Weakly Supervised Point Clouds Transformer for 3D Object Detection

The annotation of 3D datasets is required for semantic-segmentation and object detection in scene understanding. In this paper we present a framework for the weakly supervision of a point clouds transformer that is used for 3D object detection. The aim is to decrease the required amount of supervision needed for training, as a result of the high cost of annotating a 3D datasets. We propose an Unsupervised Voting Proposal Module, which learns randomly preset anchor points and uses voting network to select prepared anchor points of high quality. Then it distills information into student and teacher network. In terms of student network, we apply ResNet network to efficiently extract local characteristics. However, it also can lose much global information. To provide the input which incorporates the global and local information as the input of student networks, we adopt the self-attention mechanism of transformer to extract global features, and the ResNet layers to extract region proposals. The teacher network supervises the classification and regression of the student network using the pre-trained model on ImageNet. On the challenging KITTI datasets, the experimental results have achieved the highest level of average precision compared with the most recent weakly supervised 3D object detectors.Comment: International Conference on Intelligent Transportation Systems (ITSC), 202

Fault Prediction of Rolling Element Bearings Using the Optimized MCKD–LSTM Model

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series

Theoretical study on the reactivity of sulfate species with hydrocarbons

The abiotic, thermochemically controlled reduction of sulfate to hydrogen sulfide coupled with the oxidation of hydrocarbons, is termed thermochemical sulfate reduction (TSR), and is an important alteration process that affects petroleum accumulations in nature. Although TSR is commonly observed in high-temperature carbonate reservoirs, it has proven difficult to simulate in the laboratory under conditions resembling nature. The present study was designed to evaluate the relative reactivities of various sulfate species in order to provide greater insight into the mechanism of TSR and potentially to fill the gap between laboratory experimental data and geological observations. Accordingly, quantum mechanics density functional theory (DFT) was used to determine the activation energy required to reach a potential transition state for various aqueous systems involving simple hydrocarbons and different sulfate species. The entire reaction process that results in the reduction of sulfate to sulfide is far too complex to be modeled entirely; therefore, we examined what is believed to be the rate limiting step, namely, the reduction of sulfate S(VI) to sulfite S(IV). The results of the study show that water-solvated sulfate anions View the MathML source are very stable due to their symmetrical molecular structure and spherical electronic distributions. Consequently, in the absence of catalysis, the reactivity of SO4 2- is expected to be extremely low. However, both the protonation of sulfate to form bisulfate anions (HSO4-) and the formation of metal-sulfate contact ion-pairs could effectively destabilize the sulfate molecular structure, thereby making it more reactive. Previous reports of experimental simulations of TSR generally have involved the use of acidic solutions that contain elevated concentrations of HSO4- relative to SO4 2-. However, in formation waters typically encountered in petroleum reservoirs, the concentration of HSO4- is likely to be significantly lower than the levels used in the laboratory, with most of the dissolved sulfate occurring as SO4 2-, aqueous calcium sulfate ([CaSO4](aq)), and aqueous magnesium sulfate ([MgSO4](aq)). Our calculations indicate that TSR reactions that occur in natural environments are most likely to involve bisulfate ions (HSO4-) and/or magnesium sulfate contact ion-pairs ([MgSO4]CIP) rather than ‘free’ sulfate ions (SO4 2-) or solvated sulfate ion-pairs, and that water chemistry likely plays a significant role in controlling the rate of TSR

Justifying the Effective Use of Building Information Modelling (BIM) with Business Intelligence

Although building information modelling (BIM) is a widely acknowledged information and communication technology (ICT) in the architecture, engineering, construction, and operation (AECO) industry, its implementation is hindered by the hybrid practice of BIM and non-BIM information processing, and sometimes, it fails to add value to the AECO business. It is crucial to define, on a scientific base, how to ensure the effective use of BIM regarding the various conditions in which to apply BIM in AECO practices. Although several studies have investigated similar topics, very few have focused on the adoption of distinct BIM applications over the conventional practice from the perspective of business intelligence (BI) as a theoretical framework to justify the effective value of BIM use in the AECO. This study proposes a framework relying on BI principles to justify effective BIM use and explicates the contextual factors in AECO practices. The data were acquired from a three-round Delphi survey. The framework suggests that effective BIM use in AECO practices should follow the two principles of BI: achieving technical effectiveness and realizing business value. The pursuit of technical effectiveness should consider business objectives, business issues, business sustainability and regulatory eligibility, and the realization of business value involves willingness to adopt BIM, human-computer interoperability, visualization-based data quality and sources, data processing and system integration, and application maturity. This study provides a new perspective by which to address the issue of the technological iteration in the current hybrid BIM and non-BIM practice and could help to improve BIM implementation in the AECO industry

