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Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks.

Urban commuters and road authorities are interested in knowing how well their city can manage traffic conditions. It is challenging to make a fair comparison among global cities due to the absence of a consistent, publicly accessible and inexpensive framework. This study proposes a methodology to compare travel time and average speed in different cities. Large cities have a central core, in which many of the major activities take place. This core is called downtown or the central business district (CBD). In this study, a comparison is made by simulating the morning commute trips from origins outside to destinations inside the CBD. The proposed framework outlines how the city and the CBD borders are defined. The data are collected by an accessible location service (Google Maps Distance Matrix Application Programming Interface). The framework is conducted on 11 major cities from the five continents, namely Cairo, London, Los Angeles, Melbourne, Moscow, New York, Paris, Singapore, Sydney, Tehran and Toronto. As such Singapore and Cairo had the shortest, while London and Paris had the longest travel times. According to the distribution graphs of average speed and travel time, Asian and African cities experienced a relatively higher average speed and a lower travel time.

Software engineering (SE) research has traditionally revolved around engineering the source code. However, novel approaches that analyze software through computer vision have been increasingly adopted in SE. These approaches allow analyzing the software from a different complementary perspective other than the source code, and they are used to either complement existing source code-based methods, or to overcome their limitations. The goal of this manuscript is to survey the use of computer vision techniques in SE with the aim of assessing their potential in advancing the field of SE research. We examined an extensive body of literature from top-tier SE venues, as well as venues from closely related fields (machine learning, computer vision, and human-computer interaction). Our inclusion criteria targeted papers applying computer vision techniques that address problems related to any area of SE. We collected an initial pool of 2,716 papers, from which we obtained 66 final relevant papers covering a variety of SE areas. We analyzed what computer vision techniques have been adopted or designed, for what reasons, how they are used, what benefits they provide, and how they are evaluated. Our findings highlight that visual approaches have been adopted in a wide variety of SE tasks, predominantly for effectively tackling software analysis and testing challenges in the web and mobile domains. The results also show a rapid growth trend of the use of computer vision techniques in SE research.

Systematic design and verification of advanced control strategies for complex systems under uncertainty largely remains an open problem. Despite the promise of black-box optimization methods for automated controller tuning, they generally lack formal guarantees on the solution quality, which is especially important in the control of safety-critical systems. This letter focuses on obtaining closed-loop performance guarantees for automated controller tuning, which can be formulated as a black-box optimization problem under uncertainty. We use recent advances in non-convex scenario theory to provide a distribution-free bound on the probability of the closed-loop performance measures. To mitigate the computational complexity of the data-driven scenario optimization method, we restrict ourselves to a discrete set of candidate tuning parameters. We propose to generate these candidates using constrained Bayesian optimization run multiple times from different random seed points. We apply the proposed method for tuning an economic nonlinear model predictive controller for a semibatch reactor modeled by seven highly nonlinear differential equations.

Wide area networks for surveying applications, such as seismic acquisition, have been witnessing a significant increase in node density and area, where large amounts of data have to be transferred in real-time. While cables can meet these requirements, they account for a majority of the equipment weight, maintenance, and labor costs. A novel wireless network architecture, compliant with the IEEE 802.11ad standard, is proposed for establishing scalable, energy-efficient, and gigabit-rate backhaul across very large areas. Statistical path-loss and line-of-sight models are derived using real-world topographic data in well-known seismic regions. Additionally, a cross-layer analytical model is derived for 802.11 systems that can characterize the overall latency and power consumption under the impact of co-channel interference. On the basis of these models, a Frame Aggregation Power-Saving Backhaul (FA-PSB) scheme is proposed for near-optimal power conservation under a latency constraint, through a duty-cycled approach. A performance evaluation with respect to the survey size and data generation rate reveals that the proposed architecture and the FA-PSB scheme can support real-time acquisition in large-scale high-density scenarios while operating with minimal power consumption, thereby enhancing the lifetime of wireless seismic surveys. The FA-PSB scheme can be applied to cellular backhaul and sensor networks as well., Comment: in IEEE Transactions on Wireless Communications

Author(s): Rodrigues, Diogo; Mesbah, Ali | Abstract: Models of nonlinear dynamical systems are typically composed of unknown and known parts due to the existence of mismatch between the true nonlinear system dynamics and their models. This paper presents a multivariable control strategy for nonlinear systems that deals explicitly with the existence of incomplete models in a data-driven framework. The proposed strategy uses feedback linearization and continuous-time estimation of the unknown derivatives to achieve satisfactory closed-loop performance for fast sampling applications with simple tuning of design parameters in the absence of a fully known system model. It is shown that this strategy possesses useful steady-state properties, such as elimination of steady-state error and rejection of constant input disturbances without any integral term, and ensures exponential convergence to the setpoints. Another contribution of this paper is the development of methods for quantitative analysis of the stability and performance of the control strategy in the continuous-time case. The approach is illustrated via a simulated example of reactor control. The proposed control strategy is broadly applicable and simplifies control design significantly for a large variety of systems.

