{"title":"A 77-GHz Down-Conversion Mixer with +18.4 dB High Gain, +12.2 dBm OIP3, and Low Noise in 90-nm CMOS Technology","authors":"Hua-Bin Zhang, Sida Tang, Mengye Cai, Yanfeng Jiang","doi":"10.1007/s10762-023-00917-2","DOIUrl":"https://doi.org/10.1007/s10762-023-00917-2","url":null,"abstract":"","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"60 1","pages":"379 - 396"},"PeriodicalIF":2.9,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90271418","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}
H. Wu, Fuwei Wu, S. Shang, Zhenhua Liu, Yuhao Yang, Dasheng Li, Pin Li
Equipped with ability of high-resolution and high-speed imaging in all weather conditions, airborne terahertz synthetic aperture radar received great attention at home and abroad. Because of the high imaging speed of terahertz synthetic aperture radar, ground moving targets can create well-positioned shadows in the image. That property endows terahertz video-SAR ability to detect and associate targets through shadow features. However, lack of enough researches on moving targets detection and tracking limited functions of terahertz video-SAR presently. In this paper, start from spotlight SAR images, we completed image registration and detected moving targets through background subtraction. Based on detection results, we tracked targets through simple online and real-time tracking(SORT) algorithm. We hope this work can help expand application of terahertz video-SAR.
{"title":"Shadow tracking for airborne terahertz video-SAR based on SORT algorithm","authors":"H. Wu, Fuwei Wu, S. Shang, Zhenhua Liu, Yuhao Yang, Dasheng Li, Pin Li","doi":"10.1117/12.2662363","DOIUrl":"https://doi.org/10.1117/12.2662363","url":null,"abstract":"Equipped with ability of high-resolution and high-speed imaging in all weather conditions, airborne terahertz synthetic aperture radar received great attention at home and abroad. Because of the high imaging speed of terahertz synthetic aperture radar, ground moving targets can create well-positioned shadows in the image. That property endows terahertz video-SAR ability to detect and associate targets through shadow features. However, lack of enough researches on moving targets detection and tracking limited functions of terahertz video-SAR presently. In this paper, start from spotlight SAR images, we completed image registration and detected moving targets through background subtraction. Based on detection results, we tracked targets through simple online and real-time tracking(SORT) algorithm. We hope this work can help expand application of terahertz video-SAR.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"1 1","pages":"125651M - 125651M-7"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88599429","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}
Lin Sun, Qun Ma, Yue Zhao, Bing Liu, Tianhua Zhang, Heng Yang
The petrochemical industry plays an active role in driving the growth and structural upgrading of the entire national economy. In the storage process of refined oil, personnel theft is an important factor causing economic losses. Using infrared thermal imaging technology to monitor the perimeter of the oil depot can effectively improve the level of security monitoring. According to the application requirements of personnel intrusion detection in oil storage areas, this paper studies the moving target detection method under the static platform, and adopts the improved ViBe moving foreground target detection method to effectively extract the moving foreground and effectively eliminate the small interfering targets. Kalman filter combined with Hungarian algorithm is used to track the moving target. The simulation results show that the algorithm can effectively achieve the effective trajectory prediction and tracking of the moving target. Finally, it is transplanted on the hisilic 3519v101 embedded platform to achieve the requirements of real-time detection.