Early Fault Diagnosis of Shaft Crack Based on Double Optimization Maximum Correlated Kurtosis Deconvolution and Variational Mode Decomposition

Aiming at the problem that the vibration energy of shaft crack early weak fault is small and the feature extraction was difficult, the fault diagnosis method of cuckoo search, maximum correlation kurtosis deconvolution and variational mode decomposition (CS-MCKD-VMD) was proposed to diagnose the early weak fault of shaft cracks. Firstly, considering the periodic characteristics of fault signal, MCKD was used to highlight the fault signal, and the kurtosis correlation of deconvolution signal processed by MCKD were enhanced. Then, VMD could be used to decompose the harmonic signal, and VMD was used to process the deconvolution signal to generate some intrinsic mode functions (IMF), and the correlation coefficient method was used to calculate the components with the maximum correlation. At the same time, in order to improve the correlation of parameters in MCKD and VMD, CS was used to optimize the parameters to reduce the error of human selection. Thirdly, the optimal component was calculated by the fast spectral kurtosis (FSK), and the envelope spectrum was obtained by filtering the optimal component with the parameters obtained. Finally, the simulation and experimental results show that the transfer frequency and its 2 and 3 times frequency were prominent in the envelope spectrum, which was consistent with the analysis of shaft crack fault mechanism, Therefore, it was proved that the CS-MCKD-VMD method can effectively extract the early weak fault features of shaft cracks

New Application of Quartz Crystal Microbalance: A Minimalist Strategy to Extract Adsorption Enthalpy

The capture and separation of CO2 is an important means to solve the problem of global warming. MOFs (metal–organic frameworks) are considered ideal candidates for capturing CO2, where the adsorption enthalpy is a crucial indicator for the screening of materials. For this purpose, we propose a new minimalist solution using QCM (quartz crystal microbalance) to extract the CO2 adsorption enthalpy on MOFs. Three kinds of MOFs with different properties, sizes and morphologies were employed to study the adsorption enthalpy of CO2 using a QCM platform and a commercial gas sorption analyzer. A Gaussian simulation calculation and previously data reported were used for comparison. It was found that the measuring errors were between 5.4% and 6.8%, proving the reliability and versatility of our new method. This low-cost, easy-to-use, and high-accuracy method will provide a rapid screening solution for CO2 adsorption materials, and it has potential in the evaluation of the adsorption of other gases

Controllable Preparation of Highly Crystalline Sulfur-Doped Π-Conjugated Polyimide Hollow Nanoshell for Enhanced Photocatalytic Performance

In this study, a series of highly crystalline π-conjugated polyimide photocatalysts with porous nano hollow shell (HSPI) was prepared for the first time by the hard template method by adjusting the addition ratio of the template precursor. SiO2 nanospheres not only serve as template agents but also as dispersants to make precursors of SPI more uniform, and the degree of polymerization will be better, resulting in significantly enhanced crystallinity of HSPI relative to bulk SPI (BSPI). More strikingly, it is found that HSPI has a larger specific surface area, stronger visible light absorption, and higher separation efficiency of photogenerated electron and hole pairs compared with BSPI by various spectral means characterization analysis. These favorable factors significantly enhanced the photocatalytic degradation of methyl orange (MO) by HSPI. This work provides a promising approach for the preparation of cheap, efficient, environmentally friendly, and sustainable photocatalysts

Pollution characteristics and ecological risk of microplastic in sediments of Liaodong Bay from the northern Bohai Sea in China

Microplastics (MPs) are widely distributed in marine environments. The pollution characteristics and risk assessment of MPs in estuarine sediments are still insufficient. In this study, the MPs pollution characteristics in surface sediments of the Liao Estuary and Daliao Estuary were investigated. The characteristics of MPs in sediments were determined by stereo microscopy and micro-Fourier transform infrared spectroscopy. The results showed that the average MPs abundance ranged from 32.33 to 49.91 items center dot kg(-1) d.w. The MPs were mainly composed of 500-2000 mu m black and blue fibers. Five polymer types were identified, including rayon (RA) (87.46 %), polyethylene terephthalate (PET) (6.81 %), polyamide (PA) (2.94 %), polypropylene (PP) (2.17 %) and polyethylene (PE) (0.62 %). The pollution load index (PLI) risk assessment showed that all sampling sites were at Hazard Level I. Our results can provide useful information for assessing the environmental risks of MPs in coastal areas of China