Physical Machines Research Articles

A Digital Twin Platform Integrating Process Parameter Simulation Solution for Intelligent Manufacturing

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency.

On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card.

ACERAC: Efficient Reinforcement Learning in Fine Time Discretization.

One of the main goals of reinforcement learning (RL) is to provide a way for physical machines to learn optimal behavior instead of being programmed. However, effective control of the machines usually requires fine time discretization. The most common RL methods apply independent random elements to each action, which is not suitable in that setting. It is not feasible because it causes the controlled system to jerk and does not ensure sufficient exploration since a single action is not long enough to create a significant experience that could be translated into policy improvement. In our view, these are the main obstacles that prevent the application of RL in contemporary control systems. To address these pitfalls, in this article, we introduce an RL framework and adequate analytical tools for actions that may be stochastically dependent in subsequent time instances. We also introduce an RL algorithm that approximately optimizes a policy that produces such actions. It applies experience replay (ER) to adjust the likelihood of sequences of previous actions to optimize expected n -step returns that the policy yields. The efficiency of this algorithm is verified against four other RL methods [continuous deep advantage updating (CDAU), proximal policy optimization (PPO), soft actor-critic (SAC), and actor-critic with ER (ACER)] in four simulated learning control problems (Ant, HalfCheetah, Hopper, and Walker2D) in diverse time discretization. The algorithm introduced here outperforms the competitors in most cases considered.

Design and Development of Rice Pot-Seedling Transplanting Machinery Based on a Non-Circular Gear Mechanism

Transplanting rice pot seedlings without damaging the roots, which promotes early tillering, is an effective measure to enhance rice yield and quality. This study aimed to obtain the mechanized-transplanting trajectory and attitude of rice pot seedlings by utilizing non-circular planetary-gear trains, focusing on the three key actions of rice pot-seedling transplanting: seedling picking, conveying, and planting. A lightweight and simplified rice pot-seedling transplanting machinery was designed, referring to the motion characteristics of artificially transplanting rice pot seedlings by first pulling them out and then planting them. Key technologies such as non-circular gear trains, the rice seedling supply system, the transmission system, and the rice seedling-picking device were studied, and their key components were designed and manufactured, resulting in the creation of two physical model machines: an ordinary ride type and a high-speed type. The seedling-picking test and field-transplanting test showed that the rice pot-seedling transplanting mechanism can accomplish the rice pot-seedling picking, rice conveying, and planting actions. The designed operation efficiency yielded a planting-depth qualification rate of over 92%, a seedling injury rate of less than 1.2%, and a missed-transplanting rate of less than 2%.

Advancing service life estimation of reinforced concrete considering the coupling effects of multiple factors: Hybridized physical testing and machine learning approach

Chloride-induced corrosion is the primary factor that significantly impacts the service life of reinforced concrete structures. Therefore, it is crucial to investigate the transportation behavior of chloride ions within concrete structures to accurately estimate their service life. Previous studies have mostly examined the diffusion of chloride ions through physical tests on concrete specimens. Some data-driven models have also been developed to predict the chloride ion concentration at different depths in concrete structures. However, these prediction models often considered random factors in materials and the environment separately, neglecting their coupled effects. As a result, there can be a significant disparity between the predicted results and the actual test outcomes. To achieve a more precise evaluation of the service life, this study conducted a physical test to investigate the diffusion behavior of chloride ions in plain concrete specimens. Factors such as water-cement ratio (W/C), coarse aggregate volume fraction (v) (ratios of the mass of coarse aggregates to the total mass of concrete specimen), ratios of environmental temperature to maintenance standard temperature (T/T0), ratios of environmental humidity to maintenance humidity (h/h0), and ratios of exposure time to maintenance time (t/t0) were considered in the test. Based on the test results, machine learning approaches were employed to determine the influence of each factor on the chloride diffusion coefficient (D) and surface chloride concentration (Cs). Subsequently, a prediction model was developed using the second Fick's law, with the chloride ion concentration at each depth as the output and the aforementioned factors as the inputs. Furthermore, by analyzing the above results, the study examined the impacts of various factors on the service life of reinforced concrete structures. The findings indicate that W/C and v are the most significant factors affecting D and Cs. As W/C and T/T0 increase, both D and Cs also increase. Conversely, the volume fraction of coarse aggregate has a hindering effect on the diffusion of chloride ions in concrete structures. Additionally, the study explored the effect of each input factor on the service life of reinforced concrete structures considering the effect of the above factors. Overall, these findings provide valuable insights into improving the accuracy of estimating the service life of reinforced concrete structures by considering the coupled effects of materials and environmental factors.