{"title":"Research on target intrusion detection algorithm based on improved ViBe using infrared thermal imaging","authors":"Lin Sun, Qun Ma, Yue Zhao, Bing Liu, Tianhua Zhang, Heng Yang","doi":"10.1117/12.2662528","DOIUrl":"https://doi.org/10.1117/12.2662528","url":null,"abstract":"The petrochemical industry plays an active role in driving the growth and structural upgrading of the entire national economy. In the storage process of refined oil, personnel theft is an important factor causing economic losses. Using infrared thermal imaging technology to monitor the perimeter of the oil depot can effectively improve the level of security monitoring. According to the application requirements of personnel intrusion detection in oil storage areas, this paper studies the moving target detection method under the static platform, and adopts the improved ViBe moving foreground target detection method to effectively extract the moving foreground and effectively eliminate the small interfering targets. Kalman filter combined with Hungarian algorithm is used to track the moving target. The simulation results show that the algorithm can effectively achieve the effective trajectory prediction and tracking of the moving target. Finally, it is transplanted on the hisilic 3519v101 embedded platform to achieve the requirements of real-time detection.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"22 1","pages":"125651W - 125651W-15"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88799388","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}
Cross-age face generation refers to generating face images of other age groups by using images of known ages. It is widely used in public safety, entertainment, etc. As to the problem that the existing methods based on GANs only use age information as the generation condition and ignore the sequence of age information, we present a cross-age face generation method based on CGAN and LSTM. This method consists of four modules. The first module is a generator, which is used to generate face images of different age groups. The second module is a discriminator, whose main task is to determine whether the generated image is real or forged. The third module is a pre-trained ResNet, which is responsible for extracting the features of real images. Finally, LSTM provides age groups classification constraints for the generator by the sequence of age information.
{"title":"A cross-age face generation method based on CGAN and LSTM","authors":"Yunfei Cheng, Yuexia Liu, Wen Wang","doi":"10.1117/12.2662598","DOIUrl":"https://doi.org/10.1117/12.2662598","url":null,"abstract":"Cross-age face generation refers to generating face images of other age groups by using images of known ages. It is widely used in public safety, entertainment, etc. As to the problem that the existing methods based on GANs only use age information as the generation condition and ignore the sequence of age information, we present a cross-age face generation method based on CGAN and LSTM. This method consists of four modules. The first module is a generator, which is used to generate face images of different age groups. The second module is a discriminator, whose main task is to determine whether the generated image is real or forged. The third module is a pre-trained ResNet, which is responsible for extracting the features of real images. Finally, LSTM provides age groups classification constraints for the generator by the sequence of age information.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"26 1","pages":"125652A - 125652A-6"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81403568","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}
S. Shang, Zhan-He Ou, Yang Zhou, Yuhao Yang, Pin Li
In this paper, a novel method of heterologous image registration method based on feature inertial following is proposed, which can perform high-precision and rapid registration of SAR image and optical image. The first image pair in the sequence is registered based on improved SURF feature to achieve high-precision registration. Based on this registration result, the other image pairs in the sequence are registered by the method of feature inertia following to achieve rapid registration. The proposed method makes full use of the correlation and gradient properties between image sequence frames. It maintains the registration accuracy of the preceding images, improves the registration speed greatly.
{"title":"A novel method of heterologous image registration based on SURF and feature inertial following","authors":"S. Shang, Zhan-He Ou, Yang Zhou, Yuhao Yang, Pin Li","doi":"10.1117/12.2661631","DOIUrl":"https://doi.org/10.1117/12.2661631","url":null,"abstract":"In this paper, a novel method of heterologous image registration method based on feature inertial following is proposed, which can perform high-precision and rapid registration of SAR image and optical image. The first image pair in the sequence is registered based on improved SURF feature to achieve high-precision registration. Based on this registration result, the other image pairs in the sequence are registered by the method of feature inertia following to achieve rapid registration. The proposed method makes full use of the correlation and gradient properties between image sequence frames. It maintains the registration accuracy of the preceding images, improves the registration speed greatly.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"24 1","pages":"125650L - 125650L-6"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73441521","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}
Infrared (IR) small target detection is widely used in both civilian and military security fields. However, IR small target detection in complex backgrounds faces many challenges. For example, small targets often occupy only a few pixels in IR images, lacking texture and contour features. IR images are seriously disturbed by clutter and noise, which results in the target being easily submerged. Therefore, it is difficult to achieve a low false alarm rate and a high detection rate at the same time. In this paper, a small target detection method based on local image alignment is proposed. First, the thermal IR imaging system is combined with the range-gated technology. The range-gated technology can be used to shield the background of the non-gated area. Second, the continuous frames in the sequence are accumulated to suppress noise. The movement of the target will form a trailing line in the accumulated image, and the trailing line contains the motion information of the target. Third, the trailing line is detected and extracted. Each trailing line corresponds to a suspected target area, including the real target and edges in the background, and the motion information of the suspected target can be calculated from the length and direction of the trailing line. Then, according to the motion information, the local images of the suspected target in a certain number of consecutive frames are aligned and accumulated. In the accumulated image, the real small target will shrink to a bright spot, and the signal-to-noise ratio will be significantly improved, while the background edge still appears as a line. Finally, the target is extracted from the accumulated image.
