The drying process is fundamental for cut tobacco processing. However, there are some problems related to the drying process such as overheating, or inconsistent control of moisture content. This paper shows how an intelligent controller is designed for an industrial rotary drying system. This controller is applied to a tobacco production unit to reduce overdried cut tobacco and improve the overall unit performance. The proposed control system aims to keep the content of moisture at the dryer outlet as close as possible to the optimal value and improve the homogeneity of the product without any operator intervention. The study shows that, if a reduction of humidity in the cut tobacco drying process is achieved using AI, the quality of the final product improves. In particular, if compared to regulatory control, the proposed method constantly monitors and adjusts the moisture content level in order to reduce the amount of overdried product. The findings of this paper indicate that the suggested process can save at least 222.2 kg of cut tobacco for each batch in the first stage of the drying process.
Flue-cured tobacco grading plays a crucial role in tobacco leaf purchase and the formulation of tobacco leaf groups. However, the traditional flue-cured tobacco grading mode is usually manual, which is time-consuming, laborious, and subjective. Hence, it is essential to research more efficient and intelligent flue-cured tobacco grading methods. Most existing methods suffer from the more classes less accuracy problem. Meanwhile, limited by different industry applications, the flue-cured tobacco datasets are hard to be obtained publicly. The existing methods employ relatively small and lower resolution tobacco data that are hard to apply in practice. Therefore, aiming at the insufficiency of feature extraction ability and the inadaptability to multiple flue-cured tobacco grades, we collected the largest and highest resolution dataset and proposed an efficient flue-cured tobacco grading method based on deep densely convolutional network (DenseNet). Diverging from other approaches, our method has a unique connectivity pattern of convolutional neural network that concatenates preceding tobacco feature data. This mode connects all previous layers to the subsequent layer directly for tobacco feature transmission. This idea can better extract depth tobacco image information features and transmit each layer’s data, thereby reducing the information loss and encouraging tobacco feature reuse. Then, we designed the whole data pre-processing process and experimented with traditional and deep learning algorithms to verify our dataset usability. The experimental results showed that DenseNet could be easily adapted by changing the output of the fully connected layers. With an accuracy of 0.997, significantly higher than the other intelligent tobacco grading methods, DenseNet came to the best model for solving our flue-cured tobacco grading problem.
With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions.
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