Real Case Experiments Research Articles

Last-train timetable synchronization for service compatibility maximization in urban rail transit networks with arrival uncertainties

The last train services of urban rail transit offer all passengers the final chance to reach their destinations. In the context of multimodal transport, considering the random delayed arrivals of late-night vehicles of other transport modes at urban hub stations and their adverse effects on multimodal passengers transferring to urban rail transit, a probabilistic scenario set is established to determine the arrival uncertainties of multimodal passengers. In each scenario, the timetable synchronization problem is formulated as a mixed-integer nonlinear programming model that requires a performance trade-off between destination reachability and the remaining path distance of both non-multimodal and multimodal passengers, integrally termed last train service compatibility. Through linearization techniques, the model is transformed into an equivalent mixed-integer linear programming form, which can be efficiently solved using the Gurobi solver to obtain the optimal last train timetable for each scenario and form an alternative scheme set. An improved probabilistic scenario set-based regret value theory is then developed, in which a novel regret value calculation method is proposed. The scheme with the minimum total weighted opportunity loss in all scenarios is selected as the optimal robust. Real case experiments based on the Chengdu–Chongqing high-speed railway line and Chengdu metro network are conducted to test the performance of our model. The results show that compared with the original timetable, the optimized timetable reduces the number of unreachable passengers by 34.29% and the sum of the average remaining path distance of non-multimodal and multimodal passengers by 57.43% with the help of path planning for all passengers. The proposed approach is proven not only to balance the demands of both reachable and unreachable passengers, but also to significantly improve the robustness of the last train timetable to arrival uncertainties of multimodal passengers and reduce their service inequity.

Convolutional preprocessing Transformer-based fault diagnosis for rectifier-filter circuits in nuclear power plants

Rectifier-filter circuit, as a vital electronic component in nuclear power plants (NPPs), serves the role of converting alternating current into smooth direct current. Affected by intense radiation environmental factors and variable operating profiles, the rectifier-filter circuit is subject to degradation and soft failures, namely, a derated circuit performance but not yet to failure. It is urgent to diagnose the soft failures of the rectifier-filter circuit, so as to ensure the safe operations of NPPs. In this article, an ensemble empirical mode decomposition (EEMD) algorithm is first employed to decompose the three-phase current signals and electrical signals of electronic components. The decomposed signals are fed into a convolutional preprocessing Transformer (CPT) to deeply extract the fault features of the rectifier-filter circuit. Specifically, the multi-directional, multi-scale, and extremely sensitive long-range time-dependent features in the EEMD feature data are extracted by the multi-head attention mechanism of the Transformer. A deep Softmax classifier is, then, devised to reduce the feature dimensionality and identify the soft fault modes of the rectifier-filter circuit. The fault simulation of the rectifier-filter circuit is conducted, and a real case experiment and multiple comparative studies are conducted to validate the effectiveness and diagnosis accuracy of the CPT-based method.

A Fault Diagnosis Method for Rectifier-Filter Circuit Integrating EEMD Algorithm and Transformer Network

Rectifier-filter circuit, as a critical component of the drive circuit in instrumentation and control systems of nuclear power plants, can convert the 50 Hz AC into the smooth DC. Thus, it plays a vital role in the power control of reactors. However, the weak waveform anomalies of soft faults in the rectifier-filter circuit make fault feature extraction difficult. Therefore, in this article, an ensemble empirical modal decomposition (EEMD) algorithm is employed to decompose the signal mode components in the monitor data of the rectifier-filter circuit. The weak waveform anomalies are indirectly enhanced by the IMF and residual components. Subsequently, the Transformer network is utilized to construct the feature extractor. With the advantage of multi-head attention (MHA) mechanism in the Transformer network, the multi-directional, multi-scale, and highly sensitive long-range time-dependent features in the EEMD feature data are extracted. Then, a deep Softmax classifier is adopted to reduce the dimensionality and diagnose the soft faults of the rectifier-filter circuit. Finally, a fault simulation model of the rectifier-filter circuit is constructed and the condition monitor data are collected. The effectiveness and diagnosis accuracy of the proposed method are verified by a real case experiment and some comparative methods.