Algorithm for allocating virtual computers in energy-friendly cloud data centers

A huge proportion of centres that consume a lot of power, skyrocketing operating costs, and emit a disproportionate amount of carbon dioxide appeared due to the constantly increasing need for processing capacity. This necessitates finding a better approach to handling the energy crisis. This paper proposes a solution to address the energy crisis in cloud data centres, which consume a significant amount of power and emit a large amount of carbon dioxide. A power-conscious simulated engine allocation to physical machines reduces power consumption while maintaining an effective Class of Use. The proposed method is compared with other existing methods such as the Genetic Algorithm (GA), Modified Best Fit Decreasing Algorithm (MBFD), Random Selection (RS), and Minimum Migration Time (MMT) in terms of power usage. The proposed method outperforms the MBFD and GA procedures, showing an 8.55% and 46.81% improvement in power usage, respectively. The MBFD and GA procedures consume 389.5 and 812 kilowatts of energy, while the proposed method reduces this energy consumption. The proposed method is validated using the CloudSim simulator, which demonstrates its success as the fastest and most intelligent procedure compared to other methods. Additionally, the proposed method addresses the burdened host challenge quickly and provides valuable insights for future research by utilizing deep learning approaches.

A review of icing prediction techniques for four typical surfaces in low-temperature natural environments

As a common phenomenon in nature and industrial fields, icing always adversely affects life, and plays negative effects. Although series of anti-/de-icing techniques are widely investigated, icing prediction techniques on surfaces under low-temperature natural environment are more important due to effectively reduce or even prevent icing caused harms. To understand the current research progress of icing prediction techniques, four typical static and moving surfaces that are prone to ice formation were selected as the research objects in this study, including road, transmission line, wind turbine blade and aircraft surfaces. As summarized, prediction methods mainly include physical models, statistical models, and machine learning. For road surface, statistical analysis, theoretical analysis and data mining methods are widely used, with the average prediction accuracy reaching 80%. For wind turbine blade and transmission line surfaces, both model-driven and data-driven methods were used, resulting in average prediction accuracies of 80% and 90%, respectively. For aircraft surface, the method of numerical analysis combined with machine learning is the mainstay, with a deviation of less than 20%. Summary and outlook are finally given, which are beneficial for the optimization of ice prediction technology in different industrial fields.

Retrieval of purification ability of urban forest to SO2 stress based on the coupling of radiative transfer and AO-DELM models

Currently, hyperspectral remote sensing technology used for vegetation monitoring mainly uses empirical and semi-empirical statistical methods to calculate heavy metal content. Combining physical models and machine learning algorithms is an effective method for estimating biochemical vegetation parameters without much ground measurement data. However, a deep extreme learning machine (DELM) has a faster training speed and better generalization ability. By introducing the Aquila Optimizer (AO) algorithm, the training process of the DELM method can be accelerated. This study combined the PROSAIL model, chlorophyll concentration information, and vegetation SO2 purification ability, and comprehensively applied physical and empirical models to analyze the optical characteristics of the urban forest SO2 purification rate and other factors. A coupled model (PROSAIL + AO + DELM) was constructed to simulate urban forests canopy SO2 purification ability and was then applied to remote sensing inversion. These results indicated that the Syringa oblate Lindl. (S. oblate) and Ulmus pumila “Jinye” (U. pumila) had moderate SO2 purification capacities, whereas that of Prunus cerasifera var. atropurpurea Jack. (P. cerasifera) were low. The SO2 purification rate was highly sensitive in the green, red, and red-edge spectral ranges. In the SO2 purification rate estimation model constructed by the subset (Corresponding to Sentinel-2 band), NDI, DI, and RVI indices, the PROSAIL + AO + DELM model had the best performance, with R2, root mean square error (RMSE), and relative percent deviation (RPD) of 0.73, 0.056, 1.61, and 0.68, 0.096, and 1.06 for T2 (low concentration) and T3 (high concentration) treatments, respectively. The PROSAIL + AO + DELM model was extended to multispectral images (Sentinel-2), where the results of the NDI model inversion were the closest to those of field monitoring. These results indicate that the urban forest SO2 purification rate model constructed in this study has the potential to be applied on a large scale. This study addresses the research gap regarding rapid, non-destructive, and low-input evaluation of plant purification capacity and achieves the non-destructive detection of plant purification capacity from point to point and from static to dynamic. This enables efficient, non-destructive, and cost-effective evaluation of air purification levels, and provides a basis for predicting air purification in large regions in later stages.

