Search Results (862)

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Salt and seasoning application plays a critical role in flavor and product consistency in industrial food manufacturing; however, manual recovery of excess salt limits both hygiene and process efficiency. In this study, a closed-loop salt dispensing machine capable of operating between 120–700 kg/h was developed to automatically recover salt that does not adhere to the product surface during processing. The required motor power and torque for achieving the maximum discharge rate of 700 kg/h were analytically calculated and experimentally validated. Homogeneity tests performed on a 1.2-m conveyor indicated maximum and minimum deviations of 5.3% and 7%, respectively. Overall, the system eliminates material waste, enhances hygiene, and provides more controlled salt distribution compared to conventional manual methods. Full article

This paper presents a novel extra-X second-generation current-controlled conveyor (EX-CCCII) with controllable current gain. Unlike the conventional EX-CCCII, the proposed EX-CCCII provides a controllable current gain between the $x$- and $z$-terminals. To demonstrate the advantages of the EX-CCCII with the controllable current gain, the proposed EX-CCCII is employed to realize a universal current-mode filter and a three-phase current-mode oscillator. The universal filter can realize five standard filtering responses (low-pass, high-pass, band-pass, band-stop, and all-pass) using the same topology. The current gains of these filters can be controlled through the current gain of the EX-CCCII, while the natural frequency of the universal filter can be electronically tuned via the intrinsic resistance at the x-terminal. When the proposed EX-CCCII is used to implement the three-phase oscillator, the condition of oscillation can be adjusted through the current gain of the EX-CCCII, whereas the oscillation frequency can be tuned using the parasitic resistance of the x-terminals. The proposed EX-CCCII and its applications were verified through SPICE simulations using the transistor model parameters NR100N (NPN) and PR100N (PNP) of the bipolar array ALA400-CBIC-R from AT&T to confirm the functionality and feasibility of the proposed topologies. Furthermore, experimental verification of the EX-CCCII and its integration into a three-phase oscillator further substantiates the proposed concept and demonstrates its practical viability. Full article

Rubber–textile conveyor belts with polyester–polyamide (EP) carcasses are widely used in bulk material handling, where their mechanical performance significantly affects their reliability, safety and service life. Due to the anisotropic structure of the textile reinforcement, the deformation of EP belts is strongly dependent on the loading direction. This study investigates the deformation rate behavior of rubber–textile conveyor belts under uniaxial tensile loading, with an emphasis on the differences between the longitudinal (warp) and transverse (weft) directions. The experimental results show that the strain rate is controlled by different deformation mechanisms of the textile components, which leads to significantly different deformation kinetics under warp and weft loading. The findings provide new insights into the time-dependent tensile behavior of EP belts and support the optimization of the textile carcass design for better durability and sustainability under severe operating conditions. Full article

Conveyor belts are critical equipment in mining operations, where continuous and reliable material transport is essential for production efficiency. This systematic review aims to analyze computer vision-based techniques applied to conveyor belt condition monitoring. Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods—such as filtering, segmentation, and geometric analysis—toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption. Full article

The sorting of coal gangue is of great significance for improving coal quality, avoiding environmental pollution, and reducing labor costs. The image-based coal gangue sorting method has been proposed by a large number of researchers, but the complexity of the environment, the speed and accuracy of coal gangue detection and recognition methods, and the performance of hardware equipment all pose challenges to the accuracy of coal gangue sorting. This paper discusses the research and application of deep-learning methods in the field of coal gangue detection and proposes an improved YOLOv7 coal gangue detection model for ordinary GPU devices with large computing power and memory. In response to the feature redundancy problem of the YOLOv7 model in coal gangue detection tasks, FasterNet was introduced to improve the backbone network of YOLOv7, reducing redundant calculations and memory access, making the model more effective in extracting features. In response to the requirements for detection speed in high-speed motion of belt conveyors, VoVGSCSP was introduced to improve the efficient layer aggregation network (ELAN) of YOLOv7 neck, further enhancing the detection speed of the model. The experimental results show that when the belt speed is 0.6 m/s, the improved model’s mAP0.5 is similar to YOLOv7, FPS increases from 9 frames per second to 18 frames per second, coal gangue sorting rates reach 91.1%, and coal misselection rates are 4.8%. The proposed coal gangue detection and recognition method based on improved YOLOv7 has increased the detection speed of the recognition model and promoted the improvement of coal gangue sorting efficiency. Full article