Lithium exploration targeting through robust variable selection and deep anomaly detection: An integrated application of sparse principal component analysis and stacked autoencoders

Lithium is a strategic metal for high-technology industries that plays a vital role in realizing electromobility and effective energy storage for smartphones and electric/hybrid vehicles, in addition to being important in accumulating energy from renewable sources. Motivated by this, the development of state-of-the-art workflows is imperative to make Li exploration more efficient and guarantee a sustainable supply to the demanding markets in the future. In this paper, a computational protocol was described to address two critical challenges raised in the Li exploration: (i) selection of mineralization-related pathfinder variables and (ii) recognition of multi-element geochemical anomalies related to ore-forming processes. Robust sparse principal component analysis (SPCA) was employed to reduce active (non-zero) loading coefficients and straightforward selection of Li pathfinder variables. The selected variables were then fed into the training network of stacked autoencoders (SAE) to learn deep representations of multivariate input signals and quantify reconstruction errors linked to Li-vectoring geochemical anomalies. As a representative example, the proposed workflow, i.e., SPCA + SAE, was implemented in the R programming language and applied to a real-case experiment with stream sediment geochemical data pertaining to the Moalleman district, NE Iran. Moreover, compositional robust principal component analysis (RPCA) and Mahalanobis distance (MD) technique were adopted to constitute two comparative models, RPCA + SAE and SPCA + MD, as benchmarks to judge the competence of the SPCA + SAE model in variable selection and anomaly detection, respectively. A performance appraisal by success-rate curves and relevant area under the curves (AUCs) indicated that the SPCA + SAE model brings, by calculating the highest AUC, spatial patterns that are more promising for vectoring towards Li-mineralized grounds. Moreover, AUCSPCA+MD was measured to be greater than AUCRPCA+SAE, suggesting robust variable selection is a more important paradigm than deep anomaly detection for Li exploration targeting. Student's t–statistic method was eventually applied to the anomaly map from the SPCA + SAE model to define relevant thresholds for narrowing down prospective areas for the next round of Li exploration within the study area.

Internet of Things Positioning Technology Based Intelligent Delivery System

The fast growing of e-commerce makes the express delivery an important component of transportation system. Absent delivery as one of the main reasons of failed delivery, brings serious waste of resources every year. Although with the development of Internet of Things technology, positioning system of recipients' mobile devices can provide location information to help delivery routing optimization solving absent delivery, the lack of security makes it not practical. This paper proposes an IoT positioning technology based intelligent delivery system by introducing blockchain system and location information encryption, which overcomes three fatal problems of traditional IoT positioning technology based system: different party shares all information, location information interaction is too exposed and the system is unattractive to recipients. A set of analysis with real case experiment about efficiency improvement, incentive effect and security are conducted to validate the robustness of the system. Compared with previous research, the proposed system not only has high accuracy by utilizing real-time location information instead of unreliable prediction results, but also possesses high security.

SPATIAL MATHEMATICAL MODELING OF STATIC COMPOST PILES WITH HEAT RECOVERY

Composting experiments with heat recovery reveal spatial non-uniformity in parameters such as temperature, oxygen concentration and substrate degradation. In order to recover heat from static compost piles via integrated heat exchanger there is the need to investigate the temperature distribution for placing the heat exchangers and the interaction between heat recovery, substrate degradation and oxygen concentration to ensure quality of composting process. This study introduces a spatial model to predict the variation in controlling parameters such as temperature, oxygen concentration, substrate degradation and airflow patterns in static compost piles with heat recovery using Finite element method (FEM) in COMSOL Multiphysics ® Version 5.3. The developed two-dimensional axisymmetric numerical model considers the compaction effects and is validated to real case pilot-scale compost pile experiments with passive aeration. Strong matching with the real case experiment was achieved. The spatial model demonstrated that the compaction effect is extremely important for realistic modeling because it affects airflow, temperature distribution, oxygen consumption and substrate degradation in a compost pile. Heat recovery did not disrupt the composting process. Case studies revealed strong influence of convective heat loss through the edges and a 10 % improvement of heat recovery rate with ground insulation. The simulation indicates that an optimized placing of heat recovery pipes could increase the average heat extraction by 10-40 %.

Steam++ An Extensible End-to-end Framework for Developing IoT Data Processing Applications in the Fog

IoT applications usually rely on cloud computing services to perform data analysis such as filtering, aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT needs to deal with unique constraints. Besides the hostile environment such as vibration and electricmagnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions. In this context, we present STEAM++, a lightweight and extensible framework for real-time data stream processing and decision-making in the network edge, targeting hardware-limited devices, besides proposing a micro-benchmark methodology for assessing embedded IoT applications. In real-case experiments in a semiconductor industry, we processed an entire data flow, from values sensing, processing and analysing data, detecting relevant events, and finally, publishing results to a dashboard. On average, the application consumed less than 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets per second and reducing the output data size to 14% of the input raw data size when notifying events.

