Search Results (332)

This paper presents a stability analysis of single-channel, slow-rolling, Semi-Automatic Command to Line of Sight (SACLOS) missiles using a comparison of the Routh–Hurwitz and the Frank–Wall stability criteria and a nonlinear analysis. Beginning with a six-degree-of-freedom (6-DOF) model in the Resal frame, a linearized model for the commanded motion is developed. This linearized model, which features complex coefficients due to the coupling of longitudinal channels in rolling missiles, is used to define the structural scheme of the commanded object and its flight quality parameters. The guidance kinematic relations, guidance device equations, and actuator relations, incorporating a switching function specific to slow-rolling, single-channel missiles, are also defined and linearized within the Resal frame to construct a comprehensive structural diagram of the SACLOS missile. From this, the characteristic polynomial with complex coefficients is derived and analyzed by comparing the Routh–Hurwitz and the Frank–Wall stability criteria. This analysis determines a stability domain for the guidance gain and establishes a minimum limit for the guidance time. The stability domain defined through the linear model is then validated using a nonlinear model in the body frame. Full article

This paper studies the problem of locating wireless sensor networks (WSNs) based on time-of-arrival (TOA) measurements in mixed line of sight/non-line-of-sight (LOS/NLOS) environments. To mitigate the impacts of NLOS and improve performance both in positioning accuracy and computation time, we hereby propose an exhaustive method (i.e., EM). The EM method mainly consists of two processes. In the first process, all BSs are arranged into various combinations. For each combination, a solution and its corresponding residual vector can be obtained. For each combination, all BSs can be divided into two categories: BSs that participate in positioning and BSs that do not. Therefore, the above residual vector can also be divided into two categories in each group. In the second process, combining the comparison results of two residual vectors and the characteristics of NLOS errors, we propose a new criterion to find out solutions with only LOS-BSs. Then the final solution can be obtained by further processing these solutions. This method does not require any prior information regarding NLOS status, NLOS amplitude, or noise variance, and only needs three LOS-BSs. Numerical simulation results shows that our method greatly improves the accuracy and reduces the computation time compared to state-of-art methods. Full article

Background/Objectives: We recently discovered that tumors rely on blood fatty acids as an energy source for growth. Therefore, we investigated biomarkers in the lipid fractions of plasma from patients with pancreatic ductal adenocarcinoma (PDAC) for the screening diagnosis of PDAC. Methods: We screened common fatty acid types in human (normal 99, PDAC 103) and mouse (normal 7, KPC 22) plasma samples using a non-targeted approach. Subsequently, we identified targets in human plasma (set A: normal 68, PC 102) that could distinguish between healthy individuals and patients with cancer. Next, we verified whether the identified targets were useful in a new human set (set B: 96 normal, 78 PC). We combined sets A and B to create set C and further divided it into a training set (7:3 ratio; normal 115, pancreatic cancer 126) and a validation set (normal 49, PC 54). The identified targets were used to train three statistical models (logistic regression (LR), random forest (RF), and support vector machine (SVM) with a radial basis function (RBF) kernel). Results: The comparison of human and mouse plasma identified eight common lipid metabolites. We further identified four platforms containing these metabolites for target analysis: acylcarnitines, phospholipids, fatty acid amides, and sphingolipids. We analyzed the four platforms using sets A, B, and C and found 20 lipids (1 acylcarnitine, 1 sphingolipid, and 18 phospholipids) that met the criterion of AUC ≥ 0.75 in all three sets. Based on an average AUC for LR models with 11 or more phospholipids, the separation performance between healthy individuals and patients with cancer was 0.9207 (sensitivity, 90.74%; specificity, 86.22%; PPV, 87.90%; NPV, 89.42%), and the AUC of the validation set for CA19-9 in the same groups was 0.7354. The addition of CA19-9 to the LR models resulted in a separation performance of 0.9427 (90.74%; 88.01%; 89.32%; 89.61%) for the validation set. Conclusions: We identified 18 candidate fatty acid metabolites that could serve as biological markers in the serum lipid fractions of pancreatic cancer patients and confirmed that all of them decreased in patients. Additionally, we developed an algorithm utilizing these markers, which demonstrated a 25% increase in discriminatory power compared to the AUC value of CA19-9, an FDA-approved biomarker for pancreatic cancer. In summary, we identified candidate metabolites and algorithms that could serve as biomarkers in the lipid fractions of plasma from patients with pancreatic cancer. Full article