With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

PV modules are costly devices, so, their lifetime is an important parameter in the investment evaluation. The aim of this paper is to propose an earlier degradation detection that affects glass, EVA, wires etc … Where many researchers propose degradation evaluation based on scheduled eye observation, which becomes problematic for large scale PV power production, because it takes much more time and mobilizes skilled workers. This type of degradation evaluation is very expensive and must be carried out by expert workers. To automate PV panels self evaluation, the degradations models are embedded in a microcontroller as software which operates with instantaneous measured parameters. The degradation phenomena of each PV module’s element are also presented and discussed. For this purpose an Observing Degradation System (ODS) program is proposed and detailed, based on modeling of each recognized degradation. Where new parameters are introduced to improve the fault type detection. This recognition method of degradation types based on P–V characteristics and checklist is developed and successfully tested.

Accurate estimation of nontechnical losses (NTL) requires the real-time data acquisition of different smart meters to be accurately synchronized at a common time reference. However, real-time data reported by smart meters are usually not accurately synchronized. This article proposes a new quantity, to be measured by the smart meters that will address the problem of time synchronization. The proposed measurement quantity is also utilized to propose a modified coding-based algorithm to detect and correct NTL in distribution systems. It is worth mentioning that the measurement of the proposed quantity does not require any additional hardware, and commercially available smart meters can be reprogrammed to perform the new functionality. The proposed algorithm along with the proposed measurement quantity is tested and compared to a benchmark coding-based algorithm. The proposed algorithm is shown to outperform the benchmark algorithm in practical scenarios.

There has been an increasing interest in explicit and cheap-to-evaluate control policies that approximate (computationally expensive) control laws such as model predictive control (MPC). However, approximate control policies are subject to approximation errors, leading to asymptotic performance losses. The contribution of this paper is three-fold: (i) a closed-loop training scheme is presented for deep neural network approximation of economic MPC; (ii) an online adaptive correction scheme is presented to account for the performance losses induced by approximation errors; and (iii) an offline performance verification scheme is presented to ensure that the approximate control policy converges to an equilibrium point of the system. The proposed approach is illustrated using a Williams-Otto reactor problem.

The closed-loop performance of model predictive controllers (MPCs) is highly dependent on the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy, instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this work, we demonstrate a general approach for automating the tuning of MPC under uncertainty. In particular, we formulate the automated tuning problem as a constrained black-box optimization problem that can be tackled with derivative-free optimization. We rely on a constrained variant of Bayesian optimization to solve the MPC tuning problem that can directly handle noisy and expensive-to-evaluate functions. The benefits of the proposed automated tuning approach are demonstrated on a benchmark continuously stirred tank reactor case study.

Cost and weight optimization in ship construction are usually investigated in the form of a multiobjective optimization problem. So far, many studies have been carried out to achieve various types of existing optimization objectives and different tools have been developed. Most of the studies in the field of structural optimization have focused on comparing the available optimization algorithms. In this study, a rule-based tool is developed based on the Common Structural Rules (CSRs), which despite its simplicity in application, provides high capabilities in producing an optimal solution. In the developed tool, structural analysis of serviceability limit state is performed by using the relationships of CSRs. The computational tool is created by MATLAB software (Mathworks, Natick, Massachusetts), and the optimization technique is a genetic algorithm. The performance of the computational tool is evaluated by analyzing the midship section of a chemical tanker. In the optimization procedure, weight and cost are assumed to have the same importance. From the results of the developed tool, all components of the weight and cost of ship construction decreased in the optimal solution relative to the initial design.

The real-time implementation of closed-loop robust model predictive control (MPC) schemes is an important challenge for fast systems, as their solution complexity depends strongly on the system size, control policy parametrization, and prediction horizon. We look to address this problem by approximating the implicitly-defined MPC controller using deep learning. Although the resulting neural network approximation has a small memory footprint and can be efficiently computed, it does not guarantee robust constraint satisfaction or stability. We propose a novel projection-based strategy that is capable of providing a certificate of robust feasibility and input-to-state stability in real-time. We also show how this projection operator can be formulated as a parametric quadratic program that is solvable offline. The advantages of the proposed approach are demonstrated on a benchmark case study.

