Bipedal Robot Gait Generation Using Bessel Interpolation.

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot's joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.

Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

Calcification circumference, area, and location are associated with carotid stent expansion rate: high-resolution magnetic resonance vessel wall imaging study.

Background: The plaque imaging findings associated with the stent expansion rate (SER) of the carotid artery are not well known. The purpose of this study was to investigate the imaging findings associated with SER. Methods: It was a retrospective investigation. Based on the kind of carotid stents used, retrospective data from 89 patients who had carotid artery stenting (CAS) for atherosclerotic carotid stenosis were gathered and divided into two groups: open-cell stents and closed-cell stents. Patients underwent preoperative carotid high-resolution magnetic resonance vessel wall imaging (HR-VWI). Use HR-VWI to quantitatively evaluate carotid wall thickness and plaque components. Calculate SER using digital subtraction angiography (DSA). All patients' baseline and HR-VWI imaging features were retrospectively analyzed. Simple and multivariable linear regression analysis was used to determine the imaging findings associated with SER of open-cell and closed-cell stents. Results: A total of 89 patients (mean age, 70±8 years; 69 men) were included in the final analysis. Among 89 patients, 35 patients were treated with open-cell stents. Fifty-four patients were treated with closed-cell stents. In the open-cell stents group, the Maximum single-slice calcification circumference score, maximum wall thickness (WTmax), and total calcification location score with P<0.10 in the simple linear regression analysis were included in the multivariable linear regression analysis. The results of the multivariable linear regression revealed that only the Maximum single-slice calcification circumference score (ß=-9.35; 95% CI: -18.15 to -0.56; P=0.03) was associated with SER of open-cell stents. In the closed-cell stents group, the Maximum single-slice calcification circumference score, WTmax, maximum area percentage of calcification, calcification volume, and total calcification location score with P<0.10 in the simple linear regression analysis were included in the multivariable linear regression analysis. The results of the multivariable linear regression revealed that the Maximum area percentage of calcification (ß=-0.67; 95% CI: -1.29 to -0.05; P=0.03), Maximum single-slice calcification circumference score (ß=-8.43; 95% CI: -13.36 to -3.49; P=0.001) and total calcification location score (ß=-0.37; 95% CI: -1.08 to 0.09; P=0.02) were associated with SER of closed-cell stents. Conclusions: Calcified plaques are associated with SER of the carotid artery. Calcification circumference correlates with SER of open-cell stents. Calcification circumference, calcification area, and calcification location are related to SER of closed-cell stents, which may provide a new consideration for clinicians when choosing carotid artery stents.

A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network.

Continuous exploration of the ocean has made underwater image processing an important research field, and plenty of CNN (convolutional neural network)-based underwater image enhancement methods have emerged over time. However, the feature-learning ability of existing CNN-based underwater image enhancement is limited. The networks were designed to be complicated or embed other algorithms for better results, which cannot simultaneously meet the requirements of suitable underwater image enhancement effects and real-time performance. Although the composite backbone network (CBNet) was introduced in underwater image enhancement, we proposed OECBNet (optimal underwater image-enhancing composite backbone network) to obtain a better enhancement effect and shorten the running time. Herein, a comprehensive study of different composite architectures in an underwater image enhancement network was carried out by comparing the number of backbones, connection strategies, pruning strategies for composite backbones, and auxiliary losses. Then, a CBNet with optimal performance was obtained. Finally, cross-sectional research of the obtained network with the state-of-the-art underwater enhancement network was performed. The experiments showed that our optimized composite backbone network achieved better-enhanced images than those of existing CNN-based methods.

An AAM-Based Identification Method for Ear Acupoint Area.

Ear image segmentation and identification is for the "observation" of TCM (traditional Chinese medicine), because disease diagnoses and treatment are achieved through the massaging of or pressing on some corresponding ear acupoints. With the image processing of ear image positioning and regional segmentation, the diagnosis and treatment of intelligent traditional Chinese medicine ear acupoints is improved. In order to popularize ear acupoint therapy, image processing technology has been adopted to detect the ear acupoint areas and help to gradually replace well-trained, experienced doctors. Due to the small area of the ear and the numerous ear acupoints, it is difficult to locate these acupoints based on traditional image recognition methods. An AAM (active appearance model)-based method for ear acupoint segmentation was proposed. The segmentation was illustrated as 91 feature points of a human ear image. In this process, the recognition effects of the ear acupoints, including the helix, antihelix, cymba conchae, cavum conchae, fossae helicis, fossae triangularis auriculae, tragus, antitragus, and earlobe, were divided precisely. Besides these, specially appointed acupoints or acupoint areas could be prominent in ear images. This method made it possible to partition and recognize the ear's acupoints through computer image processing, and maybe own the same abilities as experienced doctors for observation. The method was proved to be effective and accurate in experiments and can be used for the intelligent diagnosis of diseases.

