Search

In this paper, poplar sawdust (PS), walnut shell (WS), sugarcane bagasse (SB), polyethylene (PE), and paper napkin (PN) were selected as typical organic solid waste for the study of steam (co-)gasification coupled with on-line analysis of H 2 -rich syngas. The carbon conversion efficiency of PE is only 10.8% and needs to be improved. The mixture of SB + PE exhibited the most pronounced positive synergies, with H 2 /CO reaching 2.40 without catalyst and the synergistic indices of 0.320, 0.298, and 0.226 for H 2 yield, syngas yield, and H 2 /CO, respectively, and these synergies were evident throughout the entire gasification process. The synergistic mechanism was analyzed through the interphase migration of alkali and alkaline earth metals and the morphological properties of the char. Compared with the co-gasification at 900 °C, the synergistic indices of the SB + PE co-gasification parameters at 800 °C were reduced to different degrees, and even the co-gasification at 700 °C showed a remarkable negative synergism. [Display omitted] • Steam co-gasification of various MSWs was carried out online for H 2 -rich syngas. • Co-gasification of SB + PE showed optimal synergy for both H 2 yield and H 2 /CO. • Synergistic mechanism was discussed in terms of AAEMs migration and char structure. • High temperatures of 800∼900 °C contributed to unleashing synergistic potentials. [ABSTRACT FROM AUTHOR]

[Objective] To improve the accuracy of online measurement of sugar content of grapefruit by near infrared spectroscopy. [Methods] The pomelo online non-destructive testing equipment developed by ourselves was used to collect diffuse transmission spectrum data of pomelo in three light regions. In the wavelength range of 650~950 nm, orthonormal variable transformation （SNV）, multiple scattering correction （MSC）, Normalize, Savitzky-Golay first order derivative, SG-1st preprocessed the original data, used the adaptive weighting algorithm （CARS） to screen the spectral characteristics of the grapefruit sugar content, and established a partial least squares regression （PLSR） model. 30 grapefruit samples that were not involved in the modeling were used for online verification. [Results] The modeling effect of light region C combined with SNV-CARS-PLSR method was the best. The coefficient of determination of the prediction set was 0.95 and the root-mean-square error was 0.30 °Brix. In online verification, the coefficient of determination was 0.90 and the root mean square error was 0.58 °Brix. The model had a strong ability to detect the sugar content of grapefruit on line. [Conclusion] The prediction model based on the spectral data collected under the condition that the light spot diameter is 70 mm and the light region C is 20 mm above the equator of the grapefruit can realize the online prediction of the sugar content of the grapefruit more effectively.

Real-time analysis of metallic mineral grade and slurry concentration is significant for improving flotation efficiency and product quality. This study proposes an online detection method of ore slurry combining the Prompt Gamma Neutron Activation Analysis (PGNAA) technology and artificial neural network (ANN), which can provide mineral information rapidly and accurately. Firstly, a PGNAA analyzer based on a D-T neutron generator and a BGO detector was used to obtain a gamma-ray spectrum dataset of ore slurry samples, which was used to construct and optimize the ANN model for adaptive analysis. The evaluation metrics calculated by leave-one-out cross-validation indicated that, compared with the weighted library least squares (WLLS) approach, ANN obtained more precise and stable results, with mean absolute percentage errors of 4.66% and 2.80% for Fe grade and slurry concentration, respectively, and the highest average standard deviation of only 0.0119. Meanwhile, the analytical errors of the samples most affected by matrix effects was reduced to 0.61 times and 0.56 times of the WLLS method, respectively.

