Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for med...Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.展开更多
In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interferenc...In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interference such as channel multipath fading and occlusion effect.Detecting effectively spectrum signal under low signal-to-noise ratio(SNR),directly affects the whole performance of the wireless communication network system.This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise's signal energy into useful signal energy,and improves output SNR.The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR,and promotes the performance of the whole communication system.展开更多
Stochastic resonance system is subject to the restriction of small frequency parameter in weak signal detection,in order to solve this problem,a frequency modulated weak signal detection method based on stochastic res...Stochastic resonance system is subject to the restriction of small frequency parameter in weak signal detection,in order to solve this problem,a frequency modulated weak signal detection method based on stochastic resonance and genetic algorithm is presented in this paper. The frequency limit of stochastic resonance is eliminated by introducing carrier signal,which is multiplied with the measured signal to be injected in the stochastic resonance system,meanwhile,using genetic algorithm to optimize the carrier signal frequency,which determine the generated difference-frequency signal in the lowfrequency range,so as to achieve the stochastic resonance weak signal detection. Results showthat the proposed method is feasible and effective,which can significantly improve the output SNR of stochastic resonance,in addition,the system has the better self-adaptability,according to the operation result and output phenomenon,the unknown frequency of the signal to be measured can be obtained,so as to realize the weak signal detection of arbitrary frequency.展开更多
Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
Objective:The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms.Methods:Twenty-si...Objective:The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms.Methods:Twenty-six late gadolinium enhancement cardiovascular magnetic resonance images of diseased hearts were segmented by the full width at half maximum(FWHM)method,the n standard deviations(n SD)method,and our new automatic method.The results of the three methods were compared with the consensus ground truth obtained by manual segmentation of the ventricular boundaries.Results:Our automatic method yielded the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation.The n SD method produced large variations in the Dice score and the volume difference.The FWHM method yielded the lowest Dice score and the greatest volume difference compared with the automatic,6SD,and 8SD methods,but resulted in less variation when different observers segmented the images.Conclusion:The automatic method introduced in this study is highly reproducible and objective.Because it requires no manual intervention,it may be useful for processing large datasets produced in clinical applications.展开更多
Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest can...Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.展开更多
In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every ...In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.展开更多
In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic r...In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.展开更多
Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee ...Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.展开更多
NMR logging can provide the permeability parameter and abundant stratigraphical information such as total porosity,oil,gas and water saturation,oil viscosity,etc. And these physical parameters can be obtained by T2 sp...NMR logging can provide the permeability parameter and abundant stratigraphical information such as total porosity,oil,gas and water saturation,oil viscosity,etc. And these physical parameters can be obtained by T2 spectrum inversion. NMR inversion is an important part in logging interpretation. The authors describe a multi-exponential inversion algorithm,solid iteration redress technique( SIRT),and apply the algorithm in real data and compare the results with those based on singular value decomposition( SVD). It shows that SIRT algorithm is easier to be understood and implemented,and the time spent in SIRT is much shorter than that of SVD algorithm. And the non-negative property of T2 spectrum is much easier to be implemented. It can match the results based on SVD very well. SIRT algorithm can be used in T2 spectrum inversion for NMR analysis.展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
AIM:To compare spontaneous brain regional activities between diabetic vitreous hemorrhage patients(DVHs)and healthy controls(HCs).METHODS:Thirty-two DVHs and 32 HCs were enrolled in this study.Baseline demographic and...AIM:To compare spontaneous brain regional activities between diabetic vitreous hemorrhage patients(DVHs)and healthy controls(HCs).METHODS:Thirty-two DVHs and 32 HCs were enrolled in this study.Baseline demographic and vision data were compared between groups using an independent sample t-test.Resting-state functional magnetic resonance imaging(rs-fMRI)was used in all participants.fMRI data was obtained and analyzed using MRIcro and SPM8 software.Fractional amplitude of low-frequency fluctuation(fALFF)technology was used to measure regional spontaneous brain activity,and sensitivity was tested using receiver operating characteristic curves(ROCs).The fALFF values were analyzed using REST software and two-sample t-tests were used to compare values between groups.Hospital anxiety and depression scale(HADS)score was assessed in DVHs and Pearson’s correlation was used to test relationships between mean fALFF value and both HADS score and duration of DVH.RESULTS:Except for the best-corrected visual acuity(BCVA)in both eyes,which showed a statistically significant difference(P<0.05),there were no statistically significant differences in the other indicators(P>0.05)between the HCs and DVHs group.Compared with controls,fALFF value was higher in DVH in cerebellum posterior lobe(CPL)and lower in right anterior cingulate cortex(ACC)and right medial orbitofrontal cortex(OFC).In DVH patients,mean fALFF value of CPL was positively correlated with HADS score and duration of diabetes.However,no such correlation was found,for right ACC or right medial OFC.DVH may lead to abnormal activities in certain brain regions related to visual control and mood.CONCLUSION:Visual impairment caused by DVH may lead to adjustment in regional visual brain activities and may be related to depression or reward system processing in some brain regions.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
The integration of a large number of power electronic converters,such as railway power conditioner(RPC),introduces a series of problems,including harmonic interaction,stability issues,and wideband resonance,into the r...The integration of a large number of power electronic converters,such as railway power conditioner(RPC),introduces a series of problems,including harmonic interaction,stability issues,and wideband resonance,into the railway power supply system.To address these challenges,this paper proposes a novel harmonic resonance prevention measure for RPC-network-train interaction system.Firstly,a harmonic model,a parallel resonance impedance model,a series resonance admittance model,and a control stability model are each established for the RPC-network-train interaction system.Secondly,a comprehensive resonance impact factor(CRIF)is proposed to efficiently and accurately identify the key components affecting resonance,and to provide the selection results of optimization parameters for resonance prevention.Next,the initially selected parameters are constrained by the requirements of ripple current,reactive power and stability.Subsequently,the impedance parameters(control parameters and filter parameters)of the RPC are optimized with the objective of reshaping the parallel resonance impedance and series resonance admittance of the RPC-network-train interaction system,ensuring the output current har-monics of RPC meet standards to achieve resonance prevention,while ensuring the stable operation of the RPC.Finally,the proposed resonance prevention measure is verified under both light load and heavy load conditions using a simulation platform and a hardware-in-the-loop experimental platform.展开更多
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
基金This research was supported by the Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.
