IEEE Sensors Journal最新文献

{"title":"Two-Step Graph Optimization for GNSS/INS Integration in Arctic Shipborne Navigation","authors":"Yuan Hu;Youpeng Pan;Wei Liu;Tengfei Qi","doi":"10.1109/JSEN.2025.3595561","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3595561","url":null,"abstract":"Reliable positioning in the Arctic remains a critical challenge due to low satellite elevation angles, frequent signal blockages by ice ridges, and strong ionospheric disturbances, all of which degrade the performance of global navigation satellite systems (GNSSs). Most GNSS/inertial navigation system (INS) integration techniques, ranging from extended Kalman filter (EKF) to conventional factor-graph-based frameworks, have seen limited validation in the Arctic, where sparse satellite geometry and prolonged signal outages pose conditions far more severe than typical mid-latitude environments. To bridge this gap and improve robustness in such conditions, we propose a two-step graph optimization (TSGO) framework for loosely coupled (LC) GNSS/INS integration tailored to Arctic shipborne applications. In the first stage, TSGO performs local graph-based optimization on raw GNSS pseudorange and Doppler observations to mitigate outliers and improve positioning accuracy. The second stage incorporates the optimized GNSS results into a global factor graph alongside inertial measurements to refine the trajectory through globally consistent smoothing. Field experiments conducted aboard an Arctic research vessel demonstrate that TSGO significantly outperforms conventional EKF and optimization-based GNSS/INS (OB-GINS) methods. Compared to EKF, TSGO reduces the maximum horizontal error by over 70% and the root mean square error (RMSE) by 24%. It also achieves 33% and 10% improvements in these metrics over OB-GINS. These results highlight the effectiveness and robustness of TSGO in high-latitude GNSS-challenged environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35278-35288"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Self-Contained Temperature and Angular Velocity Measurement in Interferometric Fiber-Optic Gyroscopes","authors":"Xinyu Cao;Haoyan Liu;Wenbo Wang;Fangshuo Shi;Lanxin Zhu;Huimin Huang;Ziqi Zhou;Yan He;Yanjun Chen;Zhengbin Li","doi":"10.1109/JSEN.2025.3595945","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3595945","url":null,"abstract":"Interferometric fiber-optic gyroscopes (IFOGs) are sensitive to ambient temperature variations, which can introduce drift in the measured angular velocity. To mitigate such effects, accurate access to real-time temperature information is essential. This article proposes a self-contained multiparameter measurement method that enables simultaneous acquisition of temperature and angular velocity without relying on external sensors. The approach utilizes a multiplexing four-state modulation scheme to embed temperature-related information in the output signal, which is then extracted and used to adjust the signal processing accordingly. Both simulation and experimental results confirm the effectiveness of the proposed method across a wide thermal range, with temperature measurement achieving a root-mean-square error (RMSE) of <inline-formula> <tex-math>$0.3722~^{circ } $ </tex-math></inline-formula>C. Temperature correction is carried out using the real-time measured temperature, and experimental results demonstrate improved stability, with the 100-s averaged standard deviation reduced from <inline-formula> <tex-math>$0.252~^{circ } $ </tex-math></inline-formula>/h to <inline-formula> <tex-math>$0.023~^{circ } $ </tex-math></inline-formula>/h by a factor of 10.9, and the maximum deviation suppressed from <inline-formula> <tex-math>$0.819~^{circ } $ </tex-math></inline-formula>/h to <inline-formula> <tex-math>$0.097~^{circ } $ </tex-math></inline-formula>/h by a factor of 8.4. As the method operates entirely within the existing IFOG modulation and detection framework, it offers a practical solution for improving system performance in thermally dynamic environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34655-34662"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Temperature IEPE Charge Amplifier With Enhanced Low-Frequency Performance for MEMS Piezoelectric Accelerometer","authors":"Jiachang Zhang;Anna Li;Cheng Zhang;Yongquan Su;Dalong Chen;Hao Huang;Feng Tian;Yichen Liu;Lihao Wang;Yang Wang;Zhenyu Wu","doi":"10.1109/JSEN.2025.3596050","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596050","url":null,"abstract":"The micro-electromechanical systems (MEMSs) piezoelectric (PE) accelerometers are ideal for extreme environments, such as high temperature, but extracting weak charge signals remains challenging. This article presents for the first time an integrated electronics PE (IEPE) high-temperature MEMS accelerometer, based on a self-developed PE MEMS accelerometer and IEPE charge amplifier. First, a theoretical model was established to evaluate the sensitivity, bandwidth, and noise characteristics of the charge amplifier at elevated temperatures. Meanwhile, the corresponding parameters are obtained by