The lack of personal and economic attributes in emerging public transit big data (such as smart card data) is a general issue that needs to be addressed. Passengers in the public transit network are from different socioeconomic classes, and their trip attributes usually depend on their personal and economic attributes. For instance, age as a demographic attribute plays an important role in trip attributes; adolescent passengers travel to school, young professionals travel to work, and old passengers travel to medical facilities more often. Relations between the socioeconomic and trip attributes of the passengers can be examined by developing a Bayesian network that represents the relations between the attributes by directed acyclic graphs, and calculating the joint and conditional probability values in the graph. This study infers the socioeconomic attributes of the public transit passengers from the trip attributes through developing a Bayesian network. Considered socioeconomic attributes are age, gender, and income; considered trip attributes are start time and duration of the trip, stay duration, and available origin and destination land use types. First, potential structures of the Bayesian network are examined by comparing network scores and arc strength test. After learning the network’s parameters, the reasoning is done through both prediction and diagnosis in the network. Also, the most likely combinations of the socioeconomic and trip attributes are discovered. The case study for developing the Bayesian network is a Household Travel Survey dataset from Queensland, Australia, that contains both socioeconomic and trip attributes. Results clearly show how the socioeconomic attributes can be inferred from the trip attributes. Discovered probability distributions can be used to enrich the smart card datasets with the socioeconomic attributes. Moreover, the Bayesian classifier is applied to the dataset to validate the capability of the model in predicting the socioeconomic attributes. In the end, the developed network is implemented on a set of smart card records to discuss the potential applications.

In spite of its shortcomings, faster turnaround time and cost-effectiveness make the Reynolds-averaged Navier–Stokes modeling approach still a popular and widely used methodology in many industrial applications, including the automotive industries in general, but the motorsports sector in particular. Existing literature suggests that all Reynolds-averaged Navier–Stokes models generally fail to predict pressure and velocity flow fields with a reasonable accuracy, especially in the vehicle wake region. Recent numerical works suggest that, when using two-equation eddy viscosity turbulence models, improved correlation between the experiment and computational fluid dynamics is not achievable through only mesh refinements or adding additional corrective terms in the turbulence transport equations, and additional efforts are necessary for better predictions. In this backdrop, the prediction improvement strategy adopted in this paper is based on the realization that the turbulence model closure coefficients are normally specified as constants and their values are determined from either a single observation or a simple functional form is assumed and that these coefficients are constructed and/or constrained to behave correctly in extremely limiting circumstances. Subsequently, this study investigated the influence of a few selected turbulence model closure coefficients on the veracity of computational fluid dynamics predictions by analyzing simulations run with turbulence model closure coefficient values that were different from the commonly used default ones. This was done by first investigating the individual effect of each model parameters on the prediction veracity, and then a combination of model closure coefficient values was formulated in order to obtain a prediction with the best experimental correlation. This procedure was applied to four different test objects which include NACA 4412 airfoil at 12° angle of attack, the 25 and 35° slant angle Ahmed body, and a full-scale sedan type passenger vehicle. The shear-stress transport [Formula: see text] was chosen as the turbulence model because of its popularity in automotive applications. It was observed that, irrespective of the test model, the computational fluid dynamics predictions are somewhat independent of some closure coefficients, while the prediction showed strong dependencies on certain model constant values, especially those in the dissipation equation. The present study demonstrated that, by using a modified set of closure coefficient values, very well correlated computational fluid dynamics predictions were possible. The findings from this study simply reiterate a 20-year-old conclusion by Stephen Pope that the closure coefficients for a model are not static and universal but instead are dynamic and must be calibrated for specific flow situations.

With the increasing demand and range of urban mobility, public transport systems are playing an increasingly important role in providing people with efficient and affordable access to education, employment, markets, and other key services. Public transport planners can predict passenger loads and levels of service by using transit assignment models. Therefore, having a consistent transit assignment model has become an important issue. In addition, estimating the path choice factors, considering that network cost attributes might be non-deterministic, is complicated. In view of these, this paper adapted a transit assignment model, based on the Bayesian Model proposed in the authors’ recent study, to validate the results of the model for multimodal transit networks under uncertainty and random variations of path choice parameters. In that model, path choices are represented by a multinomial logit model, and its coefficients are estimated via a Markov Chain Monte Carlo (MCMC) method. In this paper, the path choice parameters of the model are first calibrated by the individual travel history data in the AM peak period, and then are used to predict the passenger flow on different paths in the PM peak period. Second, based on the percentage error between each posterior estimate and the actual observation from AFC data on the segment level, the path segments are categorized into four groups. Then, by defining the segment attributes, the strengths and weaknesses of the model are analysed for the four groups.

One of the major issues concerning the MMC is the control of circulating currents. Through the elimination of even-order harmonic currents superimposed on the DC component, the MMC stress and losses can be reduced which leads to an increase of its lifetime. This paper proposes a new circulating current controller based on reduced order generalized integrator implemented in the stationary $$ \alpha \beta $$ reference frame; intended to eliminate the second-order harmonic current for both three-phase and single-phase systems without a need for any measurement or estimation of the DC component contained in the circulating currents. Also, the whole digital architecture of the proposed controller is designed using hardware description language and implemented on Digilent Atlys—Xilinx Spartan-6 FPGA board. The validity and effectiveness of the proposed control strategy have been verified for both continuous-time and discrete-time domains through a set of simulations and FPGA-based co-simulation.

