Real System Research Articles

Simultaneously Calibration of Multi Hand–Eye Robot System Based on Graph

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multi-eyes (cameras) and multi-hands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multi-robot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

History matching for geological carbon storage using data-space inversion with spatio-temporal data parameterization

History matching based on monitoring data will enable uncertainty reduction, and thus improved aquifer management, in industrial-scale carbon storage operations. In traditional model-based data assimilation, geomodel parameters are modified to force agreement between flow simulation results and observations. In data-space inversion (DSI), history-matched quantities of interest, e.g., posterior pressure and saturation fields conditioned to observations, are inferred directly, without constructing posterior geomodels. This is accomplished efficiently using a set of O(1000) prior simulation results, data parameterization, and posterior sampling within a Bayesian setting. In this study, we develop and implement (in DSI) a deep-learning-based parameterization to represent spatio-temporal pressure and CO2 saturation fields at a set of time steps. The new parameterization uses an adversarial autoencoder (AAE) for dimension reduction and a convolutional long short-term memory (convLSTM) network to represent the spatial distribution and temporal evolution of the pressure and saturation fields. This parameterization is used with an ensemble smoother with multiple data assimilation (ESMDA) in the DSI framework to enable posterior predictions. A realistic 3D system characterized by prior geological realizations drawn from a range of geological scenarios is considered. A local grid refinement procedure is introduced to estimate the error covariance term that appears in the history matching formulation. Extensive history matching results are presented for various quantities, for multiple synthetic true models, using the new DSI framework. Substantial uncertainty reduction in posterior pressure and saturation fields is achieved in all cases. The framework is also applied to efficiently provide posterior predictions for a range of error covariance specifications. Such an assessment would be very expensive using a traditional model-based approach.

The convergence of Digital Twins and Distributed Ledger Technologies: A systematic literature review and an architectural proposal

In recent years, the emerging Digital Twin (DT) technology is playing a key role in fostering the transition towards the Industry 4.0. DTs, representing virtual replicas of physical objects, products or processes established thanks to a bidirectional continuous flow of information between the physical and the virtual world, are currently adopted in multiple domains such as manufacturing, aerospace, automotive, energy, construction, smart cities and smart mobility, etc.. DTs live together with the physical system they replicate, receiving the same data and often triggering specific control actions that directly impact on the real system status. When dealing with DTs of complex Cyber-Physical Systems (CPSs), the data sources may be heterogeneous and untrustworthy, which requires the DT to be able to address security issues related to data in transit from/to the physical twin and data at rest. A possible way to address these data security issues consists in adopting Distributed Ledger Technologies (DLTs) which provide several security guarantees on data through cryptographic hashing techniques. In this work, we present a three-fold contribution: (i) we discuss the results of a Systematic Literature Review (SLR) on the state-of-the-art of the research related to the integration between DLT and Digital Twins, aimed at clarifying relevant aspects such as, among others, what is the favourite DLT choice in existing proposals or what is the type of DT-related information to store on-chain; (ii) leveraging the SLR results and other related research, we propose an architectural framework for the integration of DT and DLTs (more specifically, blockchains); (iii) we validate the proposed architecture by means of two proof-of-concept implementations leveraging different technological stacks and taking into account two different operational scenarios. Finally, we conduct a requirements coverage analysis and compare the PoCs through a coverage matrix.

Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.

ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations

IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions.MethodsThis work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH.ResultsModel simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms.DiscussionThe ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.

Toward a Full Configurational Accuracy Calculation of an Arbitrary Molecule via Fragment Embedding and a Stochastic Solver.

We demonstrate the feasibility of using a stochastic solver, full configuration interaction quantum Monte Carlo with the initiator approximation (i-FCIQMC), to converge fragment embedding calculations, namely bootstrap embedding (BE). We first propose and test a general protocol for converging BE-i-FCIQMC calculations and then suggest how the quality of the calculation compares against that of deterministic BE-FCI using different numbers of walkers. We then demonstrate that BE-i-FCIQMC can perform as well as BE-FCI in the large walker limit and how different factors, including the size of the Hilbert space of the fragments, the number of walkers, and the nature of the chemical system, affect the achievable matching error. We finally perform BE-FCI calculations in realistic systems like benzene and cyclohexane using a double-ζ basis set. This work demonstrates the potential of performing FCI quality calculations in realistic systems using BE.