{"title":"Infrared small target detection based on local image alignment in complex background","authors":"Fan Wang, Weixian Qian","doi":"10.1117/12.2663167","DOIUrl":"https://doi.org/10.1117/12.2663167","url":null,"abstract":"Infrared (IR) small target detection is widely used in both civilian and military security fields. However, IR small target detection in complex backgrounds faces many challenges. For example, small targets often occupy only a few pixels in IR images, lacking texture and contour features. IR images are seriously disturbed by clutter and noise, which results in the target being easily submerged. Therefore, it is difficult to achieve a low false alarm rate and a high detection rate at the same time. In this paper, a small target detection method based on local image alignment is proposed. First, the thermal IR imaging system is combined with the range-gated technology. The range-gated technology can be used to shield the background of the non-gated area. Second, the continuous frames in the sequence are accumulated to suppress noise. The movement of the target will form a trailing line in the accumulated image, and the trailing line contains the motion information of the target. Third, the trailing line is detected and extracted. Each trailing line corresponds to a suspected target area, including the real target and edges in the background, and the motion information of the suspected target can be calculated from the length and direction of the trailing line. Then, according to the motion information, the local images of the suspected target in a certain number of consecutive frames are aligned and accumulated. In the accumulated image, the real small target will shrink to a bright spot, and the signal-to-noise ratio will be significantly improved, while the background edge still appears as a line. Finally, the target is extracted from the accumulated image.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"3 1","pages":"125653Q - 125653Q-4"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73597055","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}
Automatic detection of tropical cyclone (TC) regions from satellite images can provide regions of interest for intelligent TC positioning and intensity determination, and improve the efficiency and accuracy of intelligent disaster weather forecasting. There are currently few studies on automatic detection of TCs from satellite images. In recent years, deep learning technology has developed rapidly in various fields. This paper improves the Faster-RCNN target detection model in deep learning and applies it to the TC detection. The TC detection model designed in this paper is based on the original Faster-RCNN network framework, and the feature extraction network is changed from the original VGG16 network to the ResNet50 network . On this basis, this paper designs a feature fusion network Single Output Feature Fusion Networks (SOFFN). The feature layer used for detection can combine the semantic information of the high-level feature map and the high-resolution feature information of the low-level feature map, fuse different feature layers. At the same time, a new attention mechanism, Channel Linear Weighted Networks (CLWNet), based on the Squeeze-and-Excitation Networks (SENet) channel attention mechanism improvement is added to the model designed in this paper to improve the detection performance. In this paper, China's FY-2D satellite images are used to verify the performance of the proposed model. Experimental results show that the proposed model has achieved good results in TC detection.