The ore grade is a core indicator for measuring the economic value of minerals, and its online detection capability is related to the economic benefits, environmental impact, and production intelligence level of a mining enterprise. This study discusses the application value and classification of online detection technologies for ore grade and summarizes the research and application progress of these technologies in terms of the following technical directions: radioactive, optical, electromagnetic, and machine-vision detection. Challenges faced by the development of related technologies are identified at the technical research and practical application levels. Challenges at the technical research level include (1) accuracy bottlenecks and interference factors, (2) difficulties in signal analysis and optimization, and (3) model construction and data dependency. Challenges at the practical application level include (1) radiation safety and cost-effectiveness, (2) technological breakthroughs adapted to diverse ore characteristics, and (3) stable operation and real-time feedback in harsh environments. The study further elaborates on the future development directions of online detection technologies for ore grade. Future efforts should focus on breakthroughs in exploring the forefront of multimodal fusion and intelligent perception technologies, iterating and upgrading intelligent perception and data processing algorithms, developing miniaturized/remote/intelligent equipment, and constructing and optimizing real-time dynamic monitoring network systems. Moreover, emerging technologies, such as deep learning for promoting the fusion analysis of micro and macro features, quantum computing and bioinspired algorithms, as well as intelligent sensor networks and the Internet of Things technology, are summarized. Furthermore, active actions are recommended in the following aspects: (1) technological innovation and equipment upgrading, (2) standards formulation and standardization construction, (3) deepening of the industry–education–research–application cooperation mechanism, (4) talent cultivation and team building, and (5) international cooperation and resource sharing, thereby promoting the intelligent and efficient development and utilization of mineral resources.

Digital microfluidics is a novel technique for manipulating discrete droplets with the advantages of programmability, small device size, low cost, and easy integration. The development of droplet sensing methods advances the automation control of digital microfluidics. Impedance measurement emerges as a promising technique for droplet localization and characterization due to its non-invasive nature, high sensitivity, simplicity, and cost-effectiveness. However, traditional impedance measurement approaches in digital microfluidics based on the high-voltage actuating signal are limited in sensing accuracy in practical applications. In this paper, we propose a novel droplet impedance sensing system for digital microfluidics by introducing a low-voltage and addressable measurement circuit, which enables impedance measurement over a wide frequency range. The proposed measurement system has also been used for detecting the droplet composition, size, and position in a digital microfluidic chip. The improved impedance sensing method can also promote the applications of the digital microfluidic, which requires high accuracy, real-time, and contactless sensing with automatic sample pretreatment. [ABSTRACT FROM AUTHOR]

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film's semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing. [ABSTRACT FROM AUTHOR]

Mechanical vibration in the high-temperature forging production line often causes large forging thermal dimensional measurement error in the detection task, so a vibration point cloud compensation method based on an acceleration sensor is proposed in this study. First, the vibration signal is obtained through the built-in acceleration sensor in the laser camera. After the acceleration of the camera vibration is detected, the displacement of the camera in three directions is solved by secondary integration. Subsequently, the coordinate value of the corresponding point is obtained by the rotation matrix transformation so as to compensate and correct the point cloud deviation caused by the camera vibration. Finally, the forging point cloud is matched using the surface matching algorithm in Halcon. An automatic forging production line for wheel hubs has been built, and the key dimensions of high-temperature forging products have been measured online using the developed method. After the forging point cloud is compensated, the average measurement error of dimensions is reduced from ±0.9 mm to ±0.1 mm, and the standard deviation is reduced from 0.52 mm to 0.056 mm. Using the vibration point cloud compensation method based on the acceleration sensor, as well as using silica aerogel insulation, vibration structural parts, heat insulation and constant temperature, a blue-violet 3D laser camera, and other measures, the dimensional detection accuracy of high-temperature forgings in the forging production line can be improved, and the instability of dimensional detection can be reduced. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. Graphical Abstract

This article introduces the characteristics of near-infrared spectroscopy and its application in silicone industrial production. The silicone monomers and polymers has the problems of the complex composition, complicated pretreatment, and in-teraction of sample. The application prospects of offline-online analysis in the production of silicone monomers and polymers are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. [ABSTRACT FROM AUTHOR]

In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples' maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model's multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry. [ABSTRACT FROM AUTHOR]

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC. [ABSTRACT FROM AUTHOR]

As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7- tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects. [ABSTRACT FROM AUTHOR]

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R²) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R² and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products. [ABSTRACT FROM AUTHOR]

