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.     

An interpretability security framework for intelligent decision support systems based on saliency map

International Journal of Information Security - Benefiting from the high-speed transmission and super-low latency, the Fifth Generation (5G) networks are playing an important role in contemporary...     

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods.     

Influence of Methanol on Prion Reduction during High Temperature and High Pressure Oleochemical Processes

Prion‐reduction in standard biodiesel processes is caused by acidic and alkaline conditions. In the alternative RepCat biodiesel process, efficient prion‐reduction can be achieved by high‐temperature/pressure. However, a possible increase in thermostability of prions in the presence of methanol during these conditions has not previously been investigated, and is verified in this work. Samples are spiked with prions, treated with methanol, and incubated at 220 °C at 80 bar for 30 min. No traces of protease‐resistant prion protein (as proxy for prions) are detected in tallow or glycerine (as the final by‐product) after treatment. Serial dilutions of spiked prions show at least 6 log₁₀ prion reduction. More importantly, similar effects are detected using milder conditions of 200 °C at 70 bar for 15 min, representing the worst‐case conditions of the process. In conclusion, this study shows that methanol does not increase the thermostability of prions and the RepCat process can efficiently eliminate prions and is therefore safe for the usage of category 1 tallow. Practical Applications: The study further supports the applicability of RepCat process in reducing prion‐contamination in the presence of methanol. Furthermore, the conditions leads to a considerable reduction of prions in glycerine obtained as by‐product.     

Influence of Chemical Reaction on Mass Transport in Yield Stress Exhibiting Flow Regime

Theoretical Foundations of Chemical Engineering - In this study, the simulations for first-order chemical reactions (constructive and destructive) in the flow of the Casson fluid with...     

Soft X-Ray Emission Optimization Study with Nitrogen Gas in a 1.2 kJ Plasma Focus

Measurement of soft x-ray emission from a low-energy plasma focus operated with nitrogen within the pressure range of 0.1–1.0 mbar is presented. The x-rays are detected by using an assembly of Quantrad Si PIN-diodes with differential filtering and with a multipinhole camera. In the 1.0–1.3 keV and 1.0–1.5 keV windows, the x-ray yield in 4 geometry is 1.03 J and 14.0 J, respectively, at a filling pressure of 0.25 mbar and the corresponding efficiencies are 0.04% and 1.22%. The total x-ray emission in 4 geometry is estimated at 21.8 J, which corresponds to the system efficiency of about 1.9%. The soft x-ray emission is found dominantly as a result of electron beam activity on the anode tip, which is confirmed by the images recorded by a pinhole camera.     

Distributed access management in multimedia IDCs

Future Internet data centers that provide multimedia content face security challenges requiring appropriately designed policies to manage resource access. An example from the healthcare domain shows how an access management framework can address these challenges.     

Internal model control structure using adaptive inverse control strategy

In this paper, we propose a new adaptive internal model control scheme based on adaptive finite impulse response filters. This scheme provides the same design procedure for both minimum and nonminimum phase plants. The plants under consideration may contain time delay. The tracking objective is accomplished for both invertible and noninvertible stable plants. The internal model of the plant and its inverse are estimated by recursive least-squares and least-mean-squares algorithms, respectively. The closed loop is designed such that the system from the reference input to the plant output can be approximately represented by a pure delay. The effect of the process zeros on the output is compensated by using adaptive finite impulse response filters. This avoids the cancellation of noncancellable zeros of the plant and forces the plant output to track the reference input with a delay. The stability of the closed loop for both minimum and nonminimum phase systems is guaranteed. Computer simulation and laboratory scale experimental results are included in the paper to demonstrate the effectiveness of the proposed method.