In this paper, we propose a new model based on 3D discrete orthogonal moments and deep neural networks (DNN) to improve the classification accuracy of 3D objects under geometric transformations and noise. However, the utilization of moment invariants presents some drawbacks: They can’t describe the object efficiently, and their computation process is time consuming. Discrete orthogonal moments have the property to extract pertinent features from an image even in lower orders and are robust to noise. The main goal of this work is to investigate the robustness of the proposed model to geometric transformations like translation, scale and rotation and noisy conditions. The experiment simulations are conducted on datasets formed by applying some geometric transformations and noise on selected objects from McGill database. The obtained results indicate that the proposed model achieves high performance classification rates, robust to geometric transformations and noise degradation than other methods based on moment invariants.

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro‐average of the F1‐score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high‐level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.

This paper presents a study on the flow dynamics involving vehicle interactions. In order to do so, this study first explores aerodynamic prediction capabilities of popular turbulence models used in computational fluid dynamics simulations involving tandem objects and thus, ultimately presents a framework for CFD simulations of ground vehicle platooning using a realistic vehicle model, DrivAer. Considering the availability of experimental data, the simulation methodology is first developed using a tandem arrangement of surface-mounted cubes which requires an understanding on the role of turbulence models and the impacts of the associated turbulence model closure coefficients on the prediction veracity. It was observed that the prediction accuracy of the SST k−ω turbulence model can be significantly improved through the use of a combination of modified values for the closure coefficients. Additionally, the initial validation studies reveal the inability of the Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach to resolve the far wake, and its frailty in simulating tandem body interactions. The Improved Delayed Detached Eddy Simulations (IDDES) approach can resolve the wakes with a reasonable accuracy. The validated simulation methodology is then applied to the fastback DrivAer model at different longitudinal spacing. The results show that, as the longitudinal spacing is reduced, the trailing car’s drag is increased while the leading car’s drag is decreased which supports prior explanations of vortex impingement as the reason for drag changes. Additionally, unlike the case of platooning involving Ahmed bodies, the trailing model drag does not return to an isolated state value at a two car-length separation. However, the impact of the resolution of the far wake of a detailed DrivAer model, and its implication on the CFD characterization of vehicle interaction aerodynamics need further investigations.

This paper presents a supervised machine learning (ML)-based protection approach for identifying different types of transmission lines' faults, including faults during power swing, at several locations and variable fault resistances. Descriptive statistical features used in classification were extracted from spectrograms of measured signals at the relay point. Decision trees (DT)., support vector machines (SVM), k-nearest neighbors (k-NN), boosted and bagged ensemble tree classifiers were used for classification. Four feature selection algorithms, namely neighborhood component analysis (NCA), minimum redundancy maximum relevance (mRMR), sequential feature selection (SFS), and fit ensemble of learners, were applied to reduce the number of features. The synthetic minority class oversampling technique (SMOTE) balances the data to prevent biases toward the majority (non-fault) class. The results show that ensemble bagged trees with SMOTE achieve the maximum accuracy and the minimum false-negative rates. Feature selection algorithms show no improvement in the performance of the classifiers. The best classifier was then tested using data from an unseen scenario and showed accurate detection of the fault events. The classifier, however, was not able to detect faults during power swing after integrating a photovoltaic (PV) plant behind the relay point in the protected line.

Oil and gas companies consider transforming conventional cable-based seismic acquisition to wireless acquisition as a promising step for cost and weight reduction in reservoir exploration. Wireless seismic acquisition requires large number of wireless geophone (WG) sensors to be deployed in the field. The locations of the WG sensors must be known when processing the collected data. The application of direction of arrival (DOA) estimation helps in localizing WGs and improves received signal level through beam steering and interference avoidance. Conventional DOA algorithms require high computational complexity which renders them inefficient for real-time response. In this paper, deep neural network (DNN) is proposed for DOA estimation of WGs at wireless gateway node (WGN) under different channel conditions. The estimated angle and corresponding coordinates of WGNs are used in least square estimation (LSE) to estimate the position of the WGs. The simulation results depict reasonable estimation and position accuracy in real-time.

Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model’s weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.

Studies on the quality of service thrive to quantify passenger satisfaction of public transport services which is a key factor in how these services are used. Service quality evaluation is performed by means of objective and subjective approaches. Considering that people are attracted to public transport services based on their own perception, subjective approaches are utilized to capture user judgment. However, there are various subjective approaches in the literature to model service quality in which different lists of factors and model structures are identified. This paper aims to test the spatial transferability of these approaches at various levels. All the adopted approaches are based on a Structural Equation Model (SEM) to evaluate the relationship between perceived service quality and customer satisfaction. First a synthesis of the literature on model transferability is presented to compare these approaches in terms of model characteristics (structural equivalence) and parameter values (measurement equivalence). Three classes of model characteristics, including six distinct SEM structures in total, are tested to identify the factors affecting service quality: (1) recommendations of previously published models from the literature, (2) established customer satisfaction theories, and (3) adopting an exploratory approach and developing it based on the previous two approaches and the nature of locally collected data. Tehran Metro Line 3, which is a heavy urban rail mode, is used as a case study. The dataset consists of three hundred validated responses collected through a customer satisfaction survey. The results indicate that model characteristics are not transferable to this case study (i.e., none of the SEM structures from previous studies were directly transferable). The best model is developed when the entire modeling procedure is repeated using a wide range of affecting factors and gradually narrowing them down through model development. The final model comprises four latent variables, namely major services, comfort, security, and minor services, the first two of which have the largest effect on service quality.

