Search Results (4,699)

The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system comprises three phases: assembly, data acquisition, and data processing. Environmental sensing was accomplished using a Basler Blaze 101 three-dimensional (3D) Time-of-Flight (ToF) camera. The data processing phase incorporated advanced algorithms, including Bird’s-Eye View (BEV) image conversion and You Only Look Once (YOLO) v8x-Seg instance segmentation. The system’s performance was evaluated using a comprehensive dataset of 627 point clouds, including samples from real mining environments. The system achieved efficient processing times of approximately 5 s. Segmentation accuracy was evaluated using the Intersection over Union (IoU), reaching 95.10%. Localization precision was measured by the Euclidean distance in the XY plane (EDXY), achieving 0.0128 m. The normalized error (enorm) on the X and Y axes did not exceed 2.3%. Additionally, the system demonstrated high reliability with R2 values close to 1 for the X and Y axes, and maintained performance under various lighting conditions and in the presence of suspended particles. The Mean Absolute Error (MAE) in the Z axis was 0.0333 m, addressing challenges in depth estimation. A sensitivity analysis was conducted to assess the model’s robustness, revealing consistent performance across brightness and contrast variations, with an IoU ranging from 92.88% to 96.10%, while showing greater sensitivity to rotations. Full article

Electric power is crucial for economic growth and the overall development of any country. The efficient planning of distribution system is necessary because all the consumers mainly rely on the distribution network to access the power. This paper focuses on addressing distribution system challenges and meeting consumers’ fundamental needs, such as achieving an improved voltage profile and minimizing costs within an environmentally sustainable framework. This work addressed the gap in the existing research by analysing the performance of both balanced and unbalanced systems within the same framework, specifically using the IEEE 33-bus and IEEE 118-bus test systems. Unlike prior studies that focused solely on either balanced or unbalanced systems, this work redistributed balanced loads into three-phase unequal unbalanced loads to create a more challenging unbalanced distribution network. The primary objective is to compare the effects of balanced and unbalanced loads on system the performances and to identify strategies for mitigating unbalanced load issues in each phase. Six optimization methods (PSO, TLBO, JAYA, SCA, RAO, and HBO) were employed to minimize losses, voltage variations, and other multi-objective function factors. Additionally, the study compared the cost of energy loss (CEL), emission factors, costs associated with distributed clean energy resources (DCER), and active and reactive power losses. Phase angle distortions due to unbalanced loads were also analysed. The results showed that among the optimization techniques tested (PSO, TLBO, JAYA, SCA, RAO, and HBO), the HBO method proved to be the most effective for the optimal allocation of distributed clean energy resources, yielding the lowest PF_MO values and favourable outcomes across the technical, economic, and environmental parameters. Full article

We present an efficient approach to evaluate the size dependency of elastic and plastic properties of metallic polycrystalline materials. Specifically, we consider different volume fractions of ferrite and martensite phases for the construction of three macroscopic domains. Statistical Volume Elements (SVEs) of different sizes are extracted from these domains using the moving window method. Linear and Crystal Plasticity (CP) simulations provide elastic and plastic properties of the SVEs such as the bulk and shear moduli, yield strength, and hardening modulus. We use a variation-based criterion to determine the Representative Volume Element (RVE) size of these properties. This RVE size corresponds to a size beyond which the given property can be idealized as homogeneous. We also use anisotropy indices and an additional RVE size criterion to determine the size limits beyond which these properties can be idealized as isotropic. Numerical results show that the plastic properties often reach their homogeneity and isotropy limits at larger sizes compared to elastic properties. This effect is more pronounced for the hardening modulus compared to the yield strength. Full article