A Spring Compensation Method for a Low-Cost Biped Robot Based on Whole Body Control.

At present, the research and application of biped robots is more and more popular. The popularity of biped robots can be better promoted by improving the motion performance of low-cost biped robots. In this paper, the method of the Linear Quadratic Regulator (LQR) is used to track a robot's center of mass (COM). At the same time, the whole-body-control method and value function generated in the process of tracking COM are used to construct the quadratic programming (QP) model of a biped robot. Through the above method, the torque feedforward of the robot is obtained in the Drake simulation platform. The torque feedforward information of the robot is transformed into position feedforward information by spring compensation. In this paper, open loop control and spring compensation are used, respectively, to make the robot perform simple actions. Generally, after the compensation method of spring compensation is adopted, the roll angle and pitch angle of the upper body of the robot are closer to 0 after the robot performs an action. However, as the selected motion can introduce more forward and lateral motions, the robot needs more spring clearance compensation to improve performance. For improving the motion performance of a low-cost biped robot, the experimental results show that the spring compensation method is both reasonable and effective.

Defect Severity Identification for a Catenary System Based on Deep Semantic Learning.

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F1-score value.

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection.

Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system's operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.

Anion-coordination-driven single-double helix switching and chiroptical molecular switching based on oligoureas.

Synthetic foldamers with helical conformation are widely seen, but controllable interconversion amongst different geometries (helical structure and sense) is challenging. Here, a family of oligourea (tetra-, penta-, and hexa-) ligands bearing stereocenters at both ends are designed and shown to switch between single and double helices with concomitant inversion of helical senses upon anion coordination. The tetraurea ligand forms a right-handed single helix upon chloride anion (Cl-) binding and is converted into a left-handed double helix when phosphate anion (PO4 3-) is coordinated. The helical senses of the single and double helices are opposite, and the conversion is further found to be dependent on the stoichiometry of the ligand and phosphate anion. In contrast, only a single helix is formed for the hexaurea ligand with the phosphate anion. This distinction is attributed to the fact that the characteristic phosphate anion coordination geometry is satisfied by six urea moieties with twelve H-bonds. Our study revealed unusual single-double helix interconversion accompanied by unexpected chiroptical switching of helical senses.

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

Comparison of pre-intestinal transplant and posttransplant sarcopenia and visceral adiposity measurements using computed tomography in the diagnosis of malnutrition during a nutrition-focused physical examination.

BACKGROUND: Registered dietitian nutritionists subjectively assess muscle loss as part of the nutrition-focused physical examination (NFPE), using guidelines to standardize malnutrition diagnosis. Computed tomography (CT) scans provide an objective measure of skeletal muscle mass and abdominal wall and visceral adipose tissue and can be used to determine skeletal muscle loss. METHODS: In this retrospective review, our team compared muscle measurements including the psoas, paraspinal muscles, and abdominal wall muscle area at the level of the third lumbar vertebral body (using CT)-as well as visceral and subcutaneous adipose tissue measurements-before and after gut transplant with the malnutrition diagnosis found on the NFPE. We also examined the association between CT measurements and postoperative infection, length of stay, and mortality. RESULTS: Forty-two patients were included in the study. Adipose tissue measurements on CT analysis were significantly lower in the malnutrition group compared with those without malnutrition (P ≤ 0.05) in both the pretransplant and posttransplant groups. Skeletal muscle size measurements were not significantly associated with malnutrition, but when adjusted for patients' height by calculating skeletal muscle index, an association between low skeletal muscle index scores and malnutrition diagnosis was found (P = 0.026). Pretransplant malnutrition diagnosis did not predict infection, length of stay, or mortality. CONCLUSION: Objective assessment of subcutaneous adipose tissue by CT analysis was significantly correlated with the subjective assessment of malnutrition by NFPE in both pretransplant and posttransplant patients. Skeletal muscle index scores were significantly lower in pretransplant patients who were diagnosed with malnutrition.

Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side.

As an efficient way to integrate multiple distributed energy resources (DERs) and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads (TCLs), energy storage systems (ESSs), price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process (MDP) process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor-critic (Memory A3C, M-A3C) with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training. The multithreaded working feature of M-A3C can efficiently learn the resource priority allocation on the demand side of the microgrid and improve the flexible scheduling of the demand side of the microgrid, which greatly reduces the input cost. Comparison of the researched cost optimization results with the results obtained with the proximal policy optimization (PPO) algorithm reveals that the proposed algorithm has better performance in terms of convergence and optimization economics.

