A Batch Authentication Design to Protect Conditional Privacy in Internet of Vehicles

Internet of vehicles (IoV), a novel technology, holds paramount importance within the transportation domain due to its ability to increase traffic efficiency and safety. Information privacy is of vital importance in IoV when sharing information among vehicles. However, due to the openness of the communication network, information sharing is vulnerable to potential attacks, such as impersonation, modification, side-channel and replay attacks, and so on. In order to resolve the aforementioned problem, we present a conditional privacy-preserving batch authentication (CPPBA) scheme based on elliptic curve cryptography (ECC). The proposed scheme avoids the certificate management problem, conducing to efficiency improvement. When a message is transmitted by a vehicle, its pseudo identity rather than the real identity is also broadcasted along with the shared message, which protects the privacy of the vehicle’s identity. But this privacy is conditional because TA and only the TA can reveal the real identity of the vehicle by tracing. The proposed scheme is batch verifiable, which reduces the computation costs. In addition, our scheme does not involve bilinear pairing operations and does not use the map-to-point hash function, thus making the verification process more effective. An exhaustive efficiency comparison has been carried to show that the proposed CPPBA scheme has lower computation, communication, and storage overheads than the state-of-the-art ones. A relatively comprehensive security analysis has also been carried, which not only shows that the signature design in the CPPBA scheme is unforgeable under the random oracle model but also illustrates that the CPPBA scheme is resistant to various potential attacks. The security is also verified by a popular automated simulation tool, that is, AVISPA.

Automated Workflow Based on Transient, Multiphase Technology Improves Well Control Planning Efficiency and Reduces Risk

The use of automated workflows for engineering calculations is significantly improving the efficiency of modern well planning systems. Current automated well control solutions are at large limited to single bubble considerations. Transient, multiphase technology has proven to be more accurate and reliable for well control planning, but it has been too complex to automate and integrate into automated engineering systems. The objective of this work is to improve well control planning efficiency by using an automated workflow that enables integration of transient multiphase technology into modern well-planning systems. The workflow is based around an advanced multiphase engine that covers all relevant physical processes in the wellbore including transient temperature and acceleration. The model has an accurate equations-of-state- (EOS) based pressure-volume-temperature (PVT) model with compositional tracking that, in combination with the transient temperature, can accurately predict the transition from dissolved to free gas - a key parameter in the development of a kick. The workflow is based on Driller's method and has been automated with a controller network that moves the simulation through the distinct phases of the driller's first circulation without any interaction from the user. High-performance cloud computing ensures the workflow performance. The drilling industry has focused on risk reductions after the Deepwater Horizon (BSSE 2010) accident. But the well-control risk is still high. In Norway, the reported incidents indicate a flat or increasing trend. Geological uncertainties and inaccurate mud density (static and circulating) have been identified as root causes for the majority of the reported incidents. Transient multiphase models are reducing well-control risk by accurately modeling downhole variations in fluid pressure as a function of operational mode, fluids, influx type, geometry, water depth, and pressure and temperature conditions. Such models have been regarded as expert tools because of the complexity and numerically demanding simulations. The automated workflow enables a well control engineer to run accurate multiphase simulations with the same user effort as single bubble kick tolerance tools. In special cases where more sensitivities are required, it is easy to transfer the project to the expert mode - where the automated simulation can be finetuned.

A review on coupled building physics analyses

This paper reviews methods and tools for coupled building physics analyses in the context of Building Performance Simulations (BPS) with a focus on Building Energy Simulations (BES) and Computational Fluid Dynamics (CFD) as a common application. Furthermore, requirements regarding the necessary information for simulations, data models and coupling are identified. Possibilities of automated simulation model generation, data exchange and the performance of existing multi physics simulation models are analysed and limiting factors are discussed.

A Geometry-Based Welding Distortion Prediction Tool

The prediction of welding distortion requires expertise in computer simulation programs, a clear definition of the nonlinear material properties, and mesh settings together with the nonlinear solution settings of a coupled thermal–structural analysis. The purpose of this paper is to present the validation of an automatic simulation tool implemented in Ansys using Python scripting. This tool allows users to automate the preparation of the simulation model with a reduced number of inputs. The goal was, based on some assumptions, to provide an automated simulation setup that enables users to predict accurate distortion during the welding manufacturing process. Any geometry prepared in a CAD software can be used as the input, which gave us much geometrical flexibility in the shapes and sizes to be modeled. A thermomechanical loosely coupled analysis approach together with element birth and death technology was used to predict the distortions. The automation of the setup enables both simulation and manufacturing engineers to perform welding-induced distortion prediction. The results showed that the method proposed predicts distortion with 80–98% accuracy.

