Domain Features Research Articles

Label adversarial domain adaptation network for predicting remaining useful life based on cross-domain condition

Most data-driven methods for predicting remaining useful life assume that the data under different operating conditions follow the same distribution. However, this assumption rarely holds in real-world situation. Additionally, traditional methods do not fully utilize the hidden label information from the target domain or account for the transfer quality of source domain data. To address these issues, Label Adversarial Domain Adaptation (LADA) network is introduced in this paper. Specifically, LADA aims to filter the source domain data and maximize the use of hidden label information from the target domain. Firstly, a similarity measurement indicator based on the pearson correlation coefficient (PCC) and dynamic time warping (DTW) is employed to filter source domain data similar to the target domain data distribution. Then, in order to fully utilize the hidden label information from the target domain, the cloud model and golden section are utilized to create pseudo class labels. Furthermore, a feature difference module is established that minimizes the disparity between domain features. This is realized by using the maximum mean difference (MMD) and Kolmogorov–Smirnov (K–S) statistical test. The experimental results indicate that LADA has advantages in cross-domain RUL prediction.

Correlations between Social Isolation and Functional Decline in Older Adults after Lower Limb Fractures Using Multimodal Sensors: A Pilot Study

Older adults (OAs) recovering from lower limb fractures experience social isolation (SI) and functional decline (FD) after they are discharged from inpatient rehabilitation due to reduced physical mobility. Our research used MAISON (Multimodal AI-based Sensor platform for Older iNdividuals), a multimodal sensor system comprising various smart devices collecting acceleration, heart rate, step count, frequency of indoor motion, GPS, and sleep metrics. This study aimed to investigate the correlations between SI and FD with multimodal sensor data from OAs following lower limb fractures. Multimodal sensor data from eight OAs (8 weeks per person) living at home were collected. Five clinical metrics were obtained via biweekly video calls, including three clinical questionnaires (Social Isolation Scale (SIS), Oxford Hip Score, Oxford Knee score) and two physical mobility assessments (Timed Up and Go, 30 s chair stand). From the sensor data collected, 53 statistical and domain features were extracted. Spearman correlation coefficients were calculated between the extracted features and clinical data. The results indicated strong correlations between various items of SIS and sleep metrics in OAs and various items of Oxford Knee Score with GPS and acceleration data. Strong correlations between the questions of the Oxford scores and sensor data highlight the direct impact of physical health status on measurable daily physical activities.

Dual-Spatial Domain Generalization for Fundus Lesion Segmentation in Unseen Manufacturer's OCT Images.

Optical Coherence Tomography (OCT) images can provide non-invasive visualization of fundus lesions; however, scanners from different OCT manufacturers largely vary from each other, which often leads to model deterioration to unseen OCT scanners due to domain shift. To produce the T-styles of the potential target domain, an Orthogonal Style Space Reparameterization (OSSR) method is proposed to apply orthogonal constraints in the latent orthogonal style space to the sampled marginal styles. To leverage the high-level features of multi-source domains and potential T-styles in the graph semantic space, a Graph Adversarial Network (GAN) is constructed to align the generated samples with the source domain samples. To align features with the same label based on the semantic feature in the graph semantic space, Graph Semantic Alignment (GSA) is performed to focus on the shape and the morphological differences between the lesions and their surrounding regions. Comprehensive experiments have been performed on two OCT image datasets. Compared to state-of-the-art methods, the proposed method can achieve better segmentation. The proposed fundus lesion segmentation method can be trained with labeled OCT images from multiple manufacturers' scanners and be tested on an unseen manufacturer's scanner with better domain generalization. The proposed method can be used in routine clinical occasions when an unseen manufacturer's OCT image is available for a patient.

Diverse pathways to permanent childlessness in Singapore: A latent class analysis.

