The Advanced Application of Intelligent Sensor & Artificial intelligence (AI)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 11311

Special Issue Editors

Laboratory of Sensor Fusion and Big Data, College of Control Engineering, Northeast University at Qinhuangdao, Qinhuangdao 066004, China
Interests: big data; Internet of Things; deep learning; smart sensors; BioMEMS (BioMEMS)
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Interests: micro/nano robot; cluster robot control; automation; BioMEMS (BioMEMS)
Coll Automat Engn, Nanjing Univ Aeronaut & Astronaut, Nanjing 211100, China
Interests: wearable device; human-machine interaction; wireless sensor networks; energy harvesting technologies

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has facilitated the development of human society. Its use in practice in recent years has proven the effectiveness of this technology for solving varieties of problems in industry, manufacturing, the medical field, etc. Indeed, in the last few decades, we have witnessed remarkable progress in sensor manufacturing and application. Applying artificial intelligence technology into sensors can make hardware systems more intelligent, thus accelerating the progress of practical application. Therefore, there is a pressing demand for new scalable and highly performing approaches that can cope with this explosion in the field of intelligent sensors and artificial intelligence (AI). We believe this Special Issue will serve as a timely collection of research on advantaged applications of intelligent sensors and artificial intelligence (AI).

Potential topics include but are not limited to:

  • Physical sensors: temperature, mechanical, magnetic, and others;
  • Intelligent sensor networks and systems;
  • Sensor systems: signals, processing, and interfaces;
  • Sensor signal processing for high precision and stability;
  • Intelligent sensors in industrial practice;
  • Data analysis from multisensors using artificial intelligence;
  • Vision sensors, image processing and applications;
  • Edge computing technologies, services, and applications
  • Machine/deep learning and data science in/for IoT devices;
  • Ml-enabled methods, systems, infrastructure, and open issues;
  • Application and case studies (healthcare, industry 4.0, energy, smart city, etc.);
  • Sensors for extreme environmental applications.

Dr. Zhao Yuliang
Prof. Dr. Yongliang Yang
Dr. Fei Fei
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • intelligent sensor
  • data analysis
  • machine learning
  • deep learning
  • image processing
  • edge computing
  • sensor networks

Published Papers (4 papers)

