Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food

Zhang, Haiyu; Zuo, Xuanyi; Sun, Bin; Wei, Bingqing; Fu, Jiajie; Xiao, Xinqing

doi:10.3390/app13042674

Cited by 40 publications

(5 citation statements)

References 38 publications

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“…Gaining insight into the process by which nanofibrous membranes are formed is not only a captivating scientific endeavour but also beneficial for the creation of innovative supramolecular food packaging with diverse uses. Various computational techniques have been utilized to gain insights into the intricate characteristics of molecule conformational propensity, intermolecular interaction modes, selfassembled NF structures and polymer-solvent interactions at the microscopic scale [112,113]. The advancement of ES techniques has greatly facilitated the broad application of NF materials in manufacturing.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Electrospinning in Food Packaging: Current Trend and Future Direction

Al‐Attabi,

Ahmed,

Merza

et al. 2024

Packag Technol Sci

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Food packaging is a multidisciplinary field that includes food science, chemistry, engineering materials sciences and microbiology. Food packaging has sparked great interest in protecting the freshness and quality of foods and their raw materials from oxidation and microbial spoilage. Nanotechnology, especially electrospinning, has shown promising results in fabricating sophisticated food packaging materials. The current paper aims to review, highlight, discuss and challenge the latest electrospun food packaging studies. We will not only summarize the studies but also challenge the applied methods, materials and results. Moreover, we offer a more practical point of view regarding the industrial translation of electrospun food packaging.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Electrospinning in Food Packaging: Current Trend and Future Direction

Al‐Attabi,

Ahmed,

Merza

et al. 2024

Packag Technol Sci

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“…The concept of deep learning (Wang et al, 2016;Gao et al, 2017;Lee et al, 2017) originated from artificial neural networks and has demonstrated excellent performance in feature learning and expression, which combines low-level features to form more abstract high-level features, thus discovering the distribution characteristics of data and enhancing image quality and recognition accuracy (Yang et al, 2022;Xu et al, 2023;Zhang et al, 2023). In 2020, Raikar et al (2020) classified and graded okra fingers by comparing three models (AlexNet, GoogLeNet, and ResNet50) and employed transfer learning to train the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Agricultural development driven by the digital economy: improved EfficientNet vegetable quality grading

Wen,

2024

Front. Sustain. Food Syst.

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IntroductionThe conventional manual grading of vegetables poses challenges that necessitate innovative solutions. In this context, our paper proposes a deep learning methodology for vegetable quality grading.MethodsTo address the scarcity of vegetable datasets, we constructed a unique dataset comprising 3,600 images of diverse vegetables, including lettuce, broccoli, tomatoes, garlic, bitter melon, and Chinese cabbage. We present an improved CA-EfficientNet-CBAM model for vegetable quality grading. The CA module replaces the squeeze-and-excitation (SE) module in the MobileNet convolution (MBConv) structure of the EfficientNet model. Additionally, a channel and spatial attention module (CBAM) is integrated before the final layer, accelerating model training and emphasizing nuanced features.ResultsThe enhanced model, along with comparisons to VGGNet16, ResNet50, and DenseNet169, was subjected to ablation experiments. Our method achieved the highest classification accuracy of 95.12% on the cabbage vegetable image test set, outperforming VGGNet16, ResNet50, and DenseNet169 by 8.34%, 7%, and 4.29%, respectively. Notably, the proposed method effectively reduced the model’s parameter count.DiscussionOur experimental results highlight the effectiveness of the deep learning approach in improving vegetable quality grading accuracy. The superior performance of the enhanced EfficientNet model underscores its potential for advancing the field, achieving both high classification accuracy and parameter efficiency. We hope this aligns with your expectations. If there are further adjustments or clarifications needed, please let us know.

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“…Additionally, the sliding mode control (SMC) technique was used to generate switching pulses for the voltage source converter (VSC). Haiyu Zhang et al [21] introduced a gas distribution system based on a fuzzy PID controller, where the system's mathematical model was built using the step response method, and the gas distribution control process was optimized. However, this method is subject to the influence of human factors on parameter adjustments.…”

Section: Introductionmentioning

confidence: 99%

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

Cao,

Liu,

Song

et al. 2024

Applied Sciences

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With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC’s heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values.

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Fuzzy-PID-Based Atmosphere Packaging Gas Distribution System for Fresh Food

Cited by 40 publications

References 38 publications

Electrospinning in Food Packaging: Current Trend and Future Direction

Electrospinning in Food Packaging: Current Trend and Future Direction

Agricultural development driven by the digital economy: improved EfficientNet vegetable quality grading

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

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