Synchrotron X-ray microdiffraction analysis of proton irradiated polycrystalline diamond films

X-ray microdiffraction is a non-destructive technique that allows for depth-resolved, strain measurements with submicron spatial resolution. These capabilities make this technique promising for understanding the mechanical properties of MicroElectroMechancial Systems (MEMS). This investigation examined the local strain induced by irradiating a polycrystalline diamond thin film with a dose of 2 × 10¹⁷ H⁺/cm² protons. Preliminary results indicate that a measurable strain, on the order of 10^− 3, was introduced into the film near the End of Range (EOR) region of the protons.     

Multiple households energy consumption forecasting using consistent modeling with privacy preservation

Traditional data-driven energy consumption forecasting models, including machine learning and deep learning methods, showed outstanding performance in terms of forecasting accuracy and efficiency. The superior performances are based on enough training data samples. Moreover, the derived forecasting model is only applicable to the training dataset and usually is applied to specific household. In real-world smart city development, a centralized forecasting model is required to model and forecasting energy consumption patterns for multiple households, whereas the traditional data-driven forecasting approaches may become invalid. A consistent model is demanded in this scenario modeling multiple households’ energy consumption patterns. Additionally, privacy issues are also highly concerned in such scenarios. Accurate energy consumption forecasting with privacy preservations becomes a key point for the state-of-art research. In this study, we adopt an innovative privacy-preserving structure that combines deep learning and federated learning. Under the premise of guaranteeing forecasting accuracy and privacy preservation, this structure can achieve the forecasting of various household energy consumption with a consistent model that simultaneously forecast multiple household energy consumption data by transmission control protocol.     

Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

Integrated mathematical optimisation approach for the tower crane hook routing problem to satisfy material demand requests on-site

Optimising decisions around the location and operation of tower cranes can improve the workflow in construction projects. Traditionally, the location and allocation problems involved in tower crane operations in the literature have been solved separately from the assignment of material supply points to demand points and the scheduling of the crane’s activity sequence across supply and demand points on a construction site. To address the gap, this paper proposes a binary integer programming problem, where location of the tower crane, allocation of supply points to material-demanding regions, and routing of hook of the crane based on activity sequencing of the hook across supply and material-demanding regions on site are optimised. The novelty in this work is in the way the crane’s activity scheduling is modelled via mathematical programming, based on routing the hook movement to meet material demand, through minimising tower crane operating costs. A realistic case study is solved to assess the validity of the model. The model is contrasted with results obtained from other solving algorithms commonly adopted in the literature, along with a solution proposed by an experienced practitioner. Results indicate that all instances can be solved when compared to other meta-heuristics that fail to achieve an optimum solution. Compared to the solution proposed by the practitioner, the results of the proposed model achieve a 46% improvement in objective function value. Planners should optimise decisions related to the location of the crane, the crane’s hook movement to meet service requests, and supply points’ locations and assignment to material-demanding regions simultaneously for effective crane operations.     

Optimal design of a tunable electromagnetic shunt damper for dynamic vibration absorber

Dynamic vibration absorber (DVA) is an effective device for suppressing resonant vibration of noisy machineries and structures. However, the optimum design of DVA requires precise tuning of the damping force in the DVA, which unfortunately is often not practical and prone to changes of working conditions. In this paper, a tunable electromagnetic shunt damper (EMSD) with different opposing magnet pairs configurations is tested for the optimum design of DVA. The optimum magnet pairs configuration is derived to provide the maximum damping force in the DVA. Both simulations and experiments are conducted to verify the damping coefficient variation with the number of magnet pairs in the EMSD. The experimental optimization procedure of the DVA is designed according to the fixed-points theory. The damping force generated by the EMSD can be readily adjusted by varying the external resistance of the EMSD. This is the first experimental implementation report of the optimization procedure described in the fixed-points theory. The proposed tunable EMSD can conveniently allow for onsite optimal tuning of DVA. The proposed design methodology provides fine tuning of the damping coefficient of EMSD to achieve robust optimal DVA performance, even when subject to changes of external parasitic damping.     

