The existing mine pressure monitoring system has realized the online continuous monitoring of the working-face stent resistance, roadway roof offcuts, and anchor rod/rope working resistance. However, the mine pressure monitoring information of the working face currently includes only the stent resistance and the monitoring time, and there is no information on the working-face advance. The mine pressure data cannot be precisely analyzed due to a lack of measurement point locations. Mine pressure data analysis combined with the working-face feed information is the basis for safe and efficient mining and for improving the intelligence level of the comprehensive mining face. According to the special electromagnetic environment of the underground, this system adopts UWB (ultra-wide-band) technology and the SDS-TWR (symmetric double-sided two-way ranging) ranging method, with the UWB positioning base station as the core and installs positioning tags at the end supports of the working face to collect information. The data are uploaded to the host computer via Ethernet for coordinate solving, automatically collecting the working-face footage data and providing positional information for mine pressure monitoring. The application results show that the system operates normally and can collect real-time information of working-face footage and monitor mine pressure data, and meet the requirements of coal mine positioning accuracy, positioning error is less than 30 cm, the application effect is good.
{"title":"Automatic Acquisition System for Mine Pressure Monitoring in Coal Mine Working-Face Footage","authors":"Miaoer Zhou, Yongkui Shi, Jian Hao, Xin Chen","doi":"10.1155/2024/8876210","DOIUrl":"https://doi.org/10.1155/2024/8876210","url":null,"abstract":"The existing mine pressure monitoring system has realized the online continuous monitoring of the working-face stent resistance, roadway roof offcuts, and anchor rod/rope working resistance. However, the mine pressure monitoring information of the working face currently includes only the stent resistance and the monitoring time, and there is no information on the working-face advance. The mine pressure data cannot be precisely analyzed due to a lack of measurement point locations. Mine pressure data analysis combined with the working-face feed information is the basis for safe and efficient mining and for improving the intelligence level of the comprehensive mining face. According to the special electromagnetic environment of the underground, this system adopts UWB (ultra-wide-band) technology and the SDS-TWR (symmetric double-sided two-way ranging) ranging method, with the UWB positioning base station as the core and installs positioning tags at the end supports of the working face to collect information. The data are uploaded to the host computer via Ethernet for coordinate solving, automatically collecting the working-face footage data and providing positional information for mine pressure monitoring. The application results show that the system operates normally and can collect real-time information of working-face footage and monitor mine pressure data, and meet the requirements of coal mine positioning accuracy, positioning error is less than 30 cm, the application effect is good.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"12 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chunlian An, Guyue Yang, Peng Li, Dengmei Zhou, Liangliang Tian
Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.
脉冲噪声下的到达方向（DOA）估计一直是阵列信号处理的一个重要研究领域。脉冲噪声下的传统方法大多依赖于先验参数，计算复杂度较高。基于滤波理论，我们提出了一种有效的预处理滤波技术，以去除阵列接收数据中的脉冲混杂，并采用非均匀线性阵列进一步提高估计性能。首先，根据脉冲噪声的振幅特性，提出了基于中值滤波器和滑动平均滤波器的预处理滤波技术，以滤除脉冲，该技术对强脉冲噪声和弱脉冲噪声均有效。其次，采用最小冗余阵列进行阵列虚扩展，从而增大阵列孔径，提高估计性能。最后，基于矩阵重构的思想，我们提出了通过旋转不变性技术改进的信号参数估计算法和改进的根多信号分类算法来进行 DOA 估计。理论分析和仿真结果表明，所提方法处理过程简单、计算量小、阵列扩展能力强、噪声适应性好。此外，所提出的方法大大提高了低信噪比和强脉冲噪声条件下的测向性能。
{"title":"Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array","authors":"Chunlian An, Guyue Yang, Peng Li, Dengmei Zhou, Liangliang Tian","doi":"10.1155/2024/9936133","DOIUrl":"https://doi.org/10.1155/2024/9936133","url":null,"abstract":"Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function. This leads to the proposal of a novel method for calculating mutual support among multiple sensors. Furthermore, the basic belief assignment (BBA) in evidence theory is determined by measuring the confidence distance between sensors. Subsequently, a divergence measure is employed to assess the level of conflict and difference between BBA functions, which serves as an indicator of their credibility. The credibility of BBA functions is further adjusted by calculating their information volume using Shannon entropy. This adjustment aims to increase the weight of BBA functions that exhibit less conflict with other BBA functions. Ultimately, the fusion result is obtained through an evidence combination rule based on a conflict allocation. The numerical experimental results demonstrate that the proposed approach achieves higher accuracy, better robustness, and generality compared to the existing methods.