Online viscosity information on processing lines can reflect the material flow resistance and offer valuable guidance for manufacturing across various industries. Considering the accuracy, devices, and processes involved in injection molding, characterizing the melt's flow state during material processing poses a significant challenge. To reduce investment in viscometers, avoid influencing the components' surface aesthetics due to the installation of sensors, and make the flow state detect online in mold, this study designs a rheometric mold with cylindrical runners for identifying the in situ viscosity of molten resin during injection molding. The detection conditions of injection speed and cavity pressure variations, the entrance effect, and the viscous dissipation for Polycarbonate are analyzed under various conditions. The in situ viscosity is identified and compared with the standard cross‐WLF model. The result shows that the melt velocity and cavity pressure variations during the filling process create a stable environment for in situ rheological characterization and the detected viscosity is related to the shear rate, melt temperature, and channel dimension in injection molding. The designed mold with cylindrical runners for determining the in situ thermal‐rheological behavior of polymer is distinguished successfully and exhibits prospects for the development of low‐cost, nondestructive, and inner‐mold measurement in manufacturing applications. [ABSTRACT FROM AUTHOR]

Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT We focus here on the problem of poor real-time performance and low accuracy of seed box allowance detection during wheat sowing operations, and present a real-time weighing system for a seed box allowance. The proposed weighing system obtains the seed box allowance using an ultrasonic sensor and load cell, and displays it in real time via a computer. Based on the proposed weighing system, the weighing structure is also designed for the seed box. At the software level, three filtering methods are proposed to condition the signal, namely mean filtering, median filtering, and the acceleration method, and models of these three filtering algorithms are implemented in MATLAB/Simulink. Experiments to assess the accuracy of the system and filtering algorithm were carried out on the test bench; the results show that the system error is initially in the range −16.3 g to 63.8 g, and is reduced to the range −13.4 g to 15.1 g after correction. No significant difference between the three filtering algorithms is found, but since the acceleration method can improve the response speed of the system, this is selected as the final filtering method.

High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.

Differential photoacoustic spectroscopy (PAS) for flow gas detection based on single microphone is innovatively proposed and experimentally demonstrated. Unlike the traditional systems, only one microphone is used to suppress flowing gas noise. Wavelength modulation spectroscopy and second harmonic detection technique are applied in this PAS system with Q-point demodulation for acetylene (C2H2) gas detection. The experiment is conducted at 1 atm and 300 K. Different concentrations and flow rates of C2H2 from 0 sccm to 225 sccm are detected by using nitrogen (N2) as the carrier gas, which indicates that the system can respond well to flowing gases while maintaining the noise at the same level. The system response time decreases to 3.58 s while the gas velocity is 225 sccm. The detection limit of 43.97 ppb with 1 s integration time and normalized noise equivalent absorption (NNEA) coefficient of 4.0 × 10-9 cm-1 W Hz-1/2 is achieved at the flow rate of 225 sccm. The firstly proposed differential PAS based on single microphone greatly simplifies the system structure for flow gas detection, which provides a novel route for development of PAS with significant practical implementation prospects.

Goal: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature. Methods: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters. Results: Bland–Altman analysis indicated a high estimation accuracy (R2 > 0.95, p < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (R2 = 0.8576) was achieved between measured and estimated MB count rates. Conclusions: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.

Introduction: Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods: To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results: The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion: This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes. [ABSTRACT FROM AUTHOR]

An online changepoint detection procedure based on conditional expectiles is introduced. The key contribution is threefold: nonlinearity of the underlying model improves the overall flexibility while a parametric form of the unknown regression function preserves a simple and straightforward interpretation; The conditional expectiles, well-known in econometrics for being the only coherent and elicitable risk measure, introduce additional robustness—especially with respect to asymmetric error distributions common in various types of data; The proposed statistical test is proved to be consistent and the distribution under the null hypothesis does not depend on the functional form of the underlying model nor the unknown parameters. Empirical properties of the proposed real-time changepoint detection test are investigated in a simulation study and a practical applicability is illustrated using the Covid-19 prevalence data from Prague. [ABSTRACT FROM AUTHOR]

Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things approach. An edge device, a Raspberry Pi 4 equipped with an RGB camera, is utilized to detect diseases in grapevine plants. Two lightweight deep learning models, MobileNet V2 and EfficientNet B0, were trained using a transfer learning technique on commercially available online dataset, then deployed and validated on field-site in an organic winery. The models' performance was further enhanced using semantic segmentation with the Mobile-UNet algorithm. Results were reported through a web service using FastAPI. Both models achieved high training accuracies exceeding 95%, with MobileNet V2 slightly outperforming EfficientNet B0. During validation, MobileNet V2 achieved an accuracy of 94%, compared to 92% for EfficientNet B0. In terms of IoT deployment, MobileNet V2 exhibits faster inference time (330 ms) compared to EfficientNet B0 (390 ms), making it the preferred model for online deployment. [ABSTRACT FROM AUTHOR]