Enhancing the e-learning system based on a novel tasks' classification load-balancing algorithm.

In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students.

Performance Comparisons on Parallel Optimization of Atmospheric and Ocean Numerical Circulation Models Using KISTI Supercomputer Nurion System

The characteristics of the 5th Supercomputer Nurion Knights Landing (KNL) system of the Korea Institute of Science and Technology Information (KISTI) were analyzed by developing ultra-high resolution atmospheric and ocean numerical circulation models. These models include the Weather Research and Forecasting System (WRF), Regional Ocean Modeling System (ROMS), and Unstructured Grid Finite Volume Community Ocean Model (FVCOM). Ideal and real-case experiments were simulated for each model according to the number of parallelized cores used for comparing performances. Identical experiments were performed on a general multicore system (Skylake and a general cluster system) for a performance comparison with the Nurion KNL system. Although the KNL system has more than twice as many cores per node as the Skylake system, the KNL system demonstrated 1/3 of the performance rate of the Skylake system. However, the performance rate of the Nurion KNL system was approximately 43% for all experiments. Reducing the number of cores per node in the KNL system by half (36 cores) is the most efficient method when the total number of cores is less than 256 cores, while it is more economical to use all cores when using more than 256 cores. In all experiments, the performance was continuously improved even for a maximum core experiment (1024 cores), thereby indicating that the KNL system can effectively simulate ultra-high resolution numerical circulation models.

Safety of old centrifuges in ATEX environment, how can you insure safe working conditions?

Process safety is one of the oldest priorities in process industry. With time, the ignition source mechanisms are more and more understood and the current standards are much more accurate than their first versions. Due to this evolution, some characteristics that were allowed in the past may not be tolerable any more. Besides due to the ageing of the devices, it can happen that some properties do not comply with the last versions of the concerned standards. In that case, the end user has few choices and usually end up changing its device.This presentation describes the study of old centrifuges (more than 30 years old) whose some characteristics appeared to be out of the ranges defined by the current standards. In that case, the centrifuges were supposed to be coated with a dissipative coating but some showed values of resistances to the ground several orders of magnitude higher than the values defined in the IEC 60079-32-1: 2013 (IEC, 2013). As resistance to the ground is a key parameter to avoid spark discharges, the user should change the centrifuges or recoat them with a proper coating. However, this standard describes that the threshold values for resistance were assessed to avoid that the electric potential du to static electricity build up exceeds 100 V.The basket of the centrifuge was then modelled through an RC circuit and based on measured data on the centrifuges and on the specifications of the handled products, the voltage across this circuit was defined as a function of time. It was then possible to assess the required waiting time to let the voltage decrease below 100V.The model was then tested against a real case experiment using the worse centrifuge. The results of the test were predicted by the model.

Detailed modelling of biomass steam gasification in a dual fluidized bed gasifier with temperature variation

The modelling of biomass gasification enables the optimization of the process designs, but it is a challenge due to its high complexity. Here a model for prediction of the performance of a 100-kW dual bed fluidized biomass gasifier is derived and implemented in the ASPEN plus environment. Detailed pyrolysis modelling is properly addressed, and this is believed to be a key factor of this approach and enables more accurate results. The proposed model and its basic assumptions were extensively validated on a range of operating temperature by conducting experiments using softwood pellets as fuel and fresh olivine sand as bed material. The impact of the gasifier temperature variation on the final product gas composition is measured in the experiments and used to tune the model to have a better insight on the pyrolysis process, the char heterogeneous reactions as well as the deviation from equilibrium of the water gas-shift reaction. After the assessment phase, the model was applied to to the simulation of a real case experiments and measured gas yields. The results can be considered appropriate and the difference between prediction and measurement of H2, CO and CO2 are lower than 10%, while CH4/C2H4 show values that are slightly higher than 10%.