{"title":"The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment","authors":"Lei Wang, Chenyan Fu, Junyan Qi","doi":"10.1155/2024/5581891","DOIUrl":"https://doi.org/10.1155/2024/5581891","url":null,"abstract":"An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function. This leads to the proposal of a novel method for calculating mutual support among multiple sensors. Furthermore, the basic belief assignment (BBA) in evidence theory is determined by measuring the confidence distance between sensors. Subsequently, a divergence measure is employed to assess the level of conflict and difference between BBA functions, which serves as an indicator of their credibility. The credibility of BBA functions is further adjusted by calculating their information volume using Shannon entropy. This adjustment aims to increase the weight of BBA functions that exhibit less conflict with other BBA functions. Ultimately, the fusion result is obtained through an evidence combination rule based on a conflict allocation. The numerical experimental results demonstrate that the proposed approach achieves higher accuracy, better robustness, and generality compared to the existing methods.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"8 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139647819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amritpal Kaur, Devershi Pallavi Bhatt, Linesh Raja
The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naïve Bayes, random forest, and logistic regression. Compared to other Naïve Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.
{"title":"Developing a Hybrid Irrigation System for Smart Agriculture Using IoT Sensors and Machine Learning in Sri Ganganagar, Rajasthan","authors":"Amritpal Kaur, Devershi Pallavi Bhatt, Linesh Raja","doi":"10.1155/2024/6676907","DOIUrl":"https://doi.org/10.1155/2024/6676907","url":null,"abstract":"The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naïve Bayes, random forest, and logistic regression. Compared to other Naïve Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"25 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139589020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a lack of study on fault detection methods of medical equipment at home and abroad. The main reason is that the research of fault features is diverse and not systematic. This paper aims to propose a fault recognition method for medical equipment combining the electrical performance parameter features with fault events. First, it treats the equipment as a whole system, setting up the analysis model. Then, we are going to analyze the signal for indicator. This paper chooses the multi-index electrical performance parameters (MEPP) method for the fault identification an indicator. It is proved that the electrical performance signal can evaluate the status of equipment. Thus, it can also be used to recognize the fault or other working statuses. Then, the features of current, voltage, and power are studied exhaustively using a mathematical model. After that, the weight of each parameter feature in any specific event will be determined according to the influence of each parameter feature on fault events. At that time, the recognition method basically realizes the correlation between multi-index features and fault events through weight. Next, the above method needs to be verified in the experiment. This paper chooses six monitors for setting the rules of normal status. The normal status is the baseline for fault identification. Then, feature intervals of other faults are established around this reference. Finally, each feature interval will be constantly adjusted to meet the preset recognition rate and updated to the rules in the subsequent measurement. In this paper, 10 monitors are selected as samples to update a set of basic fault judgment rules based on MEPP, and by adjusting the overlapping interval, the fault recognition rate reaches more than 90% in this study. To sum up, this paper uses the MEPP method to find out the relationship of features of current, voltage, and power with fault events. It will become a new direction for fault recognition studies on electrical medical equipment and other device.
{"title":"Fault Detection Method of Medical Equipment Based on Multi-Index Electrical Performance Parameters","authors":"Xiaoyu Chen, Haitao Guo, Zihong Wang, Feiba Chang, Xiaomei Ren, Chengqun Ma, Weiben Li, Miao Tian, Rui Yang, Xianju Yuan, Shengting Zhou","doi":"10.1155/2024/5516493","DOIUrl":"https://doi.org/10.1155/2024/5516493","url":null,"abstract":"There is a lack of study on fault detection methods of medical equipment at home and abroad. The main reason is that the research of fault features is diverse and not systematic. This paper aims to propose a fault recognition method for medical equipment combining the electrical performance parameter features with fault events. First, it treats the equipment as a whole system, setting up the analysis model. Then, we are going to analyze the signal for indicator. This paper chooses the multi-index electrical performance parameters (MEPP) method for the fault identification an indicator. It is proved that the electrical performance signal can evaluate the status of equipment. Thus, it can also be used to recognize the fault or other working statuses. Then, the features of current, voltage, and power are studied exhaustively using a mathematical model. After that, the weight of each parameter feature in any specific event will be determined according to the influence of each parameter feature on fault events. At that time, the recognition method basically realizes the correlation between multi-index features and fault events through weight. Next, the above method needs to be verified in the experiment. This paper chooses six monitors for setting the rules of normal status. The normal status is the baseline for fault identification. Then, feature intervals of other faults are established around this reference. Finally, each feature interval will be constantly adjusted to meet the preset recognition rate and updated to the rules in the subsequent measurement. In this paper, 10 monitors are selected as samples to update a set of basic fault judgment rules based on MEPP, and by adjusting the overlapping interval, the fault recognition rate reaches more than 90% in this study. To sum up, this paper uses the MEPP method to find out the relationship of features of current, voltage, and power with fault events. It will become a new direction for fault recognition studies on electrical medical equipment and other device.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"54 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139589898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ana Madevska Bogdanova, Bojana Koteska, Teodora Vićentić, Stefan D. Ilić, Miona Tomić, Marko Spasenović
Measuring blood oxygen saturation (SpO2) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO2 levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO2 estimation. The LIG sensors are attached to a subject’s chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO2 values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO2 estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and R-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO2 monitoring and other innovative applications.
{"title":"Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor","authors":"Ana Madevska Bogdanova, Bojana Koteska, Teodora Vićentić, Stefan D. Ilić, Miona Tomić, Marko Spasenović","doi":"10.1155/2024/4696031","DOIUrl":"https://doi.org/10.1155/2024/4696031","url":null,"abstract":"Measuring blood oxygen saturation (SpO<sub>2</sub>) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO<sub>2</sub> levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO<sub>2</sub> estimation. The LIG sensors are attached to a subject’s chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO<sub>2</sub> values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO<sub>2</sub> estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and <i>R</i>-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO<sub>2</sub> monitoring and other innovative applications.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"17 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139589012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.
{"title":"A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error","authors":"Qi Zhang, Wenxing Li, Si Li, Yunlong Mao","doi":"10.1155/2024/6834284","DOIUrl":"https://doi.org/10.1155/2024/6834284","url":null,"abstract":"The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"204 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139557573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pei Yu, Wei Wei, Jing Li, Qiuyang Du, Fang Wang, Lili Zhang, Huitao Li, Kang Yang, Xudong Yang, Ning Zhang, Yucheng Han, Huapeng Yu
Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.
{"title":"Fire-PPYOLOE: An Efficient Forest Fire Detector for Real-Time Wild Forest Fire Monitoring","authors":"Pei Yu, Wei Wei, Jing Li, Qiuyang Du, Fang Wang, Lili Zhang, Huitao Li, Kang Yang, Xudong Yang, Ning Zhang, Yucheng Han, Huapeng Yu","doi":"10.1155/2024/2831905","DOIUrl":"https://doi.org/10.1155/2024/2831905","url":null,"abstract":"Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"41 5 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we have investigated the performance of a resonator in 2D, in an asymmetric form using the physical method and using the Matlab software, we have analyzed it in 3D. According to the simulation results, in asymmetric 2D and 3D structures, whispering gallery modes, or resonances appeared at similar wavelengths for the same radial and polar mode number. Also, the results obtained from the simulations indicated that the resonances of the asymmetric 2D structure would occur at wavelengths close to the wavelengths of 3D structure and the resonance wavelengths for transverse electric (TE) and while transverse magnetic (TM) modes would not change by altering the lateral mode number. Accordingly, 2D structures can be used to obtain resonance wavelengths in microsphere resonators. Achieving results with high accuracy, as well as faster speed offered by smaller meshing volume is one of the advantages of 2D structures in the physical method.
{"title":"Comparison of Resonance Modes in Two-Dimensional and Three-Dimensional Microsphere Structures","authors":"Sajjad Heshmati, Abolfazl Rahmani","doi":"10.1155/2024/6642397","DOIUrl":"https://doi.org/10.1155/2024/6642397","url":null,"abstract":"In this article, we have investigated the performance of a resonator in 2D, in an asymmetric form using the physical method and using the Matlab software, we have analyzed it in 3D. According to the simulation results, in asymmetric 2D and 3D structures, whispering gallery modes, or resonances appeared at similar wavelengths for the same radial and polar mode number. Also, the results obtained from the simulations indicated that the resonances of the asymmetric 2D structure would occur at wavelengths close to the wavelengths of 3D structure and the resonance wavelengths for transverse electric (TE) and while transverse magnetic (TM) modes would not change by altering the lateral mode number. Accordingly, 2D structures can be used to obtain resonance wavelengths in microsphere resonators. Achieving results with high accuracy, as well as faster speed offered by smaller meshing volume is one of the advantages of 2D structures in the physical method.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"17 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139408185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Zhang, Xianjun Zhou, Yike Shi, Xuan Guo, Hang Liu
Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object recognition accuracy image detection. The method first reduces the computation and memory consumption by introducing the RepConv structure, further improves the detection accuracy and speed of the model by embedding the C2F structure. This method finally is further optimized neural network by the Meta-ACON activation function. The results indicate that the average detection accuracy of the improved YOLOv5 network can reach 96.9%, which is 2.2% higher than before. Additionally, corresponding detection speed can reach 258.36 frames/second, which surpasses existing mainstream target detection models, performing better in terms of the balance of inference speed and detection accuracy. Consequently, the effectiveness and superiority of the algorithm have been proved.
{"title":"Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework","authors":"Hao Zhang, Xianjun Zhou, Yike Shi, Xuan Guo, Hang Liu","doi":"10.1155/2024/5977332","DOIUrl":"https://doi.org/10.1155/2024/5977332","url":null,"abstract":"Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object recognition accuracy image detection. The method first reduces the computation and memory consumption by introducing the RepConv structure, further improves the detection accuracy and speed of the model by embedding the C2F structure. This method finally is further optimized neural network by the Meta-ACON activation function. The results indicate that the average detection accuracy of the improved YOLOv5 network can reach 96.9%, which is 2.2% higher than before. Additionally, corresponding detection speed can reach 258.36 frames/second, which surpasses existing mainstream target detection models, performing better in terms of the balance of inference speed and detection accuracy. Consequently, the effectiveness and superiority of the algorithm have been proved.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"9 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139408160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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