Nano-tweezers, especially those based on photonic crystals and plasmonic structures, are powerful tools for trapping, manipulating, or accelerating nano-sized objects. However, the precise control of the inter-distance between trapped nanoparticles has rarely been considered. In this paper, we propose a mirror-symmetric optical conveyor belt, in which each unit contains three graded nano-slots. Through the optimized design of spacing between these nano-slots, the structure generates multiple trapping centers, enabling wavelength-selective control over trapping positions. The results show that, through dynamically shifting excitation wavelengths, the programmable bidirectional optical manipulation of nanoparticles can be achieved. Also, the inter-distance between trapped particles can be tuned with subwavelength precision. The proposed structure provides a versatile solution for lab-on-a-chip systems, especially for systems aiming to study the interactions between objects. Full article

A 0.3 V nanowatt CCII⁻ is presented in 0.18 $\mathsf{\mu }$m TSMC CMOS, targeting ultra-low-power current-mode interfaces. Post-layout extracted simulations demonstrate correct conveying operation with a total DC power consumption of less than 2.40 nW. The low-frequency tracking factors evaluated at 1 Hz are ${\beta }_0=0.9452$ (−0.48 dB) and ${\alpha }_0=0.9609$ (≈−0.35 dB), with $-3$ dB bandwidths of 22.95 kHz and 63.95 kHz for the voltage and current transfers, respectively. Small-signal extraction confirms the intended impedance profile, yielding $R_X=46.73$ M$\mathsf{\Omega }$, $R_Z=1.204$ G$\mathsf{\Omega }$, and a very high input resistance $R_Y=392$ G$\mathsf{\Omega }$. Robustness is verified through full PVT and mismatch analyses, showing stable functionality across process corners, a 0–80 °C temperature range, and 270–330 mV supply variations while maintaining nanowatt-level dissipation. Full article

Several hydrogen explosions have been reported in ash treatment facilities at municipal solid waste incineration plants, highlighting the need for effective suppression measures. This study focuses on controlling temperature changes during the conveyor transportation process as a method to reduce hydrogen generation from incineration ash. Experiments were conducted in which heated simulated incineration ash was immersed in water inside a vessel and allowed to cool naturally. The results show that higher water temperatures during immersion significantly decreased the amount of hydrogen gas generated. Specifically, it was found that hydrogen generation ceased when the simulated incineration ash was immersed in water at 90 °C and subsequently allowed to cool naturally. These findings suggest that temperature control may contribute to preventing hydrogen explosion hazards in ash handling systems. Full article

This paper proposes and investigates two novel worldwide non-invasive, low-cost, online automatic diagnostic technologies for conveyor belt looseness by motor current signature analysis. Belt looseness causes impulsive transient spikes due to intermittent belt–motor engagement, which are captured and essentially enhanced using spectral kurtosis (SK). Two diagnostic technologies are as follows: Cross-Correlations of Spectral Moduli of orders three and four to extract supply frequency harmonic cross-correlations from SK-filtered current signals, and Consolidated Spectral Kurtosis, a band-independent technology, which enables effective diagnosis by summing essential spectral kurtosis values across the entire frequency range. Comprehensive experimental trials on an industrial grain belt conveyor system demonstrate that the proposed technologies are effective for conveyor belt looseness diagnosis. The Cross-Correlations of Spectral Moduli technologies achieved a maximum total probability of correct diagnosis value of 98%. The Consolidated Spectral Kurtosis technology captures overall impulsive energy across the whole frequency range, achieving a maximum total probability of correct diagnosis value of 99.6%. This study highlights the diagnostic effectiveness and computational efficiency of the proposed technologies for the reliable diagnosis of conveyor belt looseness. Experimental comparison of the proposed technologies is undertaken. Full article

This study involves the design of an integrated machine dedicated to the core processes of classifying, shelling, and cleaning to address the critical drawbacks of existing Camellia oleifera fruit processing equipment, including the high manual labor requirement, low operating efficiency, unsatisfactory shelling and cleaning performance, and severe camellia seed damage. The classifying system employed a slat drum structure, and response surface methodology (RSM) was utilized to determine and optimize its operating parameters: spiral blade speed: 20 rpm; drum speed: 10 rpm; and rise angle: 9.6°. The shelling system employed a horizontal flexible structure, and polyurethane was the core material. We determined through single-factor experiments that the shelling drum rotation speed was 200 rpm. For the cleaning system, a composite mode integrating drum screening and friction separation was adopted, and single-factor experiments further determined the optimal operating parameters: cleaning drum rotation speed: 20 rpm; friction conveyor shaft rotation speed: 150 rpm; and cleaning inclination angle: 25°. The performance test verified that the integrated machine achieved outstanding results: the shelling rate reached 97.52%, the camellia seed breakage rate did not exceed 2.42%, the impurity content rate did not exceed 1.99%, the loss rate was less than 3.66%, and the processing capacity reached 2614 kg/h. Full article

