Pub Date : 2024-07-01DOI: 10.1088/1361-6501/ad574b
Guohao Sun, Shouming Zhang, Sen Yang, Yuhao Zhao
With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.
{"title":"Application of improved Wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles","authors":"Guohao Sun, Shouming Zhang, Sen Yang, Yuhao Zhao","doi":"10.1088/1361-6501/ad574b","DOIUrl":"https://doi.org/10.1088/1361-6501/ad574b","url":null,"abstract":"With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.
{"title":"Periodic group-sparse method via generalized minimax-concave penalty for machinery fault diagnosis","authors":"Wangpeng He, Zhihui Wen, Xuan Liu, Xiaoya Guo, Juanjuan Zhu, Weisheng Chen","doi":"10.1088/1361-6501/ad5860","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5860","url":null,"abstract":"\u0000 Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1088/1361-6501/ad5861
Baoquan Hu, Jun Liu, Rongzhen Zhao, Yue Xu, Tianlong Huo
Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field (MTF), continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field (MMTF) method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, two datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.
{"title":"A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis","authors":"Baoquan Hu, Jun Liu, Rongzhen Zhao, Yue Xu, Tianlong Huo","doi":"10.1088/1361-6501/ad5861","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5861","url":null,"abstract":"\u0000 Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field (MTF), continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field (MMTF) method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, two datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.1088/1361-6501/ad5862
Xuyao Lu, Yongjie Huang, Ruiqi Liu, Xiaofei Huang, Chuanzhu Liu
Controller area network (CAN) buses are widely used as low-cost, highly flexible field buses in various scenarios, such as in vehicle networks for automobiles and communication networks for industrial sites. They typically operate in harsh environments, and faults inevitably occur. CAN bus faults cannot be efficiently diagnosed via traditional manual detection. Herein, we propose a lightweight MDSCA-Net for CAN bus fault diagnosis. Deep separable convolution (DSConv) is used in the model instead of ordinary convolution to reduce the number of parameters and floating-point operations. Additionally, the noise immunity of the model is improved by designing a multiscale denoising module (MDM). A multiscale deep separable convolutional fusion SE at tention (MDSCSA) module is designed to capture the channel dimension details of the features. Furthermore, a spatial attention module (SAM) is utilized to capture the spatial dimension details of the features. Finally, a residual (Res) module stabilizes the model performance. Experimental results on the CAND dataset indicated that the proposed method achieved a diagnostic accuracy of 99% in a noise-free environment, and compared with other fault diagnosis methods, it had better noise immunity and robustness in a noisy environment, which is of considerable practical significance for ensuring the stable operation of CAN buses.
控制器局域网（CAN）总线作为低成本、高灵活性的现场总线被广泛应用于各种场合，如汽车的车载网络和工业现场的通信网络。它们通常在恶劣的环境中运行，不可避免地会出现故障。传统的人工检测无法有效诊断 CAN 总线故障。在此，我们提出了一种用于 CAN 总线故障诊断的轻量级 MDSCA 网络。该模型采用深度可分离卷积（DSConv）代替普通卷积，以减少参数和浮点运算的数量。此外，还通过设计多尺度去噪模块（MDM）提高了模型的抗噪能力。设计了一个多尺度深度可分离卷积融合 SE at tention（MDSCSA）模块，以捕捉通道维度的特征细节。此外，空间关注模块（SAM）用于捕捉特征的空间维度细节。最后，残差（Res）模块可稳定模型性能。在 CAND 数据集上的实验结果表明，所提出的方法在无噪声环境下的诊断准确率达到了 99%，与其他故障诊断方法相比，在噪声环境下具有更好的抗噪性和鲁棒性，这对于确保 CAN 总线的稳定运行具有重要的现实意义。
{"title":"Lightweight MDSCA-Net: An end-to-end CAN bus fault diagnosis framework","authors":"Xuyao Lu, Yongjie Huang, Ruiqi Liu, Xiaofei Huang, Chuanzhu Liu","doi":"10.1088/1361-6501/ad5862","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5862","url":null,"abstract":"\u0000 Controller area network (CAN) buses are widely used as low-cost, highly flexible field buses in various scenarios, such as in vehicle networks for automobiles and communication networks for industrial sites. They typically operate in harsh environments, and faults inevitably occur. CAN bus faults cannot be efficiently diagnosed via traditional manual detection. Herein, we propose a lightweight MDSCA-Net for CAN bus fault diagnosis. Deep separable convolution (DSConv) is used in the model instead of ordinary convolution to reduce the number of parameters and floating-point operations. Additionally, the noise immunity of the model is improved by designing a multiscale denoising module (MDM). A multiscale deep separable convolutional fusion SE at tention (MDSCSA) module is designed to capture the channel dimension details of the features. Furthermore, a spatial attention module (SAM) is utilized to capture the spatial dimension details of the features. Finally, a residual (Res) module stabilizes the model performance. Experimental results on the CAND dataset indicated that the proposed method achieved a diagnostic accuracy of 99% in a noise-free environment, and compared with other fault diagnosis methods, it had better noise immunity and robustness in a noisy environment, which is of considerable practical significance for ensuring the stable operation of CAN buses.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57e0