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

To address the challenges of narrow peak characteristics and low signal-to-noise ratio (SNR) detection in Laser-Induced Breakdown Spectroscopy (LIBS), in this paper, we combine the Sparse Bayesian Learning–Baseline Correction (SBL-BC) algorithm with residual skewness monitoring to propose a spectral estimation method tailored for LIBS. In LIBS spectra, discrete peaks are susceptible to baseline fluctuations and noise, while the Gaussian dictionary modeling and fixed convergence criterion of the existing SBL-BC lead to the inaccurate characterization of narrow peaks and high computational complexity. To overcome these limitations, we introduce a residual skewness dynamic tracking mechanism to mitigate residual negative skewness accumulation caused by positivity constraints under high noise levels, preventing traditional convergence criterion failure. Simultaneously, by eliminating the dictionary matrix and directly modeling the spectral peak vector, we transform matrix operations into vector computations, better aligning with LIBS’s narrow peak features and high-channel-count processing requirements. Through simulated and real spectral experiments, the results demonstrate that this method outperforms the SBL-BC algorithm in terms of spectral peak fitting accuracy, computational speed, and convergence performance across various SNRs. It effectively separates spectral peaks, baseline, and noise, providing a reliable approach for both quantitative and qualitative analysis of LIBS spectra. Full article

In this work we present the shape optimization of a 2000 L water tank placed behind the rear axle of a forestry skidder. The main criterion is the static stability of the vehicle. The purpose of the research is to decrease the impact of the tank on stability of the vehicle. The stability is determined in the form of distances of vectors of a stability triangle and a gravity vector. The tank is divided into small elements and their impact on stability is evaluated independently. Then, the gravity vector, placed in the center of gravity of the vehicle with the tank, combines the gravities of the vehicle and the tank composed of as many elements as required for the desired volume. The Python 3.13 programming language is used to implement the solution. The results for various shapes of the tank are displayed in the form of heatmaps. A slope angle of 20 degrees is used for the analysis. The results show that the longitudinal or lateral stability can be improved by shape modifications of the tank. The most interesting output is the final shape of the tank that improves terrain accessibility of the vehicle. The optimization method is universal and can also be used for different vehicles, tank placements and also auxiliary devices added in general positions. Full article

Transmission towers constitute critical power infrastructure, yet structural damage may accumulate over their long-term service, underscoring the paramount importance of research on damage identification. This paper presents a cross-correlation function amplitude vector (CorV) method for damage localization based on time-domain response analysis. The approach involves calculating the CorV of structural members before and after damage using dynamic response data, employing the CorV assurance criterion (CVAC) to quantify changes in CorV, and introducing first-order differencing for damage localization. Taking an actual transmission tower in Jiangmen as the engineering backdrop, a finite element model is established. Damage conditions are simulated by reducing the stiffness of specific members, and parameter analyses are conducted to validate the proposed method. Furthermore, experimental validation in a lab is performed to provide additional confirmation. The results indicate that the CVAC value of the damaged structure is significantly lower than that in the healthy state. By analyzing the relative changes in the components of CorV, the damage location can be accurately determined. Notably, this method only requires acquiring the time-domain response signals of the transmission tower under random excitation to detect both the existence and location of damage. Consequently, it is well suited for structural health monitoring of transmission towers under environmental excitation. Full article

