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.     

Concept-based evidential reasoning for multimodal fusion in human–computer interaction

Considerable research has been done on using information from multiple modalities, like hands, facial gestures or speech, for better interaction between humans and computers, and many promising human–computer interfaces (HCI) have been developed in recent years. However, most of the current HCI systems have a few drawbacks: firstly, they are highly dependent on the performance of individual sensors. S econdly, the information fusion process from these sensors tends to ignore the semantic nature of the modalities, which may reinforce or clarify each other over time. Finally, they are not robust enough at representing the imprecise nature of human gestures, since individual gestures are highly ambiguous in themselves. In this paper, we propose an approach for the semantic fusion of different input modalities, based on transferable belief models. We show that this approach allows for a better representation of the ambiguity involved in recognizing gestures. Ambiguity is resolved by combining the beliefs of the individual sensors on the input information, to form new extended concepts, based on a pre-defined domain specific knowledge base, represented by conceptual graphs. We apply this technique to a multimodal system consisting of a hand gesture recognition sensor and a brain computing interface. It is shown that the technique can successfully combine individual gestures obtained from the two sensors, to form meaningful concepts and resolve ambiguity. The advantage of this approach is that it is robust even if one of the sensors is inefficient or has no input. Another important feature is its scalability, wherein more input modalities, like speech or facial gestures, can be easily integrated into the system at minimal cost, to form a comprehensive HCI interface.     

A Framework for Coordinated Control of Multiagent Systems and Its Applications

In this paper, a framework is proposed for the distributed control and coordination of multiagent systems (MASs). In the proposed framework, the control of MASs is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a coordinated hybrid agent, which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input, and workspace input. In the proposed framework, we describe the coordination mechanism in a domain-independent way, i.e., as simple abstract primitives in a coordination rule base for certain dependence relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision making, and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. To verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous MASs are implemented. The proposed framework is applied to a multicrane system, a multiple robot system, and a MAS consisting of an overhead crane, a mobile robot, and a robot manipulator. It is demonstrated that the proposed framework can model the three MASs. The agents in these systems are able to cooperate and coordinate to achieve a global goal. In addition, the stability of systems modeled using the proposed framework is also analyzed.     

Numerical and sensitivity analysis of MHD bioconvective slip flow of nanomaterial with binary chemical reaction and Newtonian heating

The impact of Stefan blowing on the MHD bioconvective slip flow of a nanofluid towards a sheet is explored using numerical and statistical tools. The governing partial differential equations are nondimensionalized and converted to similarity equations using apposite transformations. These transformed equations are solved using the Runge–Kutta–Fehlberg method with the shooting technique. Graphical visualizations are used to scrutinize the effect of the controlling parameters on the flow profiles, skin friction coefficient, local Nusselt, and Sherwood number. Moreover, the sensitivities of the reduced Sherwood and Nusselt number to the input variables of interest are explored by adopting the response surface methodology. The outcomes of the limiting cases are emphatically in corroboration with the outcomes from preceding research. It is found that the heat transfer rate has a positive sensitivity towards the haphazard motion of the nanoparticles and a negative sensitivity towards the thermomigration. The thermal field is enhanced by the Stefan blowing aspect. Moreover, the fluid velocity can be controlled by the applied magnetic field.     

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.     

A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach     

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.