Immune Evolution Particle Filter for Soil Moisture Data Assimilation

Data assimilation (DA) has been widely used in land surface models (LSM) to improve model state estimates. Among various DA methods, the particle filter (PF) with Markov chain Monte Carlo (MCMC) has become increasingly popular for estimating the states of the nonlinear and non-Gaussian LSMs. However, the standard PF always suffers from the particle impoverishment problem, characterized by loss of particle diversity. To solve this problem, an immune evolution particle filter with MCMC simulation inspired by the biological immune system, entitled IEPFM, is proposed for DA in this paper. The merit of this approach is in imitating the antibody diversity preservation mechanism to further improve particle diversity, thus increasing the accuracy of estimates. Furthermore, the immune memory function refers to promise particle evolution process towards optimal estimates. Effectiveness of the proposed approach is demonstrated by the numerical simulation experiment using a highly nonlinear atmospheric model. Finally, IEPFM is applied to a soil moisture (SM) assimilation experiment, which assimilates in situ observations into the Variable Infiltration Capacity (VIC) model to estimate SM in the MaQu network region of the Tibetan Plateau. Both synthetic and real case experiments demonstrate that IEPFM mitigates particle impoverishment and provides more accurate assimilation results compared with other popular DA algorithms.

Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

Data-driven prognostics can be described in two sequential steps: a training stage, in which, the data-driven model is constructed based on observations; and a prediction stage, in which, the model is used to compute the end of life and remaining useful life of systems. Often, these predictions are noisy and difficult to integrate. A technique well known for its integrative and robustness abilities is the Kalman filter. In this paper we study the applicability of the Kalman filter to filter the estimates of remaining useful life. Using field data from an aircraft bleed valve we conduct a number of real case experiments investigating the performance of the Kalman filter on five data-driven prognostics approaches: generalized linear models, neural networks, k-nearest neighbors, random forests and support vector machines. The results suggest that Kalman-based models are better in precision and convergence. It was also found that the Kalman filtering technique can improve the accuracy and the bias of the original regression models near the equipment end of life. Here, the approach with the best overall improvement was the nearest neighbors, which suggests that Kalman filters may work the best for instance-based methods.

Correcting circulation biases in a lower-resolution global general circulation model with data assimilation

In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz ’96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents.

A Research on an Effective Method for Embedded Software Testing

This paper discusses and compares the current development of the methods and techniques of embedded software testing. A compromised and practical embedded software testing method, which is between intervention and non-intervention, is proposed and the flow process is provided as well. The method has been verified by a real-case experiment and the result is satisfactory. Key words: Embedded software; testing method

The influence of erroneous background, beam-blocking and microphysical non-linearity on the application of a four-dimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts

A series of observation system simulation experiments (OSSEs) and a real case study are conducted to investigate the application of the Doppler radar data assimilation technique for numerical model quantitative precipitation forecasts (QPFs). A four-dimensional variational Doppler radar analysis system (VDRAS) is adopted for all experiments. The first set of OSSEs demonstrates that when the background field contains the imperfect information predicted from a mesoscale model, the incorrect convective-scale perturbations in the background can result in spurious scattered precipitation. However, a smoothing procedure can be used to remove the fine structures from the primitive model output in order to avoid this over-prediction. Results from the second set of OSSEs indicate that the lack of low-elevation data owing to radar scan and/or beam blockage could significantly alter the retrieved low-level thermal and dynamical structures when a different number of data assimilation cycles is applied. These impacts could lower the rainfall forecast capability of the model. The third set of OSSEs shows that, when the rainwater is assimilated over a long assimilation window, the non-linearity embedded in the microphysical process could lead the minimization algorithm in a wrong direction, causing a further degradation of the rainfall prediction. However, using multiple short assimilation cycles produces better minimization and forecast results than those obtained with a single long cycle. A real case experiment based on data collected during Intensive Operation Period (IOP) #8 of the 2008 Southwest Monsoon Experiment (SoWMEX) is conducted to provide a verification of the conclusions obtained from OSSEs under a realistic framework.

Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications

Pose estimation methods in robotics applications frequently suffer from inaccuracy due to a lack of correspondence and real-time constraints, and instability from a wide range of viewpoints, etc. In this paper, we present a novel approach for estimating the poses of all the cameras in a multi-camera system in which each camera is placed rigidly using only a few coplanar points simultaneously. Instead of solving the orientation and translation for the multi-camera system from the overlapping point correspondences among all the cameras directly, we employ homography, which can map image points with 3D coplanar-referenced points. In our method, we first establish the corresponding relations between each camera by their Euclidean geometries and optimize the homographies of the cameras; then, we solve the orientation and translation for the optimal homographies. The results from simulations and real case experiments show that our approach is accurate and robust for implementation in robotics applications. Finally, a practical implementation in a ping-pong robot is described in order to confirm the validity of our approach.