Learning-assisted optimization for transmission switching

AbstractThe design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

LFC performance advancement of two-area RES penetrated multi-source power system utilizing CES and a new CFOTID controller

This study addresses the challenges of load fluctuations and the assimilation of renewable energy sources (RES) causing undesirable frequency deviations. To avoid widespread deviations in frequency, an intelligent, competent and robust load frequency control (LFC) scheme and energy storage system are obligatory for disturbance rejection in RES penetrated realistic interconnected multi-source power system (IMSPS). Hence, a new cascade fractional order tilt integral derivative (CFOTID) controller is suggested with capacitive energy storage (CES) for a pragmatic RES based thermal-hydro-gas IMSPS having governor dead-band and generation rate constraint nonlinearities. The salp swarm algorithm (SSA) is utilized successfully for fine-tuning of the controller parameters. Scrutiny of the outcome results reveals the sovereignty of the CFOTID controller over several existing well proven controllers and SSA-based FOTID. It is observed that power generation due to CES further advance the system results in terms of minimized overshoot, undershoot, and settling time. Lastly, a sensitivity analysis is conducted to confirm the robustness of the controller.

Orbit while in service

In various real-life queueing systems, part of the service can be rendered without involvement or presence of the customers themselves. In those queues, customers whose service order is still in process may leave the service station, go to ‘orbit’ for a random length of time, and then return to find out if their order has been completed. Common examples are car’s annual maintenance works, food ordering, etc. In this paper, a thorough analysis of a single-server ‘orbit while in service’ queueing model with general service time is presented. Assuming an Exponentially distributed orbit time, we derive general formulae for the distributions of (i) a customer’s total residence time in the system; (ii) a customer’s net actual residence time in the system during service (not including orbit time); (iii) the time an orbiting customer is late to return, i.e., remains in orbit after his/her service has been completed; and (iv) the total number of customers in the system. Considering the family of Gamma-distributed service times (spanning the range of distributions between the Exponential and the Deterministic), as well as the Uniform distribution, we further derive explicit formulae for the distributions of the above variables. Under linear cost assumptions, the optimal mean orbit time is numerically calculated for each of the above service-time distributions. Figures depicting the behavior of the measures as functions of the parameters are presented.

Wave optical propagation in realistic lens systems through multi-slice decomposition with phase compensation (MSDP)

Wave optical propagation in realistic lens systems through multi-slice decomposition with phase compensation (MSDP)

Temperature change-induced linear and nonlinear axial responses of internal replacement pipe (IRP) systems for pipeline rehabilitation incorporating the effects of soil friction

The internal replacement pipe (IRP) is a developing trenchless system utilised for restoring buried steel and cast-iron legacy pipelines. It is crucial to ensure that this advanced system is appropriately designed to reinstate the functionality of damaged pipelines effectively and safely. The present paper investigates the structural response of IRP systems used in repairing pipelines with circumferential discontinuities subjected to seasonal temperature changes. Analytical and numerical approaches verified via experimental data and available closed-form solutions were implemented to analyse a total of 180 linear and nonlinear finite element (FE) simulations. A set of analytical expressions was developed to describe the loading and induced responses of the system. Based on an extensive FE parametric study, five modification factors were derived and applied to developed analytical expressions to characterise the structural response incorporating the effects of soil friction. Results showed that there is a major difference between the results of linear and nonlinear analyses highlighting the importance of including the material nonlinearities in the FE analysis. A significant difference was observed between the discontinuity openings with and without the consideration of soil friction implying that appropriate inclusion of soil friction in the FE model is crucial to get realistic system responses subjected to temperature change. Although the application of IRP holds immense promise as a trenchless solution for rehabilitating legacy pipelines, the lack of established design procedures and standards for these technologies has restricted their application in gas pipelines. Results obtained from numerical and analytical models developed in the present research will provide valuable insights for the design and development of safe and efficient IRP systems urgently needed in the pipeline industry.

Design of Real Time Intelligent System for Women Safety

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women.

