Clarifying the impact of engine operating parameters of heavy-duty diesel vehicles on NOx and CO emissions using multimodal fusion methods.

The issue of air pollution from transportation sources remains a major concern, particularly the emissions from heavy-duty diesel vehicles, which pose serious threats to ecosystems and human health. China VI emission standards mandate On-Board Diagnostics (OBD) systems in heavy-duty diesel vehicles for real-time data transmission, yet the current data quality, especially concerning crucial parameters like NOx output, remains inadequate for effective regulation. To address this, a novel approach integrating Multimodal Feature Fusion with Particle Swarm Optimization (OBD-PSOMFF) is proposed.

Detecting candidate high NO emitting light commercial vehicles using vehicle emission remote sensing.

Vehicle emission remote sensing devices have been widely used for monitoring and assessing the real-world emission performance of vehicles. They are also well-suited to identify candidate high emitting vehicles as remote sensing surveys measure the on-road, real-driving emissions (RDE) of a high proportion of the operational vehicle fleet passing through a testing site. This study uses the Gumbel distribution to characterize the fuel-specific NO emission rates (g·kg) from diesel vans (formally referred to as light commercial vehicles or LCVs) and screen candidate high emitting vehicles.

Real-world CO and NO emissions from refrigerated vans.

Refrigerated vans used for home deliveries are attracting attention as online grocery shopping in the UK is expanding rapidly and contributes to the increasing greenhouse gas (CO) and nitrogen oxides (NO) emissions. These vans are typically 3.5-tonne gross weight vehicles equipped with temperature-controlled units called Transport Refrigeration Units (TRUs), which are usually powered off the vehicles' engine.

Non-rigid image registration with dynamic Gaussian component density and space curvature preservation.

Image registration plays an important role in military and civilian applications, such as natural disaster damage assessment, environmental monitoring, ground change detection and military damage assessment, etc. This work presents a new feature-based non-rigid image registration method. The main contributions of this work are: (i) a dynamic Gaussian component density is designed to better exploit available potential image information and provide sufficient inlier pairs for image transformation; (ii) a spatial structure preservation, which consists of an image transformation space curvature preservation and a local spatial structure constrain, is proposed to constrain the image transforming cost as well as the local structure of feature points during feature point set registration.