Semantic correction, enrichment and enhancement of social and transport infrastructure BIM models

The use of Building Information Modelling (BIM) models in the design, construction and operation of buildings and infrastructure is leading to a stronger focus on the quality of the models. Models may need correction, enrichment or enhancement to meet the expectations for quality and completeness, especially if models are to be taken as legal documents, for example for regulatory approval. Past work on semantic development has looked at specific scenarios such as scanned geometry or missing classification. This paper describes an innovative unified approach to the documentation of semantic expectations by actors in the AECO (Architectural, Engineering, Construction and Operations) domain and the means to put them into effect. RASE (Requirements, Applications, Selections and Exceptions) semantic mark-up is used to make both the requirements and any supporting resources both human-readable and machine-operable. Two example models from industry, a motorway bridge and a healthcare space, are used to demonstrate applying geometric, schema and classification knowledge. This knowledge is represented in a number of different styles. This extends our understanding of the nature of the knowledge found in dictionaries, classifications and development specifications, demonstrating how this knowledge can be made operable. This bridges the gap between the application of static compliance knowledge and the accurate and efficient application of correction, enrichment and enhancement knowledge

Microbial electrolysis contribution to anaerobic digestion of waste activated sludge, leading to accelerated methane production

Methane production rate (MPR) in waste activated sludge (WAS) digestion processes is typically limited by the initial steps of complex organic matter degradation, leading to a limited MPR due to sludge fermentation speed of solid particles. In this study, a novel microbial electrolysis AD reactor (ME-AD) was used to accelerate methane production for energy recovery from WAS. Carbon bioconversion was accelerated by ME producing H-2 at the cathode. MPR was enhanced to 91.8 gCH(4)/m(3) reactor/d in the microbial electrolysis ME-AD reactor, thus improving the rate by 3 times compared to control conditions (30.6 gCH(4)/m(3) reactor/d in AD). The methane production yield reached 116.2 mg/g VSS in the ME-AD reactor. According to balance calculation on electron transfer and methane yield, the increased methane production was mostly dependent on electron contribution through the ME system. Thus, the use of the novel ME-AD reactor allowed to significantly enhance carbon degradation and methane production from WAS. (C) 2016 Elsevier Ltd. All rights reserved

Spectroscopic study of the morphology development of closed-cell polyurethane foam using bio-based malonic acid as chain extender

In this study the advantages of using bio-based multi-functional malonic acid salt as chain extender in preparing closed cell polyurethane foams is demonstrated. We found that the presence of malonic acid salt can alter the reaction kinetics, segmental mobility and hence the relative kinetics of polyurethane and polyurea formation. By analyzing the infrared active vibrations, it is demonstrated that the formation of urethane and urea linkages is accelerated at the initial stage of reaction, resulting in enhanced viscosity build-up attributed to the catalytic activity of the triethylamine complexed with malonic acid. The relative kinetics in formation of different morphological features thus can be used to control the cell opening process. These results, combining information on polymerization kinetics, gas evolution and phase separation, illustrate the capability of using malonic acid salt as the chain extender to manipulate polyurethane foam morphology and its potential application in polyurethane closed cell foam systems

Advance in mechanism of plant leaf colour mutation

As a common mutation trait in plants, leaf colour mutation is related to the degree of chlorophyll and anthocyanin changes and the destruction of chloroplast structure. This study summarizes the latest research progress in leaf colour mutation mechanism, including the metabolic basis of plant leaf colour mutation, leaf colour mutation caused by gene mutation in the chlorophyll metabolism pathway, leaf colour mutation caused by blocked chloroplast development, leaf colour mutation controlled by key transcription factors and non-coding RNAs, leaf colour mutation caused by environmental factors, and leaf colour mutation due to the involvement of the mevalonate pathway. These results will lay a theoretical foundation for leaf colour development, leaf colour improvement, and molecular breeding for leaf colour among tree species

Automatic unpaired shape deformation transfer

Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user effort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a high-dimensional space. To obtain a more compact and effective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light field distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required

Towards General-Purpose Representation Learning of Polygonal Geometries

Neural network representation learning for spatial data is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made in representation learning for points, polylines, and networks, whereas little progress has been made for polygons, especially complex polygonal geometries. In this work, we focus on developing a general-purpose polygon encoding model, which can encode a polygonal geometry (with or without holes, single or multipolygons) into an embedding space. The result embeddings can be leveraged directly (or finetuned) for downstream tasks such as shape classification, spatial relation prediction, and so on. To achieve model generalizability guarantees, we identify a few desirable properties: loop origin invariance, trivial vertex invariance, part permutation invariance, and topology awareness. We explore two different designs for the encoder: one derives all representations in the spatial domain; the other leverages spectral domain representations. For the spatial domain approach, we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons. For the spectral domain approach, we develop NUFTspec based on Non-Uniform Fourier Transformation (NUFT), which naturally satisfies all the desired properties. We conduct experiments on two tasks: 1) shape classification based on MNIST; 2) spatial relation prediction based on two new datasets - DBSR-46K and DBSR-cplx46K. Our results show that NUFTspec and ResNet1D outperform multiple existing baselines with significant margins. While ResNet1D suffers from model performance degradation after shape-invariance geometry modifications, NUFTspec is very robust to these modifications due to the nature of the NUFT.Comment: 58 pages, 20 figures, Accepted to GeoInformatic

Semantic correction, enrichment and enhancement of social and transport infrastructure BIM models

The use of Building Information Modelling (BIM) models in the design, construction and operation of buildings and infrastructure is leading to a stronger focus on the quality of the models. Models may need correction, enrichment or enhancement to meet the expectations for quality and completeness, especially if models are to be taken as legal documents, for example for regulatory approval. Past work on semantic development has looked at specific scenarios such as scanned geometry or missing classification. This paper describes an innovative unified approach to the documentation of semantic expectations by actors in the AECO (Architectural, Engineering, Construction and Operations) domain and the means to put them into effect. RASE (Requirements, Applications, Selections and Exceptions) semantic mark-up is used to make both the requirements and any supporting resources both human-readable and machine-operable. Two example models from industry, a motorway bridge and a healthcare space, are used to demonstrate applying geometric, schema and classification knowledge. This knowledge is represented in a number of different styles. This extends our understanding of the nature of the knowledge found in dictionaries, classifications and development specifications, demonstrating how this knowledge can be made operable. This bridges the gap between the application of static compliance knowledge and the accurate and efficient application of correction, enrichment and enhancement knowledge