Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, image synthesis, event reconstruction, intelligent driving and pose measurement. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy.

The continuous development of industry big data technology requires better computing methods to discover the data value. Information forecast, as an important part of data mining technology, has achieved excellent applications in some industries. However, the existing deviation and redundancy in the data collected by the sensors make it difficult for some methods to accurately predict future information. This paper proposes a semi-supervised prediction model, which exploits the improved unsupervised clustering algorithm to establish the fuzzy partition function, and then utilize the neural network model to build the information prediction function. The main purpose of this paper is to effectively solve the time analysis of massive industry data. In the experimental part, we built a data platform on Spark, and used some marine environmental factor datasets and UCI public datasets as analysis objects. Meanwhile, we analyzed the results of the proposed method compared with other traditional methods, and the running performance on the Spark platform. The results show that the proposed method achieved satisfactory prediction effect.

With the development of the Internet-of-things (IoT) industry, more and more fields are involved such as multimedia data. Currently, users rely on videos and images with high data volume, so it has brought more challenges for wireless communication and transmission. For multimedia data, it is obviously different from traditional communication data. So new method is required to solve the problem of high data volume in communication. The proactive content caching and the unmanned aerial vehicle(UAV) relaying techniques are deployed over IoT network, enabling the maximum throughput for the served IoT devices. Even though these two existing technologies are important to solve the problem of throughput, there are still other challenges for efficiently improving the system throughput. We mainly study the cache-enabled UAV to maximize throughput among IoT devices in the IoT with the placement of content caching and UAV location. Specially, we divide the joint optimization problem into two parts. Firstly, the UAV deployment problem is decomposed into vertical and horizontal dimensions to ensure the optimal deployment height and 2D position. The enumeration search method is employed to obtain the 2D position. Then, we also formulate a concave problem for probabilistic caching placement. Experimental results have indicated that the cache-enabled UAV scheme can obtain a better throughput, which can bring new approach for multimedia data throughput maximization in Internet-of-Things system.

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Especially, explosive time-series data pose enormous challenges to the information mining and processing for modern industry. How to identify and detect the multidimensional industrial time-series anomaly is an important issue. However, most of the existing studies fail to handle with large amounts of unlabeled data, thus generating the undesirable results. In this article, we propose a novel integrated deep generative model, which is built by generative adversarial networks based on bidirectional long short-term memory and attention mechanism (AMBi-GAN). The structure for the generator and the discriminator is the bidirectional long short-term memory with attention mechanism, which can capture time-series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multidimensional time-series anomaly toward IIoT in the era of artificial intelligence.

With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.