Current Issue : January-March Volume : 2023 Issue Number : 1 Articles : 5 Articles
Food safety issues are inextricably linked to people’s lives and, in extreme cases, endanger public safety and social stability. People are becoming increasingly concerned about food safety issues in a modern society with high-quality economic development. People’s incomes are increasing day by day as the economy continues to grow, and the tourism industry has grown by leaps and bounds. However, many problems arose, such as the issue of food safety in tourism. Tourism food safety issues affect not only the development of the food industry but also the development of tourism. Food safety oversight of tourist attractions has always been a relatively concerning issue in the country, and it is also something that the general public is concerned about. It can be said that food safety supervision of tourist attractions is the most important thing in food safety supervision. In this context, it becomes an important task to evaluate the safety of tourist food. This work proposes a multiscale convolutional neural network (AMCNN) combined with neural networks and attention layers to realize the safety and quality evaluation of tourist food. The algorithm uses the lightweight Xception network as a basic model and utilizes multiscale depth-separable convolution modules of different sizes for feature extraction and fusion to extract richer food safety feature information. Furthermore, the convolutional attention module (CBAM) is embedded on the basis of the multiscale convolutional neural network, which makes the network model focus more on discriminative features....
Quality inspection and defect detection play a critical role in infrastructure safety and integrity specially when it comes to aging infrastructure mostly owned by governments around the world. One of the prevalent inspections performed in the industry is nondestructive testing (NDT) using radiography imaging. Growing demand, shortage of experts, diversity of required skills, and specific regional standards with a time-limited requirement of inspection results make automated inspection an urgent need. Therefore, utilizing artificial intelligence- (AI-) based tools as an assistive technology has become a trend for industrial applications, which automates repeated tasks and provides increased confidence before and during the inspection operation. Most of the works in quality assessment are focused on the classification of few categories of defects and mostly performed on public or noncomprehensive research datasets. In this work, a scalable, efficient, and real-time deep learning family of models for detection and classification of 10 various categories of weld characteristics on a real-world industrial dataset is presented. The models are evaluated and compared against each other, various critical hyperparameters and components are optimized, and local explainability of models is discussed. Additionally, AutoAugment for object detection and various techniques are utilized and investigated. The best performance for object detection and classification for 10 class models is reached by mean average precision of 72.4% and top-1 accuracy of 90.2%, respectively. Also, the fastest object detection model is able to evaluate a full 15360 ×1024 pixels weld image in 0.39 seconds. Finally, the proposed models are deployable on edge-devices to perform as assistant to NDT experts or auditing professionals....
Parametric uncertainties should always be considered when setting design criteria in order to ensure safe and cost effective design of engineered structures. This paper presents the results of the reliability assessment of a fully laterally restrained steel floor I-beam to Eurocode 3 design rules. The failure modes considered are bending, shear and deflection. These were solved to obtain reliability indices using first order reliability method coded in MATLAB environment. Parametric sensitivity analyses were carried out at varying values of the design parameters to show their relative contributions to the safety of the beam. It was seen that reliability indices generally decreased with an increase in load ratio, imposed load, beam span in bending, shear stress and deflection respectively. In addition, increasing the beam span beyond 10 m, load ratio above 1.4 and imposed load beyond 30 kN/m made the beam fail as these parameters gave negative reliability indices. For failure in deflection, reliability index rose with an increase in the radius of gyration and overall depth of the beam section accordingly. Furthermore, the reliability index surged as the thickness of the web increased when taking into account, shear failure. The results of the analysis showed that the steel beam is very safe in shear and at some load ratios and imposed loads for failure in bending and deflection respectively. The average values of reliability indices obtained for load ratios ranging from 1.0 to 1.4 fell from 3.017 to 3.457 for all failure mode studied. These values are within the recommended reliability indices by the Joint Committee on Structural Safety for structure with moderate failure consequences and beams in flexure....
In the primary processing of cotton, it is important to increase the productivity of ginning and reduce the wear of working bodies. The working body of the genie is the working chamber, the saw cylinder and the rib grate. The main wear is on the saw cylinder shaft and saw teeth. The wear of these parts leads to additional material costs, as well as to a decrease in the quality of the fiber. The wear of the shaft is affected by the number of saws and the mass of raw cotton in the working chamber of the gin. To prevent wear of the saw cylinder, the article determines the optimal static load on the shaft by calculating saw gins consisting of 90, 100, 110, 120, 130 saws. An analysis of tables shows that maximum value to bending shows 120 and 130 saws cylinder, because shaft bending angles along the length appear. This leads to a 2% - 3% reduction in the distance between the saws, serves for the premature wear of the saw, the exit of short fibers....
In view of the low control precision, low degree of intelligence, low utilization rate of soybean milk, and poor conjunctiva quality in current Yuba skin production technology, a system of multifactor control and intelligent quality detection for Yuba skin was studied and designed in this paper. The system uses LabVIEW as the upper computer and the single chip microcomputer as the lower computer. Uses programming electronic control technology to set process parameters in advance, precisely control each factor to achieve adaptive control of wind speed, concentration, liquid level, and temperature, the control error of each factor is within 1%, and the quality of Yuba skin was detected by image processing technology. The application of the system greatly improves the production efficiency and intellectualization of the production line and reduces the damage degree of Yuba skin. An orthogonal test and response surface optimization were carried out for the process parameters. The test results show that the accuracy of the test results using radial basis kernel function (RBF) can reach 92.06% when the characteristic combination mode is all the characteristics, and the optimal yield will be 48.57% and the optimal quality score is 9.20 when the export angle is 36.75°, the export diameter is 9.90 mm, the export wind speed is 1.22 m/s, the slurry concentration is 7.66%, the slurry temperature is 83.26°C, and the liquid level is 30.52 mm. Comparison between the results from the regression model validation test and those from the response surface methodology is made. The relative errors of yield and quality were 0.14% and 0.87%, which indicates that the response surface methodology can effectively optimize the process parameters of Yuba skin....
Loading....