Pareto optimality solution of the multi-objective photogrammetric resection-intersection problem

Reconstruction of architectural structures from photographs has recently experienced intensive efforts in computer vision research. This is achieved through the solution of nonlinear least squares (NLS) problems to obtain accurate structure and motion estimates. In Photogrammetry, NLS contribute to the determination of the 3-dimensional (3D) terrain models from the images taken from photographs. The traditional NLS approach for solving the resection-intersection problem based on implicit formulation on the one hand suffers from the lack of provision by which the involved variables can be weighted. On the other hand, incorporation of explicit formulation expresses the objectives to be minimized in different forms, thus resulting in different parametric values for the estimated parameters at non-zero residuals. Sometimes, these objectives may conflict in a Pareto sense, namely, a small change in the parameters results in the increase of one objective and a decrease of the other, as is often the case in multi-objective problems. Such is often the case with error-in-all-variable (EIV) models, e.g., in the resection-intersection problem where such change in the parameters could be caused by errors in both image and reference coordinates. This study proposes the Pareto optimal approach as a possible improvement to the solution of the resection-intersection problem, where it provides simultaneous estimation of the coordinates and orientation parameters of the cameras in a two or multistation camera system on the basis of a properly weighted multi-objective function. This objective represents the weighted sum of the square of the direct explicit differences of the measured and computed ground as well as the image coordinates. The effectiveness of the proposed method is demonstrated by two camera calibration problems, where the internal and external orientation parameters are estimated on the basis of the collinearity equations, employing the data of a Manhattan-type test field as well as the data of an outdoor, real case experiment. In addition, an architectural structural reconstruction of the Merton college court in Oxford (UK) via estimation of camera matrices is also presented. Although these two problems are different, where the first case considers the error reduction of the image and spatial coordinates, while the second case considers the precision of the space coordinates, the Pareto optimality can handle both problems in a general and flexible way.

Simulation of non-local effects of convection with the hybrid mass flux convection scheme HYMACS

The hybrid mass flux convection scheme HYMACS has been proposed to simulate moist convection in non-hydrostatic numerical weather prediction models with grid sizes at which convection is partially resolved. Different to classical mass flux schemes HYMACS produces a net convective mass flux due to updrafts and downdrafts with resulting grid-scale pressure gradient forces. Thus, as a grid-scale reaction the environmental subsidence may cover several grid columns. Here, HYMACS is extended concerning the non-local effects of gust fronts and cell transport and aging in a way that convective cells can communicate also directly on the subgrid-scale without passing information only via grid-scale averages. Subgrid scale information of gust fronts from parameterized convective cells is used to initiate secondary cells by distributing a trigger bonus to neighboring grid columns. In addition, a cell age is assigned to convective cells to parameterize a temporal development in cloud top height and precipitation. This cell age is then transported on the grid-scale to simulate the advection of convective cells. The extentions of HYMACS are demonstrated by means of both idealized and real case experiments.

The development of a nonhydrostatic global spectral model

With the development of numerical weather prediction technology, the traditional global hydrostatic models used in many countries of the world for operational weather forecasting and numerical simulations of general circulation have become more and more unfit for high-impact weather prediction. To address this, it is important to invest in the development of global nonhydrostatic models. Few existing nonhydrostatic global models use consistently the grid finite difference scheme for the primitive equations of dynamical cores, which can subsequently degrade the accuracy of the calculations. A new nonhydrostatic global spectral model, which utilizes the Eulerian spectral method, is developed here from NCAR Community Atmosphere Model 3.0 (CAM3.0). Using Janjic’s hydrostatic/nonhydrostatic method, a global nonhydrostatic spectral method for the primitive equations has been formulated and developed. In order to retain the integrity of the nonhydrostatic equations, the atmospheric curvature correction and eccentricity correction are considered. In this paper, the Held-Suarez idealized test and an idealized baroclinic wave test are first carried out, which shows that the nonhydrostatic global spectral model has similar climate states to the results of many other global models for long-term idealized integration, as well as better simulation ability for short-term idealized integration. Then, a real case experiment is conducted using the new dynamical core with the full physical parameterizations of subgrid-scale physical processes. The 10-day numerical integration indicates a decrease in systematic error and a better simulation of zonal wind, temperature, and 500-hPa height.