Atmospheric pressure plasma jets (APPJs) are increasingly used for biomedical applications. Reproducible and effective operation of APPJs hinges on controlling the nonlinear effects of plasma on a target substrate in the face of intrinsic variabilities of the plasma as well as exogenous disturbances. This paper presents a low-memory fast approximate nonlinear model predictive control (NMPC) strategy for an APPJ with prototypical applications in plasma medicine. The NMPC objective is to regulate the delivery of the cumulative thermal effects of plasma to a substrate, while adhering to constraints pertaining to a patient’s safety and comfort. Deep neural networks are used to approximate the implicit NMPC law with a cheap-to-evaluate explicit control law that has low memory requirements. Robust constraint satisfaction is guaranteed by projecting the output of the neural network onto a set that ensures the state stays within an appropriately defined invariant set. Closed-loop simulations and real-time control experiments indicate that the proposed approximate NMPC strategy is effective in handling nonlinear control costs at fast sampling times, while guaranteeing satisfaction of safety-critical system constraints. This work takes a crucial step toward fast NMPC of safety-critical plasma applications using resource-limited embedded control hardware.

Mars life support system models consist of numerous mission-critical, interrelated, and scenario-specific parameters. The large size and involved nature of these models make them computationally expensive, with parameters that are subject to several sources of uncertainty. Accurately characterizing these uncertainties and their impact on overall model predictions is crucial for decision-support and mission optimization. This paper focuses on uncertainty quantification of a model of a space crop cultivation system, which is one of several systems that are required on a long-duration manned Mars mission. The model performs constrained optimization of the equivalent system mass (ESM) metric, which augments shipped mass costs with those of pressurized volume, demanded power, thermal control, and needed crew time. This paper uses surrogate modeling for fast quantification of the effect of probabilistic uncertainty in mission-critical parameters of semi-empirical equations that describe crop growth and equipment operation. This work shows sparse polynomial chaos-Kriging (PCK) yields a computationally cheap-to-evaluate surrogate for the minimum ESM that accounts for probabilistic uncertainty in 86 model parameters. This surrogate model accelerates a global sensitivity analysis that elucidates which crop growth and equipment operation parameters are critical to mission outcome variability. The PCK surrogate model realizes a 100-fold computational speed gain in the estimation of the probability distribution of the minimum ESM.

Individual chance constraints can be used to systematically seek trade-offs between control performance and constraint violation for a given disturbance description. This paper presents a Stochastic Model Predictive Control (SMPC) approach with adaptive individual chance constraints that relies on online adaptation of the disturbance description using the empirical cumulative distribution (ECDF). Individual chance constraints are ensured by using a suitable worst-case confidence interval derived from the ECDF. The confidence interval may, however, be excessively conservative due to the empirical nature of the ECDF. To reduce this conservatism, the proposed approach accounts for the updated disturbance information, which is sampled online from the one-step ahead prediction error. Hence, the initial ECDF can be obtained from a reduced number of samples since the conservative handling of the chance constraint is continuously mitigated. This will also allow for using simpler models of the stochastic system disturbances. Convergence and recursive feasibility of the proposed adaptive approach are established. A DC-DC converter benchmark problem is used to illustrate the usefulness of the proposed approach.

Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and the data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. Therefore, a three layer wireless network architecture is proposed in this work, which consists of wireless geophones (WG) and a data center with an intermediate wireless gateway node (WGN). This paper investigates the aggregate data throughput, transmission time, and energy consumption from WGs to the WGN in a wireless geophone network architecture based on the IEEE 802.11af standard. This standard is considered in order to have the maximum possible range and low power consumption due to operating in TV bands. Analytical expressions of the aforementioned quantities are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling the access method that allows multiple WGs to connect to a WGN and the other for representing a buffer in a WG. Since seismic data is recorded at regular intervals, arrivals of data packets in the buffer of the WG is deterministic. On the other hand, departure is random due to the multiple access method. Hence, in this work D/M/1/B queue is used for the first time to model the buffer in a wireless geophone. Furthermore, the physical layer constraints are also taken into account together with proper wireless path-loss channel models. The results obtained are useful for designing such wireless seismic networks without extensive simulations. In particular, the proposed joint medium access control, physical layer, and D/M/1/B model enables us to optimize the required number of WGNs. Finally, sectoring is also introduced in order to minimize the total number of WGNs needed to cover the whole surveying area.

