Remote sensing data is a complex transformation process from data to information, from acquisition to application. These data come from a variety of sources and have a complex structure. It is extremely difficult for different researchers to access each other, which seriously affects the efficiency of scientific research. How to use artificial intelligence technology to manage these scattered files in a systematic multi-level classification is the key to remote sensing data management. In this paper, we propose a specially optimized file classification method based on deep learning technology, which aims to classify the random heterogeneous data generated by satellite remote sensing and expeditions in the field of fisheries expertise by means of artificial intelligence. The main contributions of this paper are threefold: (1) The binary data itself and its summary information in remote sensing data are input as different modalities for classification. (2) The BiLSTM model and the CNN model are improved for remote sensing data classification scenarios based on the multi-headed attention mechanism. (3) Feature extraction is performed based on the improved model and compared with the model of traditional classification methods.(3) The experimental results show that the improved file classification method has higher accuracy than the traditional machine learning classification methods in classifying fisheries remote sensing data files.
KEYWORDS: Deep learning, Machine learning, Sensors, Sensor networks, Data modeling, Detection and tracking algorithms, Modulation, Intelligent sensors, Education and training, Industry
Fisheries IoT realizes real-time online detecting and precise modulation of aquaculture process, which greatly enhances the informationization, automation and intelligence level of farms, reduces production costs, promotes the scale of fishery industry, and improves the quality level and market competitiveness of agricultural products. However, the key to large-scale fishery farming lies in accurate water environment modulation, the IoT gateway system is the key to realize the ability of intelligent fishery to achieve accurate water environment modulation. Once the aquatic product networking is attacked by malicious IoT commands, it may cause rapid disintegration of the farming environment ecosystem at any time, resulting in huge economic losses. Due to the size and power consumption of fishery IoT gateways, traditional security protection means are often difficult to be applied in such scenarios. Therefore, this paper proposes an improved malicious command detection method for smart fisheries sensor gateways based on deep learning techniques, aiming to accurately and efficiently identify malicious control commands of smart fisheries sensor networks on IoT gateways. The main contributions of this paper are three: (1) Transforming various types of signals from gateways into feature texts, which are used as inputs for deep learning for command classification. (2) Based on the BiLSTM model, it is improved by adding a multi-headed self-attention mechanism to obtain a smaller size and higher accuracy malicious command detection model applicable to smart fisheries gateways (3) Experimental results show that compared with other models, highly confusing malicious commands elaborated for the command characteristics of short, small size and high change rate of smart fisheries sensor commands The improved BiLSTM model has a higher recognition rate.
With the information age coming, the explosion of data is creating opportunities just in the management of the text-documents data in and of itself. Although several information technology is applied in web crawl and information category widely, some vital characteristics of such text classification and natural language processing are not able to obtain directly in specific fields. Therefore, this technical paper propose a multi-head-attention method to classify for Fishery Science and Technology Literature, which aims to help scholars or intelligence agent to obtain the classification results of prepared literature from the database directly. There are mainly three contributions in this paper: (i) the multi-head-attention method and improved training mechanisms is first developed for the text classification research; (ii) a novel compression scheme, referred as PP-MiniLM, based on task-agnostic distillation, pruning and quantization is presented to build the fast, small, accurate classification model; (iii) Furthermore, the experimental results indicate that the prediction accuracy is greatly improved based on the pruning and pooling.
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