从卫星图像中自动探测热带气旋区域，可以为热带气旋智能定位和强度确定提供感兴趣的区域，提高灾害天气智能预报的效率和精度。目前关于从卫星图像中自动检测tc的研究很少。近年来，深度学习技术在各个领域发展迅速。本文改进了深度学习中的Faster-RCNN目标检测模型，并将其应用于TC检测。本文设计的TC检测模型是基于原来的Faster-RCNN网络框架，特征提取网络由原来的VGG16网络改为ResNet50网络。在此基础上，设计了单输出特征融合网络(Single Output feature fusion Networks, SOFFN)。用于检测的特征层可以结合高级特征图的语义信息和低级特征图的高分辨率特征信息，融合不同的特征层。同时，在基于挤压激励网络(SENet)通道注意机制改进的基础上，在本文设计的模型中加入了一种新的注意机制——通道线性加权网络(Channel Linear Weighted Networks, CLWNet)，以提高检测性能。本文使用中国的FY-2D卫星图像来验证所提出模型的性能。实验结果表明，该模型在TC检测中取得了较好的效果。
{"title":"Improved Faster-RCNN algorithm combined with infrared satellite image for tropical cyclone detection","authors":"Liu Zhang, Changjiang Zhang, Feng Guo, Wanle Zhao","doi":"10.1117/12.2661625","DOIUrl":"https://doi.org/10.1117/12.2661625","url":null,"abstract":"Automatic detection of tropical cyclone (TC) regions from satellite images can provide regions of interest for intelligent TC positioning and intensity determination, and improve the efficiency and accuracy of intelligent disaster weather forecasting. There are currently few studies on automatic detection of TCs from satellite images. In recent years, deep learning technology has developed rapidly in various fields. This paper improves the Faster-RCNN target detection model in deep learning and applies it to the TC detection. The TC detection model designed in this paper is based on the original Faster-RCNN network framework, and the feature extraction network is changed from the original VGG16 network to the ResNet50 network . On this basis, this paper designs a feature fusion network Single Output Feature Fusion Networks (SOFFN). The feature layer used for detection can combine the semantic information of the high-level feature map and the high-resolution feature information of the low-level feature map, fuse different feature layers. At the same time, a new attention mechanism, Channel Linear Weighted Networks (CLWNet), based on the Squeeze-and-Excitation Networks (SENet) channel attention mechanism improvement is added to the model designed in this paper to improve the detection performance. In this paper, China's FY-2D satellite images are used to verify the performance of the proposed model. Experimental results show that the proposed model has achieved good results in TC detection.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"111 1","pages":"125650I - 125650I-6"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77611801","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}
Chiyuan Zhang, Nan Chen, L. Yao, Shengyou Zhong, Jiqing Zhang, Changkun Cui
With the development of science and technology, image sensors are more and more widely used, such as digital cameras and surveillance cameras. However, due to the physical characteristics of the photodetector, which performance is sensitive to the variation of the operating temperature. Therefore, a digital temperature sensor integrated on the chip is required to measure the operating temperature and assist in correction and compensation. Traditional scheme integrates one temperature sensor on the whole image sensor chip, which can’t reflect the temperature distribution for each pixel. It’s desirable to implement temperature measurement in pixel level for accurate correction, but existing temperature sensor occupying area of hundred μm2, which can’t be input to the pixel of image sensor. In additional, the power consumption of each temperature sensor is μW-level, which will dissipate considerable power for million temperature sensors. In this paper, a pixel-level integrated temperature sensor is proposed. The circuit is composed of only a capacitor and a conventional diode. The readout circuit is similar to that of the active pixel of image sensor, thus the ADC (Analog-to-Digital Converter) and other readout circuits and be multiplexed. The temperature sensor integrated in pixel is designed, which area is only 0.21 μm2. The simulation results show the increased power consumption for 50Hz working pixels don’t exceed 4%. It’s confirmed that the proposed pixel-level integrated temperature sensor can measure the temperature of each pixel and assisting in the accurate correction of image sensor in pixel level.
