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Special Issue "Deep Reinforcement Learning and IoT in Intelligent System"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 7387

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Special Issue Editors

Prof. Dr. Jiachen Yang

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300000, China
Interests: artificial intelligence and deep learning; image quality assessment; image processing; information security
Special Issues, Collections and Topics in MDPI journals

Dr. Dezong Zhao

E-Mail Website
Guest Editor

James Watt School of Engineering, University of Glasgow, Glasgow, UK
Interests: machine learning; autonomous vehicles; intelligent control

Special Issue Information

Dear Colleagues,

The rapid development of information technology is driving a new industrial revolution. More and more industrial practices have introduced artificial intelligence approaches, such as deep reinforcement learning, Internet of Things (IoT), Internet of Underwater Things (IoUT), etc. They have shown great potential and ave hbeen applied in a number of intelligent systems. For example, reinforcement learning has been applied in recommendation systems, financial transactions, intelligent transportation, path planning, and other areas. These novel algorithms and solutions based on artificial intelligence allow for new possibilities, injecting vitality into the traditional field and driving the rapid development of IOT intelligent systems. However, there are still

many open issues in these areas; for example, how should the interconnected and intelligent autonomous systems and infrastructure cooperate with humans for trustworthy joint decisions?

This Special Issue focuses on intelligent algorithms such as deep reinforcement learning and machine learning, and aims to promote the application of intelligent information technology in intelligent systems such as the IoT. We are interested in the implementation of deep reinforcement learning algorithms for applications in different intelligent systems to try to enhance the application and diffusion of intelligent technologies in modern industry by improving the robustness, adaptability, and generalizability of intelligent algorithms. This Special Issue hopes to provide a platform for researchers to share their novel research on the application, performance, and theory of intelligent algorithms.

Prof. Dr. Jiachen Yang
Dr. Dezong Zhao
Guest Editors

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Keywords

deep reinforcement learning
machine learning
internet of things
intelligent system
industrial applications
human-autonomy-infrastructure teaming

Published Papers (7 papers)

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Research

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Open AccessArticle

A Model for Cognitive Personalization of Microtask Design

and

Sensors 2023, 23(7), 3571; https://doi.org/10.3390/s23073571 - 29 Mar 2023

Viewed by 581

Abstract

The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Open AccessArticle

A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning

and

Sensors 2022, 22(23), 9331; https://doi.org/10.3390/s22239331 - 30 Nov 2022

Cited by 1 | Viewed by 649

Abstract

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Open AccessArticle

Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters

and

Sensors 2022, 22(21), 8543; https://doi.org/10.3390/s22218543 - 06 Nov 2022

Viewed by 777

Abstract

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Open AccessArticle

No-Reference Quality Assessment of Stereoscopic Video Based on Temporal Adaptive Model for Improved Visual Communication

Fenghao Gu

and

Zhichao Zhang

Sensors 2022, 22(21), 8084; https://doi.org/10.3390/s22218084 - 22 Oct 2022

Viewed by 660

Abstract

An objective stereo video quality assessment (SVQA) strives to be consistent with human visual perception while ensuring a low time and labor cost of evaluation. The temporal–spatial characteristics of video make the data processing volume of quality evaluation surge, making an SVQA more challenging. Aiming at the effect of distortion on the stereoscopic temporal domain, a stereo video quality assessment method based on the temporal–spatial relation is proposed in this paper. Specifically, a temporal adaptive model (TAM) for a video is established to describe the space–time domain of the video from both local and global levels. This model can be easily embedded into any 2D CNN backbone network. Compared with the improved model based on 3D CNN, this model has obvious advantages in operating efficiency. Experimental results on NAMA3DS1-COSPAD1 database, WaterlooIVC 3D Video Phase I database, QI-SVQA database and SIAT depth quality database show that the model has excellent performance. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Open AccessArticle

Image Classification Method Based on Multi-Agent Reinforcement Learning for Defects Detection for Casting

Chaoyue Liu

Yulai Zhang

and

Sijia Mao

Sensors 2022, 22(14), 5143; https://doi.org/10.3390/s22145143 - 08 Jul 2022

Viewed by 1092

Abstract

A casting image classification method based on multi-agent reinforcement learning is proposed in this paper to solve the problem of casting defects detection. To reduce the detection time, each agent observes only a small part of the image and can move freely on the image to judge the result together. In the proposed method, the convolutional neural network is used to extract the local observation features, and the hidden state of the gated recurrent unit is used for message transmission between different agents. Each agent acts in a decentralized manner based on its own observations. All agents work together to determine the image type and update the parameters of the models by the stochastic gradient descent method. The new method maintains high accuracy. Meanwhile, the computational time can be significantly reduced to only one fifth of that of the GhostNet. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Open AccessArticle

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

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Sensors 2022, 22(12), 4316; https://doi.org/10.3390/s22124316 - 07 Jun 2022

Cited by 9 | Viewed by 1592

Abstract

Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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Review

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Open AccessReview

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

Razvan Bocu

Dorin Bocu

and

Maksim Iavich

Sensors 2023, 23(1), 294; https://doi.org/10.3390/s23010294 - 27 Dec 2022

Viewed by 1415

Abstract

The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)

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