In order to provide new technical support for the variable temperature drying process and realize the adaptive control of drying temperature based on the change of moisture content, the study designed a temperature adaptive control system with the function of online detection of material moisture content. A moisture content online detection model with weight detection value, air velocity, the temperature of load sensor elastic substrate, airflow impingement distance as inputs, and the real material weight as outputs was established by using a convolutional neural network. A validation test of the moisture content online detection model was carried out. The results showed that the model meets the accuracy requirements of online moisture content detection in the variable temperature drying process, and the coefficient of determination R² and root mean square error (RMSE) of the five groups of model validation tests were 0.993 4 and 1.20% in that order. In this reasearch, an improved neural network-PID (INN-PID) controller was designed to realize temperature control in the variable temperature drying process. The dynamic performance of PID, neural network-PID (NN-PID), and INN-PID controllers was simulated in MATLAB software with unit step signal as input. The three controllers were tested for drying temperature control at 50-55 ℃. The results showed that the control stability and regulation time of the INN-PID controller were significantly better than the other two controllers in the simulation test, the drying temperature control results had approximately the same law with the simulation results, and the peak time of the INN-PID controller was 208.00 s, the regulation time was 120.59 s, and the maximum overshooting was 4.87%, which meets the requirements of temperature control in the variable-temperature drying process. In this paper, a temperature adaptive control system was built in the air-impingement dryer, and the temperature adaptive control test based on the moisture content change was carried out. The results showed that the system could quickly and effectively regulate the drying temperature in the variable temperature drying process based on the change in moisture content. This research is of great significance for improving the automation level of drying equipment, developing new variable-temperature drying processes, and providing reference for multi-information fusion detection and control strategy research in other fields. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quality of the stator winding coil directly affects the performance of the motor. A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed. A vision detection platform was designed to capture individual winding images, and an image processing algorithm was used for image pre-processing, template matching and positioning of the coil lead area to set up a coordinate system. After eliminating image noise by Blob analysis, the improved Canny algorithm was used to detect the location of the coil lead paint stripped region, and the time was reduced by about half compared to the Canny algorithm. The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset, and the detect file was converted to the Open Neural Network Exchange (ONNX) model for the detection of winding cross features with an average accuracy of 99.0%. The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces, and the detection data were recorded and statistically analyzed. The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%, and the average detection time of a single workpiece was about 300 ms, while YOLOv5s took less than 30 ms. [ABSTRACT FROM AUTHOR]

As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7-tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects.

Abstract Background Bacillus subtilis is one of the workhorses in industrial biotechnology and well known for its secretion potential. Efficient secretion of recombinant proteins still requires extensive optimization campaigns and screening with activity-based methods. However, not every protein can be detected by activity-based screening. We therefore developed a combined online monitoring system, consisting of an in vivo split GFP assay for activity-independent target detection and an mCherry-based secretion stress biosensor. The split GFP assay is based on the fusion of a target protein to the eleventh β-sheet of sfGFP, which can complement a truncated sfGFP that lacks this β-sheet named GFP1-10. The secretion stress biosensor makes use of the CssRS two component quality control system, which upregulates expression of mCherry in the htrA locus thereby allowing a fluorescence readout of secretion stress. Results The biosensor strain B. subtilis PAL5 was successfully constructed by exchanging the protease encoding gene htrA with mCherry via CRISPR/Cas9. The Fusarium solani pisi cutinase Cut fused to the GFP11 tag (Cut11) was used as a model enzyme to determine the stress response upon secretion mediated by signal peptides SPPel, SPEpr and SPBsn obtained from naturally secreted proteins of B. subtilis. An in vivo split GFP assay was developed, where purified GFP1-10 is added to the culture broth. By combining both methods, an activity-independent high-throughput method was created, that allowed optimization of Cut11 secretion. Using the split GFP-based detection assay, we demonstrated a good correlation between the amount of secreted cutinase and the enzymatic activity. Additionally, we screened a signal peptide library and identified new signal peptide variants that led to improved secretion while maintaining low stress levels. Conclusion Our results demonstrate that the combination of a split GFP-based detection assay for secreted proteins with a secretion stress biosensor strain enables both, online detection of extracellular target proteins and identification of bottlenecks during protein secretion in B. subtilis. In general, the system described here will also enable to monitor the secretion stress response provoked by using inducible promoters governing the expression of different enzymes.