Maximum elastic scheduling of virtual machines in general graph cloud data center networks

ABSTRACT In this research, we pioneer a novel method to evaluate the maximum admissible load (MAL) for virtual machines (VMs) in physical machines (PMs) in data centre networks (DCNs), without restricting DCN topologies. This unique approach simplifies the issue into a single-source, multiple-sink maximum flow problem. It also resolves the maximum elastic scheduling problem by determining the optimal load for consistent growth without reassigning tasks. An effective strategy for these challenges is introduced and validated through extensive simulations.

A Dynamic Workflow Scheduling Method based on MCDM Optimization that Manages Priority Tasks for Fault Tolerance

Because it offers efficient on-demand service delivery over the internet, cloud computing has become more and more popular. An architectural model for cloud computing energy management is provided by the suggested Ant Lion algorithm. Virtual Machines (VMs) in cloud systems are assigned to hosts based not so much on their overall and long-term use, but rather on their immediate resource consumption, including RAM availability. The placement and scheduling processes are frequently computationally demanding and have the potential to affect the performance of deployed virtual machines. In this research work, we offer a strategy that considers the historical resource use of virtual machines (VMs) over time while scheduling them in the cloud. Our goal is to use the Ant lion approach to schedule virtual machines (VMs) in a way that maximizes performance by evaluating the utilization levels of prior VMs. The goal is to reduce the degradation of performance brought about by Cloud administration tasks like as virtual machine deployment, which might impact systems that have already been installed. Furthermore, congested virtual machines (VMs) sometimes take up resources from nearby VMs, increasing the VMs' actual CPU use. Our results show that by learning and adjusting to system behavior over time, our strategy outperforms conventional instant-based physical machine selection. We offer the idea of scheduling virtual machines (VMs) using resource monitoring data from past VM resource use. By using the Ant lion classifier, four fewer physical machines are needed.

Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration.

Design and implementation of a digital twin for a stone-cutting machine: a case study

The use of digital twins in the development and management of stone-cutting devices presents real challenges in the field of industrial stone manufacturing. Digital twins help manufacturers achieve increased precision, efficiency, and cost-effectiveness, ultimately transforming the landscape of stone manufacturing operations. Such digital twins enable real-time monitoring, predictive maintenance, and virtual simulations. This paper explores two options for connecting a physical stone-cutting machine in a company to its digital twin. The first option involves using Industrial Internet of Things (IIoT) devices and a Message Queuing Telemetry Transport (MQTT) broker to enable real-time data exchange. This approach provides agile and efficient communication between the physical machine and its digital twin. The second option employs conventional automation devices and a VPN connection to ensure data security during transmission. The two options were evaluated by considering criteria such as communication speed, security, scalability, ease of implementation, cost, complexity, and technical requirements. The results of the comparative analysis provide valuable insights for the implementation of the digital twin of the stone-cutting machine, allowing the selection of the most suitable option based on these considerations.

An Efficient Hybrid Optimizer for Resource Reuse in a Cloud Environment

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre’s enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%.