Qiushi Huang, Guanghui Liang, Chao Tan, Feng Dong
It is challenging to monitor the multiphase flow distribution in the industrial processes in order to optimize the production. Electrical resistance tomography (ERT) can be used to visualize the inner distribution of multiphase flow. The image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problem make the image reconstruction of ERT a challenge, and the development of advanced imaging algorithm has attracted much attention in the past. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of multi-scale feature extraction of UNet++ and residual feature fusion of Res2Net. The network is used to post-process the prereconstruction result of traditional ERT image reconstruction methods, where the generalization ability of the traditional methods and the flexible feature extraction advantage of deep learning methods can be combined. Simulations and experiments are designed to verify the generalization ability and the effectiveness of the proposed model. Both simulation and experimental results show that the proposed U-shaped network approach outperforms other deep learning methods, and the proposed deep learning model is fit for post-processing tasks of ERT.
监测工业流程中的多相流分布以优化生产是一项具有挑战性的工作。电阻断层扫描（ERT）可用于观察多相流的内部分布。图像重建在 ERT 中起着至关重要的作用。然而，逆问题的非线性和多拟性使得 ERT 的图像重建成为一项挑战，而先进成像算法的开发在过去一直备受关注。本研究提出了一种改进的 U 型深度学习模型，它结合了 UNet++ 的多尺度特征提取和 Res2Net 的残差特征融合的优点。该网络用于对传统 ERT 图像重建方法的预重建结果进行后处理，将传统方法的泛化能力和深度学习方法的灵活特征提取优势结合起来。为了验证所提模型的泛化能力和有效性，我们设计了仿真和实验。仿真和实验结果表明，所提出的 U 型网络方法优于其他深度学习方法，所提出的深度学习模型适合 ERT 后处理任务。
{"title":"Res2-UNet++: A Deep Learning Image Post-Processing Method for Electrical Resistance Tomography","authors":"Qiushi Huang, Guanghui Liang, Chao Tan, Feng Dong","doi":"10.1088/1361-6501/ad57e0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57e0","url":null,"abstract":"\u0000 It is challenging to monitor the multiphase flow distribution in the industrial processes in order to optimize the production. Electrical resistance tomography (ERT) can be used to visualize the inner distribution of multiphase flow. The image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problem make the image reconstruction of ERT a challenge, and the development of advanced imaging algorithm has attracted much attention in the past. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of multi-scale feature extraction of UNet++ and residual feature fusion of Res2Net. The network is used to post-process the prereconstruction result of traditional ERT image reconstruction methods, where the generalization ability of the traditional methods and the flexible feature extraction advantage of deep learning methods can be combined. Simulations and experiments are designed to verify the generalization ability and the effectiveness of the proposed model. Both simulation and experimental results show that the proposed U-shaped network approach outperforms other deep learning methods, and the proposed deep learning model is fit for post-processing tasks of ERT.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57de
Zongjun Zhang, Wei He, Hongyu Li, Ning Ma, Guohui Zhou
Health status assessment is an important measure for maintaining the safety of spacecraft flywheel systems. The influence of noise, sensor quality, and other disturbance factors can lead to a decrease in the reliability of the collected information. This can affect the model accuracy. Moreover, a loss of belief in the model is frequently caused by the opaque nature of the procedure and the incomprehensibility of the outcomes, particularly in fields such as aerospace. It is urgent to maintain the interpretability of the model and successfully identify the unreliability of the observed data. Therefore, this paper proposes a spacecraft flywheel system health status assessment method under perturbation based on interpretable belief rule base with attribute reliability (IBRB-r). First, the attribute reliability is calculated based on the average distance method, and a new fusion method of attribute reliability is proposed to reduce the interference of unreliable information. Then, a new interpretable constraint strategy is proposed to improve the rationality and interpretability of the parameters. Finally, the proposed method is validated by a case study of the health status assessment of a spacecraft flywheel system. Experiments show that the IBRB-r maintains high accuracy and interpretability under unreliable observation data.