simulation. Second, a high-temperature lower corner frequency test system was established based on the electric excitation discharge time constant (EEDTC) principle, resolving the issue of inadequate load in low-frequency response testing system. Finally, feedback capacitance and bias resistance parameters are optimized to maintain the zero temperature coefficient (ZTC) operating point in the IEPE charge amplifier, reducing low-frequency temperature drift. Above all, the developed IEPE high-temperature MEMS accelerometer achieves a wide 3-dB bandwidth of 0.11–7000 Hz, a sensitivity of 2.72 mV/g, a noise spectral density of <inline-formula> <tex-math>$4.45~mu $ </tex-math></inline-formula>V/<inline-formula> <tex-math>$surd $ </tex-math></inline-formula>Hz at 10 Hz, a nonlinearity of 0.14% at 500 g, and a transverse sensitivity of 0.42% at room temperature, and it achieves a -3-dB lower corner frequency at 0.24 Hz, a sensitivity of 2.24 mV/g, a noise spectral density of <inline-formula> <tex-math>$10~mu $ </tex-math></inline-formula>V/<inline-formula> <tex-math>$surd $ </tex-math></inline-formula>Hz at 10 Hz, and an equivalent noise of <inline-formula> <tex-math>$le 0.0278~{g}_{text {rms}}$ </tex-math></inline-formula> at <inline-formula> <tex-math>$175~^{circ }$ </tex-math></inline-formula>C, which is of significant importance for the application of MEMS PE accelerometers in high-temperature environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34819-34829"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exosome Biosensor Based on Quantum Dots-Modified Field Effect Transistor","authors":"Jingqiu Chen;Qing Huang;Jing Huang;Tucan Chen;Lanpeng Guo;Yunong Zhao;Dandan Li;Wenjian Zhang;Huayao Li;Yang Zhang;Liang Hu;Huan Liu","doi":"10.1109/JSEN.2025.3593869","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3593869","url":null,"abstract":"The escalating demand for high-throughput and miniaturized biosensors has spurred significant advancements in biosensor chips. However, challenges remain in achieving high-sensitivity and specificity in miniaturized devices. Herein, we construct a sensitive exosome biosensor based on quantum dots (QDs)-modified field-effect transistor. PbS QDs were drop-coated onto the gate of a high-electron mobility transistor (HEMT), followed by surface modification with CD63 antibodies (anti-CD63) through a ligand exchange strategy, and subsequent bovine serum albumin (BSA) blocking treatment. Exosomes with CD63 proteins enriched in the surface were isolated from MCF-7 breast cancer cell lines to serve as target analytes. The specific binding events between the CD63 antigen and antibody capture exosomes onto the gate of HEMT, affecting the output current (<inline-formula> <tex-math>${I}_{text {D}}text {)}$ </tex-math></inline-formula>. The synergy of the capacitance coupling effect of the modification layer and the intrinsic signal amplification capability of HEMT assists in significantly modulating the 2-D electron gas (2DEG), resulting in amplified <inline-formula> <tex-math>${I}_{text {D}}$ </tex-math></inline-formula>. With constant gate voltage applied, the <inline-formula> <tex-math>${I}_{text {D}}$ </tex-math></inline-formula> of the biosensor sensitively increases with exosome concentration within a wide range of <inline-formula> <tex-math>$10^{{5}}$ </tex-math></inline-formula>–<inline-formula> <tex-math>$10^{{9}}$ </tex-math></inline-formula> particles/mL at <inline-formula> <tex-math>${V}_{text {D}} =3.5$ </tex-math></inline-formula> V, and the limit of detection (LOD) is estimated to be <inline-formula> <tex-math>$9times 10^{{4}}$ </tex-math></inline-formula> particles/mL. This exosome biosensor demonstrates its potential for clinical applicability and paves the way for the development of miniaturized exosome biosensor chips.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34355-34362"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Activity Recognition Based on the Method of CFAR-SOPC Using Millimeter-Wave Radar","authors":"Fang Zhou;Xinyu Liao;Jing Fang;Mengdao Xing;Marina Gashinova","doi":"10.1109/JSEN.2025.3595930","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3595930","url":null,"abstract":"Human activity recognition (HAR) has become a research hotspot due to its broad application prospects in search and rescue, health monitoring, safety monitoring, and sports science. Millimeter-wave radar, with its low cost and noncontact sensing method, provides an ideal technical solution for protecting user privacy, so it is widely used in HAR research. In the study, a human motion classification method based on the constant false alarm rate-sampling the overall point cloud (CFAR-SOPC) is proposed. First, the human motion data from a millimeter-wave radar are resized into a 2-D cube. Second, the phasor average cancellation (PAC) method is applied to the data to filter out clutter, and then, the data are resized into a 3-D cube, and CFAR-SOPC is performed on the data to generate a range–Doppler (RD)–time stereo contour point cloud (SCPC), which effectively reduces the size of the features. Finally, the sample is input into the PointNet network that specializes in processing point cloud data for feature extraction and activity recognition, with an accuracy rate of 96.7%. The experimental results present a fact that compared with the existing approaches, CFAR-SOPC improves the accuracy of classification and reduces the cost of memory and time.