Sketch-Based Mechanism Simulation Using Machine Learning

This paper presents a machine learning approach for building an object detector for interactive simulation of planar linkages from handmade sketches and drawings found in patents and texts. Touch- and pen-input devices and interfaces have made sketching a more natural way for designers to express their ideas, especially during early design stages, but sketching existing complex mechanisms can be tedious and error-prone. While there are software applications available to help users make drawings, including that of a linkage mechanism, it is both educational and instructive to see existing sketches come to life via automated simulation. However, texts and patents present rich and diverse styles of mechanism drawings, which makes automated recognition difficult. Modern machine learning algorithms for object recognition require an extensive number of training images. However, there are no data sets of planar linkages available online. Therefore, our first goal was to generate images of sketches similar to hand-drawn ones and use state-of-the-art deep generation models, such as β-VAE, to produce more training data from a limited set of images. The latent space of β-VAE was explored by linear and spherical interpolations between sub-spaces and by varying latent space’s dimensions. This served two-fold objectives — 1) examine the possibility of generating new synthesized images via interpolation and 2) develop insights in the dependence of latent space dimension on bar linkage parameters. t-SNE dimensionality reduction technique was implemented to visualize the latent space of a β-VAE in a 2D space. Training images produced by animation rendering were used for fine-tuning a real-time object detection system — YOLOv3.

Semi-automated simulations: Know your schedules

This study tests semi-automated simulation measures such as schedules and occupancy profiles in B I M software packages to establish energy performance predictions for the purposes of providing evidence of compliance. These predictions are tested against an archetypal range of household operation figures that are based on the data collected of approximately 400 households monitored for 11 months each. This study identifies that standardising the simulations by using the archetypal range of dwelling occupation predictions produces a more consistent outcome in energy evaluation across both software packages. However, both of the B I M software packages tested in this study are unable to establish energy performance predictions that align with the real-world measured data. This suggests that in-built semi-automated simulation measures, beyond the optimised schedules and occupancy profiles, investigated in this study, need to be examined in greater detail.

Evaluating computer aided design tools for building performance: Trusting and defining the predetermined automated inputs

BIM use is on the rise in New Zealand with popular software packages, including Revit and ARCHICAD, adopting a semi-automated simulation platform. This allows architects and designers to calculate the thermal and energy performance of their designs. This paper identifies the strengths and weaknesses of these semi-automated simulation platforms. The objective is to investigate how accurate their assumptions are in determining a reliable output for use in achieving compliance with Clause H1 of the New Zealand Building Code. To achieve this, this paper reports a comparative study that examines the program’s ability to calculate construction R-values, interpret thermal properties and simulate energy performance. The results from this study show that if used as delivered there is a significant difference between the simulation results of the two software packages, due to the assumptions built into the default settings. It also identifies the disadvantages of the inbuilt construction R-value calculators and explores a potential path to resolving this through redefining the inputs of thermal properties.

Evaluating computer aided design tools for building performance: Trusting and defining the predetermined automated inputs

BIM use is on the rise in New Zealand with popular software packages, including Revit and ARCHICAD, adopting a semi-automated simulation platform. This allows architects and designers to calculate the thermal and energy performance of their designs. This paper identifies the strengths and weaknesses of these semi-automated simulation platforms. The objective is to investigate how accurate their assumptions are in determining a reliable output for use in achieving compliance with Clause H1 of the New Zealand Building Code. To achieve this, this paper reports a comparative study that examines the program’s ability to calculate construction R-values, interpret thermal properties and simulate energy performance. The results from this study show that if used as delivered there is a significant difference between the simulation results of the two software packages, due to the assumptions built into the default settings. It also identifies the disadvantages of the inbuilt construction R-value calculators and explores a potential path to resolving this through redefining the inputs of thermal properties.

Semi-automated simulations: Know your schedules

This study tests semi-automated simulation measures such as schedules and occupancy profiles in B I M software packages to establish energy performance predictions for the purposes of providing evidence of compliance. These predictions are tested against an archetypal range of household operation figures that are based on the data collected of approximately 400 households monitored for 11 months each. This study identifies that standardising the simulations by using the archetypal range of dwelling occupation predictions produces a more consistent outcome in energy evaluation across both software packages. However, both of the B I M software packages tested in this study are unable to establish energy performance predictions that align with the real-world measured data. This suggests that in-built semi-automated simulation measures, beyond the optimised schedules and occupancy profiles, investigated in this study, need to be examined in greater detail.

Automated Simulation Framework for Urban Wind Environments Based on Aerial Point Clouds and Deep Learning

Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China.