{"title":"An interpretable spacecraft flywheel system health status assessment method under perturbation","authors":"Zongjun Zhang, Wei He, Hongyu Li, Ning Ma, Guohui Zhou","doi":"10.1088/1361-6501/ad57de","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57de","url":null,"abstract":"\u0000 Health status assessment is an important measure for maintaining the safety of spacecraft flywheel systems. The influence of noise, sensor quality, and other disturbance factors can lead to a decrease in the reliability of the collected information. This can affect the model accuracy. Moreover, a loss of belief in the model is frequently caused by the opaque nature of the procedure and the incomprehensibility of the outcomes, particularly in fields such as aerospace. It is urgent to maintain the interpretability of the model and successfully identify the unreliability of the observed data. Therefore, this paper proposes a spacecraft flywheel system health status assessment method under perturbation based on interpretable belief rule base with attribute reliability (IBRB-r). First, the attribute reliability is calculated based on the average distance method, and a new fusion method of attribute reliability is proposed to reduce the interference of unreliable information. Then, a new interpretable constraint strategy is proposed to improve the rationality and interpretability of the parameters. Finally, the proposed method is validated by a case study of the health status assessment of a spacecraft flywheel system. Experiments show that the IBRB-r maintains high accuracy and interpretability under unreliable observation data.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57e2
Chuan Xu, Shuai Chen, Zhikuan Hou
To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit (GRU) neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.
{"title":"A hybrid information fusion method for SINS/GNSS integrated navigation system utilizing GRU-Aided AKF during GNSS outages","authors":"Chuan Xu, Shuai Chen, Zhikuan Hou","doi":"10.1088/1361-6501/ad57e2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57e2","url":null,"abstract":"\u0000 To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit (GRU) neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57dc
Gang Peng, Chong Cao, Bocheng Chen, Lu Hu, Dingxin He
The traditional visual inertial simultaneous localisation and mapping (SLAM) system does not fully consider the dynamic objects in the scene, which can reduce the quality of visual feature point matching. In addition, dynamic objects in the scene can cause illumination changes which reduce the performance of the visual front end and loop closure detection of the system. To address this problem, this study combines 3D light detection and ranging (LiDAR), camera, and inertial measurement units (IMUs) in a tightly coupled manner to estimate the pose of mobile robots, thereby proposing a robust LiDAR visual inertial odometry that can effectively filter out dynamic feature points. In addition, a dynamic feature point detection algorithm with attention mechanism is introduced for target detection and optical flow tracking. In experimental analyses on public datasets and real indoor scenes, the proposed method improved the accuracy and robustness of pose estimation in scenes with dynamic objects and varying illumination compared with traditional methods.
{"title":"Robust LiDAR visual inertial odometry for dynamic scenes","authors":"Gang Peng, Chong Cao, Bocheng Chen, Lu Hu, Dingxin He","doi":"10.1088/1361-6501/ad57dc","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57dc","url":null,"abstract":"\u0000 The traditional visual inertial simultaneous localisation and mapping (SLAM) system does not fully consider the dynamic objects in the scene, which can reduce the quality of visual feature point matching. In addition, dynamic objects in the scene can cause illumination changes which reduce the performance of the visual front end and loop closure detection of the system. To address this problem, this study combines 3D light detection and ranging (LiDAR), camera, and inertial measurement units (IMUs) in a tightly coupled manner to estimate the pose of mobile robots, thereby proposing a robust LiDAR visual inertial odometry that can effectively filter out dynamic feature points. In addition, a dynamic feature point detection algorithm with attention mechanism is introduced for target detection and optical flow tracking. In experimental analyses on public datasets and real indoor scenes, the proposed method improved the accuracy and robustness of pose estimation in scenes with dynamic objects and varying illumination compared with traditional methods.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57da
Qunying Li, Changying Wu
A microstrip air line system for measuring the complex permittivity of anisotropic materials in the band from 0.3 to 1 GHz is proposed. The multireflect-thru method is used to calibrate the measurement system in the whole band with a single microstrip air line without suffering from the limited space resolution of time-gating technique. During the measurement, the material under test (MUT) is placed both above and below the strip. With this deployment, the TEM mode propagates along the microstrip in the MUT. Therefore, it is possible to measure the anisotropic permittivity. Since a small portion of the electric field is parallel to the ground plane around two edges of the strip, the extracted property is not purely along one direction. To obtain higher accuracy, with the help of linear combination, the properties along two directions are disentangled by two measurements. For validation of the method, an isotropic material and an anisotropic material in the microstrip air line were simulated and their permittivities were extracted from simulation results. An anisotropic material polytetrafluoroethylene (PTFE) and two anisotropic materials, FR4 and honeycomb absorber, were measured. The results of PTFE show that there is a maximum relative error of 1.4% and 2.5% for the permittivity extracted from simulation and measurement, respectively. The validity and the accuracy of the system for measuring anisotropic materials are verified by the simulation and measurement results.