基于三维地球的多源空间科学数据可视化管理系统的设计与实现

如何高效地组织和管理日益增长的多源海量空间科学数据、提高数据的可用性和易用性,是空间科学目前需要重点解决的技术问题。在充分分析现有空间科学数据管理技术和数据特点的基础上,提出了一种有效的系统架构,研究并利用空间关系数据库及分布式数据库技术,实现了海量异构数据的高效存储、检索与定位;研究基于三维数字地球的多层次细节展示方法、三维数据剪裁、多线程并行加载等关键技术,提高了空间科学数据的集成可视化显示与应用效率。最后设计并集成了多源空间科学数据可视化组织与管理系统,在实际工程应用中验证了设计的合理性与有效性。     

Radiometric Calibration and Validationof TG-1 Hyper-spectral Imager

TG-1,the first Chinese space laboratory module launched on 29 September 2011,has accumulated large numbers of high-resolution image data by the hyper-spectral imager.However,these images still can’t be applied to quantitative analysis because of the huge spectral and radiance difference between ground and onboard conditions.So a radiometric calibration is very necessary to correct these laboratory and on-board radiance calibration parameters for better quantitative application.This paper aims to calibrate TG-1 hyper-spectral imager using reflectance-based calibration method through performing ground calibration experiments on 3 February and 6 March 2011.Firstly,this study used the ground experiment data on 6 March to obtain each channel’s calibration correction coefficients,which were different a lot from laboratory and on-board ones,relative error in all channels are bigger than 10 percent.Then,the calibration experiment data on 3 February 2011 were used to validate the result.Results show that calibration correction coefficients improved the image accuracy,the calibration and validation experiments’ results are in good agreement.In most channels,the relative errors are less than 10 percent,except that bigger error appears in absorption channels.Therefore,this calibration experiment renewed the calibration coefficients,and improved the quantitative level of the TG radiance products.     

Upgrading of low rank coal with solvent

In this study, thermal upgrading of low-rank coal with solvent at 380–440 °C under an initial nitrogen pressure of 2 MPa was studied as a possible method for producing clean solid fuel with a high heating value and less spontaneous ignition behavior. Upgrading of Buckskin coal (USA, subbituminous coal) in the presence of t-decalin (non hydrogen-donor solvent) at 440 °C gave 11.4 wt.% of gas, 5.3 wt.% of oil and 74.1 wt.% of upgraded solid product with a small amount of water. The gaseous product consisted mainly of carbon dioxide (67 wt.%), methane, carbon monoxide, hydrogen and a trace of C₂ and C₃ hydrocarbon gases. The oil product from coal contained BTX, phenol, and their alkyl-derivatives. The heating value of the upgraded solid product from the Buckskin coal increased to 31.0 MJ/kg in dry base as compared to the heating value of wet base of the untreated raw coal, which was 19.3 MJ/kg. Spontaneous ignition behavior was greatly reduced by the upgrading. The effect of catalyst and additives on the upgrading was investigated in terms of product distribution and the quality of the solid product. Taiheiyo (Japan, subbituminous) and Yallourn (Australia, brown) coals were also studied.     

Hydrothermal degradation of polybenzimidazole coating

The usefulness of a room-temperature film-forming polybenzimidazole (PBI) resin as a high-temperature anti-corrosion coating for carbon steel in geothermal environments was evaluated. PBI displayed thermal stability at temperatures up to 600 °C. However, when its film was exposed to 300 °C brine, it underwent hydrolysis, which causes the opening and breakage of imidazole rings in the PBI structure, followed by the formation of two hydrolysate derivatives, biphenyl tetra-amine and benzodicarboxylic acid. This shortcoming lowered the film's maximum effectiveness in minimizing the rate of transportation through it of corrosive electrolytes in hydrothermal environments. As a result, the PBI coating film was hydrothermally degraded, and it delaminated from the underlying steel substrates after a 14-day exposure.