With the increase of cable operating age, various latent defects gradually appear, so it is important to identify and locate the defects accurately for power operation and maintenance. The common methods for cable failure detection primarily work with cables in an offline state, which causes a certain degree of economic loss. In this research, SSTDR is used to achieve online defect detection, which can avoid further damage to the cable in a working state. This paper established the numerical model of the cable distribution characteristics and investigated how various incident signals affect the effect of defect localization. Based on the obtained results, the localization curve is wavelet decomposed in order to filter the operating frequency signal. The findings of the experiments and simulations demonstrate that SSTDR and its enhanced algorithm may achieve online cable defect detection and serve as an effective method for cable defect locating technology advancement.

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film’s semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing.

This is a paper in the field of mining engineering. With the increasing demands for energy conservation and consumption reduction in mineral processing plants, ball mill load measurement is the key technology to realize mill control and optimize the grinding process. This paper summarizes the measurement methods of mill load in the recent years: differential pressure method, grinding sound method, vibration method, power method, ultrasonic method, indirect detection method based on multi-source signal fusion. Moreover, it summarizes and analyzes the mill load modeling methods that have emerged inrecent years. In the future, the indirect methods of multi-source information fusion will still be the main methods to detect mill load, modeling methods based on improvement of neural network and new online detection methods, as well as the establishment of efficient and accurate load detection models will be the main development direction of mill load detection.

Power cable conductor ampacity directly correlates with conductor temperature. Traditional thermocouple measurement fails to ensure real-time monitoring, risking cable operation. Leveraging Raman scattering principles, this study establishes a method for continuous surface temperature detection of long-distance power cables using distributed fiber optics. This approach surpasses OTDR-based measurement in speed and reliability, offering superior online monitoring for cable systems, suitable for widespread adoption.

Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies. [ABSTRACT FROM AUTHOR]

High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak, which has a devastating impact on the health and well-being of the global population, is a respiratory disease. It is vital to determine, isolate and treat people with the disease as soon as possible to fight against the COVID-19 pandemic. Even though the reverse transcription polymerase chain reaction (RT-PCR) test, the accuracy of which is about 63%, seems to be a good option for determining COVID-19, it is a disadvantage is that test kits are few, are difficult to obtain in remote rural areas and have low accuracy. Chest X-ray (CXR) has become essential for rapidly diagnosing the rapidly spreading COVID-19 disease worldwide, so it is urgent to develop an online system that will help specialists identify infected patients with CXR images. In this study developed a transfer learning-based diagnosis system for online diagnosis of COVID-19 patients using CXR images. Transfer learning-based deep learning models VGG16, VGG19, ResNet50, InceptionV3, Xception, MobileNet, DenseNet121 and DenseNet201 were used for the experimental studies. We explored the COVID-19 radiography database from Kaggle, which is open to the public, using image preprocessing techniques and data augmentation. The images captured by the various terminals are transferred to the web server in the created system. Similar to the ensemble learning approach, the percentage accuracy of the model with the highest prediction value among the eight deep learning models is displayed on the screen. The results show that the proposed online diagnosis system performs better than others with the highest accuracy, precision, recall and F1 values of 98%, 99%, 97% and 97%, respectively. The results show that deep learning models help to increase the efficiency of chest radiograph scanning and have promising potential in predicting COVID-19 cases. The online diagnostic system will be a helpful tool for radiologists as it diagnoses COVID-19 quickly and with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Bacillus subtilis is one of the workhorses in industrial biotechnology and well known for its secretion potential. Efficient secretion of recombinant proteins still requires extensive optimization campaigns and screening with activity-based methods. However, not every protein can be detected by activity-based screening. We therefore developed a combined online monitoring system, consisting of an in vivo split GFP assay for activity-independent target detection and an mCherry-based secretion stress biosensor. The split GFP assay is based on the fusion of a target protein to the eleventh β-sheet of sfGFP, which can complement a truncated sfGFP that lacks this β-sheet named GFP1-10. The secretion stress biosensor makes use of the CssRS two component quality control system, which upregulates expression of mCherry in the htrA locus thereby allowing a fluorescence readout of secretion stress. Results: The biosensor strain B. subtilis PAL5 was successfully constructed by exchanging the protease encoding gene htrA with mCherry via CRISPR/Cas9. The Fusarium solani pisi cutinase Cut fused to the GFP11 tag (Cut11) was used as a model enzyme to determine the stress response upon secretion mediated by signal peptides SPPel, SPEpr and SPBsn obtained from naturally secreted proteins of B. subtilis. An in vivo split GFP assay was developed, where purified GFP1-10 is added to the culture broth. By combining both methods, an activity-independent high-throughput method was created, that allowed optimization of Cut11 secretion. Using the split GFP-based detection assay, we demonstrated a good correlation between the amount of secreted cutinase and the enzymatic activity. Additionally, we screened a signal peptide library and identified new signal peptide variants that led to improved secretion while maintaining low stress levels. Conclusion: Our results demonstrate that the combination of a split GFP-based detection assay for secreted proteins with a secretion stress biosensor strain enables both, online detection of extracellular target proteins and identification of bottlenecks during protein secretion in B. subtilis. In general, the system described here will also enable to monitor the secretion stress response provoked by using inducible promoters governing the expression of different enzymes. [ABSTRACT FROM AUTHOR]