{"title":"Anisotropic complex permittivity measurement using a microstrip air line","authors":"Qunying Li, Changying Wu","doi":"10.1088/1361-6501/ad57da","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57da","url":null,"abstract":"\u0000 A microstrip air line system for measuring the complex permittivity of anisotropic materials in the band from 0.3 to 1 GHz is proposed. The multireflect-thru method is used to calibrate the measurement system in the whole band with a single microstrip air line without suffering from the limited space resolution of time-gating technique. During the measurement, the material under test (MUT) is placed both above and below the strip. With this deployment, the TEM mode propagates along the microstrip in the MUT. Therefore, it is possible to measure the anisotropic permittivity. Since a small portion of the electric field is parallel to the ground plane around two edges of the strip, the extracted property is not purely along one direction. To obtain higher accuracy, with the help of linear combination, the properties along two directions are disentangled by two measurements. For validation of the method, an isotropic material and an anisotropic material in the microstrip air line were simulated and their permittivities were extracted from simulation results. An anisotropic material polytetrafluoroethylene (PTFE) and two anisotropic materials, FR4 and honeycomb absorber, were measured. The results of PTFE show that there is a maximum relative error of 1.4% and 2.5% for the permittivity extracted from simulation and measurement, respectively. The validity and the accuracy of the system for measuring anisotropic materials are verified by the simulation and measurement results.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-13DOI: 10.1088/1361-6501/ad57d8
Jiangang Yang, Shenggang Yang, Yuquan Yao, Jie Gao, Shuyi Wang
To assess three-dimensional ground-penetrating radar (GPR) applicability for evaluating interlayer bonding in asphalt pavements with semi-rigid base layers, and analysis the GPR detection mechanism. Using forward simulation to create various medium models and analyze electromagnetic wave transmission in air, water, and sand. Four distinct pavement structures were subjected to GPR testing, and the amplitude intensity levels and image processing techniques to assess asphalt pavement interlayer bonding, and validated by comparing the results with core samples. The findings revealed that electromagnetic wave transmission processes were significantly influenced by medium uniformity. Non-uniform medium models generated considerable stray waves, akin to typical "noise," closely mirroring real pavement conditions. Poorly bonded areas exhibited clearer hyperbolic ripples, primarily due to significant differences in dielectric constants of filling materials. Amplitude strength effectively evaluated bonding across different asphalt pavement configurations and lanes, typically following a normal distribution. Enhanced interlayer contact correlated with smaller amplitudes, while weaker bonding led to larger amplitudes. The amplitude distribution in the center of lanes differed significantly from wheel track areas, and the interlayer bonding condition of center lanes was better than wheel track belt. Additionally, radar plan views exhibited considerable variation across different interlayer contact conditions. The image processing method can evaluate the interlayer contact condition of different pavement structures across full cross-sections.
{"title":"Three-Dimensional Orthorectified Simulation and Ground Penetrating Radar Detection of Interlayer Bonding Condition in Asphalt Pavements","authors":"Jiangang Yang, Shenggang Yang, Yuquan Yao, Jie Gao, Shuyi Wang","doi":"10.1088/1361-6501/ad57d8","DOIUrl":"https://doi.org/10.1088/1361-6501/ad57d8","url":null,"abstract":"\u0000 To assess three-dimensional ground-penetrating radar (GPR) applicability for evaluating interlayer bonding in asphalt pavements with semi-rigid base layers, and analysis the GPR detection mechanism. Using forward simulation to create various medium models and analyze electromagnetic wave transmission in air, water, and sand. Four distinct pavement structures were subjected to GPR testing, and the amplitude intensity levels and image processing techniques to assess asphalt pavement interlayer bonding, and validated by comparing the results with core samples. The findings revealed that electromagnetic wave transmission processes were significantly influenced by medium uniformity. Non-uniform medium models generated considerable stray waves, akin to typical \"noise,\" closely mirroring real pavement conditions. Poorly bonded areas exhibited clearer hyperbolic ripples, primarily due to significant differences in dielectric constants of filling materials. Amplitude strength effectively evaluated bonding across different asphalt pavement configurations and lanes, typically following a normal distribution. Enhanced interlayer contact correlated with smaller amplitudes, while weaker bonding led to larger amplitudes. The amplitude distribution in the center of lanes differed significantly from wheel track areas, and the interlayer bonding condition of center lanes was better than wheel track belt. Additionally, radar plan views exhibited considerable variation across different interlayer contact conditions. The image processing method can evaluate the interlayer contact condition of different pavement structures across full cross-sections.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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