Experimental testing of the Prandtl–Tomlinson model: Molecular origin of rotational friction

Structural superlubricity, one of the most important concepts in modern tribology, has attracted lots of interest in both fundamental research and practical applications. However, the underlying model, known as the Prandtl–Tomlinson (PT) model, is oversimplified and not for real processes, despite its prevalence in frictional and structural lubricant studies. Here, with a realistic system, cholesteric liquid crystals confined between two atomically smooth surfaces, we measure both the surface torque during rotational friction and the molecular rotation from the commensurate to incommensurate configuration at the onset of structural lubricity. Furthermore, by changing the surface potential or the strain, the Aubry transition is confirmed. The results agree well with the description by a quasi-one-dimensional version of the PT model and provide molecular evidence for rupture nucleation during static friction. Our study bridges the gap between theories and experiments and reinforces the connection between friction and fracture.

A tri-variate moment projection method for multi-dimensional particle population balance dynamics

This study develops a tri-variate moment projection method (TVMPM) for solving particle dynamics problems governed by the population balance equations involving any three internal coordinates of particles, such as volume, surface area, component mass, etc. By leveraging the concept of conditional moments, the multi-dimensional moments are expressed as the product of marginal moments and several conditional moments, facilitating the solution of high-dimensional moments. To obtain abscissas and conditional weights in different dimensions for reconstructing moments, a three-dimensional (3-D) adaptive Blumstein-Wheeler algorithm is proposed and implemented, which can also improve the stability in the third dimension, where ill-conditioning issues are more likely to arise. More importantly, by fixing the smallest particle abscissas, TVMPM can directly track the weight of the smallest particles, thereby closing the shrinkage and fragmentation terms. An analysis of the sources of errors arising from this approach is also presented. To mitigate potential interference from external factors, constant kernels for inception, growth, coagulation, shrinkage and fragmentation are employed to validate the effectiveness of TVMPM. The resulting moments from TVMPM are computed for both individual and combined processes, and subsequently compared with the moments derived from the direct simulation algorithm (DSA). The results demonstrate that TVMPM maintains a high level of accuracy across various numbers of quadrature nodes for particle dynamics with 3-D internal coordinates, while significantly reducing computational efforts compared to DSA. This study reveals that the developed algorithms and framework are promising for further extending MPM to higher dimensions. Moreover, owing to its accuracy and efficiency, TVMPM shows great potential for implementation in computational fluid dynamics (CFD) for particle dynamics in realistic systems.

Joint Service Caching, Resource Allocation and Task Offloading for MEC-Based Networks: A Multi-Layer Optimization Approach

To provide reliable and elastic Multi-access edge computing services, one feasible solution is to federate geographically proximate edge servers to form a logically centralized resource pool. Optimization of such systems, however, becomes challenging. In this paper, we study the problem of maximizing users' QoE in a MEC-based network, through jointly optimizing service caching, resource allocation and task offloading decisions. We formulate a mixed-integer nonlinear programming (MINLP) problem for the task and establish its NP-hardness. To tackle it efficiently, we propose a novel two-stage algorithmic solution based on approximation and decomposition theory. The proposed algorithm achieves high system performance while at the same time, ensures all constraints from different layers are satisfied. Meanwhile, the structure of the algorithm also fits the multi-layer optimizing feature, making it suitable to be implemented at different layers. In addition, we propose a distributed and online version of our mechanism with very limited information exchange between MEC servers, and further demonstrate how the cost of service switches from real MEC systems can be incorporated into our framework. We evaluate our mechanisms through simulations with both synthetic and real-world traces, and results indicate they are effective as compared to representative baseline algorithms.

Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.

Natural carbonation of portland cement with synthetic zeolite Y as a supplementary cementitious material

