Sheng Li, Zewen Yao, Li Wang, Li Ma, Yongze He, Xuefeng Ban
The present study investigated the influence of key design parameters on the seismic performance of prefabricated precast assembled piers’ connection parts to better adapt to the industrialized construction of prefabricated precast assembled pier connected using grouting sleeves. Relying on a prefabricated assembled bridge in the actual project, the ABAQUS software was used to establish a refined solid finite element model of prefabricated assembled piers connected with grouting sleeves. Numerical simulation analysis was conducted for the piers with low circumferential reciprocating loading. The seismic performance of the prefabricated assembled piers was evaluated in terms of hysteresis characteristics, dissipation characteristics, and damage development. The effects of the length of the grouting sleeve and the diameter of the longitudinal reinforcement on the seismic performance were also investigated. The maximum error between the numerical simulation results and the test results was 5.7%, and the plastic region of the precast assembled pier obtained from the numerical simulation was consistent with the test results, indicating that the numerical simulation method is accurate and reliable. When the length of the grouting sleeve increased from 0.6 to 1.2 m, the yield load, peak load, and dissipation of energy of prefabricated assembled piers increased by 11.6%, 10.9%, and 11.4%, respectively; no significant change in residual displacement; ductility coefficient decreased by a small amount. When the longitudinal reinforcement diameter increased from 20 to 50 mm, prefabricated assembled piers yield load, peak load, and dissipation increased by 99.6%, 89.3%, and 218.9%, respectively, whereas the residual displacement increased by 137.3%, and the ductility coefficient decreased more. Increasing the length of the grouting sleeve or increasing the diameter of longitudinal reinforcement improved the stiffness of the piers, causing the piers to displace less and damage less under the same force, but the residual displacement would increase.
本研究探讨了关键设计参数对预制装配式墩台连接部位抗震性能的影响，以更好地适应采用灌浆套筒连接预制装配式墩台的工业化施工。依托实际工程中的预制装配式桥梁，采用 ABAQUS 软件建立了灌浆套筒连接预制装配式桥墩的实体有限元精化模型。在低圆周往复荷载作用下，对桥墩进行了数值模拟分析。从滞后特性、耗散特性和损伤发展等方面评估了预制装配式桥墩的抗震性能。此外，还研究了灌浆套筒长度和纵向钢筋直径对抗震性能的影响。数值模拟结果与试验结果的最大误差为 5.7%，数值模拟得到的预制装配式桥墩塑性区与试验结果一致，表明数值模拟方法准确可靠。当灌浆套筒长度由 0.6 m 增加到 1.2 m 时，预制装配式桥墩的屈服荷载、峰值荷载和耗能分别增加了 11.6%、10.9% 和 11.4%；残余位移无明显变化；延性系数有少量下降。当纵向钢筋直径从 20 mm 增加到 50 mm 时，预制装配式桥墩的屈服荷载、峰值荷载和耗散分别增加了 99.6%、89.3% 和 218.9%，而残余位移增加了 137.3%，延性系数下降较多。增加灌浆套筒的长度或增大纵向钢筋的直径可提高桥墩的刚度，使桥墩在相同的力作用下位移更小，破坏更小，但残余位移会增加。
{"title":"Research on the Factors Influencing the Seismic Performance of Grouting Sleeve Assembled Double-Column Piers","authors":"Sheng Li, Zewen Yao, Li Wang, Li Ma, Yongze He, Xuefeng Ban","doi":"10.1155/2024/6938870","DOIUrl":"https://doi.org/10.1155/2024/6938870","url":null,"abstract":"The present study investigated the influence of key design parameters on the seismic performance of prefabricated precast assembled piers’ connection parts to better adapt to the industrialized construction of prefabricated precast assembled pier connected using grouting sleeves. Relying on a prefabricated assembled bridge in the actual project, the ABAQUS software was used to establish a refined solid finite element model of prefabricated assembled piers connected with grouting sleeves. Numerical simulation analysis was conducted for the piers with low circumferential reciprocating loading. The seismic performance of the prefabricated assembled piers was evaluated in terms of hysteresis characteristics, dissipation characteristics, and damage development. The effects of the length of the grouting sleeve and the diameter of the longitudinal reinforcement on the seismic performance were also investigated. The maximum error between the numerical simulation results and the test results was 5.7%, and the plastic region of the precast assembled pier obtained from the numerical simulation was consistent with the test results, indicating that the numerical simulation method is accurate and reliable. When the length of the grouting sleeve increased from 0.6 to 1.2 m, the yield load, peak load, and dissipation of energy of prefabricated assembled piers increased by 11.6%, 10.9%, and 11.4%, respectively; no significant change in residual displacement; ductility coefficient decreased by a small amount. When the longitudinal reinforcement diameter increased from 20 to 50 mm, prefabricated assembled piers yield load, peak load, and dissipation increased by 99.6%, 89.3%, and 218.9%, respectively, whereas the residual displacement increased by 137.3%, and the ductility coefficient decreased more. Increasing the length of the grouting sleeve or increasing the diameter of longitudinal reinforcement improved the stiffness of the piers, causing the piers to displace less and damage less under the same force, but the residual displacement would increase.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140107040","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 protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.