{"title":"Pixel-level temperature sensor design for image sensors","authors":"Chiyuan Zhang, Nan Chen, L. Yao, Shengyou Zhong, Jiqing Zhang, Changkun Cui","doi":"10.1117/12.2664572","DOIUrl":"https://doi.org/10.1117/12.2664572","url":null,"abstract":"With the development of science and technology, image sensors are more and more widely used, such as digital cameras and surveillance cameras. However, due to the physical characteristics of the photodetector, which performance is sensitive to the variation of the operating temperature. Therefore, a digital temperature sensor integrated on the chip is required to measure the operating temperature and assist in correction and compensation. Traditional scheme integrates one temperature sensor on the whole image sensor chip, which can’t reflect the temperature distribution for each pixel. It’s desirable to implement temperature measurement in pixel level for accurate correction, but existing temperature sensor occupying area of hundred μm2, which can’t be input to the pixel of image sensor. In additional, the power consumption of each temperature sensor is μW-level, which will dissipate considerable power for million temperature sensors. In this paper, a pixel-level integrated temperature sensor is proposed. The circuit is composed of only a capacitor and a conventional diode. The readout circuit is similar to that of the active pixel of image sensor, thus the ADC (Analog-to-Digital Converter) and other readout circuits and be multiplexed. The temperature sensor integrated in pixel is designed, which area is only 0.21 μm2. The simulation results show the increased power consumption for 50Hz working pixels don’t exceed 4%. It’s confirmed that the proposed pixel-level integrated temperature sensor can measure the temperature of each pixel and assisting in the accurate correction of image sensor in pixel level.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"26 1","pages":"125653X - 125653X-9"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81455120","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}
Qun Ma, Mei-rong Zhao, Lin Sun, Yue Zhao, Yelong Zheng, B. Liu
Oil is one of the most important energy supplies for economic development. In recent years, the fire safety problems of petrochemical enterprises have become prominent, with serious casualties and property losses. The continuously monitoring of key areas through the low-cost and intelligent infrared thermal imaging video monitoring system has important engineering application significance for the improvement of petrochemical site safety problems. According to the characteristics of infrared thermal imaging fire target, this paper proposes a method of deep neural network combined with time-domain feature analysis to realize fire detection. Firstly, high thermal pixels are extracted from the infrared image, and the gray-scale image is converted into a binary gray-scale image. Based on the YOLOv4 tiny framework, multi-level channel prediction and attention mechanism are added to detect the fire candidate target of the binary image, Finally, the candidate target is finally determined by analyzing the time-domain characteristics. Compared with the traditional temperature threshold judgment infrared temperature measurement fire alarm system, it can achieve high detection rate and effectively reduce the false alarm rate of the system. The intelligent security monitoring system in Petrochemical area designed in this paper has been applied in practical engineering, and the fire detection effect is good, which realizes the requirements of low power consumption, low cost and high reliability of the security monitoring system in Petrochemical area based on infrared thermal imaging.
{"title":"Fire detection algorithm of infrared thermal imaging in petrochemical area based on improved YOLOv4-tiny framework and time-domain feature analysis","authors":"Qun Ma, Mei-rong Zhao, Lin Sun, Yue Zhao, Yelong Zheng, B. Liu","doi":"10.1117/12.2661626","DOIUrl":"https://doi.org/10.1117/12.2661626","url":null,"abstract":"Oil is one of the most important energy supplies for economic development. In recent years, the fire safety problems of petrochemical enterprises have become prominent, with serious casualties and property losses. The continuously monitoring of key areas through the low-cost and intelligent infrared thermal imaging video monitoring system has important engineering application significance for the improvement of petrochemical site safety problems. According to the characteristics of infrared thermal imaging fire target, this paper proposes a method of deep neural network combined with time-domain feature analysis to realize fire detection. Firstly, high thermal pixels are extracted from the infrared image, and the gray-scale image is converted into a binary gray-scale image. Based on the YOLOv4 tiny framework, multi-level channel prediction and attention mechanism are added to detect the fire candidate target of the binary image, Finally, the candidate target is finally determined by analyzing the time-domain characteristics. Compared with the traditional temperature threshold judgment infrared temperature measurement fire alarm system, it can achieve high detection rate and effectively reduce the false alarm rate of the system. The intelligent security monitoring system in Petrochemical area designed in this paper has been applied in practical engineering, and the fire detection effect is good, which realizes the requirements of low power consumption, low cost and high reliability of the security monitoring system in Petrochemical area based on infrared thermal imaging.","PeriodicalId":16181,"journal":{"name":"Journal of Infrared, Millimeter, and Terahertz Waves","volume":"31 1","pages":"125650J - 125650J-19"},"PeriodicalIF":2.9,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81911967","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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