To circumvent the computational bottlenecks associated with the intermediate steps (e.g., least squares fitting) in conventional sine wave similarity principles and directly acquire the energy metrics required for stabilized sinusoidal waveform characterization, this study leverages time domain probability distribution theory. From a complementary advantage perspective, a novel transformer inrush current identification criterion is developed using the Wasserstein distance metric. The methodology employs feature discretization to extract target/template signals, transforming them into state vectors for sample labelling. By quantifying inter-signal energy distribution disparities through this framework, it achieves a precise waveform similarity assessment in sinusoidal regimes. The theoretical analysis and simulations demonstrate that the approach eliminates frequency domain computations while maintaining implementation simplicity. Compared with conventional sine wave similarity methods, the solution streamlines protection logic and significantly enhances practical applicability with accelerated response times. Furthermore, tests conducted on field-recorded circuit breaker closing waveforms using MATLAB R2022a confirm the effectiveness of the proposed method in improving transformer protection performance. Full article

This paper presents some of the most significant findings in the design of a hydraulic servomechanism for flight controls, which were primarily achieved by the first author during his activity in an aviation institute. These results are grouped into four main topics. The first one outlines a classical theory, from the 1950s–1970s, of the analysis of nonlinear automatic systems and namely the issue of absolute stability. The uninformed public may be misled by the adjective “absolute”. This is not a “maximalist” solution of stability but rather highlights in the system of equations a nonlinear function that describes, for the case of hydraulic servomechanisms, the flow-control dependence in the distributor spool. This function is odd, and it is therefore located in quadrants 1 and 3. The decision regarding stability is made within the so-called Lurie problem and is materialized by a matrix inequality, called the Lefschetz condition, which must be satisfied by the parameters of the electrohydraulic servomechanism and also by the components of the control feedback vector. Another approach starts from a classical theorem of V. M. Popov, extended in a stochastic framework by T. Morozan and I. Ursu, which ends with the description of the local and global spool valve flow-control characteristics that ensure stability in the large with respect to bounded perturbations for the mechano-hydraulic servomechanism. We add that a conjecture regarding the more pronounced flexibility of mathematical models in relation to mathematical instruments (theories) was used. Furthermore, the second topic concerns, the importance of the impedance characteristic of the mechano-hydraulic servomechanism in preventing flutter of the flight controls is emphasized. Impedance, also called dynamic stiffness, is defined as the ratio, in a dynamic regime, between the output exerted force (at the actuator rod of the servomechanism) and the displacement induced by this force under the assumption of a blocked input. It is demonstrated in the paper that there are two forms of the impedance function: one that favors the appearance of flutter and another that allows for flutter damping. It is interesting to note that these theoretical considerations were established in the institute’s reports some time before their introduction in the Aviation Regulation AvP.970. However, it was precisely the absence of the impedance criterion in the regulation at the appropriate time that ultimately led, by chance or not, to a disaster: the crash of a prototype due to tailplane flutter. A third topic shows how an important problem in the theory of automatic systems of the 1970s–1980s, namely the robust synthesis of the servomechanism, is formulated, applied and solved in the case of an electrohydraulic servomechanism. In general, the solution of a robust servomechanism problem consists of two distinct components: a servo-compensator, in fact an internal model of the exogenous dynamics, and a stabilizing compensator. These components are adapted in the case of an electrohydraulic servomechanism. In addition to the classical case mentioned above, a synthesis problem of an anti-windup (anti-saturation) compensator is formulated and solved. The fourth topic, and the last one presented in detail, is the synthesis of a fuzzy supervised neurocontrol (FSNC) for the position tracking of an electrohydraulic servomechanism, with experimental validation, in the laboratory, of this control law. The neurocontrol module is designed using a single-layered perceptron architecture. Neurocontrol is in principle optimal, but it is not free from saturation. To this end, in order to counteract saturation, a Mamdani-type fuzzy logic was developed, which takes control when neurocontrol has saturated. It returns to neurocontrol when it returns to normal, respectively, when saturation is eliminated. What distinguishes this FSNC law is its simplicity and efficiency and especially the fact that against quite a few opponents in the field, it still works very well on quite complicated physical systems. Finally, a brief section reviews some recent works by the authors, in which current approaches to hydraulic servomechanisms are presented: the backstepping control synthesis technique, input delay treated with Lyapunov–Krasovskii functionals, and critical stability treated with Lyapunov–Malkin theory. Full article

Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface electromyography (sEMG) recorded muscle activity. Participants were guided to perform both proper and commonly observed incorrect forms of each movement. Statistical analyses compared joint angles and sEMG signals, and a support vector machine (SVM) was trained to classify movement correctness. The analysis showed that correct executions consistently produced distinct joint angle patterns and significantly higher sEMG activity than incorrect ones (p < 0.001). After modifying the selection of joint angle features for Movement 5 (M5), the classification accuracy improved to 96.26%. Including M5, the average classification accuracy across all eight exercises reached 97.77%, demonstrating the overall robustness and consistency of the proposed approach. In contrast, high variability across individuals made sEMG less reliable as a standalone indicator of correctness. The strong classification performance based on joint angles highlights the potential of this approach for real-world applications. While sEMG signals confirmed the physiological differences between correct and incorrect executions, their individual variability limits their generalizability as a sole criterion. Joint angle data derived from a simple single-camera setup can effectively distinguish movement quality in older adults, offering a low-cost, user-friendly solution for real-time feedback in home and community settings. This approach may help support independent exercise and reduce reliance on professional supervision. Full article

Sugarcane yield prediction plays a pivotal role in enabling farmers to monitor crop development and optimize cultivation practices, guiding harvesting operations for sugar mills. In this study, we established three experimental fields, which were planted with three main sugarcane cultivars in Guangxi, China, respectively, implementing a multi-gradient fertilization design with 39 plots and 810 sampling grids. Multispectral imagery was acquired by unmanned aerial vehicles (UAVs) during five critical growth stages: mid-tillering (T1), late-tillering (T2), mid-elongation (T3), late-elongation (T4), and maturation (T5). Following rigorous image preprocessing (including stitching, geometric correction, and radiometric correction), 16 VIs were extracted. To identify yield-sensitive vegetation indices (VIs), a spectral feature selection criterion combining gray relational analysis and correlation analysis (GRD-r) was proposed. Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. Results demonstrated that multi-stage models consistently outperformed their single-stage counterparts. Among the single-stage models, the RF model using T3-stage features achieved the highest accuracy (R² = 0.78, RMSE_V = 7.47 t/hm²). The best performance among multi-stage models was obtained using a GBDT model constructed from a combination of DVI (T1), NDVI (T2), TDVI (T3), NDVI (T4), and SRPI (T5), yielding R² = 0.83 and RMSE_V = 6.63 t/hm². This study highlights the advantages of integrating multi-temporal spectral features and advanced machine learning techniques for improving sugarcane yield prediction, providing a theoretical foundation and practical guidance for precision agriculture and harvest logistics. Full article

The performance of multiple-input single-output (MISO) transmission is highly dependent on the accuracy of the channel state information (CSI) at the base station (BS), which necessitates precise CSI estimation and reliable feedback from the user equipment. However, the overhead of the CSI feedback occupies substantial uplink bandwidth resources. To alleviate the overhead, this paper proposes a novel task-based quantizer for uplink MISO visible light communication (VLC) systems. In particular, a hybrid radio frequency (RF)/VLC system is considered, where VLC links are mainly used for large-volume downlink transmissions and RF links are used for uplink CSI feedback. Since the RF bandwidth resources are limited, the CSI is quantified to reduce the uplink resource requirements, which, however, inevitably causes CSI estimation errors at the BS. To guarantee the CSI estimation accuracy while minimizing the RF resource cost, a task-based quantization scheme for channel estimation (TQ-CE) is proposed. In the TQ-CE, both the quantized codebook and the post-processing matrix are optimized to minimize the mean square error (MSE) of the channel estimation. Taking the minimum MSE as the target task, the TQ-CE leverages vector quantization (VQ) to generate a codebook, which is designed to reduce the feedback overhead without compromising the precision of the channel estimation. Then, an optimal closed-form solution of the post-processing matrix is derived based on the minimum mean square error (MMSE) criterion. The simulation results demonstrate that the proposed TQ-CE achieved $0.25\mathrm{Mbit}/\mathrm{s}$ and $0.62\mathrm{Mbit}/\mathrm{s}$ higher data rates compared with the conventional scalar quantization-based channel estimation (SQ-CE) schemes and vector quantization-based channel estimation (VQ-CE) schemes, respectively. Moreover, in terms of the feedback overhead, compared with the 18-bit SQ-CE, the 4-bit TQ-CE achieved a 22.2% reduction in uplink bits. Full article

