these challenges,this paper innovatively combines equipment mechanisms with expert knowledge to build an optimized model that considers the impact of component replacement based on the traditional Belief Rule Base(BRB-h).Meanwhile,under the framework of traditional independent component analysis,this paper proposes an Independent Component Analysis(ICA)method that considers Expert knowledge(ICA-E).Furthermore,to quantify the impact of component replacement on equipment performance,this paper delves into the transparency and traceability of replacement impact factors and conducts a sensitivity analysis.Through empirical case studies,the advancement and practicability of this new method in the field of fault diagnosis are verified.
基金supported by Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(award number:K2024X010).
文摘Purpose–This paper conducts a joint analysis of monitoring data in the hidden danger areas of railway subgrade deformation using a data-driven method,thereby realizing the systematic risk identification of regional hidden dangers.Design/methodology/approach–The paper proposes a regional systematic risk identification method based on Bayesian and independent component analysis(ICA)theories.Firstly,the Gray Wolf Optimization(GWO)algorithm is used to partition each group of monitoring data in the hidden danger area,so that the data distribution characteristics within each sub-block are similar.Then,a distributed ICA early warning model is constructed to obtain prior knowledge such as control limits and statistics of the area under normal conditions.For the online evaluation process,the input data is partitioned following the above-mentioned procedure and the ICA statistics of each sub-block are calculated.The Bayesian method is applied to fuse online parameters with offline parameters,yielding statistics under a specific confidence interval.These statistics are then compared with the control limits–specifically,checking whether they exceed the pre-set confidence parameters–thus realizing the systematic risk identification of the hidden danger area.Findings–Through simulation experiments,the proposed method can integrate prior knowledge such as control limits and statistics to effectively determine the overall stability status of the area,thereby realizing the systematic risk identification of the hidden danger area.Originality/value–The proposed method leverages Bayesian theory to fuse online process parameters with offline parameters and further compares them with confidence parameters,thereby effectively enhancing the utilization efficiency of monitoring data and the robustness of the analytical model.
基金supported by the Deanship of Graduate Studies and Scientific Research at Najran University through funding code NU/GP/MRC/13/771-1.
文摘Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
基金the National Science and Technology Council,Taiwan,for financially supporting this research(grant No.NSTC 113-2221-E-018-011)the Ministry of Education's Teaching Practice Research Program,Taiwan(PSK1134099).
文摘This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave(FMCW)automotive radar performance under high noise and interference.The four-stage pipeline is applied consecutively:(i)an improved independent component analysis(ICA)blindly separates the two-channel echoes,isolating target and interference components;(ii)a recursive least-squares(RLS)filter compensates amplitude-and phase-mismatches,restoring signal fidelity;(iii)variational mode decomposition(VMD)followed by the Hilbert-Huang Transform(HHT)extracts noise-free intrinsic mode functions(IMFs)and sharpens their time-frequency signatures;and(iv)HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information.Finally,key IMFs are reconstructed into a clean signal,and a beat-frequency estimation via HHT confirms accurate distance results,closely aligning with theoretical predictions.On synthetic data with an input signal-to-noise ratio(SNR)of 12.7 dB,the pipeline delivers a 7.6 dB SNR gain,yields a mean-squared error of 0.25 m2,and achieves a range root-mean-square error(Range-RMSE)of 0.50 m.Empirical evaluations demonstrate that this enhanced ICA and VMD/HHT scheme effectively restores the fundamental echo signature,providing a robust approach for advanced driver assistance systems(ADAS).
基金supported by National Natural Science Foundation of China (Grant No. 50975192)Tianjin Municipal Natural Science Foundation of China (Grant No. 10YFJZJC14100)
文摘Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis（ICA）.However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition（EMD） and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.
文摘In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.
基金This project is supported by National Natural Science Foundation of China (No.50275154) Municipal Natural Science Foundation of Chongqing, China (No.8773).
文摘It is an important precondition for machine fault diagnosis that vibrationsignal can be extracted effectively. Based on the characteristic of noise interfused during thecourse of sampling vibration signal, independent component analysis (ICA) method is combined withwavelet to de-noise. Firstly, The sampled signal can be separated with ICA, then the function offrequency band chosen with multi-resolution wavelet transform can be used to judge whether thestochastic disturbance singular signal is interfused. By these ways, the vibration signals can beextracted effectively, which provides favorable condition for subsequent feature detection ofvibration signal and fault diagnosis.