基金the Natural Science Foundation of Heilongjiang Province(No.F2015019)the Postdoctoral Foundation of Heilongjiang Province(No.LBHZ16054)the Undergraduate Basic Scientific Research Service Fee Project of Heilongjiang Province(No.Hkdqg201806)
文摘In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interference such as channel multipath fading and occlusion effect.Detecting effectively spectrum signal under low signal-to-noise ratio(SNR),directly affects the whole performance of the wireless communication network system.This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise's signal energy into useful signal energy,and improves output SNR.The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR,and promotes the performance of the whole communication system.
基金supported by the National Natural Science Foundation of China (Grant No. 61072133)the Production,Learning and Research Joint Innovation Program of Jiangsu Province,China (Grant Nos. BY2013007-02,SBY201120033)+2 种基金the Industrialization of Research Findings Promotion Program of Institution of Higher Education of Jiangsu Province,China (Grant No. JHB2011-15)the advantage discipline platform "information and Communication Engineering" of Jiangsu Province,Chinathe "Summit of the Six Top Talents" Program of Jiangsu Province,China
文摘Stochastic resonance system is subject to the restriction of small frequency parameter in weak signal detection,in order to solve this problem,a frequency modulated weak signal detection method based on stochastic resonance and genetic algorithm is presented in this paper. The frequency limit of stochastic resonance is eliminated by introducing carrier signal,which is multiplied with the measured signal to be injected in the stochastic resonance system,meanwhile,using genetic algorithm to optimize the carrier signal frequency,which determine the generated difference-frequency signal in the lowfrequency range,so as to achieve the stochastic resonance weak signal detection. Results showthat the proposed method is feasible and effective,which can significantly improve the output SNR of stochastic resonance,in addition,the system has the better self-adaptability,according to the operation result and output phenomenon,the unknown frequency of the signal to be measured can be obtained,so as to realize the weak signal detection of arbitrary frequency.
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
基金This work was supported by grants from the National Key Research and Development Program of China(2016YFC1301002 to Jianzeng Dong)the National Natural Science Foundation of China(81901841 to Dongdong Deng,81671650 and 81971569 to Yi He)Dongdong Deng also acknowledges support from Dalian University of Technology(DUT18RC(3)068).
文摘Objective:The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms.Methods:Twenty-six late gadolinium enhancement cardiovascular magnetic resonance images of diseased hearts were segmented by the full width at half maximum(FWHM)method,the n standard deviations(n SD)method,and our new automatic method.The results of the three methods were compared with the consensus ground truth obtained by manual segmentation of the ventricular boundaries.Results:Our automatic method yielded the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation.The n SD method produced large variations in the Dice score and the volume difference.The FWHM method yielded the lowest Dice score and the greatest volume difference compared with the automatic,6SD,and 8SD methods,but resulted in less variation when different observers segmented the images.Conclusion:The automatic method introduced in this study is highly reproducible and objective.Because it requires no manual intervention,it may be useful for processing large datasets produced in clinical applications.
文摘Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era.Among various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal world.The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease.Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time.From the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)technique.Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells.Number of image processing techniques are available for processing the images to identify the affected area.This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI image.Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique.The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism.The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its standard.The experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.
文摘In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.
基金National Natural Science Foundations of China(Nos.61362001,61365013,51165033)the Science and Technology Department of Jiangxi Province of China(Nos.20132BAB211030,20122BAB211015)+1 种基金the Jiangxi Advanced Projects for Postdoctoral Research Funds,China(o.2014KY02)the Innovation Special Fund Project of Nanchang University,China(o.cx2015136)
文摘In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.