Growth differentiation factor 15 predicts poor prognosis in patients with heart failure and reduced ejection fraction and anemia: results from RED-HF.

AIMS: We aimed to assess the value of GDF-15, a stress-responsive cytokine, in predicting clinical outcomes in patients with heart failure (HF) with reduced ejection fraction (HFrEF) and anemia METHODS AND RESULTS: Serum GDF-15 was assessed in 1582 HFrEF and mild-to-moderate anemia patients who where followed for 28 months in the Reduction of Events by Darbepoetin alfa in Heart Failure (RED-HF) trial, an overall neutral RCT evaluating the effect darbepoetin alfa on clinical outcomes in patients with systolic heart failure and mild-to-moderate anemia. Association between baseline and change in GDF-15 during 6 months follow-up and the primary composite outcome of all-cause death or HF hospitalization were evaluated in multivariable Cox-models adjusted for conventional clinical and biochemical risk factors. The adjusted risk for the primary outcome increased with (i) successive tertiles of baseline GDF-15 (tertile 3 HR 1.56 [1.23-1.98] p < 0.001) as well as with (ii) a 15% increase in GDF-15 levels over 6 months of follow-up (HR 1.68 [1.38-2.06] p < 0.001). Addition of change in GDF-15 to the fully adjusted model improved the C-statistics (p < 0.001). No interaction between treatment and baseline or change in GDF-15 on outcome was observed. GDF-15 was inversely associated with several indices of anemia and correlated positively with ferritin. CONCLUSIONS: In patients with HF and anemia, both higher baseline serum GDF-15 levels and an increase in GDF-15 during follow-up, were associated with worse clinical outcomes. GDF-15 did not identify subgroups of patients who might benefit from correction of anemia but was associated with several indices of anemia and iron status in the HF patients.

Effectiveness of collateral arteries embolization before endovascular aneurysm repair to prevent type II endoleaks: A systematic review and meta-analysis.

OBJECTIVES: This study aimed to evaluate the effect of preventive collateral arteries embolization before endovascular aneurysm repair (EVAR) to reduce type II endoleaks (T2EL), aneurysm enlargement, and re-interventions. METHODS: A comprehensive search of PubMed, MEDLINE, Web of Science, and Embase was conducted to identify articles in English, related to preventive collateral arteries embolization before EVAR, published until October 2020. RESULTS: A total of 12 relevant studies, including 11 retrospective studies and one randomized controlled trial, were identified and fulfilled the specified inclusion criteria. A total of 1706 patients in 11 studies were involved in the meta-analysis. The overall incidence of T2EL was 17.3% in the embolization group vs. 34.5% in the control group (OR 0.36, p < 0.01). The incidence of persistent T2EL was 15.3% vs. 30.0% (OR 0.37, p < 0.01). Five studies reported the incidence of sac enlargement, with the rate 10.2% vs. 24.9% (OR 0.25, p < 0.01). Nine studies reported T2EL related re-interventions, and it was 1.3% in the embolization group and 10.4% in control (OR 0.14, p < 0.01). The technical success of collateral arteries embolization was 92.1% (455/494) in the 12 studies. 1.2% (10/829) patients suffered a mild complication of collateral arteries embolization, and 2/829 patients died because of the embolization. CONCLUSION: Collateral arteries embolization is a promising measure to prevent the occurrence of T2EL, sac enlargement, and re-intervention. High-quality studies need to be conducted to provide stronger evidence-based medical suggestions about the embolize operation.

Paediatric Ulcerative Colitis Is a Fibrotic Disease and Is Linked with Chronicity of Inflammation.

BACKGROUND AND AIMS: Intestinal fibrosis has recently been characterised in adult ulcerative colitis and may affect motility, diarrhoea, and the symptom of urgency. We aimed to charactersze the presence of fibrosis in paediatric patients with ulcerative colitis, and its link to severity and chronicity of mucosal inflammation, as well as clinical factors of severity. METHODS: We performed a single-centre cross-sectional study in children ages 1-18 years with ulcerative colitis, undergoing colectomy or proctocolectomy. Tissue cross-sections were derived from proximal, mid, and distal colon and rectum, and inflammation and fibrosis were graded based on previously developed scores. Clinical data were collected prospectively. RESULTS: From 62 patients, 205 intestinal sections were evaluated. Median age at diagnosis was 13 years, 100% had extensive colitis, and all resections were done for refractory disease. The presence, chronicity, and degree of inflammation were linked with the presence of fibrosis. Thickness of the muscularis mucosa was also linked with presence and chronicity of inflammation. The overall submucosal fibrosis burden was associated with prior anti-tumour necrosis factor use. CONCLUSIONS: Paediatric patients with ulcerative colitis exhibit colorectal submucosal fibrosis and muscularis mucosa thickening, which correlate with the presence, chronicity, and degree of mucosal inflammation. Fibrosis should be recognised as a complication of paediatric ulcerative colitis, and ulcerative colitis should be considered a progressive disease.