A Dual-Objective Approach for Allocation of Virtual Machine with improved Job Scheduling in Cloud Computing

In Cloud Computing (CC) environment, requests of user are maintained via workloads that are allocated to Virtual Machines (VMs) using scheduling techniques which primarily focus on reducing the time for processing by generating efficient schedules of smaller lengths. The efficient processing of requests also needs larger usage of resources that incurs higher overhead in the form of utilization of energy and optimization of cost utilized by Physical Machines (PMs). Assignment of VMs optimally in the environment of CC for jobs submitted by users is a challenge. In order to obtain better solution involving scheduling of jobs to VMs, considering two parameters utilization of energy and cost, we present a dual-objective approach for VM allocation with improved scheduling of jobs in CC environment. The proposed work aimed to build a dual-objective scheduling model for improved job scheduling, focusing on minimization of cost and utilization of energy at a time. For evaluating performance of dual-objective approach, we utilized two types of benchmark datasets and compared with existing approaches such as Whale, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Metaheuristic Dynamic VM Allocation (MDVMA) techniques. The results obtained from simulation demonstrated that dual-objective approach performs better in the form of minimization of utilization of energy and cost

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

Application of Digital Twin in Structural Health Monitoring of Civil Structures: A Systematic Literature Review Based on PRISMA

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and longevity of structures. Digital twin technology has emerged as a promising tool to improve the accuracy and efficiency of SHM systems. This paper presents a systematic literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the applications of digital twin for SHM of civil structures. And this systematic literature review examines the current trends and developments in the application of digital twin technology in SHM systems for structures. The review identified 45 relevant articles published between 2010 and 2023 that covered various applications of digital twin in SHM systems, including buildings, bridges, wind turbines, trains, and gas turbines. Key themes identified from the literature review included the integration of physical models and machine learning algo-rithms, the use of distributed sensor networks, and the importance of real-time data analysis for effective SHM. The findings suggest that digital twin technology has the potential to significantly improve the accuracy and efficiency of SHM systems, but further research is needed to develop more advanced and integrated Digital Twin-Based SHM systems for different types of civil structures.

Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications

Abstract The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of PEML methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate the application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different “genres” of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities.

Data-Driven Research on Energy-Efficient Retrofit and Multi-Objective Optimization of Urban Building Clusters

Urban energy-efficient retrofit is an important path to reduce energy consumption and carbon emission. However, balancing environmental and economic benefits and making choices among numerous retrofit packages is challenging for decision-makers. This study proposes a data-driven framework that integrates physical UBEM and machine learning model to evaluate the energy retrofit performance of urban building clusters and assists decision-makers in rapidly selecting the optimal energy-efficient retrofit packages through NSGA-II. The feasibility of the proposed framework is validated using the building clusters of Sipailou campus in Southeast University as a case study and identified 25, 19, and 13 optimal retrofit packages for office, public and education building clusters that minimizes the total energy consumption and carbon emissions while maximizing economic benefits from 343 retrofit packages of each cluster in a 30-year retrofit period.

Molecular machines working at interfaces: physics, chemistry, evolution and nanoarchitectonics.

As a post-nanotechnology concept, nanoarchitectonics combines nanotechnology with advanced materials science. Molecular machines made by assembling molecular units and their organizational bodies are also products of nanoarchitectonics. They can be regarded as the smallest functional materials. Originally, studies on molecular machines analyzed the average properties of objects dispersed in solution by spectroscopic methods. Researchers' playgrounds partially shifted to solid interfaces, because high-resolution observation of molecular machines is usually done on solid interfaces under high vacuum and cryogenic conditions. Additionally, to ensure the practical applicability of molecular machines, operation under ambient conditions is necessary. The latter conditions are met in dynamic interfacial environments such as the surface of water at room temperature. According to these backgrounds, this review summarizes the trends of molecular machines that continue to evolve under the concept of nanoarchitectonics in interfacial environments. Some recent examples of molecular machines in solution are briefly introduced first, which is followed by an overview of studies of molecular machines and similar supramolecular structures in various interfacial environments. The interfacial environments are classified into (i) solid interfaces, (ii) liquid interfaces, and (iii) various material and biological interfaces. Molecular machines are expanding their activities from the static environment of a solid interface to the more dynamic environment of a liquid interface. Molecular machines change their field of activity while maintaining their basic functions and induce the accumulation of individual molecular machines into macroscopic physical properties molecular machines through macroscopic mechanical motions can be employed to control molecular machines. Moreover, research on molecular machines is not limited to solid and liquid interfaces; interfaces with living organisms are also crucial.