Journal of Sensors
 Journal metrics
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Acceptance rate47%
Submission to final decision56 days
Acceptance to publication24 days
CiteScore4.100
Journal Citation Indicator0.450
Impact Factor2.137

Article of the Year 2020

Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

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 Journal profile

Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.

 Editor spotlight

Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

In the past 20 years, although there are many achievements in the model analysis and research, there are still problems of low data utilization and low accuracy. This paper analyzes the mental health level of college students based on chaotic algorithm. At the same time, the application of computer monitoring algorithm to students’ real life psychology is discussed. According to different types of mental health analysis models, the high-precision matching analysis of different students is realized. At the same time, according to the personality characteristics and psychological changes of different students, the model is established and analyzed. Finally, an experiment is designed to carry out practical application and data analysis of the mental health analysis model. The results show that the intelligent analysis model based on computer chaos algorithm has better classification effect. In addition, the algorithm can also make different evaluation strategies according to the different personality of students and can carry out multidimensional classification for college students of different majors. It has effectively increased the proportion of college students’ mental health groups. Compared with the current mainstream algorithms, the algorithm used in this study can adaptively classify college students of different majors. The accuracy of the experimental results is improved by at least 37% compared with the traditional method, and the error is low.

Research Article

Assessing Usability and Accessibility of Indian Tourism Websites for Visually Impaired

The tourism industry cannot ignore the needs of people with special needs. Providing accessible tourism is essential because of social and legal obligations, but also because they have large business opportunities. These people with special needs face challenges in every social, economic, and digital environment. One of the greatest barriers they face is the lack of accessible and usable information on the Internet, which thwarts their travel plans. This research is aimed at identifying the usability and accessibility status of official state tourism websites of India. The usability evaluation was done on various web quality parameters using automated online tools. The accessibility evaluation was done to check the compliance of Web Content Accessibility Guideline version 2.0 by the tourism website using the automated tool TAW. Further manual inspection was applied to identify accessibility and language options on the webpage. The result revealed that Indian state tourism websites had low usability and accessibility status, and they need much improvement to make them accessible to people with special needs.

Research Article

A Collaborative Filtering Method for Operation Maintenance Behavior in Power Monitoring Systems

As an important part of power infrastructure, a power monitoring system provides real-time data acquisition, state detection, and remote control of power equipment for the power grid and can deal with sudden anomalies in time. The operation and maintenance of the power monitoring system are very important to ensure the stable operation of power grid. The current mainstream remote operation and maintenance mode has internal threats such as misoperation of operation and maintenance personnel or malicious damage caused by attackers stealing operation and maintenance authority. Meanwhile, the existing operation and maintenance audit has the problems of high human resource cost and limited supervision of operation and maintenance personnel. To solve this problem, this paper proposes a collaborative filtering method for operation and maintenance behavior of power monitoring system called CFomb. Exploiting a keyword matching algorithm, CFomb determines the power resources accessed by operation and maintenance users from multiple operation instructions and extracts operation and maintenance behaviors. Referring to the collaborative filtering idea, the feature matrix decomposition scheme is introduced to train the access probability model based on the historical normal behavior of multiple operation and maintenance users, which provides a basis for real-time prediction of the access behavior probability of target operation and maintenance users. The OTSU binarization technique is used to determine the probability threshold of abnormal operation and maintenance behaviors, identify abnormal behaviors through threshold comparison, and send real-time alarms to operation and maintenance audit. The simulation experiment results show that the method in this paper can effectively identify the abnormal behavior of operation and maintenance users, reduce the overhead of manual audit, and help improve the power monitoring system’s ability to respond to internal threats of operation and maintenance.

Research Article

Effects of Variable Proportions of Concrete Fragments on Urban Soil Moisture Transport: An Experimental and Simulation Study

In order to investigate the effects of typical anthropogenic concrete fragments on moisture infiltration and evaporation in urban soils, the effects of typical anthropogenic concrete fragments on wetting peak transport distance, cumulative infiltration, cumulative evaporation, evaporation rate, and soil profile moisture at four levels (0, 5%, 10%, and 20%) were investigated by indoor soil column experiments. The results showed that the presence of concrete fragments promoted the wetting peak transport distance and cumulative infiltration, and the promotion effect increased gradually with the increase of the ratio, but there was a threshold value, and the promotion effect was least when the ratio was 20%. When the evaporation period was 35 d, concrete fragment treatment can increase the cumulative evaporation and promote the evaporation of urban soil moisture; the promotion effect increases with the increase of the proportion, but there is a threshold value; when the proportion is 20%, the promotion effect is the smallest. The evaporation rate was consistent with the different stages of evaporation process during evaporation. The concrete fragment treatment reduced the time required for moisture to reach the same depth during infiltration; the moisture coefficient of variation of the concrete fragment treatment during evaporation showed a trend of decreasing, then increasing, and then decreasing, which increased the uncertainty of moisture in the evaporation process. The model simulation results show that the models such as the power function, Kostiakov model, and Rose model fit well, and the coefficient of determination is greater than 0.99, among which the Kostiakov model fits best. The research results can provide a theoretical scientific basis for building an efficient ecological city.

Research Article

Nonlinear Identification of PMSM Rotor Magnetic Linkages Based on an Improved Extended Kalman Filter

The permanent magnet synchronous motor (PMSM) has complex nonlinear, strongly coupled characteristics and the variation of motor parameters makes its control more difficult. Therefore, parameter identification is of great significance for the stable operation of its closed-loop control system. In this paper, a method based on an improved extended Kalman filter (EKF) for the identification of the rotor flux () of a permanent magnet synchronous motor is investigated for this nonlinear and strongly coupled model. Simulation results show that the method has a more fast convergence rate and more accurate identification result than traditional EKF algorithm.

Research Article

A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition

In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted great concern. However, because the data feature distributions present differences in applications with varying working conditions, the deep learning models cannot provide satisfactory performance of fault prediction in such scenarios. To address this problem, this paper proposes a domain adversarial-based rolling bearing fault transfer diagnosis model EMBRNDNMD. First of all, an EEMD-based time-frequency feature graph (EEMD-TFFG) construction method is proposed, and the time-frequency information of nonlinear nonstationary vibration signal is extracted; secondly, a multi-branch ResNet (MBRN) structure is designed, which is used to extract deep features representing the bearing state from EEMD-TFFG; finally, to solve the model domain adaptation transfer problem under varying working conditions, the adversarial network module and MK-MMD distribution difference evaluation method are introduced to optimize MBRN, so as to reduce the probability distribution difference between the deep features of source domain and target domain, and to improve the accuracy of EMBRNDNMD in state diagnosis of target domain. The results of experiments carried out on two bearing fault test platforms prove that EMBRNDNMD can maintain an average accuracy above 97% in fault transfer diagnosis tasks, and this method also has high stability and strong ability of scene adaptation.

Journal of Sensors
 Journal metrics
See full report
Acceptance rate47%
Submission to final decision56 days
Acceptance to publication24 days
CiteScore4.100
Journal Citation Indicator0.450
Impact Factor2.137
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.