The proportions of adults reaching midlife without having children have been rising rapidly across the globe, particularly in Asia. However, little is known about the pathways to permanent childlessness within the region's childless population. This study utilized latent class analysis (LCA) to typologize pathways to childlessness based on dynamic characteristics of multiple life domains (i.e., partnership, education, and occupation) among 489 childless Singaporeans aged 50 and above from a 2022 nationwide survey. Additionally, we utilized multinomial logistic regressions to examine the sociodemographic correlates of pathway profiles and Shannon's entropy index to assess the heterogeneity in pathways to childlessness among successive cohorts. Results revealed five distinct profiles of pathways to childlessness: the Never-Married Semi-Professionals, the Low-Flex Blue-Collars, the Highly Educated Professionals, the Ever-Married Semi-Professionals, and the Flexible Blue-Collars. These pathway profiles were significantly associated with sociodemographic characteristics such as gender and family background. Women's pathways to childlessness were more standardized and heavily influenced by partnership characteristics, compared to those of men. The childless from privileged family background were less likely to follow pathways characterized by disadvantageous education and occupational status. There were also rising trends of voluntary childlessness among married childless individuals and increasing heterogeneity in pathways to childlessness across successive birth cohorts. In sum, our findings are consistent with some of the predictions of the Second Demographic Transition theory, suggesting that Singapore may be experiencing a demographic transition characterized by rising childlessness, decoupling of marriage and childbearing, and de-standardization of the life course.

Efficient and novel multidomain feature analysis model with incremental optimizations for enhancing pediatric myelodysplastic syndrome detection

Pediatric myelodysplastic syndromes (MDS) are complicated, thus early and accurate diagnosis is essential for treatment planning and patient care. Diagnostic processes often use discrete data domain analysis, which reduces accuracy and delays diagnosis. This work addresses these limitations by introducing an advanced Multi domain Feature Analysis Model (MFAM) enhanced with incremental optimizations to improve pediatric MDS detection. Traditional pediatric MDS diagnosis relies on subjective evaluations and limited data fusion, not modern computational methods. These constraints may reduce diagnosis accuracy and postpone action. The proposed MFAM integrates data from Clinical History, Physical Examination, Blood Cell Counts, Peripheral Blood Smear, Bone Marrow Aspiration and Biopsy, Cytogenetic Analysis, Flow Cytometry, Genetic Testing, Iron Studies, and Bone Marrow Cytology to overcome these challenges. The MFAM increases feature variance by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Bidirectional Gated Recurrent Units (BiGRU). Deep Q Learning with Graph Recurrent Convolutional Neural Networks (DQGRCNN) boosts efficiency. Additionally, the model integrates the Vector Autoregressive Moving Average with Exogenous Inputs (VARMAX) to facilitate early prediction of paediatric MDS. These enhancements have resulted in significant improvements in the precision of paediatric MDS detection by 4.5%, accuracy by 3.5%, recall by 2.3%, Area Under the Curve (AUC) by 1.5%, and specificity by 2.4% while reducing diagnostic delays by 8.5%. Furthermore, the model enhances the precision of predictive analysis by 2.9%, accuracy by 3.5%, recall by 2.5%, AUC by 2.9%, specificity by 5.5%, and reduces delays in predictive analysis by 8.5%. The MFAM presented in this paper revolutionizes the diagnosis and treatment of paediatric MDS by efficiently combining diverse diagnostic data, employing advanced transformation and fusion techniques, and optimizing responses through DQGRCNN. The integration of VARMAX further enables early prediction of the disease. MFAM will enhance diagnostic precision, therapy start, and clinical outcomes for young MDS patients.

Nonlinear damage dynamics modeling and characteristics simulation of bevel gear set

Abstract As a key core device for power transmission in the main reducer of a helicopter, the bevel gear set affects the flight performance of the helicopter directly. A nonlinear damage dynamic model of bevel gear set was constructed using the centralized parameter method. The inherent characteristics and nonlinear behavior characteristics of bevel gear sets were studied, and the time-frequency domain characteristics under different health states were simulated. The fault response transmission law and fault mechanism of the system were deeply revealed, which provides guidance and support for the optimization design of helicopter transmission system and the diagnosis algorithms development.