A two-dimensional physical and mathematical model is proposed for determining the components of the stress tensor and stress intensity in an electroconductive plate of rectangular cross-section under the action of an unsteady electromagnetic field. The two-dimensional thermomechanics problem for such a plate is formulated. The determining functions are the component of the magnetic field intensity vector tangent to the plate bases, temperature, and components of the stress tensor. The methodology for solving the formulated thermomechanics problem by approximating all the determining functions by the thickness variable with cubic polynomials is developed. As a result, the original two-dimensional problems related to the determining functions are reduced to one-dimensional problems on the integral characteristics of these functions. To solve the obtained systems of equations for the integral characteristics of the determining functions, a finite integral transformation in the transverse variable is used. During induction heating of a copper plate by a homogeneous quasi-steady-state electromagnetic field, the change in the stress intensity depending on the Fourier time, as well as its distribution across the plate cross-section depending on the parameters of induction heating and the Biot criterion, is numerically analyzed. Full article

The paper focuses on the optimal state-estimation algorithm for discrete-continuous systems. The research aim is to create an effective strategy for combining data from continuous and discrete information sources to improve the state estimation accuracy and reliability of complex dynamic systems. The paper discusses, in detail, the theoretical foundations of the proposed method, including the mathematical description of continuous and discrete models, and its optimality criterion formulation. State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. The conclusions are drawn about the promising application for the developed algorithm in various fields related to information processing in complex technical systems, such as navigation, motion control, and state and processes monitoring. It is noted that the proposed approach can be generalized to the case of more sources’ fusion. The paper is considered to be valuable for specialists in control theory and signal and information processing, as well as for navigation and motion-control system designers. The results obtained may find practical application in the development of high-precision state-estimation systems in various technical applications. Full article

Aiming to solve the difficult problem of the miniaturization of servo valves, this paper designs a subminiature two-dimensional (2D) electro-hydraulic servo valve, which realizes the integration of the pilot stage and the power stage and significantly improves the work-to-weight ratio. Meanwhile, a high-power-density brushless DC motor (BLDC) is adopted as the electro-mechanical converter to further reduce the volume and mass. Firstly, the structure and working principle of the two-dimensional (2D) servo valve are described, and the mathematical model of the electro-mechanical converter is established. Aiming at the special working condition of the electro-mechanical converter with high-frequency oscillation at a small turning angle, this paper designs a position–current double closed-loop PID control algorithm based on the framework of the vector control algorithm (FOC). At the same time, the current feedforward compensation technique is included to cope with the high-frequency nonlinear disturbance problem of the electro-mechanical converter. The stability conditions of the electro-mechanical converter and the main valve were established based on the Routh–Hurwitz criterion, and the effects of the control algorithm of the electro-mechanical converter and the main parameters of the main valve on the stability of the system were analyzed. The dynamic and static characteristics of the 2D valve were simulated and analyzed by establishing a joint simulation model in Matlab/Simulink and AMESim. The prototype was fabricated, and the experimental bench was built; the size of the experimental prototype was 31.7 mm × 29.3 mm × 31 mm, and its mass was 73 g. Under a system pressure of 7 MPa, the flow rate of this valve was 5 L/min; the hysteresis loop of the spool-displacement input–output curve was 4.8%, and the linearity was 2.54%, which indicated that it had the ability of high-precision control and that it was suitable for the precision fluid system. The step response time was 7.5 ms, with no overshoot; the frequency response amplitude bandwidth was about 90 Hz (−3 dB); the phase bandwidth was about 95 Hz (−90°); and the dynamic characterization experiment showed that it had a fast response characteristic, which can satisfy the demand of high-frequency and high-dynamic working conditions. Full article