{"title":"Monitoring Analysis of Urban Subsidence in Northern Henan Province Based on TS-InSAR Technology","authors":"Huabin Chai, Yahui Ding, Jibiao Hu, Sijia Geng, Pengju Guan, Hui Xu, Yuqiao Zhao, Mingtao Xu","doi":"10.1155/2024/1190407","DOIUrl":"https://doi.org/10.1155/2024/1190407","url":null,"abstract":"The protracted and pervasive incidence of land subsidence emerges as a pivotal factor exerting a substantial impact on the sustainable development of urban landscapes. A nuanced comprehension of the spatiotemporal evolution characteristics of land subsidence within the Northern Henan Plain assumes paramount significance in the context of mitigating potential urban geological disasters. This study endeavors to redress the deficiency in information concerning the temporal and spatial evolution characteristics of enduring deformation in cities within the northern plain of Henan Province. To this end, the authors leveraged Sentinel-1A radar data processed through persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to elucidate the distribution patterns of ground deformation and temporal evolution characteristics within the expansive 24-scene coverage research area. Empirical findings illuminate conspicuous surface deformation in Anyang, Puyang, and Hebi throughout the monitoring period. Spatially, land subsidence in the study area predominantly clusters in the suburban peripheries of the cities, with Hebi and Puyang registering a maximum subsidence rate exceeding 25 mm per annum. Temporally, land subsidence manifests predominantly during autumn and winter, whereas spring and summer display relatively stable land subsidence interspersed with a slight ground uplift. In order to rectify the spatial disparities observed between leveling data and PS-InSAR monitoring data, this experiment employed an averaging procedure on the PS-InSAR monitoring data, subsequently subjecting it to comparative analysis with the leveling data. Additionally, through the integration of the singular spectrum analysis (SSA) method and the time series deformation model, this study aspires to attain a comprehensive understanding of the temporal dynamics manifested in the PS-InSAR monitoring outcomes, while concurrently elucidating the factors influencing the observed deformations. Ultimately, this analysis discloses that the monitoring outcomes derived via PS-InSAR technology exhibit a root mean square error of ±12.9 mm and a standard deviation of ±13.31 mm. These statistical metrics furnish valuable insights into the precision and consistency of the PS-InSAR monitoring data. Drawing upon a comparative scrutiny of on-site data and historical remote sensing imagery within the study area, it has been discerned that excessive groundwater extraction and expansive surface engineering initiatives stand as the principal instigators of land subsidence in the research domain. Consequently, this experiment assumes the role of a salient reference for the mitigation of urban ground subsidence within the study area.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070990","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}
Diego R. Guevara-Torres, José M. Facelli, Bertram Ostendorf
Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.
