Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

The Critical Role of Passive Permeability in Designing Successful Drugs

Passive permeability is a key property in drug disposition and delivery. It is critical for gastrointestinal absorption, brain penetration, renal reabsorption, defining clearance mechanisms and drug-drug interactions. Passive diffusion rate is translatable across tissues and animal species, while the extent of absorption is dependent on drug properties, as well as in vivo physiology/pathophysiology. Design principles have been developed to guide medicinal chemistry to enhance absorption, which combine the balance of aqueous solubility, permeability and the sometimes unfavorable compound characteristic demanded by the target. Permeability assays have been implemented that enable rapid development of structure-permeability relationships for absorption improvement. Future advances in assay development to reduce nonspecific binding and improve mass balance will enable more accurately measurement of passive permeability. Design principles that integrate potency, selectivity, passive permeability and other ADMET properties facilitate rapid advancement of successful drug candidates to patients.     

COVID-19 hospitalization rates rise exponentially with age,inversely proportional to thymic T-cell production

Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R² = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus, MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49–75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.     

Dual command travel times and miniload system throughput with turnover-based storage

We analyze travel times for automated storage/retrieval systems. In particular, we apply our travel time model to turnover-based storage systems and determine the mean and variance of dual command travel times. We present detailed numerical results for selected rack shape factors and ABC inventory profiles. We then investigate the effect of alternative rack configurations on travel time performance measures. We also show how to determine the throughput of miniload systems with turnover-based storage and exponentially distributed pick times.     

Performance of Indoor Infrared Wireless CDMA Systems with Angle Diversity

Indoor infrared communication systems is one of the possible ways of offering data rates in excess of 100 Mbit/s without the need for wiring. Multiple users can share an infrared channel by code division-multiple access (CDMA) techniques. However, the CDMA system performance is limited by both background noise and co-channel interference. In this paper we study the use of angle diversity for mitigating the effects of the noise and interference. The system considered uses on-off shift keying modulation with multibeam transmitters and imaging receivers. The overall system performance for different diversity combining techniques is evaluated and compared to a system without diversity. Numerical results for a 2-user CDMA system indicate that signal to noise and interference ratio (SNIR) improvement (over systems with no diversity) of 5 dB is obtained for at least 50% of an ensemble of 10000 sample evaluations. The generalized selection combining (GSC)--a new diversity technique yet to be implemented for infrared systems--offers the best performance even with its reduced complexity.