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In this paper, we introduce a non-linear Lanchester’s model of Network Centric Warfare type and investigate an optimization problem for this model, where only the Red force is supplied by several supply agents. Optimal fire allocation of the Blue force is sought in the form of a piece-wise constant function of time. A “threatening rate” is computed for the Red force and each of its supply agents at the beginning of each stage of the combat. These rates can be used to derive the optimal decision for the Blue force to focus its firepower to the Red force itself or one of its supply agents. This optimal fire allocation is derived and proved by considering an optimization problem of the number of Blue force troops. Numerical experiments are included to demonstrate the theoretical results.

Many existing studies have investigated component-level Prognostics and Health Management (PHM) problems. In the real field, the PHM for the system is more important, which deals with the exploration of the system health derived from components degradations, from which the decision can be made to which components to repair. While there have been few recent studies in this direction, no studies are found that have investigated this issue from the systems perspective. Motivated by this, appropriate architecture for the system-level PHM is proposed for the physics-based and data-driven approaches. The architecture is demonstrated using a direct current (DC) motor system, which addresses the system health by the degradation of two components: bearing and permanent magnet. Due to the lack of real field data, simulation data are made using the motor dynamic equation. The two approaches are compared from the perspective of model construction and required information. In conclusion, the proposed architecture enables the estimation of components and system health, as well as the prediction of their remaining useful life. Furthermore, a what-if study allows us to investigate how long the system can be operated by repairing each component, from which the optimum maintenance plan can be made.

This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

Uncertainty quantification (UQ) is an important step in the verification and validation of scientific computing. Validation can be inconclusive when uncertainties are larger than acceptable ranges for both simulation and experiment. Therefore, uncertainty reduction (UR) is important to achieve meaningful validation. A unique approach in this paper is to separate model error from uncertainty such that UR can reveal the model error. This paper aims to share lessons learned from UQ and UR of a horizontal shock tube simulation, whose goal is to validate the particle drag force model for the compressible multiphase flow. First, simulation UQ revealed the inconsistency in simulation predictions due to the numerical flux scheme, which was clearly shown using the parametric design of experiments. By improving the numerical flux scheme, the uncertainty due to inconsistency was removed, while increasing the overall prediction error. Second, the mismatch between the geometry of the experiments and the simplified 1D simulation model was identified as a lack of knowledge. After modifying simulation conditions and experiments, it turned out that the error due to the mismatch was small, which was unexpected based on expert opinions. Last, the uncertainty in the initial volume fraction of particles was reduced based on rigorous UQ. All these UR measures worked together to reveal the hidden modeling error in the simulation predictions, which can lead to a model improvement in the future. We summarized the lessons learned from this exercise in terms of empty success, useful failure, and deceptive success.

Shrinking bias problem is a practical limitation of graph-cut-based methods for binary image segmentation. The detail parts with thin elongated object are not well preserved in graph cut minimisation as a result. The authors propose to use structure transferring to overcome the problem with an expanded L2 norm of colour difference. They also show that the structure-transferring output can be integrated into graph cuts as a counter-balance to shrinking problem. Experimental results show that the proposed alpha-cutting technique is effective and efficient in improving segmentation of thin elongated objects.

The dynamic traffic assignment (DTA) problem has been studied intensively in the literature. However, there is no existing linear framework to solve the user equilibrium (UE) DTA problem. In this paper, we develop a novel linear programming framework to solve the UE-DTA problem in a dynamic capacity network that exploits the linkage between the UE and system optimal (SO) solutions underpinned by a first-in-first-out (FIFO) principle. This important property enables us to develop an incremental loading method to obtain the UE solutions efficiently by solving a sequence of linear programs. The proposed solution methodology possesses several nice properties such as a predictable number of iterations before reaching the UE solution, and a linear system of equations to be solved in each of the iterations. In contrast to the related iterative methods, such as Frank–Wolfe algorithm, successive average (MSA) or projection and their extensions where the purpose of iteration is to seek the solution convergence, whereas ours is to solve a linear problem over multiple iterations but only for a single unit of demand in each iteration. Furthermore, we provide a theoretical proof that in the limit, the SO objective can be used to obtain the UE solution as the system time step goes to zero given the satisfaction of the FIFO constraint. We show via numerical examples the significant improvements in the obtained UE solutions both in terms of accuracy and computational complexity.