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35077-35089"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Online Fountain Code With Component Feedback for Underwater Acoustic Sensor Networks","authors":"Lei Zhao;Xiujuan Du;Xiuxiu Liu;Xiaojing Tian","doi":"10.1109/JSEN.2025.3595610","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3595610","url":null,"abstract":"Improving the security of data transmission in underwater acoustic sensor networks (UASNs) remains a critical challenge for current underwater acoustic communication systems. In this article, a novel security transmission scheme for underwater data based on online fountain codes (OFCs) is proposed. Specifically, an underwater security system model is established, and the security of underwater data transmission, as well as the coding overhead of OFC based on the theory of random graphs, is analyzed. Furthermore, three optimization objectives for the security of underwater data transmission are investigated. In addition, a component-based OFC without completion phase (COFCNC) precede is presented to optimize the encoding and feedback mechanism. The optimized feedback mechanism of the decoding state reduces the overhead while lowering the eavesdropping probability of illegal nodes. On this basis, an <sc>xor</small> encryption scheme is proposed, enabling our scheme to achieve confidentiality in most cases. Experimental results show that, compared with the traditional fountain codes and other fountain code security mechanisms, this scheme ensures data transmission efficiency while achieving security.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35505-35523"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Error Compensation Framework for MEMS Sensor Array Applied to Seabed Terrain Deformation Monitoring","authors":"Qixiao Zhou;Yongqiang Ge;Peng Zhou;Jiawang Chen;Deqing Mei","doi":"10.1109/JSEN.2025.3596131","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596131","url":null,"abstract":"Micro-electromechanical systems (MEMS) sensor array plays a significant role in in situ and long-term monitoring of seabed terrain deformation. However, multisource errors in the monitoring process are difficult to avoid, such as natural disaster, communication issues, and calculation models. A novel error compensation framework based on a MEMS sensor array is presented and validated in this article. An elaborate laboratory test is carried out, and two datasets with an interval of 5 mm between 0 and 150 mm are collected using two MEMS sensor arrays. The correlation coefficient (CC) is introduced and applied for constructing optimal model input features. The window size, sliding step, differential coefficient, differential step, and lag length are determined by CC analysis and 12 model input features are identified. Seven models are comprehensively compared with two datasets, including 1-D convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), BiLSTM, 1D-CNN, gated recurrent unit (GRU), long short-term memory (LSTM), linear regression (LR), and support vector regression (SVR). The proposed hybrid 1D-CNN-BiLSTM can achieve the lowest average RMSE (2.9677 mm; 2.3319 mm) and the highest average <inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula> (0.9269; 0.9356) on two datasets with k-fold cross validation. Also, it outperforms other models in the compensation experiment, with RMSE reduction of up to 34.46% and 31.00%, respectively. The results demonstrate that the proposed error compensation framework can effectively compensate deformation monitoring errors and provide practical guidelines for seabed terrain monitoring and deformation error compensation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35101-35111"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource Efficient Framework for Remote Sensing Visual Recognition","authors":"Unse Fatima;Zafran Khan;Yechan Kim;Joonmo Kim;Witold Pedrycz;Moongu Jeon","doi":"10.1109/JSEN.2025.3595936","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3595936","url":null,"abstract":"In the rapidly evolving field of remote sensing (RS), the need for efficient and accurate scene classification is paramount. RS imagery comprising satellite and aerial imagery often faces challenges such as varying scales and diverse environmental conditions, which can significantly affect the discernibility of important features. To address these challenges, this article introduces a lightweight dual-branch network architecture that adequately handles scale variations and complex scene compositions. The first branch, progressive feature processing branch (PFPB), of the proposed framework is engineered to extract rich multiple-scale features through collaborative parallel stages and intrabranch and interbranch