Comparative biochemical characterization of pufferfish saxitoxin and tetrodotoxin-binding protein (PSTBP) homologs in the plasma from four Takifugu species: Conservation of heat-stable PSTBP orthologs having three and two tandemly repeated lipocalin domains in genus Takifugu

To study the relationship between domain characteristics of pufferfish saxitoxin and tetrodotoxin binding protein (PSTBP) proteoforms and their thermal stability, a comparative biochemical characterization of PSTBPs from the plasma of four Takifugu species (T. flavipterus, T. pardalis, T. alboplumbeus and T. rubripes) was conducted by Western blot analysis. The heat-tolerance tetrodotoxin (TTX)-binding ability of PSTBP proteoforms in T. rubripes plasma was verified by ultrafiltration and liquid chromatography tandem mass spectrometry (LC-MS/MS). These results suggest that the heat-stable PSTBP proteoforms, composed of three and two tandemly repeated lipocalin domains, are genetically conserved and ubiquitous in the genus Takifugu. This study builds on our knowledge of the structural and functional properties of PSTBP proteoforms, which is vital for understanding how toxins are transmitted and accumulate in organisms and is essential for evaluating the potential risks of toxins in seafood.

Investigating Modifiable Risk Factors Across Dementia Subtypes: Insights from the UK Biobank.

This study investigates the relationship between modifiable risk factors and dementia subtypes using data from 460,799 participants in the UK Biobank. Utilizing univariate Cox proportional hazards regression models, we examined the associations between 83 modifiable risk factors and the risks of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VD). Composite scores for different domains were generated by aggregating risk factors associated with ACD, AD, and VD, respectively, and their joint associations were assessed in multivariable Cox models. Additionally, population attributable fractions (PAF) were utilized to estimate the potential impact of eliminating adverse characteristics of the risk domains. Our findings revealed that an unfavorable medical history significantly increased the risk of ACD, AD, and VD (hazard ratios (HR) = 1.88, 95% confidence interval (95% CI): 1.74-2.03, p < 0.001; HR = 1.80, 95% CI: 1.54-2.10, p < 0.001; HR = 2.39, 95% CI: 2.10-2.71, p < 0.001, respectively). Blood markers (PAF = 12.1%; 17.4%) emerged as the most important risk domain for preventing ACD and VD, while psychiatric factors (PAF = 18.3%) were the most important for preventing AD. This study underscores the potential for preventing dementia and its subtypes through targeted interventions for modifiable risk factors. The distinct insights provided by HR and PAF emphasize the importance of considering both the strength of the associations and the population-level impact of dementia prevention strategies. Our research provides valuable guidance for developing effective public health interventions aimed at reducing the burden of dementia, representing a significant advancement in the field.

A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.

FCLFusion: A frequency-aware and collaborative learning for infrared and visible image fusion

Infrared and visible image fusion (IVIF) aims to integrate the advantages of different modal images. Most existing deep learning-based methods often focus on a single IVIF task and ignore the effect of frequency information on the fusion results, which do not fully preserve saliency structures and important texture details. The core idea of this paper is based on the following observation: (1) image content can be characterized by different frequency domain components, low frequency represents base information, such as saliency structure, while high frequency contains texture detail information. (2) multi-tasks learning generally achieve better performance than single-task. Based on these observations, we propose a fusion model called Frequency-aware and Collaborative Learning (FCLFusion) for infrared and visible images. This model takes image fusion as the main task and introduces image reconstruction as an auxiliary task to collaboratively optimize the network, thereby improving the fusion quality. Specifically, we transform spatial domain features to the frequency domain and develop a frequency feature fusion module for guiding the primary network to generate the fused image. The sub-network generates the reconstructed images. Also, we preserve the saliency and detail features via frequency skip connections. Moreover, we propose a hybrid loss function that consists of two terms: frequency loss and self-supervised reconstruction loss. The former aims to prevent information loss in the frequency domain, while the latter improves the extraction of vital information. Extensive experiments verified on three public datasets demonstrate that our FCLFusion outperforms ten state-of-the-art fusion models.