Computer-aided engineering (CAE) is now an essential instrument that aids in engineering decision-making. Statistical model calibration and validation has recently drawn great attention in the engineering community for its applications in practical CAE models. The objective of this paper is to review the state-of-the-art and trends in statistical model calibration and validation, based on the available extensive literature, from the perspective of uncertainty structures. After a brief discussion about uncertainties, this paper examines three problem categories—the forward problem, the inverse problem, and the validation problem—in the context of techniques and applications for statistical model calibration and validation.

Multifidelity surrogates are essential in cases where it is not affordable to have more than a few high-fidelity samples, but it is affordable to have as many low-fidelity samples as needed. In the...

5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service.

Accurate prediction of the remaining useful life (RUL) of plant turbines is a major scientific challenge for effective operation and maintenance in the power plant industry. This paper proposes an RUL prediction methodology that incorporates a damage index into the damage growth model. A Bayesian inference technique is used to consider uncertainties while estimating the probability distribution of a damage index from on-site hardness measurements. A Bayesian approach is proposed for the damage growth model for use with aged steam turbines. The predictive distribution of the damage index is estimated using its mean and standard deviation. As a case study, real steam turbines from power plants are examined to demonstrate the effectiveness of the proposed Bayesian approach. The results from the proposed damage growth model can be used to predict the RULs of the steam turbines of power plants regardless of load types (peak-load or base-load) of the power plant.

This study was designed to test the hypothesis that: “A properly designed implant that harnesses the principle of the incompressibility of fluids can improve the weight carrying ability of an amputee’s residual femur and reduce the frictional forces at the stump external socket interface.” The hypothesis was tested both mechanically on an Amputee Simulation Device (ASD) and through Finite Element Analysis (FEA) modeling software. With the implant attached to the femur, the FEA and ASD demonstrated that the femur carried 90% and 93% respectively of the force of walking. Without the implant, the FEA model and ASD femur carried only 35% and 77%, respectively, of the force of walking. Statistical calculations reveal three (3) degrees of separation (99% probability of non-random significant difference) between with and without implant data points. FEA modeling demonstrates that the normal contact forces and shear forces are pushed the distal weight-bearing area of the amputee stump, relieving the lateral stump of frictional forces. The ASD mechanical and FEA modeling data validate each other with both systems supporting the hypotheses with confidence intervals of three degrees of separation between with implant and without implant models.

Surrogate models have been developed to infer the response of engineering systems based on scattered tests/simulations. An effective sampling scheme enables surrogates to have a desirable accuracy ...

Counter-terrorism is a global task that every nation is concerned about. To improve operations against terrorism, many nations carry out counter-terroristic operations not only by themselves but also in cooperation with other nations. In this paper, we propose an extended the Kaplan-Kress-Szechtman model to cope with multi-party counter-terrorism. The optimal control problem for this model is studied. Our main tool is Pontryagin’s maximal principle. The optimal intelligence level and individual reinforcement of each party are found. The numerical results show that counter-terrorism operations in cooperative models are more effective than that in single models.

In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers and design variables. Different from the conventional optimality criteria method, the proposed method does not satisfy constraints at every iteration. Rather, it improves the Lagrange multipliers and design variables such that the optimality criteria are satisfied upon convergence. The main advantages of the proposed method are its capability of handling multiple constraints and computational efficiency. In numerical examples, the proposed method was found to be more than 100 times faster than the optimality criteria method and more than 1000 times faster than the method of moving asymptotes.

Short cantilever beams appear in both civil and industrial projects such as bearing supports in assembled structures, connections between steel and concrete or heavy load-bearing components such as crane pillars, supporting bridge, etc. However, the distributed stress in the short cantilever reinforced concrete beam under concentrated load usually has a discontinuity zone and varies, which depend on the ratio between span length and height, making it difficult to calculate from the viewpoint as conventional beam. The strut and tie model has been studied by many authors, the application results have been recorded and included in the Euro code and ACI standards but the application to designers in civil engineering field is not efficient. Whenever designers need to design short cantilever beams, a difficult research must be done by designers, and it takes period to finish calculations. In this article, we discuss the usage of short cantilever reinforced concrete beams, apply strut and tie model to calculate this structure in accordance with ACI and Euro code standards and compare the calculation results to short cantilever reinforced concrete beams of TCVN 5574-2018 for an overview of these structural calculations in 3 different criteria. The article will be a practical reference source for designers and students to apply in real building design and reduce time in designing short cantilever beams.

Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV’s energy and charging time on the system performance.

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

Deep neural networks (DNNs) give state-of-the-art accuracy in many tasks, but they can require large amounts of memory storage, energy consumption, and long inference times. Modern DNNs can have hundreds of million parameters, which make it difficult for DNNs to be deployed in some applications with low-resource environments. Pruning redundant connections without sacrificing accuracy is one of popular approaches to overcome these limitations. We propose two $\ell_{0}$ -constrained optimization models for pruning deep neural networks layer-by-layer. The first model is devoted to a general activation function, while the second one is specifically for a ReLU. We introduce an efficient cutting plane algorithm to solve the latter to optimality. Our experiments show that the proposed approach achieves competitive compression rates over several state-of-the-art baseline methods.

Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

An approach is proposed to quantify the uncertainty in probability of failure using a Gaussian process (GP) and to estimate uncertainty change before actually adding samples to GP. The approach estimates the coefficient of variation (CV) of failure probability due to prediction variance of GP. The CV is estimated using single-loop Monte Carlo simulation (MCS), which integrates the probabilistic classification function while replacing expensive multi-loop MCS. The methodology ensures a conservative estimate of CV, in order to compensate for sampling uncertainty in MCS. Uncertainty change is estimated by adding a virtual sample from the current GP and calculating the change in CV, which is called expected uncertainty change (EUC). The proposed method can help adaptive sampling schemes to determine when to stop before adding a sample. In numerical examples, the proposed method is used in conjunction with the efficient local reliability analysis to calculate the reliability of analytical function as well as the battery drop test simulation. It is shown that the EUC converges to the true uncertainty change as the model becomes accurate.

One application of deep learning in medical applications is the use of deep neural networks to classify human speech as healthy or pathological. In such applications, the audio signal is transformed into a spectrogram that captures its time-varying content and the latter “images” are fed into a classifier for classification. A challenge in applying this approach is the shortage of suitable speech data for training purposes. Labelled data acquisition requires significant human effort and/or time-consuming experiments. In this paper, we propose a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of insufficient training data. We compare the classification performance of a traditional classifier and our semi-supervised classifier. We observe that the GAN-based semi-supervised approach demonstrates a significant improvement in terms of accuracy and ROC curve when supplied an equivalent number of training samples.

Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing., Comment: submitted for inclusion in Quantum Machine Learning, a book to be published by Cambridge

A challenge in applying machine learning algorithms to pathological speech classification is the labelled data shortage problem. Labelled data acquisition often requires significant human effort and time-consuming experimental design. Further, for medical applications, privacy and ethical issues must be addressed where patient data is collected. While labelled data are expensive and scarce, unlabelled data are typically inexpensive and plentiful. In this paper, we propose a semi-supervised learning approach that employs a generative adversarial network to incorporate both labelled and unlabelled data into training. We observe a promising accuracy gain with this approach compared to a baseline convolutional neural network trained only on labelled pathological speech data.

We develop in this paper a comprehensive linear mathematical framework to study the benefit of real-time information and the impact of resulting user adaptive route choice behaviours on network performance. The framework formulates the information-based stochastic user equilibrium (ISUE) dynamic traffic assignment (DTA) problem for a multiple origin-destination (OD) network. Using the framework, we prove the linkage between the user equilibrium (UE) and system optimal (SO) solutions underpinned by the first-in-first-out (FIFO) principle. This important property then enables us to develop an incremental loading method to obtain the ISUE solutions efficiently by solving a sequence of linear programs. Moreover, the proposed method is more scalable that avoids a huge enumeration of paths in large-scale networks as done in path-based methods of the existing literature on this topic. We show via numerical examples the impact of information on both route choices and network performance, and demonstrate the significant improvements in the obtained ISUE solution both in terms of accuracy and computational complexity.

In this paper, a temperature control system based on Halogen lamp is designed for education and training purpose, which is low cost, energy saving and less time consuming for student’s experimental task. The designed system consists of a box made of plastic and aluminum, a 300 W Halogen lamp, a temperature sensor, an Arduino Uno R3 based microprocessor, a triac BT137-600E by NXP Semiconductors and 220 V–50 Hz power supply. First, a transfer function from power to temperature is obtained through system identification based on a unit step response. Then, a PID controller is designed for the temperature. Finally, the temperature control system is verified through experimental tests. The results show that the temperature converges to the set-point with short settling time and small overshoot. The average time period for doing system identification and real-time control is quite small for student analyzing and performing experimental tasks. Moreover, since the cost of the designed system is cheap, it is possible to provide a system to each student for experiments. In addition, it can be also used as a testbed for verifying new control algorithms.

Due to the expensive cost of full-scale tests, more and more designs rely on simulation. For highly nonlinear crash simulation, numerical uncertainty is an inherent by-product, which refers to the oscillation of results when the simulation is repeated at the same design or the design variables are slightly changed. This oscillation directly influences the quality and reliability of the optimal design. This paper shows how these issues can be addressed by proposing a simple uncertainty quantification method for numerical uncertainty (noise) and surrogate model uncertainty (error) in the optimization process. Three engineering problems, a tube crush example, an automotive front-rail crush example and a multi-cell structure crush example, are used to illustrate this method. Firstly, the level of numerical uncertainty is quantified in terms of noise frequency and amplitude, and the convergence study of these two criteria is employed to determine an appropriate data size to describe numerical noise. Secondly, an estimation method considering both numerical noise and surrogate model error is proposed based on the prediction variance of the polynomial response surface. Finally, the tube and front rail structures are optimized according to the proposed uncertainty quantification method. It was found that by considering the two sources of uncertainty, the optimal designs are more reliable than the deterministic solutions.

This study explores why the use of the low-fidelity scale factor can substantially improve the accuracy of the Bayesian multi-fidelity surrogate (MFS). It is shown analytically that the Bayesian MFS framework utilizes the scale factor to reduce the waviness and variation of the discrepancy function by maximizing the Gaussian process-based likelihood function. Less wavy functions are more accurately fitted, and variation reduction mitigates the effect of fitting error. Bumpiness is another way used to combine waviness and variation. Two examples, Borehole3 and Hartmann6, illustrated that indeed the Bayesian MFS reduced bumpiness using the scale factor. The finding may be useful for MFS using surrogates lacking uncertainty structure, so that likelihood is not an option, but bumpiness may be.

In this paper, we propose a novel mathematical framework to formulate a unified Bus Rapid Transit (BRT) with Transit Signal Priority (TSP) system for single destination networks, namely, Bus Priority System Optimal Dynamic Traffic Assignment with Signal Control (BP SODTA-SC). This framework considers dedicated bus lanes, bus routes, and priority for public bus transport in mixed bus-car scenarios. Furthermore, this approach assures fairness to all road users. It is linear and can be applied to analyze city-size BRT-TSP systems. Our numerical results show that bus priority significantly reduces Total System-wide Passenger Travel Time (TSPT).

Prognostics methods model the degradation of system performance and predict remaining useful life using degradation data measured during service. However, obtaining degradation data from in-service systems in practice is either difficult or expensive. Therefore, accelerated life testing (ALT) is instead frequently performed for validating designs using considerably heavy loads. This work discusses the methods and effectiveness of utilizing ALT degradation data for the prognostics of a system. Depending on the degradation model and loading conditions, four different ways of utilizing ALT data for prognostics are discussed. A similar transformation method used in ALT is adopted to convert accelerated loading conditions to field loading conditions. To demonstrate the proposed approach, synthetic data are generated for crack growth under accelerated loading conditions; these data are used for training a neural network model or identifying model parameters in a particle filter. The applied example shows that the use of ALT data increases the accuracy of prognostics in the early stages in all four cases and compensates for the problem posed by data insufficiency through the proposed method.

In this paper, we investigate the M/M/1 retrial queue with working vacations and a constant retrial rate. In the queue, customers decide about the entry based on the information upon their arrival instants. Scenarios regarding the availability of information (i.e., the server is occupied or not, and the server is on the vacation or not) for customers are compared. We derive the closed form solution for the stationary probabilities of the queue. Social optimizing and Nash equilibrium strategies for joining the system are investigated. Based on numerical results, the social benefit rate is best when customers know all information about the server.