The pattern of activities by transit passengers and the factors affecting this pattern are recently addressed in the literature. Also, the pattern of trips and the factors affecting trips are the subject of many studies. However, little is known about the correlation between the activity and trip of passengers in the public transit network. This study investigates how similar are activities of passengers if they have similar trips (or vice versa)? And what factors impact the similarity of the activity and trip of passengers? Answering these questions is useful in understanding the mobilization patterns of passengers and developing group-based transit services. Also, smart card data have provided an opportunity, which was not available before, to analyze the activity and trip of the passengers in a large scale network. In this paper, the correlation between activity similarity and trip similarity of public transit passengers is investigated. The correlation between the activity and trip of the passengers is analyzed using histograms, Pearson correlation coefficient, conditional probabilities, and hexagonal binning technique. In addition, the impact of trip length and duration on the activity and trip similarity are examined using histograms and hexagonal binning diagrams. The proposed methodology is implemented for two-day smart card data in Brisbane, Australia. Results show that it is more likely to have the activity similarity when there is a trip similarity than having the trip similarity when there is activity similarity. Also, there is a nonlinear correlation between the activity and trip similarity with the trip length and duration.

Seismic surveys are conducted by oil exploration companies to keep up with the global demand for oil and gas. Although seismic cables offer fast and reliable data transfer, they account for a majority of the equipment weight, logistics, and labor costs. A novel wireless geophone network architecture, compliant with the IEEE 802.11af standard, is described. Operation in television white spaces can achieve long transmission ranges, allowing for scalable coverage of large seismic survey areas. Two methods for data collection are proposed: a Geophone-Polling (GP) scheme and an Adaptive Geophone Time Division Multiple Access Scheduling (AGTS) scheme. The two schemes are analyzed and evaluated in terms of the total time taken for data acquisition and the average power consumption, in comparison to the default IEEE 802.11 channel access schemes. The problem of hexagonal clustering for orthogonal deployment of geophones is also tackled, and the impact of co-channel interference is considered. The proposed schemes not only reduce data acquisition time, but also the average power consumption of the geophones, thereby improving the lifetime of wireless seismic surveys.

Chaos computing provides a large number of functions from a single hardware. Large-scale reconfigurability can be achieved flexibly by tuning only a few parameters from a chaos-based computing system. Implementation of reconfigurable complex functions from a single chaos circuit can alleviate area and power concerns due to decreasing technology nodes. It is possible to make a multi-input multi-output complex instruction set using the chaos-generated functionalities where operations are more uniform than conventional implementations. Lack of uniformity in implementation of instructions in traditional computing system provides opportunity for attackers to reverse engineer based on side channel power analysis. In this paper, it is proposed that chaos-based implementation of a complex instruction set is immune to classification-based reverse engineering attack. Cross obfuscation and self obfuscation schemes are proposed in this work which leverage reconfigurability of chaotic system for obfuscating the power profile of the instruction set and it has been made immune to reverse engineering attacks. The design utilizes 3-input multi-output instructions by using a single chaotic iterative map. We analyzed the immunity of this design against classification-based reverse engineering attack for six different classification algorithms with five dimensionality reduction techniques.

This paper presents a stochastic-tube model predictive control (MPC) strategy that systematically handles plant-model mismatch, guarantees stability in the presence of changing operating conditions, and ensures offset-free tracking for all reachable operating conditions. The key notion of this work is to separate the system uncertainty into two distinct sources of additive bounded state error. The first source is a non-random uncertainty that represents mismatch between the linear model of the controller and the true plant (as well as other types of persistent disturbances). The second source represents random fluctuations due to either the intrinsic stochastic variability of the system, or exogenous disturbances. Recursive feasibility and stability of the stochastic-tube MPC strategy is guaranteed by defining the terminal invariant set to include the effect of changes in the steady-state operating conditions. Offset-free tracking is achieved with a disturbance estimator that can be tuned independently of the controller. To reduce the conservatism inherent to robust control performance, a new filter model for deterministic disturbances is proposed that predicts uncertainty in the future evolution of the disturbances based on a bound on how quickly the plant-model mismatch can vary. The stochastic-tube MPC strategy is demonstrated on two simulation case studies—a benchmark DC-DC converter and an industrially-motivated fluidized-bed catalytic cracking unit.

Structural model uncertainty is prevalent in control design and arises from incomplete knowledge of the system or the existence of different modes of dynamic behavior, such as those arising from system faults and malfunctions. This paper addresses control of stochastic nonlinear systems using model predictive control, or mpc , under structural model uncertainty. Inspired by dual control, the mpc strategy with active learning presented here can probe the uncertain system to select, among a set of candidates, the model that best describes the observed closed-loop system data. The proposed controller involves online model selection based on estimation of the model-hypothesis probabilities and minimization of a computationally tractable measure of the predicted Bayes risk of selection error. The performance of the proposed approach is compared to that of nominal mpc with no learning, mpc with passive learning, and a robust mpc approach that systematically accounts for structural model uncertainty but has no learning mechanism. Simulation results on a nonlinear bioreactor demonstrate that active learning can have significant advantages in maintaining adequate control performance in the presence of structural uncertainty. Active learning can be particularly beneficial for improving online model discrimination and active fault diagnosis under closed-loop control.

Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using information-rich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.

Lipreading or Visual speech recognition is the process of decoding speech from speaker's mouth movements. It is used for people with hearing impairment, to understand patients attained with laryngeal cancer, people with vocal cord paralysis and in noisy environment. In this paper we aim to develop a visual-only speech recognition system based only on video. Our main targeted application is in the medical field for the assistance to laryngectomized persons. To that end, we propose Hahn Convolutional Neural Network (HCNN), a novel architecture based on Hahn moments as first layer in the Convolutional Neural Network (CNN) architecture. We show that HCNN helps in reducing the dimensionality of video images, in gaining training time. HCNN model is trained to classify letters, digits or words given as video images. We evaluated the proposed method on three datasets, AVLetters, OuluVS2 and BBC LRW, and we show that it achieves significant results in comparison with other works in the literature.

Summary This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system app...

While transit exclusive lanes are statically introduced for an entire day (or for the peak period), the total number and distribution of trips vary from one hour to another during a day. In this st...

This paper describes a practical automated procedure to calibrate and validate a transit assignment model. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. Lastly, the model is validated using another weekday data. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The South-East Queensland (SEQ) network in Australia is used as a case study. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model.

The optimal design of experiments is crucial for maximizing the information content of data across a wide-range of experimental goals. This paper presents a Bayesian approach to optimal experiment design (OED) for parameter inference in constrained, dynamic, and nonlinear systems under noisy, incomplete, and indirect measurements. Bayesian OED maximizes an expected utility objective, which accounts for prior and posterior uncertainty in the model parameters from an information-theoretic standpoint. Due to the complicated form of the expected utility, it must be estimated using sample-based methods and, in particular, a nested Monte Carlo estimator that is expensive to evaluate using the full dynamic model. We propose a novel surrogate model based on arbitrary polynomial chaos (aPC), which readily applies to any type of prior distribution. The aPC expansions are constructed locally at each design visited during the iterative optimization procedure. The main cost in aPC, which is the determination of the expansion coefficients, is minimized by estimating these coefficients from only a minimal set of dynamic model evaluations. Although sample-based estimators can also be applied to the chance constraints, this leads to a potentially large number of binary variables, such that a smooth moment-based approximation is preferred in this work. Numerical simulations indicate that the proposed surrogate can significantly lower the computational cost of the Bayesian OED, while guaranteeing the original chance constraints are satisfied without noticeably increasing the average time to find a solution. As such, this methodology appears to have the potential to pave the way for real-time or sequential dynamic experiment design in a fully Bayesian setting.

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

Public transport planners can predict passenger loads and levels of service by applying the prior knowledge about the transit network and using transit assignment models. The individual travel history data available from automated fare collection (AFC) systems bring the opportunity of understanding the individual’s travel behavior, which is necessary to develop a transit assignment model. By combining the prior knowledge about the transit network with the AFC data, a transit assignment model can be calibrated. This paper proposes a Bayesian hierarchical model to estimate attributes of travel time components and to calibrate a transit assignment model. In this model, route choices are represented by a multinomial logit model, and its coefficients are estimated via a Markov chain Monte Carlo method. The proposed model is specified in two ways, and in order to consider travel time variability, it is assumed that travel time on links follows a gamma distribution. In the first specification, route choice variables and parameters are the same for all transit modes of bus, train, and ferry. In the second specification, mode-specific route choice variables and parameters are defined. In order to assess the model fitness, the root-mean-square error (RMSE) between each posterior estimate and the actual observation is computed. The lowest %RMSE belongs to the third-model specification (at 15%), which indicates its high predictive power.

Because of the large size of reservoirs and complexity of the fluid behavior, especially in fractured porous media, simulation of hydrocarbon reservoirs is costly to compute using common simulators based on an Eulerian grid. For this reason, the streamlines method, which offers high computational speed and low computational hardware requirements with suitable accuracy can replace common simulators. The current study investigated parallel processing of numerical simulation for two-phase flow in two-dimensional fractured porous media using the streamlines method in OpenMp programming model. For validation, the results of water flooding simulation were compared with the IMPES method and the effect of natural flow barriers for the injection wells has been modeled. The dual porosity model is used to model the fractured reservoirs and transport equations are solved independently on 1D streamlines. It was found that the ability and performance of the streamlines method is better for parallel processing than for other methods based on the Eulerian grid.