{"title":"Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition","authors":"Diego R. Guevara-Torres, José M. Facelli, Bertram Ostendorf","doi":"10.1155/2024/6668228","DOIUrl":"https://doi.org/10.1155/2024/6668228","url":null,"abstract":"Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (<i>Lomandra</i> spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (<i>Avena barbata</i>), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070970","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}
Sizwe Zondo, Aline Ferreira-Correia, Kate Cockcroft
Human immunodeficiency virus (HIV) infection is associated with disturbed neurotransmission and aberrant cortical networks. Although advances in the imaging of brain microarchitecture following neuroHIV has added to our knowledge of structural and functional changes associated with HIV, no data exists on paediatric HIV using optical neuroimaging techniques. This study investigated the feasibility of optical neuroimaging in paediatric HIV using functional near-infrared spectrometry (fNIRS). We measured prefrontal brain activation while participants executed a sustained attention task. We specifically tested whether patients living with HIV and study controls could perform the study protocol and whether we could measure the typical fNIRS haemodynamic response associated with neuronal activity. Eighteen participants (10 HIV participants, mean age: 13.9, SD = 1.66 years; 8 controls, mean age: 14.8, SD = 1.28 years), matched for sex, grade, and socio-economic status, were included in the study. All participants completed the Stroop colour word test (SCWT). Oxygenated haemoglobin concentration and the deoxygenated haemoglobin signal were recorded from the dorsolateral prefrontal cortex and the frontopolar area (FA) using fNIRS. The control group performed significantly better in terms of reaction time on the congruent and incongruent condition (congruent: t (16) = −3.36, : incongruent:
{"title":"A Feasibility Study on the Efficacy of Functional Near-Infrared Spectrometry (fNIRS) to Measure Prefrontal Activation in Paediatric HIV","authors":"Sizwe Zondo, Aline Ferreira-Correia, Kate Cockcroft","doi":"10.1155/2024/4970794","DOIUrl":"https://doi.org/10.1155/2024/4970794","url":null,"abstract":"Human immunodeficiency virus (HIV) infection is associated with disturbed neurotransmission and aberrant cortical networks. Although advances in the imaging of brain microarchitecture following neuroHIV has added to our knowledge of structural and functional changes associated with HIV, no data exists on paediatric HIV using optical neuroimaging techniques. This study investigated the feasibility of optical neuroimaging in paediatric HIV using functional near-infrared spectrometry (fNIRS). We measured prefrontal brain activation while participants executed a sustained attention task. We specifically tested whether patients living with HIV and study controls could perform the study protocol and whether we could measure the typical fNIRS haemodynamic response associated with neuronal activity. Eighteen participants (10 HIV participants, mean age: 13.9, SD = 1.66 years; 8 controls, mean age: 14.8, SD = 1.28 years), matched for sex, grade, and socio-economic status, were included in the study. All participants completed the Stroop colour word test (SCWT). Oxygenated haemoglobin concentration and the deoxygenated haemoglobin signal were recorded from the dorsolateral prefrontal cortex and the frontopolar area (FA) using fNIRS. The control group performed significantly better in terms of reaction time on the congruent and incongruent condition (congruent: <i>t</i> (16) = −3.36, <span><svg height=\"11.7782pt\" style=\"vertical-align:-3.42938pt\" version=\"1.1\" viewbox=\"-0.0498162 -8.34882 18.973 11.7782\" width=\"18.973pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,11.342,0)\"></path></g></svg><span></span><span><svg height=\"11.7782pt\" style=\"vertical-align:-3.42938pt\" version=\"1.1\" viewbox=\"22.555183800000002 -8.34882 21.921 11.7782\" width=\"21.921pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,22.605,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,28.845,0)\"></path></g><g transform=\"matrix(.013,0,0,-0.013,31.809,0)\"><use xlink:href=\"#g113-49\"></use></g><g transform=\"matrix(.013,0,0,-0.013,38.049,0)\"></path></g></svg>:</span></span> incongruent: <span><svg height=\"11.7782pt\" style=\"vertical-align:-3.42938pt\" version=\"1.1\" viewbox=\"-0.0498162 -8.34882 21.148 11.7782\" width=\"21.148pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,0,0)\"><use xlink:href=\"#g113-113\"></use></g><g transform=\"matrix(.013,0,0,-0.013,13.517,0)\"><use xlink:href=\"#g117-91\"></use></g></svg><span></span><span><svg height=\"11.7782pt\" style=\"vertical-align:-3.42938pt\" version=\"1.1\" viewbox=\"24.730183800000002 -8.34882 21.921 11.7782\" width=\"21.921pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g transform=\"matrix(.013,0,0,-0.013,24.78,0)\"><use xlink:href=\"#g113-49\"></use></g><g transform=\"matri","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139955686","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}
Miriam Cristina Reyes-Fernandez, Rubén Posada-Gomez, Albino Martinez-Sibaja, Alberto A. Aguilar-Lasserre, J. J. Agustín Flores Cuautle
In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist’s movement and to configure a multilayer perceptron (MLP) capable of classifying such movements.
{"title":"Wrist EMG Monitoring Using Neural Networks Techniques","authors":"Miriam Cristina Reyes-Fernandez, Rubén Posada-Gomez, Albino Martinez-Sibaja, Alberto A. Aguilar-Lasserre, J. J. Agustín Flores Cuautle","doi":"10.1155/2024/5526158","DOIUrl":"https://doi.org/10.1155/2024/5526158","url":null,"abstract":"In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient’s subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist’s movement and to configure a multilayer perceptron (MLP) capable of classifying such movements.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754178","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 study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.
{"title":"Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO","authors":"Kai Guo, Jun Ma, Xin Xiong, Yuming Hu, Xiang Li","doi":"10.1155/2024/8551009","DOIUrl":"https://doi.org/10.1155/2024/8551009","url":null,"abstract":"The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754134","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}
Cheng Li, Gang Yao, Teng Long, Xiwen Yuan, Peijie Li
In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.