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations of true quantum supremacy remain rare, quantum computing applications managing to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts. We develop a systematic method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. The marginal probabilities associated with root nodes (nodes without any parent nodes) are represented using rotation gates, and the conditional probability tables associated with non-root nodes are represented using controlled rotation gates. The controlled rotation gates with more than one control qubit are represented using ancilla qubits. The proposed approach is demonstrated for three examples: a 4-node oil company stock prediction, a 10-node network for liquidity risk assessment, and a 9-node naive Bayes classifier for bankruptcy prediction. The circuits were designed and simulated using Qiskit, a quantum computing platform that enables simulations and also has the capability to run on real quantum hardware. The results were validated against those obtained from classical Bayesian network implementations.

This research presents a new method of degradation feature extraction to predict remaining useful life, the remaining time to the maintenance, of rolling element bearings. Since bearing fault is the foremost cause of failure in rotating machinery, there are many studies for evaluating bearings’ health status to prevent a catastrophic failure. Most of these studies are based on health monitoring data, such as vibration signals that are indirectly related to bearing fault, from which degradation feature can be extracted. It is, however, challenging to extract a degradation feature that can be applied to all rolling elements. This study focuses on the amplitude decrease at specific frequencies, from which a robust degradation feature is extracted by employing the information entropy. Some important attributes are found from the degradation feature, which is used to predict the remaining useful life of bearings. This method is demonstrated using the real test data provided by FEMTO-ST Institute. The results show that bearings can be used up to 87% of their whole life and 59%–74% of life in average.

A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations and experiments). New samples are often selected based on the estimated uncertainty in the design space. While some surrogates such as kriging have readily available uncertainty models for their predictions, other surrogates do not. Consequently, there have been studies of using the process of leaving-samples-out (LSO) used in cross-validation tools to estimate prediction uncertainty, such as the universal prediction distribution (UPD). In this paper, an adaptive sampling scheme for general surrogates is proposed based on LSO, similar to cross-validation and interquartile range (IQR). Multiple submodels are first developed from LSO. The uncertainty is then estimated from the IQR of these surrogates at a given point. New samples are added iteratively at the point with maximum IQR for design space exploration. The proposed scheme is illustrated using kriging, radial basis function, and neural network surrogates. The proposed scheme is evaluated using four algebraic test functions. Multiple sets of initial samples were produced to account for randomness. For these test functions, the proposed scheme was found to be more accurate and robust than kriging with its own uncertainty model. The proposed scheme was more accurate than the UPD for three out of the four test functions. For a fixed number of samples, the IQR-based adaptive sampling also proved to be more accurate than all-at-once sampling in most cases even when the estimated uncertainty was only mildly correlated with prediction errors.

While equalization is usually a positively contributing factor towards opening eye diagrams further, sometimes over equalization can occur and degrade an eye opening as opposed to improve it. This paper explores the effectiveness of using post-cursors FFE transmitter equalization on 25.78Gb/s communication links with different transmitter and receiver equalization. The effect is observed on two topologies with different loss levels and impedance profiles. Simulation and experimental results are presented and discussed.

The power of quantum computers is still somewhat speculative. While they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work over the past three decades we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results., Revised substantially, and resubmitted to IEEE Transactions on Neural Networks and Learning Systems

A novel approach is introduced to estimate the change in the variance of the probability of failure by adding a sample to the Gaussian process (GP) in a conservative manner. Uncertainty in probability stems from prediction uncertainty and GP is used to represent the uncertainty. In the estimation of variance, a single-loop Monte Carlo Simulation (MCS) alleviates the computational burden. The result shows that the proposed methodology well predicts the change by a sample, maintaining the conservativeness by ignoring correlation in GP, yet the computational cost is at the same level as single-loop MCS.

This paper presents a tradeoff between shifting design and controlling sampling uncertainty in system reliability-based design optimization (RBDO) using the Bayesian network. The sampling uncertainty is caused by a finite number of samples used in calculating the reliability of a component, and it propagates to the system reliability. A conservative failure probability is utilized to consider sampling uncertainty. In this paper, the sensitivity of a conservative system failure probability is derived with respect to the design change and the number of samples in a component using Bayesian network along with global sensitivity analysis (GSA). In the sensitivity analysis, GSA is used for local sensitivity calculation. The numerical results show that sampling uncertainty can significantly affect the conservative system reliability and needs to be controlled to achieve the desired level of system reliability. Numerical examples show that both shifting design and reducing sampling uncertainty are crucial in the system RBDO.

Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is "learned," thus obviating the algorithm design difficulty. This works quite well for small systems. But the goal is macroscopic physical computation. Here, we implement our learned pairwise entanglement witness on Microsoft's Q#, one of the commercially available gate model quantum computer simulators; we perform statistical analysis to determine reliability and reproduceability; and we show that using the machine learning technique called "bootstrapping", we can infer the pattern for mesoscopic N from simulation results for three-, four-, five-, six-, and seven-qubit systems. Our results suggest a fruitful pathway for general quantum computer algorithm design and computation., Comment: Submitted to Quantum Information Processing

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) uses radioactive contrast agents as a tracer to provide tumor morphology and contrast kinetics information of tumor regions, which are crucial in breast cancer diagnosis and treatment. The effectiveness of the imaging modality relies on its capacity to acquire dynamic data at a sufficient sampling rate to gain the desired temporal resolution. This can be achieved by sparsely sampling the k-space data and applying advanced image reconstruction method that exploits compressed sensing, such as the recently proposed GenSeT (Generalized Series with Temporal constraint) method. Due to the highly nonlinear nature of compressed-sensing-based approach, computational complexity of the reconstruction algorithm is a practical challenge, especially for large breast DCE-MRI datasets. In this study, the GenSeT algorithm was implemented in GPU using CUDA platform to significantly reduce reconstruction time, yielding a much more practical solution. Experimental results showed that for a breast DCE-MRI data, the proposed GPU-based GenSeT implementation achieved approximately 48 times faster in the reconstruction time as compared to the CPU approach, without sacrificing the image quality. Although this work focuses on accelerating image reconstruction for sparsely-sampled breast DCE-MRI, the proposed GPU-based algorithm can be easily applied for sparsely sampled DCE-MRI of other organs.

In the data-driven approaches for engineering prognostics, the lack of run-to-fail (RTF) data has been one of the bottlenecks that hinders practical applications in the field. In order to tackle this issue, the dynamic time warping (DTW) method is presented to augment the RTF data obtained from different operating conditions or systems to the current system, which plays the role of virtual RTF data under the current condition. Once the virtual RTF data are available, they are used to train a neural network model to predict the remaining useful life of the current system. When multiple RTF data are available with different behavior under different failure modes, an RMSE-based performance criterion is proposed that can adaptively choose the closest match to the current data and use it as the virtual RTF data during the prognosis process. Numerical examples are given to show that the proposed DTW-based data augmentation can predict the RUL with less uncertainty than the conventional neural network model without data mapping.

The main objective of this paper is to quantify the effect of surrogate model uncertainty on reliability in addition to the aleatory randomness of the input variables, especially when Kriging surrogate model is utilized where the prediction uncertainty is modeled with a normal distribution. A novel approach is presented which requires only a single set of Monte Carlo Simulation (MCS) to precisely estimate the variance of reliability that is used as an uncertainty measure. It is found that the method only requires the bivariate cumulative distribution function, and the result shows that the uncertainty is well quantified without going through multiple numbers of MCS.

Although structural health monitoring (SHM) technologies using sensors have dramatically been developed recently, their capability should be evaluated from the perspective of the maintenance industry. As a first step toward utilizing sensors, the objective of the paper is to investigate the possibility of using sensors for inspecting the entire fuselage during C-check. First, we reviewed various sensors for their detection range, detectable damage size, and installed weight, which revealed that the piezoelectric wafer active sensor (PWAS) is the most promising sensor for aircraft SHM. Second, we performed a case study of inspecting the fuselage of Boeing-737NG using PWAS. To maintain the same detecting capability of manual inspection in C-check, we estimated the total number of sensors required. It turned out that utilizing sensors can reduce the maintenance downtime and thus, maintenance cost. However, even with a very conservative estimate, the lifetime cost was significantly increased due to the weight of sensor systems. The cost due to the weight increase was an order of magnitude higher than the cost saved by using SHM. We found that a large number of sensors were required to detect damage at unknown locations, which was the main cause of the weight increase. We concluded that to make SHM cost-effective, it would be necessary either to improve the current sensor technologies so that a less number of sensors are used or to modify the aircraft design concept for SHM.

In this demonstration, we present a prototype of Big Switch Abstraction to support network as service through the realization of Virtual Network Function Forwarding Graphs and Network forwarding paths.