Detecting Attacks in CyberManufacturing Systems: Additive Manufacturing Example

CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms - (i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection - have been adopted in the research and shown to be effective in detecting such defects

A systematic collection of medical image datasets for deep learning

The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to medical data. Thus, as comprehensively as possible, this article provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected the information of approximately 300 datasets and challenges mainly reported between 2007 and 2020 and categorized them into four categories: head and neck, chest and abdomen, pathology and blood, and others. The purpose of our work is to provide a list, as up-to-date and complete as possible, that can be used as a reference to easily find the datasets for medical image analysis and the information related to these datasets

Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model

The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics. For rear-end crashes, potential correlation in injury severity may present between the two drivers involved in the same crash. Moreover, there may exist unobserved heterogeneity considering parameter effects, which may vary across both crashes and individuals. To address these concerns, a random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars. Taking both the within-crash correlation and unobserved heterogeneity into consideration, the proposed model outperforms the two separate ordered probit models with fixed parameters. The value of the correlation parameter demonstrates that there indeed exists significant correlation between two drivers’ injuries. Driver age, gender, vehicle, airbag or seat belt use, traffic flow, etc., are found to affect injury severity for both the two drivers. Some differences can also be found between the two drivers, such as the effect of light condition, crash season, crash position, etc. The approach utilized provides a possible use for dealing with similar injury severity analysis in future work

Mixing characteristics of inclined dense jets with different nozzle geometries

In the present study, we performed a laboratory investigation to examine the mixing characteristics of 45° inclined dense jets from different nozzle geometries using the technique of Planar Laser Induced Fluorescence (PLIF). The geometries included the circular, square and diamond shapes, as well as the duckbill shape of non-return duckbill valves that are now commonly used for brine outfalls and the star shape of non-return star valves that are available commercially but have not been adopted so far. The concentration centrelines, cross-sectional profiles and spread widths were quantified in the experiments. The results showed that the circular, square and diamond nozzle geometries have similar behavior, implying that the differences of their discharge length scale are not sufficiently large to induce a significant effect. On the other hand, the duckbill and star nozzle geometries have relatively higher dilutions both at the centreline peak and return points. Interestingly, the duckbill nozzle has a relatively lower rise height compared to the others, potentially due to the strong influence of axis-switching effect. In addition, we also performed numerical simulations using the Large Eddy Simulations (LES) approach with the Dynamic Smagorinsky sub-grid model for the diamond, duckbill and star shape nozzle geometries in the experiments. The comparison showed that the time-averaged geometrical characteristics from the three different nozzle geometries can be simulated reasonably well before the centreline peak but with slight over-predictions after that. Meanwhile, the dilution characteristics are underestimated by ~25% generally, which are similar to previous LES results with the reference circular nozzle. The spectral density distribution of the concentration fluctuations clearly demonstrated that the production of turbulence energy in the larger eddies is enhanced by the non-circular nozzles, which is also consistent with the increase in dilutions with these nozzles