Study on Mechanical Characteristics of Segmental Joints of a Large-Diameter Shield Tunnel under Ultrahigh Water Pressure.

At present, there is no clear design standard for segmental joints of large-diameter shield tunnels under high water pressure. In this paper, a theoretical calculation model for the bending stiffness of segmental joints under high water pressure is proposed. The numerical simulation method is used to investigate the failure and crack formation processes of single-layer and double-layer lining segments under large axial forces. The effects of axial force, bolt strength, and concrete strength on the bending stiffness of joints are then studied using a theoretical calculation model of segmental joints. The results show that under extremely high water pressure, the influence of double lining on joint stiffness is limited. It is more rational and safe to compute the bending stiffness of segmental joints using this theoretical model rather than the numerical simulation method. The parameter analysis reveals that increasing the bolt strength has a minor impact on bending stiffness and deformation, whereas increasing the concrete strength has the opposite effect. The influence of ultimate bearing capacity and deformation decreases non-linearly as the axial force increases.

The Efficacy of Transcarotid Artery Revascularization With Flow Reversal System Compared to Carotid Endarterectomy: A Systematic Review and Meta-Analysis.

Background: Carotid artery stenosis has long been a critical cause of stroke and death, and it can seriously affect the life quality. Transcarotid artery revascularization (TCAR) and carotid endarterectomy (CEA) are both feasible therapies for this disease. This systematic review and meta-analysis aim to evaluate if the efficacy of the two approaches is comparable. Methods: Clinical studies up to March 2021 were searched through PubMed, Embase, and Scopus from a computer. The screening process was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Newcastle-Ottawa Scale (NOS) was used for methodological quality assessment of works of literature meeting the inclusion criteria, and Review Manager 5.4 was used for data synthesis. The I2 statistic was performed to measure the heterogeneity, and M-H/I-V fixed or random model was utilized depending on the I2 value. The evidence evaluation was accomplished based on grades of recommendation, assessment, development, and evaluation (GRADE) online tool. Results: A total of 14,200 subjects (six comparative studies) were finally included in this pooled study. There is no statistical discrepancy between the two treatments on reducing stroke/death/myocardial infarction (odds ratio [OR] 0.85, 95% CI 0.67-1.07), stroke (OR 1.03, 95% CI 0.77-1.37), or death (OR 1.14, 95% CI 0.67-1.94). Besides, TCAR is associated with a lower incidence of myocardial infarction (P = 0.004), cranial nerve injury (P < 0.00001), and shorter procedure time (P < 0.00001) than CEA among the overall cohort. Conclusions: TCAR is a rapidly developing treatment that reaches a comparable prognosis to CEA and significantly reduces the risk of myocardial infarction under the well-matched condition, which is a dependable choice for patients with carotid stenosis.

Howling Detection and Suppression Based on Segmented Notch Filtering.

The existing adaptive echo cancellation based howling (typically in hearing aids) removal methods have several drawbacks such as insufficient attenuation of the howling component, slow response and nonlinear distortion. To solve these problems, we propose a segmented notch filtering based scheme. Specifically, firstly, it is proved that the attenuation value can reach -330 dB at any detected howling frequency; secondly, the filter coefficients can be readily calculated by a closed-form formula, yielding a fast response to the sudden howling accident; thirdly, the closed-form formula of this filter is theoretically an even function, indicating that this filter possesses a linear transfer characteristic. In combination with proper segmentation and precisely removing these transient samples arising from FIR (Finite Impulsive Response) filtering, nonlinear distortion can be entirely avoided. Experimental results show that our proposed scheme can not only accurately estimate the howling frequency, but can also completely remove it, which yields a high-quality output waveform with a recovery SNR of about 22 dB. Therefore, the proposed segmented notching based scheme possesses vast potential for hearing aid development and other relevant applications.

Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network.

As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot meet the requirements. To solve this problem, a real-time closed-loop detection method based on a dynamic Siamese networks is proposed in this paper. First, a dynamic Siamese network-based fast conversion learning model is constructed to handle the impact of external changes on key frame judgments, and an elementwise convergence strategy is adopted to ensure the accurate positioning of key frames in the closed-loop judgment process. Second, a joint training strategy is designed to ensure the model parameters can be learned offline in parallel from tagged video sequences, which can effectively improve the speed of closed-loop detection. Finally, the proposed method is applied experimentally to three typical closed-loop detection scenario datasets and the experimental results demonstrate the effectiveness and robustness of the proposed method under the interference of complex scenes.

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach.

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.