Graph neural network incorporating time-varying frequency domain features with application in spatial wind speed field prediction

Precise forecasting of wind speed is vital to guarantee safe travel on bridges. Nevertheless, current research primarily concentrates on single-point prediction. When it comes to forecasting spatial wind fields, graph neural networks prove to be powerful methods, but many of them have yet to take into account the impact of frequency domain attributes. To address this, a novel model called the graph neural network with time-varying frequency features (GTF) is proposed. Its core layer employs wavelet transforms in matrix form to extract various frequency sub-series, followed by the use of adaptive adjacency matrices to identify their spatial characteristics. Furthermore, aggregating all frequency band information and exploring spatial features further through multi-kernel convolutions. The predictive performance of different models for wind speed forecasting is assessed by utilizing spatial wind field data collected on a large-span bridge. Experimental results demonstrate that spatiotemporal model GTF outperforms the other models in terms of predictive accuracy. The quantity of wind observation points and their level of correlation exert a certain impact on the GTF model, while hyperparameters such as stacking depth and wavelet decomposition parameters have minimal effects. Finally, recommended parameters for the GTF model are provided.

AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain

This paper introduces AVATAR (adversarial self-supervised domain adaptation network for target domain), a novel unsupervised domain adaptation method designed to address the challenge of transferring knowledge from a well-labeled source domain to an unlabeled target domain. AVATAR’s key contributions lie in its unique combination of adversarial learning and self-supervised learning techniques, which enhance the model’s adaptability and accuracy in new, unlabeled environments. The technical innovations of AVATAR include a domain adversarial learning component that reduces domain discrepancy by aligning source and target domain features in a shared feature space, a self-supervised learning module that leverages pseudo-labels and instance weights to refine target domain discrimination boundaries, and a sample selection strategy guided by within-cluster distances to focus on aligning informative samples while mitigating the impact of outliers. By integrating these components, AVATAR provides a comprehensive framework for tackling complex unsupervised domain adaptation tasks, particularly when the domain gap is significant. The method balances domain-invariant feature learning with domain-specific adjustments, enabling effective adaptation to dynamically changing environments. Extensive experiments on multiple benchmark datasets demonstrate AVATAR’s superior performance compared to existing domain adaptation methods, especially in scenarios with limited labeled target domain data. These results highlight AVATAR’s potential as a versatile and robust solution for domain adaptation challenges in various real-world applications.

Machine Learning Predicts Phenoconversion from Polysomnography in Isolated REM Sleep Behavior Disorder.

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total of 66 patients with iRBD were included, of whom 18 converted to an overt alpha-synucleinopathy within 2.7 ± 1.0 years. For each patient, a baseline PSG was available. Sleep stages were scored automatically, and time and frequency domain features were derived from electromyography (EMG) and electroencephalography (EEG) signals in REM and non-REM sleep. Random survival forest was employed to predict the time to phenoconversion, using a four-fold cross-validation scheme and by testing several combinations of features. The best test performances were obtained when considering EEG features in REM sleep only (Harrel's C-index: 0.723 ± 0.113; Uno's C-index: 0.741 ± 0.11; integrated Brier score: 0.174 ± 0.06). Features describing EEG slowing had high importance for the machine learning model. This is the first study employing machine learning applied to PSG to predict phenoconversion in patients with iRBD. If confirmed in larger cohorts, these findings might contribute to improving the design of clinical trials for neuroprotective treatments.

A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

Entropy-guided robust feature domain adaptation for electroencephalogram-based cross-dataset drowsiness recognition

To accurately identify driver drowsiness using Electroencephalogram (EEG), a significant amount of time is typically required to gather data from either the same subject or multiple subjects under the same experimental condition. The prolonged period required for data collection impedes the practical application of EEG. In this paper, we consider the challenging task of cross-dataset driver drowsiness recognition, where the EEG data collected by different devices are expected to be conveniently recognized by a classifier trained on an existing dataset. In order to solve the problem of distribution drift, we propose an Entropy-Guided Robust Feature (EGRF) adaptation framework. This framework utilizes the Entropy Minimization (EM) technique for domain alignment and an ensemble-like structure, consisting of multiple branch classifiers and a dropout layer, to enhance the robustness. The proposed method has been tested across two public datasets and achieves 2-class recognition accuracies of 77.4% and 85.9%. The innovative feature-robust block, combined with the EM loss, leads to substantial performance improvement, proving the effectiveness of our framework for cross-dataset driver drowsiness recognition. Our research demonstrates a promising direction for development of a calibration-free driver drowsiness monitoring system in the future.

