Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge

3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics. These applications are commonly deployed on edge devices to promptly interact with the environment, and often require near real-time response. With limited computation power, it is challenging to execute 3D detection on the edge using highly complex neural networks. Common approaches such as offloading to the cloud induce significant latency overheads due to the large amount of point cloud data during transmission. To resolve the tension between wimpy edge devices and compute-intensive inference workloads, we explore the possibility of empowering fast 2D detection to extrapolate 3D bounding boxes. To this end, we present Moby, a novel system that demonstrates the feasibility and potential of our approach. We design a transformation pipeline for Moby that generates 3D bounding boxes efficiently and accurately based on 2D detection results without running 3D detectors. Further, we devise a frame offloading scheduler that decides when to launch the 3D detector judiciously in the cloud to avoid the errors from accumulating. Extensive evaluations on NVIDIA Jetson TX2 with real-world autonomous driving datasets demonstrate that Moby offers up to 91.9% latency improvement with modest accuracy loss over state of the art.Comment: Accepted to ACM International Conference on Multimedia (MM) 202

Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

Aiming at the difficulty of extracting rolling bearing fault features under strong background noise conditions, a method based on the Fourier decomposition method (FDM), robust independent component analysis (RobustICA), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the FDM method is introduced to decompose the single-channel bearing fault signal into several Fourier intrinsic band functions (FIBF). Secondly, by setting the cross-correlation coefficient and kurtosis as a new selection criterion, it can effectively construct the virtual noise channel and the observation signal channel, which makes RobustICA complete the separation of the useful signal and noise well. Finally, MOMEDA is introduced to enhance the periodic impact components in the denoised signal, and then the filtered signal is analyzed by the Hilbert envelope spectrum to extract the fault characteristic frequency. Through the experimental analysis of the simulated signals and the actual bearing fault signals, the results show that the proposed method not only has the ability to suppress noise and accurately extract fault feature information but also has better performance than the traditional method of local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD), highlighting its practicality in the fault diagnosis of rotating machinery

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained

A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD

In modern industry, due to the poor working environment and the complex working conditions of mechanical equipment, the characteristics of the impact signals caused by faults are often submerged in strong background signals and noises. Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method

Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions

A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD

In modern industry, due to the poor working environment and the complex working conditions of mechanical equipment, the characteristics of the impact signals caused by faults are often submerged in strong background signals and noises. Therefore, it is difficult to effectivelyextract the fault features. In this paper, a fault feature extraction method based on improved VMD multi-scale dispersion entropy and TVD-CYCBD is proposed. First, the marine predator algorithm (MPA) is used to optimize the modal components and penalty factors in VMD. Second, the optimized VMD is used to model and decompose the fault signal, and then the optimal signal components are filtered according to the combined weight index criteria. Third, TVD is used to denoise the optimal signal components. Finally, CYCBD filters the de-noised signal and then envelope demodulation analysis is carried out. Through the simulation signal experiment and the actual fault signal experiment, the results verified that multiple frequency doubling peaks can be seen from the envelope spectrum, and there is little interference near the peak, which shows the good performance of the method

Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions

Downregulation of AMP-activated protein kinase by Cidea-mediated ubiquitination and degradation in brown adipose tissue

We previously showed that Cidea−/− mice are resistant to diet-induced obesity through the upregulation of energy expenditure. The AMP-activated protein kinase (AMPK), consisting of catalytic α subunit and regulatory subunits β and γ, has a pivotal function in energy homoeostasis. We show here that AMPK protein levels and enzymatic activity were significantly increased in the brown adipose tissue of Cidea−/− mice. We also found that Cidea is colocalized with AMPK in the endoplasmic reticulum and forms a complex with AMPK in vivo through specific interaction with the β subunit of AMPK, but not with the α or γ subunit. When co-expressed with Cidea, the stability of AMPK-β subunit was dramatically reduced due to increased ubiquitination-mediated degradation, which depends on a physical interaction between Cidea and AMPK. Furthermore, AMPK stability and enzymatic activity were increased in Cidea−/− adipocytes differentiated from mouse embryonic fibroblasts or preadipocytes. Our data strongly suggest that AMPK can be regulated by Cidea-mediated ubiquitin-dependent proteosome degradation, and provide a molecular explanation for the increased energy expenditure and lean phenotype in Cidea-null mice