connectivity with optimized computational resources. The second branch, InXformer branch (IXB) enhances the system’s capability to assimilate global context and long-range dependencies essential for comprehensive scene analysis utilizing an involution-based transformer approach. Experimental validation in three challenging datasets sourced from diverse aerial platforms demonstrates the greater effectiveness of the proposed network. The proposed network achieves a weighted <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> of 97.15% in the AIDERSv2 dataset, surpassing other methods such as DecoupleNet by more than 2%, while maintaining high efficiency with 0.41M parameters, lower computational overhead with 0.96 GFLOPs, and a higher processing speed of 4616 frames/s (FPS). With regards to WHU-RS19 and UCM datasets, the devised network achieves 93.69% and 94.57% weighted-<inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> score, respectively. These results underscore the ability of the proposed network to efficiently handle diverse scene compositions by delivering state-of-the-art performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"34793-34802"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VR-DataAug: An Efficient Data Augmentation Method for Multicamera Vehicle Tracking","authors":"Hanyang Zhuang;Yeqiang Qian;Minghu Wu;Chunxiang Wang;Ming Yang","doi":"10.1109/JSEN.2025.3596067","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596067","url":null,"abstract":"Multicamera vehicle tracking refers to tracking the same vehicle in multiple cameras in various locations, which aids in traffic flow analysis and prediction. But, collecting and labeling extensive multicamera vehicle tracking datasets for cities is challenging due to the spatio-temporal complexity, hindering the performance of multicamera vehicle tracking algorithm development. Simulations can produce vast, automatically labeled datasets. However, there is a significant domain gap between virtual and real vehicles, affecting style features like texture and illumination, as well as apparent features like scale and pose. We introduce VR-DataAug, a data augmentation method merging virtual and real data with consistent style and apparent features. A Background Modeling With Detection Feedback module creates a clean background and extracts vehicle instances. A Multiattribute Vehicle Apparent Modeling module utilizes a classifier to learn apparent features from various camera viewpoints, preserving scale, position, and orientation information between virtual and real vehicles. A Virtual Vehicle and Real Background Fusion module uses a generative model to ensure texture consistency and merge virtual vehicles into real traffic scenes. Extensive experiments on the CityFlow dataset demonstrate that our approach improves detection performance by 3.4% mAP, enhances the vehicle re-identification model by 3.84%, boosts multi camera vehicle tracking by increasing IDF1 metrics by 4.25%, and highlighting its potential to expand training sets while minimizing domain offset.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35426-35437"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Self-Enhancement Compound Dimension Interpretable Wavelet Network and Its Application in Rotating Component Fault Diagnosis Under Varying Speed","authors":"Qijian Lin;Tianyang Wang;Zhaoye Qin;Fulei Chu","doi":"10.1109/JSEN.2025.3596155","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3596155","url":null,"abstract":"For rotating components’ fault diagnosis, the existing interpretable networks mostly focus on rotors at constant speeds, neglecting interpretable networks for fault diagnosis in rotors with variable speeds. In industrial settings, rotors mostly operate at variable speeds, leading to challenges in identifying rotor health due to spectral aliasing in faulty rotors under variable speeds. This article proposes a hybrid 1-D and 2-D neural network incorporating a feature enhancement layer and wavelet transformation feature self-enhanced compound dimension wavelet network (FSCDWN). FSCDWN transforms 1-D feature maps generated by wavelet convolution kernels into 2-D maps, facilitating better analysis of interband relationships and easier feature extraction from variable frequency signals. By enhancing fault features before the wavelet convolution layer, downstream networks can more easily extract fault features. Through experiments, the convergence speed of FSCDWN is significantly higher than that of the control group, with accuracy mostly around 90%, markedly surpassing the control group’s 80% and 60%. Based on the principles of signal processing, the feature maps of the network are visualized. FSCDWN enhances and retains the fault characteristics in the variable speed signals, while the significance of the feature maps of the control group network is unclear. This demonstrates the interpretability of FSCDWN and explains the reason for its high accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 18","pages":"35121-35130"},"PeriodicalIF":4.3,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}