{"title":"A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud","authors":"Cheng Li, Gang Yao, Teng Long, Xiwen Yuan, Peijie Li","doi":"10.1155/2024/5854745","DOIUrl":"https://doi.org/10.1155/2024/5854745","url":null,"abstract":"In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754471","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}
Félix Pérez-Martínez, Susan Martínez-Cordero, Jaime Calvo-Gallego, Francisco-Javier Romero-Paisano
The purpose of this article is to present the advantages that the use of arc-scanning synthetic aperture radar (Arc-SAR) would provide for accurate location of target to the weapon systems. Arc-SAR systems have an extraordinary capacity of angular discrimination of the targets, this fact make possible they can be used for the precise location of targets by replacing the large antennas required by the monopulse systems, used in this kind of applications, with a small rotating antenna. However, to carry out the real-time location of targets with the current processors a much more complex signal processing is needed. A simulator has been developed in this work, which allows the comparison of the theoretical and experimental results obtained by the classical systems against those obtained with the proposed Arc-SAR system working in the millimeter frequency bands. The results obtained demonstrate the ability of the proposed technique to accurately locate targets. This confirms that the obtained precisions allow the use the presented system in some important applications as Sense & Avoid (S&A) and weapon-pointing systems. Finally, possible applications of these techniques are described, especially useful is onboard sensors, because of the small size and weight of the antenna needed to implement an Arc-SAR system.
{"title":"Arc-Scanning Synthetic Aperture Radar for Accurate Location of Targets","authors":"Félix Pérez-Martínez, Susan Martínez-Cordero, Jaime Calvo-Gallego, Francisco-Javier Romero-Paisano","doi":"10.1155/2024/1308712","DOIUrl":"https://doi.org/10.1155/2024/1308712","url":null,"abstract":"The purpose of this article is to present the advantages that the use of arc-scanning synthetic aperture radar (Arc-SAR) would provide for accurate location of target to the weapon systems. Arc-SAR systems have an extraordinary capacity of angular discrimination of the targets, this fact make possible they can be used for the precise location of targets by replacing the large antennas required by the monopulse systems, used in this kind of applications, with a small rotating antenna. However, to carry out the real-time location of targets with the current processors a much more complex signal processing is needed. A simulator has been developed in this work, which allows the comparison of the theoretical and experimental results obtained by the classical systems against those obtained with the proposed Arc-SAR system working in the millimeter frequency bands. The results obtained demonstrate the ability of the proposed technique to accurately locate targets. This confirms that the obtained precisions allow the use the presented system in some important applications as Sense & Avoid (S&A) and weapon-pointing systems. Finally, possible applications of these techniques are described, especially useful is onboard sensors, because of the small size and weight of the antenna needed to implement an Arc-SAR system.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754171","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}
As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.
{"title":"A Deep Learning Method for Building Extraction from Remote Sensing Images by Fuzing Local and Global Features","authors":"Yitong Wang, Shumin Wang, Jing Yuan, Aixia Dou, Ziying Gu","doi":"10.1155/2024/5575787","DOIUrl":"https://doi.org/10.1155/2024/5575787","url":null,"abstract":"As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754194","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}
Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach that leverages existing surveillance cameras within the car park and a self-collected dataset of Saudi license plates. The approach combines YOLOv5 for license plate detection, YOLOv8 for character detection, and a new convolutional neural network architecture for improved character recognition. We show that our approach outperforms the single-stage approach, achieving an overall accuracy of 96.1% versus 83.9% of the single-stage approach. The approach is also integrated into a web-based dashboard for real-time visualization and statistical analysis of car park occupancy and vehicle movement with an acceptable time efficiency. Our work demonstrates how existing technology can be leveraged to improve the efficiency and sustainability of smart cities.
{"title":"Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems","authors":"Mejdl Safran, Abdulmalik Alajmi, Sultan Alfarhood","doi":"10.1155/2024/4917097","DOIUrl":"https://doi.org/10.1155/2024/4917097","url":null,"abstract":"Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach that leverages existing surveillance cameras within the car park and a self-collected dataset of Saudi license plates. The approach combines YOLOv5 for license plate detection, YOLOv8 for character detection, and a new convolutional neural network architecture for improved character recognition. We show that our approach outperforms the single-stage approach, achieving an overall accuracy of 96.1% versus 83.9% of the single-stage approach. The approach is also integrated into a web-based dashboard for real-time visualization and statistical analysis of car park occupancy and vehicle movement with an acceptable time efficiency. Our work demonstrates how existing technology can be leveraged to improve the efficiency and sustainability of smart cities.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754388","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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