In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study proposes a machine vision-based methodology for the accurate identification of Fuji apples’ maturity and diameter. Firstly, maturity level detection employed an improved YOLOv5s object detection model. The feature fusion section of the YOLOv5s network was optimized by introducing the cross-level partial network module VoVGSCSP and lightweight convolution GSConv. This optimization aimed to improve the model’s multiscale feature information fusion ability while accelerating inference speed and reducing parameter count. Within the enhanced feature fusion network, a dual attention mechanism combining channel and spatial attention (GAM) was introduced to refine the color and texture feature information of apples and to increase spatial position feature weights. In terms of diameter determination, the contours of apples are obtained by integrating the dual features of color and depth images within the target boxes acquired using the maturity detection model. Subsequently, the actual area of the apple contour is determined by calculating the conversion relationship between pixel area and real area at the current depth value, thereby obtaining the diameter of the apples. Experimental results showed that the improved YOLOv5s model achieved an average maturity level detection precision of 98.7%. Particularly noteworthy was the detection accuracy for low maturity apples, reaching 97.4%, surpassing Faster R-CNN, Mask R-CNN, YOLOv7, and YOLOv5s models by 6.6%, 5.5%, 10.1%, and 11.0% with a real-time detection frame rate of 155 FPS. Diameter detection achieved a success rate of 93.3% with a real-time detection frame rate of 56 FPS and an average diameter deviation of 0.878 mm for 10 apple targets across three trials. Finally, the proposed method achieved an average precision of 98.7% for online detection of apple maturity level and 93.3% for fruit diameter features. The overall real-time inference speed was approximately 56 frames per second. These findings indicated that the method met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization–extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

To meet the requirements of rail-bottom flaw detection, we use a differential-triple-coil sensor at low frequencies to detect rail-bottom flaws. Aiming at its online application to railways, we propose a single-cycle demodulation algorithm. To increase the ability to remove multiple interference signals in an electromagnetic environment, we analyze the anti-interference performance of the proposed algorithm in the frequency domain. We also discuss a method of achieving high anti-noise performance in rail-bottom flaw detection. In addition, we form artificial horizontal rail-bottom flaws in a standard rail specimen and carry out experiments on four horizontal rail-bottom flaws with different depths using the embedded inspection system. The experimental results demonstrate that the differential-triple-coil sensor with the proposed single-cycle demodulation algorithm can be used for horizontal rail-bottom flaw detection. Furthermore, horizontal rail-bottom flaws with a depth of less than 7 mm can be detected. [ABSTRACT FROM AUTHOR]