Existing approaches for image deraining often rely on synthetic or unpaired real-world rainy datasets, leading to sub-optimal generalization ability when processing the complex and diverse real-world rain degradation. To address these challenges, we propose a novel iterative semantic-guided detail fusion model with implicit neural representations (INR-ISDF). This approach addresses the challenges of complex solution domain variations, reducing the usual negative impacts found in these situations. Firstly, the input rainy images are processed through implicit neural representations (INRs) to obtain normalized images. Residual calculations are then used to assess the illumination inconsistency caused by rain degradation, thereby enabling an accurate identification of the degradation locations. Subsequently, the location information is incorporated into the detail branch of the dual-branch architecture, while the normalized images obtained from the INR are used to enhance semantic processing. Finally, we use semantic clues to iteratively guide the progressive fusion of details to achieve improved image processing results. To tackle the partial correspondence between real rain images and the given ground truth, we propose a two-stage training strategy that utilizes adjustments in the semantic loss function coefficients and phased freezing of the detail branch to prevent potential overfitting issues. Extensive experiments verify the effectiveness of our proposed method in eliminating the degradation in real-world rainy images. Full article

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments. Full article

From the late Carboniferous to the early Permian, multiple pulses of glaciation and deglaciation have been caused by the LPIA. The Pennsylvanian period experienced phases of recovery, proliferation, and decline, ultimately forming a reef system distinctly different from that of the Mississippian period. During the late Bashkirian to Moscovian, the metazoan reef experienced a limited resurgence, with reef predominantly formed by chaetetid developing in the United States, northern China, and Japan. During the Kasimovian to Gzhelian, the phylloid algal reef dominated the global reef systems. In the late Pennsylvanian, bioconstruction cases and paleoenvironmental proxies in southern Guizhou Province were studied to investigate the composition, recovery, and evolutionary processes of the bioconstructions as well as their response to environmental variations during this period. Several bioconstructions have been reported in the Lumazhai section of Houchang Town, Guizhou Province, southern China, from the Moscovian to the Gzhelian. The upper Carboniferous strata are well-preserved and continuously exposed. The continuous strata, abundant fossils, and diverse bioconstructions provide excellent research materials for exploring the mutual constraints between organisms and their environment. This study identified ten microfacies, whose vertical evolution indicated significant changes in the depositional environment related to relative sea-level fluctuations. Skeletal grains are widely present in these facies. Among them, foraminifera, algae, bryozoans, crinoids, and Tubiphytes are the most common and exhibit distinct distribution characteristics in various environments. Quantitative statistics, CCA and theoretical ecospace have been utilized to examine and interpret environmental impact factors. Quantitative analysis of their relative abundance and distribution patterns provides insights into the complex interactions between organisms and environmental factors. The relative abundances of different organisms and factors controlling their bioconstructions are influenced by relative sea-level changes. CCA analysis reveal that hydrodynamic conditions are the primary influencing factor. Variation trends in average tiering and motility reveal the characteristics of biological communities during environmental changes in phylloid algae and microbial bioconstructions. These bioconstructions are not directly correlated with changes in environmental factors, and the biological communities in phylloid algae mounds and biostromes exhibit similar organism compositions and ecological niches across different environments. Full article

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. Full article

This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application. Full article

The presented article addresses the issue of the maintenance factor, which forms a part of the design variables in artificial lighting within engineering practices from a sustainability perspective. The maintenance factor was monitored using two simulation tools—Dialux, version 5.12.0.5527 and Relux, version 2024.2.8.0. In a production hall, inadequate lighting was identified with a value below 300 lx, prompting a redesign of the lighting system. The overall methodology of the Ergonomic Rationalization Sequence was expanded in the “Design of Lighting System” phase to include the determination of the maintenance factor as a necessary parameter for sustainability, which was subsequently verified in a virtual environment using two options in a practical study. According to the in situ measurements, the virtual environments of the production hall were created for both software, in which four alternatives for the lighting system were developed. The illuminance values met the normative requirements in each alternative; however, the first two (illuminance values 1000 lx–1200 lx) were predicted to have long-term high-energy consumption. In alternatives 3 and 4, the number of luminaires was therefore reduced from 6 pieces to 4, with a total illuminance in the range of 680 lx–780 lx. The determination of the variations in the methods for establishing the maintenance factor identified a deviation of 5%, which, indicating the changes in illuminance values, can be considered as the occurrence of a gross error in lighting design. Full article