Does mental fatigue affect performance in racket sports? A systematic review

Mental fatigue impairs performance across several sports domains. However, a systematic review on its effects on racket sports performance has been lacking due to the previous scarcity of studies. This review aims to provide a comprehensive review the effects of mental fatigue on racket players’ performance, with a discussion of the underlying mechanisms. A thorough search was conducted across five databases, including Web of Science, PubMed, SCOPUS, SPORTDiscus (via EBSCOhost), and the Psychological and Behavioral Science Collection (via EBSCOhost). The PICOS framework established the inclusion criteria: (1) healthy racket sports players; (2) induction of mental fatigue in both field and laboratory settings; (3) comparison of mental fatigue interventions with a control group (e.g., watching a movie or reading a magazine); (4) assessment of performance outcomes, including physical performance, skilled performance, and perceptual-cognitive performance; and (5) randomized controlled trials (RCTs), non-randomized controlled trials (non-RCTs), and non-randomized non-controlled trials. Mental fatigue manipulation, subjective evaluation, and (neuro)physiological markers were synthesized to support the successful induction of mental fatigue. Performance was categorized into tennis, table tennis, badminton, and padel based on the characteristics of specific racket sports domains. Secondary outcomes, such as the rate perception of effort (RPE) and motivation, were synthesized to explain the mechanisms based on the prominent theory of the Psychobiological model of endurance performance. Six studies revealed that mental fatigue impacts stroke performance in table tennis, affecting speed, accuracy, faults, and only second-serve accuracy in tennis. The response time of psychomotor performance increased in table tennis, padel, and badminton. Meanwhile, mental fatigue increased the RPE and remained unchanged in heart rate, blood glucose, and lactate, consistent with the Psychobiological model of endurance performance. Additionally, attention is suggested as a significant underlying psychobiological factor.

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction

Predicting tool wear significantly improves cutting efficiency and quality in intelligent manufacturing. Traditional recurrent neural networks suffer from vanishing or exploding gradients and need to be improved prediction accuracy. Therefore, this paper proposes a gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism (TCN-BiGRU-SA). Monotonicity, robustness, and trendability indicators are used to assess time and frequency domain features. Then, evaluation results of these three indicators are weighted to identify the sensitive features with the degradation trend. The attention mechanism is proposed to adaptively adjust the soft thresholding, and the SA is added to TCN-BiGRU to remain helpful features. Finally, the proposed model is validated using an experimental dataset. Compared to the TCN-BiGRU model without the SA mechanism, the predicted results show that the average mean absolute error and root mean square error are reduced by 48.29 % and 36.39 %, respectively.

A one-step fabrication method of flexible magnetostrictive fiber ribbon based on Fe50Co50 alloy and its characteristics study

Magnetostrictive materials are widely used in sensors, actuators and micro-nano electronics because of their excellent performance. However, traditional rigid magnetostrictive materials are difficult to adapt to the flexible and deformable structure of flexible electronic devices. In order to solve the deformation, sensing and control problems in the field of flexible electronics, a one-step new method of is proposed to deposit Fe50Co50 fiber ribbons with reciprocated and loop structures on flexible substrate. Helmholtz coil is designed to test the magnetostrictive positive effect ability of Fe50Co50 ribbon, and its magnetostrictive strain curve is analyzed. Furthermore, frequency domain characteristics of Fe50Co50 ribbon are tested, and the influence of externally driving magnetic field on elastic layer materials and resonance output characteristics is analyzed. The experimental results show that Fe50Co50 ribbon exhibits good low-frequency magnetic field characteristics. And free end displacement of Fe50Co50 ribbon based on loop structure, under 12 mT alternating magnetic field and 134 mT bias magnetic field, reaches a peak-to-peak value of 870 μm for first-order resonance, and 320 μm for second-order resonance. This verifies that Fe50Co50 ribbon possesses the expected properties that magnetostrictive materials should have, and innovatively breaks through the technical bottleneck of manufacturing flexible micro-nano magnetostrictive energy exchange components.