基金Project(217/s/458)supported by Azarbaijan Shahid Madani University,Iran
文摘A statistical signal processing technique was proposed and verified as independent component analysis(ICA) for fault detection and diagnosis of industrial systems without exact and detailed model.Actually,the aim is to utilize system as a black box.The system studied is condenser system of one of MAPNA's power plants.At first,principal component analysis(PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones.Then,the fault sources were diagnosed by ICA technique.The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states,and it can distinguish main factors of abnormality among many diverse parts of a power plant's condenser system.This selectivity problem is left unsolved in many plants,because the main factors often become unnoticed by fault expansion through other parts of the plants.
基金Project (No. 50575203) supported by the National Natural ScienceFoundation of China
文摘Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.
基金supported by a grant from the national High Technology Research and development Program of China(863 Program)(No.2012AA01A502)National Natural Science Foundation of China(No.61179006)Science and Technology Support Program of Sichuan Province(No.2014GZX0004)
文摘To overcome the inter-carrier interference （ICI） of orthogonal frequency division multiplexing （OFDM） systems subject to unknown carrier frequency offset （CFO） and multipath, this paper develops a blind adaptive interference suppression scheme based on independent component analysis （ICA）. Taking into account statistical independence of subcarriers＇ signals of OFDM, the signal recovery mechanism is investigated to achieve the goal of blind equalization. The received OFDM signals can be considered as the mixed observation signals. The effect of CFO and multipath corresponds to the mixing matrix in the problem of blind source separation （BSS） framework. In this paper, the ICA- based OFDM system model is built, and the proposed ICA-based detector is exploited to extract source signals from the observation of a received mixture based on the assumption of statistical independence between the sources. The blind separation technique can increase spectral efficiency and provide robustness performance against erroneous parameter estimation problem. Theoretical analysis and simulation results show that compared with the conventional pilot-based scheme, the improved performance of OFDM systems is obtained by the proposed ICA-based detection technique.
基金Funded by the National 863 Program of China (No.2002AA783050)
文摘A small target detection approach based on independent component analysis for hyperspectral data is put forward. In this algorithm, firstly the fast independent component analysis(FICA) is used to collect target information hided in high-dimensional data and projects them into low-dimensional space.Secondly, the feature images are selected with kurtosis .At last, small targets are extracted with histogram image segmentation which has been labeled by skewness.
基金Supported by Tianjin Municipal Science and Technology Commission (No.09JCYBJC02200)
文摘Speech signals in frequency domain were separated based on discrete wavelet transform (DWT) and independent component analysis (ICA). First, mixed speech signals were decomposed into different frequency domains by DWT and the subbands of speech signals were separated using ICA in each wavelet domain; then, the permutation and scaling problems of frequency domain blind source separation (BSS) were solved by utilizing the correlation between adjacent bins in speech signals; at last, source signals were reconstructed from single branches. Experiments were carried out with 2 sources and 6 microphones using speech signals at sampling rate of 40 kHz. The microphones were aligned with 2 sources in front of them, on the left and right. The separation of one male and one female speeches lasted 2.5 s. It is proved that the new method is better than single ICA method and the signal to noise ratio is improved by 1 dB approximately.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(12CX06071A)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.
基金Supported by the National Scientific Research Fund of China(No.31201133)
文摘The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.
基金supported by the National Natural Science Foundation of China, No.81974331(to XYZ)Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant, No.20161429(to XYZ)
文摘Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity(FC) in upper-limb amputees(ULAs). This observational study included 40 ULAs and 40 healthy control subjects;all participants underwent resting-state functional magnetic resonance imaging. Changes in intra-and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus(precuneus gyrus) within the dorsal attention network and left precentral(precentral gyrus) within the auditory network;but increased in the left Parietal_Inf(inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2(crus Ⅱ of cerebellum) and left Temporal_Mid(middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper(rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping;rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain.
文摘A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
基金Supported by the Key Project of the National Natural Science Foundation of China(No.90104030)the National Natural Science Foundation of China(No.60401015)
文摘Subspace modeling plays an important role in face recognition. Independent Component Analysis （ICA）, a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Com- ponent Analysis （PCA） technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis （DICA） is proposed, which combines the strength＂ of unsupervised learning of ICA and supcrvised learning of Linear Discriminant Analysis （LDA）, and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called ＂discriminant independent faces＂ are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA＋LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.