This paper introduces a multiloop stabilized low-dropout regulator with a DC power supply rejection ratio of 85 dB and a phase margin of 80°. It is suitable for low-power, low-voltage and area-efficient applications since it consumes less than 100 μA. The dropout voltage is only 400 mV and the power supply rails are 1 V. Furthermore, a full mathematical analysis is conducted for stability and noise before the circuit verification. To confirm the proper operation of the implementation process, voltage and temperature corner variation simulations are extracted. The proposed regulator is designed and verified utilizing the Cadence IC Suite in a TSMC 90 nm CMOS process. Full article

This paper presents advancements in the performance of digital phase-locked loop (DPLL)s, with a special focus on addressing the issue of required gain calibration in the time-to-digital converter (TDC) within phase-domain DPLL structures. Phase-domain DPLLs are preferred for their simplicity in implementation and for eliminating the delta–sigma modulator (DSM) noise inherent in conventional fractional-N designs. However, this advantage is countered by the critical need to calibrate the gain of the TDC. The previously proposed dual-interpolated TDC(DI-TDC) was proposed as a solution to this problem, but strong spurs were still generated due to the TDC resolution, which easily became non-uniform due to PVT variation, degrading performance. To overcome these problems, this work proposes a DPLL with a new calibration system that ensures consistent TDC resolution matching the period of the digitally controlled oscillator (DCO) and operating in both the foreground and background, thereby maintaining consistent performance despite PVT variations. This study proposes a DPLL using a calibrated dual-interpolated TDC that effectively compensates for PVT variations and improves the stability and performance of the DPLL. The PLL was fabricated in a 28-nm CMOS process with an active area of only 0.019 mm², achieving an integrated phase noise (IPN) performance of −17.5 dBc, integrated from 10 kHz to 10 MHz at a PLL output of 570 MHz and −20.5 dBc at 1.1 GHz. This PLL operates within an output frequency range of 475 MHz to 1.1 GHz. Under typical operating conditions, it consumes only 930 µW with a 1.0 V supply. Full article

Cavitation phenomena in pumps are major determinants of the lifespan of both the impeller and the pump itself, causing significant vibration and noise, which are critical concerns for pump designers. This study focuses on the influence of various geometric factors of the impeller, including the shape of the blade leading edge, blade inlet angle, number and thickness of blades, surface roughness, wrap angle, impeller outlet width, inlet hub diameter, and tip clearance. The pump analyzed in this study, which exhibited issues of vibration and noise in actual industrial settings, was evaluated by varying only the shroud diameter based on Gulich’s theory, while keeping other parameters constant, to assess the effects on cavitation phenomena across five different impellers. Single-phase analysis was initially conducted to evaluate the performance of each pump model, with the reliability of the numerical analysis methods validated by comparison with experimental data. Furthermore, to analyze cavitation phenomena, a multiphase flow analysis was performed using the Rayleigh–Plesset model within a computational fluid dynamics framework. Quantitative analysis of cavitation occurrence, NPSH3% head-drop performance, and bubble volume was conducted. The results confirmed that the M1 model, featuring a shroud diameter of 560 mm, exhibited superior cavitation resistance. Variations in cavitation occurrence observed under three different flow conditions demonstrated a nonlinear trend, but overall, improvements were noted within a specific diameter range. This study offers valuable insights and data for pump design applicable in real-world industrial settings. Full article

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading. Full article

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions. Full article

In order to investigate the interphase mass transfer and component distribution characteristics of the CO₂–water system under micro-scale and nano-scale transport conditions, a micro-scale kinetic model representing interphase mass transfer in the CO₂–water/saline system is developed in this paper. The molecular dynamics method is employed to delineate the diffusion and mass transfer processes of the system’s components, revealing the extent of the effects of variations in temperature, pressure, and salt ion concentration on interphase mass transfer and component distribution characteristics. The interphase mass transfer process in the CO₂–water system under transport conditions can be categorized into three stages: approach, adsorption, and entrance. As the system temperature rises and pressure decreases, the peak density of CO₂ molecules at the gas–liquid interface markedly drops, with their aggregation reducing and their diffusion capability enhancing. The specific hydration structures between salt ions and water molecules hinder the entry of CO₂ into the aqueous phase. Additionally, as the salt concentration in water increases, the density peak of CO₂ molecules at the gas–liquid interface slightly increases, while the density value in the water phase region significantly decreases. Full article