Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Computer science & Population. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Real-Time Locating Systems Using Active RFID for Internet of Things

Abstract: The proliferation of the Internet of Things (IoT) has fostered growing attention to real-time locating systems (RTLSs) using radio frequency identification (RFID) for asset management, which can automatically identify and track physical objects within indoor or confined environments. Various RFID indoor locating systems have been proposed. However, most of them are inappropriate for large-scale IoT applications owing to severe radio multipath, diffraction, and reflection. In this paper, we propose a newly fashioned RTLS using active RFID for the IoT, i.e., iLocate, which locates objects at high levels of accuracy up to 30 cm with ultralong distance transmission. To achieve fine-grained localization accuracy, iLocate presents the concept of virtual reference tags. To overcome signal multipath, iLocate employs a frequency-hopping technique to schedule RFID communication. To support large-scale RFID networks, iLocate leverages the ZigBee. We implement all hardware using 2.45-GHz RFID chips so that each active tag can communicate with readers that are around 1000 m away in a free space. Our empirical study and real project deployment show the superiority of the proposed system with respect to the localization accuracy and the data transmission rate for large-scale active RFID networks.

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Abstract: Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

Recent studies on mechanical properties of recycled aggregate concrete in China—A review

Abstract: Numerous experimental and theoretical studies on recycled aggregate concrete have been carried out in China in the past 10 years. This paper provides a comprehensive review of the related findings of research on the mechanical properties of RAC in China. The influences of the RCA on the strength and deformation characteristics of concrete, the statistical characteristics for the strength of RAC, fracture energy, stress-strain relationships under uniaxial compression, uniaxial tension as well as pure shear, and the residual strength of RAC after exposure to high temperatures, the bond between RAC and different kinds of steel rebar were also reviewed. Furthermore, some recent studies on the numerical simulation of the failure mechanism for RAC at the meso-structure level were discussed.