In this paper, a new method to model the time-dependent corrosion wastage for main deck panel in bulk carrier is presented based on empirical data. In this modeling, recorded data of thickness measurements performed on bulk carrier of the Islamic Republic of Iran Shipping Lines from 1983 to 2016, which had the age of 6–28 years at the time of measurement were used. After monitoring and deleting suspicious data, 3814 and 3828 measured points from the deck plate and deck longitudinals elements were used, respectively. Due to non-sufficient information for all the years of ships lifetime, one of the previous models was considered as basic model. The coefficients were updated in modeling using the BAYESIAN method and applying the Markov chain Monte Carlo simulation. The parameters in this method were statistically considered and this method is useful to predict the corrosion wastage thickness of all ship structure elements per year of the ships lifetimes. Even a new data can update the modeling coefficients and provide a better and more accurate model. The resulting models with updated coefficients were used to estimate the corrosion wastage thickness for consecutive years of ship lifetime and its achievements, and important results were summarized.

The characterization of a racecar’s aerodynamic behavior at various yaw and pitch configurations has always been an integral part of its on-track performance evaluation in terms of lap time predictions. Although computational fluid dynamics has emerged as the ubiquitous tool in motorsports industry, a clarity is still lacking about the prediction veracity dependence on the choice of turbulence models, which is central to the prediction variability and unreliability for the Reynolds Averaged Navier–Stokes simulations, which is by far the most widely used computational fluid dynamics methodology in this industry. Subsequently, this paper presents a comprehensive assessment of three commonly used eddy viscosity turbulence models, namely, the realizable [Formula: see text] (RKE), Abe–Kondoh–Nagano [Formula: see text], and shear stress transport [Formula: see text], in predicting the aerodynamic characteristics of a full-scale NASCAR Monster Energy Cup racecar under various yaw and pitch configurations, which was never been explored before. The simulations are conducted using the steady Reynolds Averaged Navier–Stokes approach with unstructured trimmer cells. The tested yaw and pitch configurations were chosen in consultation with the race teams such that they reflect true representations of the racecar orientations during cornering, braking, and accelerating scenarios. The study reiterated that the prediction discrepancies between the turbulence models are mainly due to the differences in the predictions of flow recirculation and separation, caused by the individual model’s effectiveness in capturing the evolution of adverse pressure gradient flows, and predicting the onset of separation and subsequent reattachment (if there be any). This paper showed that the prediction discrepancies are linked to the computation of the turbulent eddy viscosity in the separated flow region, and using flow-visualizations identified the areas on the car body which are critical to this analysis. In terms of racecar aerodynamic performance parameter predictions, it can be reasonably argued that, excluding the prediction of the %Front prediction, shear stress transport is the best choice between the three tested models for stock-car type racecar Reynolds Averaged Navier–Stokes computational fluid dynamics simulations as it is the only model that predicted directionally correct changes of all aerodynamic parameters as the racecar is either yawed from the 0° to 3° or pitched from a high splitter-ground clearance to a low one. Furthermore, the magnitude of the shear stress transport predicted delta force coefficients also agreed reasonably well with test results.

Recent advances in resistive random access memory (RRAM) as high density, low power, and faster memory systems drive the need for devising a more lightweight integrity checking system for RRAM. In this paper, we design a new tag generation system for integrity checking of RRAM. A single read operation to a crossbar RRAM in the presence of sneak path currents can output a tag for the memory data that can be used for integrity checking. An analytical approach to model such a tag generation process is described in this paper. Security results predicted by the analytical model provide various design options leading to an optimal system from the perspective of considered security properties. The proposed design is simulated to investigate and verify the security properties of the system for a number of optimal design options predicted by the analytical model. Reliability of the proposed system is also measured for varying conditions of device parameters, operating temperatures, load resistances, and read voltage. Finally, the performance of the proposed system is compared against another existing lightweight tag generation method from the perspective of energy consumption, transistor count, and delay.

For several decades, urban congestion causes various problems such us pollution, road wares, and congestion in intersections which deteriorates the quality of life of citizens who live in big cities. Different methods proposed to reduce urban congestion, notably traffic regulation that attend tremendous attention recently. In past years, the usage of tools from artificial intelligence, particularly distributed methods and multi-agent systems, which allow to design new methods for traffic regulation. In this context, a Multi-Agent approach for intersection management system based on the principle of trajectory reservation has been proposed to reduce the travel time average and air pollution.

Fraud is a very costly criminal activity. Insurance companies face the very challenging task of identifying and preventing fraudulent claims. Just like any big problem in recent years, Machine Learning has been heavily applied to fraud detection in both a supervised and non-supervised manner. But, usually supervised models do not perform well in the presence of awry, asymmetrical Datasets. This paper presents a novel approach for auditing claims in automobile insurance. Our data pipeline consists of preprocessing, feature selection, data balancing, and classification. This robust fraud detection model, built upon existing fraud detection research, gives very promising results compared to state of the art in the industry.