Lack of lubrication in rolling-element bearings is a leading root cause of premature failure in induction motors and other electromechanical drives. This study proposes novel vibration-based technologies for diagnosing a lack of lubrication in bearings of induction motors. Two technologies are proposed: the Filter-less spectral kurtosis (FLSK), which quantifies impulsive energy generated by a lack of bearing lubrication, and the fundamental rotational harmonic technology, which captures an increase in the fundamental rotational harmonic magnitude, also induced by a lack of bearing lubrication. Comprehensive experimental trials are performed on a Siemens induction gearmotor, used in airport baggage handling conveyor systems. The experimental results show that both technologies exhibit effective diagnostics. Full article

Open-pit mining increasingly substitutes truck-based haulage with continuous systems—such as mobile bridges and relocatable conveyors—to mitigate operational costs and environmental impacts. This PRISMA 2020-compliant systematic review (2010–2025) maps transferable evidence in structural analysis, optimization, and maintenance for truss-type mobile assets. Following a systematic search in Scopus and Web of Science, 94 studies were selected via MMAT quality appraisal and analyzed through cluster-based synthesis. Results reveal sustained publication growth since 2018, with a corpus dominated by finite element (FE) research on steel bridges and capacity assessment, supplemented by emerging areas in AI-driven structural health monitoring (SHM). Given the scarcity of mining-specific literature, bridge engineering serves as a structural proxy for mobile applications. Critical research gaps include full-scale operational validation, soil–structure interaction, and design–maintenance co-optimization. The study concludes with an evidence-anchored agenda toward validated, predictive, and sustainable monitoring frameworks, positioning digital-twin integration as a promising future horizon rather than a current industry-wide convergence. Full article

A remote analysis of coastal sedimentation in northern KwaZulu-Natal (KZN), South Africa, describes how summer runoff and winter wave-action operate within a highly variable climate. Despite rising sea levels, the sediment flux can sustain beaches under certain conditions. Daily satellite red-band reflectivity and ocean–atmosphere reanalysis datasets were studied over the period of 2018–2025. Statistical results indicate that streamflow discharges are spread northward by oblique wave-driven currents. Sediment concentrations peak during late winter (>1 mg/L, May–October) when deep turbulent mixing (>40 m) mobilizes sand from the seabed. A case study from September 2021 revealed that ridging high-pressure/cut-off low weather patterns can simultaneously increase streamflow, wave energy, and wind power, creating a surf-zone sediment conveyor along the coast of northern KZN. Long-term climate diagnostics from 1981 to 2025 reveal upward trends in coastal runoff, vegetation, and turbidity (0.29 σ/yr) that point to an increasingly vigorous water cycle. The warming of the southeast Atlantic intensifies the sub-tropical upper-level westerlies and late winter storms over southeast Africa. These processes occur in 5–8 year cycles and drive shoreline advance and retreat, from accretion ~1 T/m and storm surge inundations up to 5.5 m. Using Digital Earth, it was noted that ~1/4 of beaches around Africa are gaining sediment while ~1/3 are eroding. Although remote information could not close the sediment budget, realistic estimates of long-shore transport in the surf-zone (>10⁴ kg/yr/m) and on the beach (>10³ kg/yr/m) were calculated. These provide an emerging explanation for the resilience of northern KZN beaches, as sea levels rise at a rate of 0.6 cm/yr. Full article

Plywood has emerged as a key sustainable material in modern building. Yet, ensuring its consistent performance requires rigorous quality control of the rotary-cut veneers used in its manufacture. This task is complicated by the high-speed nature of industrial conveyors, where motion blur and the complex, varying textures of eucalyptus wood drastically reduce the effectiveness of real-time surface inspection. This study proposes an intelligent, real-time defect detection system specifically optimized for the diverse defect morphology of eucalyptus veneers. A lightweight model, YOLOv11-DMS-Veneers, was developed by integrating MobileNetV4 as the backbone, a Dynamic Head for multi-scale feature extraction, and a Shape-IoU loss function to precisely localize irregular defects like cracks and knots. Additionally, an ERD video enhancement framework (combining ESRGAN, RIFE, and DnCNN) was implemented to mitigate motion blur in dynamic environments. Experimental results demonstrate that the proposed model achieves a mean Average Precision (mAP@50) of 96.0% and a Precision of 95.7% with a low computational cost of only 4.5 GFlops, significantly outperforming traditional algorithms. Notably, the detection precision for challenging linear cracks reached 93.9%. In dynamic tests at conveyor speeds up to 24 m/min, the video enhancement strategy increased the average detection confidence by 0.288, maintaining a maximum confidence of 0.890. This technology offers a robust solution for the automated quality control of eucalyptus veneers, facilitating the production of high-performance plywood and advancing the efficient application of engineered wood in the building industry. Full article