基金The authors extend their appreciation to the Jilin Provincial Natural Science Foundation for funding this research work through Project Number(20220101128JC).
文摘Knee osteoarthritis(OA)is a common disease that impairs knee function and causes pain.Currently,studies on the detection of knee OA mainly focus on X-ray images,but X-ray images are insensitive to the changes in knee OA in the early stage.Since magnetic resonance(MR)imaging can observe the early features of knee OA,the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered.Firstly,the knee MR images are preprocessed before training,including a region of interest clipping,slice selection,and data augmentation.Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model.The method divides the training process into two stages.The first stage freezes all the base layers and only trains the weights of the embedding neural networks.The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network.In this step,we design a block-by-block fine-tuning strategy for training based on the dense blocks,which improves detection accuracy.We have conducted training experiments with different depth modules,and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer.We achieve an accuracy of 0.921,a sensitivity of 0.960,a precision of 0.885,a specificity of 0.891,an F1-Score of 0.912,and an MCC of 0.836.The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%.
文摘NMR logging can provide the permeability parameter and abundant stratigraphical information such as total porosity,oil,gas and water saturation,oil viscosity,etc. And these physical parameters can be obtained by T2 spectrum inversion. NMR inversion is an important part in logging interpretation. The authors describe a multi-exponential inversion algorithm,solid iteration redress technique( SIRT),and apply the algorithm in real data and compare the results with those based on singular value decomposition( SVD). It shows that SIRT algorithm is easier to be understood and implemented,and the time spent in SIRT is much shorter than that of SVD algorithm. And the non-negative property of T2 spectrum is much easier to be implemented. It can match the results based on SVD very well. SIRT algorithm can be used in T2 spectrum inversion for NMR analysis.
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金the National Key Research and Development Program of China(Grant No.2022YFF0711400)which provided valuable financial support and resources for my research and made it possible for me to deeply explore the unknown mysteries in the field of lunar geologythe National Space Science Data Center Youth Open Project(Grant No.NSSDC2302001),which has not only facilitated the smooth progress of my research,but has also built a platform for me to communicate and cooperate with experts in the field.
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Zhejiang Traditional Chinese Medicine Science and Technology Plan Project(No.2025ZR172).
文摘AIM:To compare spontaneous brain regional activities between diabetic vitreous hemorrhage patients(DVHs)and healthy controls(HCs).METHODS:Thirty-two DVHs and 32 HCs were enrolled in this study.Baseline demographic and vision data were compared between groups using an independent sample t-test.Resting-state functional magnetic resonance imaging(rs-fMRI)was used in all participants.fMRI data was obtained and analyzed using MRIcro and SPM8 software.Fractional amplitude of low-frequency fluctuation(fALFF)technology was used to measure regional spontaneous brain activity,and sensitivity was tested using receiver operating characteristic curves(ROCs).The fALFF values were analyzed using REST software and two-sample t-tests were used to compare values between groups.Hospital anxiety and depression scale(HADS)score was assessed in DVHs and Pearson’s correlation was used to test relationships between mean fALFF value and both HADS score and duration of DVH.RESULTS:Except for the best-corrected visual acuity(BCVA)in both eyes,which showed a statistically significant difference(P<0.05),there were no statistically significant differences in the other indicators(P>0.05)between the HCs and DVHs group.Compared with controls,fALFF value was higher in DVH in cerebellum posterior lobe(CPL)and lower in right anterior cingulate cortex(ACC)and right medial orbitofrontal cortex(OFC).In DVH patients,mean fALFF value of CPL was positively correlated with HADS score and duration of diabetes.However,no such correlation was found,for right ACC or right medial OFC.DVH may lead to abnormal activities in certain brain regions related to visual control and mood.CONCLUSION:Visual impairment caused by DVH may lead to adjustment in regional visual brain activities and may be related to depression or reward system processing in some brain regions.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
基金supported in part by the National Natural Science Foundation of China under Grant No.52277126.
文摘The integration of a large number of power electronic converters,such as railway power conditioner(RPC),introduces a series of problems,including harmonic interaction,stability issues,and wideband resonance,into the railway power supply system.To address these challenges,this paper proposes a novel harmonic resonance prevention measure for RPC-network-train interaction system.Firstly,a harmonic model,a parallel resonance impedance model,a series resonance admittance model,and a control stability model are each established for the RPC-network-train interaction system.Secondly,a comprehensive resonance impact factor(CRIF)is proposed to efficiently and accurately identify the key components affecting resonance,and to provide the selection results of optimization parameters for resonance prevention.Next,the initially selected parameters are constrained by the requirements of ripple current,reactive power and stability.Subsequently,the impedance parameters(control parameters and filter parameters)of the RPC are optimized with the objective of reshaping the parallel resonance impedance and series resonance admittance of the RPC-network-train interaction system,ensuring the output current har-monics of RPC meet standards to achieve resonance prevention,while ensuring the stable operation of the RPC.Finally,the proposed resonance prevention measure is verified under both light load and heavy load conditions using a simulation platform and a hardware-in-the-loop experimental platform.