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Research

15 pages, 5480 KiB  
Article
Time Series Forecasting Performance of the Novel Deep Learning Algorithms on Stack Overflow Website Data
by Mesut Guven and Fatih Uysal
Appl. Sci. 2023, 13(8), 4781; https://doi.org/10.3390/app13084781 - 11 Apr 2023
Cited by 1 | Viewed by 4749
Abstract
Time series forecasting covers a wide range of topics, such as predicting stock prices, estimating solar wind, estimating the number of scientific papers to be published, etc. Among the machine learning models, in particular, deep learning algorithms are the most used and successful [...] Read more.
Time series forecasting covers a wide range of topics, such as predicting stock prices, estimating solar wind, estimating the number of scientific papers to be published, etc. Among the machine learning models, in particular, deep learning algorithms are the most used and successful ones. This is why we only focus on deep learning models. Even though it is a hot topic, there are only a few comprehensive studies, and in many studies, there is not much detail about the tested models, which makes it impossible to constitute a comparison chart. Thus, one of the main motivations for this work is to present comprehensive research by providing details about the tested models. In this study, a corpus of the asked questions and their metadata were extracted from the software development and troubleshooting website. Then, univariate time series data were created from the frequency of the questions that included the word “python” as the tag information. In the experiments, deep learning models were trained on the extracted time series, and their prediction performances are presented. Among the tested models, the model using convolutional neural network (CNN) layers in the form of wavenet architecture achieved the best result. Full article
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12 pages, 3070 KiB  
Article
Shooting Prediction Based on Vision Sensors and Trajectory Learning
by Yuliang Zhao, Xinyue Zhang, Mingliang Yang, Qingchao Zhang, Jian Li, Chao Lian, Changbo Bi, Zhiping Wang and Guanglie Zhang
Appl. Sci. 2022, 12(19), 10115; https://doi.org/10.3390/app121910115 - 08 Oct 2022
Viewed by 1831
Abstract
Basketball has become one of the most popular sports and is generally popular in international sports events. However, how to effectively achieve shooting prediction and then guide shooting has become a major challenge. Different from the classical manual observation method, this paper proposes [...] Read more.
Basketball has become one of the most popular sports and is generally popular in international sports events. However, how to effectively achieve shooting prediction and then guide shooting has become a major challenge. Different from the classical manual observation method, this paper proposes a real-time shooting prediction method based on vision sensors and trajectory learning. In the research, we first extracted the basketball trajectory information on template matching and centroid calculation and then obtained a smooth trajectory curve through interpolation. Taking the change of x, y coordinate position, height Y, and distance D from the shooting point during the instantaneous movement as basic features, four machine learning algorithms were used to analyze the impact of different feature combinations on the shooting prediction. Finally, we analyzed the minimum trajectory point requirements predicted when making a shot. The experimental results show that our method can effectively predict the effect of shooting when the feature combination is basketball height and time. When the interpolation density is high (the total number of trajectory points is 116), the overall accuracy can reach more than 90%, and only one-third of the effective trajectory length is required, which effectively helps athletes improve their shooting percentage and assist referees in daily training. Full article
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20 pages, 15627 KiB  
Article
Evaluation and Recognition of Handwritten Chinese Characters Based on Similarities
by Yuliang Zhao, Xinyue Zhang, Boya Fu, Zhikun Zhan, Hui Sun, Lianjiang Li and Guanglie Zhang
Appl. Sci. 2022, 12(17), 8521; https://doi.org/10.3390/app12178521 - 25 Aug 2022
Cited by 2 | Viewed by 2376
Abstract
To accurately recognize ordinary handwritten Chinese characters, it is necessary to recognize the normative level of these characters. This study proposes methods to quantitatively evaluate and recognize these characters based on their similarities. Three different types of similarities, including correlation coefficient, pixel coincidence [...] Read more.
To accurately recognize ordinary handwritten Chinese characters, it is necessary to recognize the normative level of these characters. This study proposes methods to quantitatively evaluate and recognize these characters based on their similarities. Three different types of similarities, including correlation coefficient, pixel coincidence degree, and cosine similarity, are calculated between handwritten and printed Song typeface Chinese characters. Eight features are derived from the similarities and used to verify the evaluation performance and an artificial neural network is used to recognize the character content. The results demonstrate that our proposed methods deliver satisfactory evaluation effectiveness and recognition accuracy (up to 98%~100%). This indicates that it is possible to improve the accuracy in recognition of ordinary handwritten Chinese characters by evaluating the normative level of these characters and standardizing writing actions in advance. Our study can offer some enlightenment for developing methods for the identification of handwritten Chinese characters used in transaction processing activities. Full article
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15 pages, 2438 KiB  
Article
Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
by Shengli Zhou, Fei Fei and Kuiying Yin
Appl. Sci. 2022, 12(7), 3374; https://doi.org/10.3390/app12073374 - 25 Mar 2022
Cited by 2 | Viewed by 1398
Abstract
Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used [...] Read more.
Currently, the classification accuracy of surface electromyography (sEMG) signals is high in literature, but the conventional recognition system may classify untrained movements or the trained movements of low reliability to one of its target classes by mistake. If such a system is used for prosthetic control, sometimes it may cause a disaster. A two-layer classifier that fuses the Gaussian mixture model (GMM) and k-nearest neighbor (kNN) in a sequential structure is proposed in this study. The proposed algorithm can reject the trained movements with low reliability and is efficient in rejecting the untrained movements, thus enhancing the reliability of the myoelectric control system. The results show that the proposed algorithm can produce 95.7% active accuracy in recognizing 12 trained movements and a 30.3% error rate for rejecting 12 untrained movements. When the movement number is six, the active accuracy for trained movements can reach 99.2%, and the error rate of untrained movement is only 17.4%, which is much better than previous studies. Therefore, the proposed classifier can accurately recognize the trained movements and reject untrained movement patterns effectively. Full article
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