Risks associated with carbonation are a key limitation to greater replacement levels of ordinary portland cement (OPC) by supplementary cementitious materials (SCMs). The addition of pozzolanic SCMs in OPC alters the hydrate assemblage by forming phases like calcium-(alumina)-silicate-hydrate (C-(A)-S-H). The objective of the present study was to elucidate how such changes in hydrate assemblage influence the chemical mechanisms of carbonation in a realistic OPC system. Here, we show that synthetic zeolite Y (faujasite) is a highly reactive pozzolan in OPC that reduces the calcium content of hydration products via prompt consumption of calcium hydroxide from the evolving phase assemblage prior to CO2 exposure. Suppression of portlandite at moderate to high zeolite Y content led to a more damaging mechanism of carbonation by disrupting the formation of a passivating carbonate layer. Without this layer, carbonation depth and CO2 uptake are increased. Binders containing 12–18% zeolite Y by volume consumed all the calcium hydroxide from OPC during hydration and reduced the Ca/(Si+Al) ratio of the amorphous products to near 0.67. In these cases, higher carbonation depths were observed after exposure to ambient air with decalcification of C-(A)-S-H as the main source of CO2 buffering. Binders with either 0% or 4% zeolite Y contained calcium hydroxide in the hydrated microstructure, had higher Ca/(Si+Al) ratios, and formed a calcite-rich passivation layer that halted deep carbonation. Although the carbonated layer in the samples with 12% and 18% zeolite Y contained 70% and 76% less calcite than the OPC respectively, their higher carbonation depths resulted in total CO2 uptakes that were 12x greater than the OPC sample. Passivation layer formation in samples with calcium hydroxide explains this finding and was further supported by thermodynamic modeling. High Si/Al zeolite additives to OPC should be balanced with the calcium content for optimal carbonation resistance.

A Comparative Study of 9-Level Inverter and 15-Level Multi Inverter for a Grid Connected System

Abstract: Recently, the emergence of single phase multilevel inverter has been increased due to its advantages over traditional one. Multilevel Inverters [MLI] are the ones which can give out a stepwise output waveform with the use of power electronic switches, power diodes and some DC voltage source which might be a series/parallel connected PV cells, a renewable source or a battery. Multilevel inverters not only produce low harmonic distortion but also decrease the dv/dt stresses on the equipment Multilevel inverters is a good option as the output voltage of a Multilevel inverter is a stepped waveform which approaches a sinusoid providing lesser harmonic distortion. As the number of levels are increased the harmonic distortion reduces but at the same time the number of switching devices and the DC voltage sources required increases, thus increasing the complexity of the system design and control. Multilevel inverter proposed in this paper uses only 7 switches to provide 9 levels and 15 levels of output voltage. A symmetrical Cascaded H bridge Multi level inverter (SCHBMLI) and Asymmetrical Cascaded H bridge Multilevel Inverter (ASCHBMLI) using PD-PWM technique have been analysed in this paper for grid connection. The Asymmetric Cascade H Bridge [ASCHMLI] implemented in real time system using PIC16F877A microcontroller with RL load. This type of proposed system is used for high power applications in photovoltaic and it’s reduce overall cost as well as size of the system.

Numerical modeling of temperature-reporting nanoparticle tracer for fractured geothermal reservoir characterization

Information on the temperature distribution of subsurface reservoirs is essential for geothermal energy development. One of the promising tools to detect the reservoir temperature distribution is temperature-reporting nanoparticle tracers whose functionality has been extensively investigated in both theoretical and experimental ways in the last decade. However, most related studies were limited to simplified geometries and ignored the dynamic interplays of fluid flow, heat transfer, transport and reaction of the temperature-reporting nanoparticle tracer. The response behavior and working mechanisms of such nanotracers in a realistic three-dimensional system still have not been fully revealed through a systematic study. In this work, we develop a numerical modeling approach to simulate field implementation of these nanotracers in a fractured geothermal reservoir. This study aims to evaluate whether the injection of multiple temperature-reporting nanoparticle tracers with different thresholds can be used to estimate the temperature distribution and provide information on the thermal and geological heterogeneities. Several scenarios have been investigated for the geothermal reservoir including homogeneous and non-homogeneous cases (e.g., thermal and geological heterogeneities). Our obtained results from the nanotracer breakthrough curves show that the deviation temperatures in peak concentration values provide an upper limit of the lowest temperature and precise highest temperature for the reservoir temperature range. The deviation temperature of the peak arrival time curve accurately estimates the highest temperature along the main streamlines between the wells. The proposed analysis curves based on the nanotracer breakthrough data were visibly affected by geological heterogeneities including their conductivities and orientations as well as thermal heterogeneities in the geothermal reservoir.

AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction

Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose Auxiliary Match Tasks for enhancing Click-Through Rate (AT4CTR) prediction accuracy by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue.