Extensive Simulations Research Articles

Accelerating deep neural network learning using data stream methodology

This paper introduces a novel machine learning approach, called BBATDD (Boosting-Based Algorithm Trained with Drift Detector), that departs from traditional epoch-based methods by adopting a data stream generation strategy from the original dataset. The proposed method is versatile, applicable to a broad range of deep structures and other machine learning architectures. The key to its effectiveness is the conversion of original data into a streaming sequence, which is then fed into the neural network structure. To monitor the learning process and prevent overfitting, a drift detector, commonly used in stream data mining, is employed. Additionally, two methods, ELB and ENT, are introduced to prioritize problematic data elements during the learning process, enhancing overall model performance. Extensive simulations on benchmark datasets (MNIST and CIFAR) validate that BBATDD achieves higher accuracy with significantly fewer batches, without sacrificing generalization capability. For instance, when applied to the MNIST dataset and processing 10 000 batches while comparing loss function values, our approach demonstrates a remarkable 45.6% improvement.

A novel single-channel edge computing LoRa gateway for real-time confirmed messaging

LoRaWAN has become the technology of choice for increasing Internet of Things applications owing to its long range and low power consumption characteristics. However, in the uplink confirmed messaging cases, the entire retransmission could take several seconds, so it cannot be used in scenarios that require rapid confirmed messaging, such as emergency alerting and real-time controlling applications. Nevertheless, there has been limited work targeting this issue. This study presents a novel LoRaWAN gateway using edge computing to expedite the confirmed messaging process by generating the acknowledgment (ACK) locally, so that the confirmed messaging time can be significantly reduced. Additionally, the resource utilization of the network server can also be decreased due to the use of edge computing. We verified the effectiveness of our solution through extensive simulations and experiments. The confirmed messaging time between the end nodes and the gateway averaged 43 ms for a maximum of 2 retransmissions. With the adoption of edge computing on the gateway, the network server’s central processing unit (CPU), memory, and bandwidth peak utilization decrease from 53.51 to 39.46, 73.88 to 72.11%, and 4422.68 kbps to 3271.27 kbps, respectively. In addition, the network server’s system load decreases from 2.15 to 1.69, while the gateway cost is reduced by almost $\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\$$$\\end{document}38 compared to the benchmark products.

Toeplitz covariance estimation with applications to MUSIC

We consider the maximum likelihood estimation of a covariance matrix that is known to be Toeplitz. The corresponding optimization problem is nonconvex, and to compute a local minimizer we propose an efficient implementation of Newton’s method. The cost per iteration of a straightforward implementation is of order O(n4), where n+1 is the dimension of the covariance matrix. By exploiting fast Fourier transforms to assemble the gradient and Hessian we reduce the iteration cost to order O(n3). Through extensive simulations we demonstrate that for direction-of-arrival estimation, the performance of MUSIC is significantly enhanced by using it together with the maximum likelihood Toeplitz covariance estimate. The simulations also demonstrate that our open-source implementation is orders of magnitude faster than two existing Toeplitz covariance estimators.

Model order estimation in multi-component chirp signals

Real datasets exhibiting non-constant frequencies, can be analyzed with the help of multi-component chirp signals adequately. This requires estimation of the number of chirp components in practice. The problem of estimation becomes difficult when the data is composed of moderate to large number of chirp components. We propose Bayesian approach to estimate the number of components of a multi-component chirp model using an approximation to the mode of the posterior distribution. This approximation is motivated from the relation between mode of the obtained posterior and the sequential least squares estimators. Extensive numerical simulations highlight superior performance of the proposed estimators than that of the estimators based on Akaike information criterion, Bayesian information criterion and asymptotic maximum a posteriori probability, for a large range of signal-to-noise ratio levels. We further illustrate the implementation of the proposed estimators through two real data examples.

Absence of Barren Plateaus in Finite Local-Depth Circuits with Long-Range Entanglement.

Ground state preparation is classically intractable for general Hamiltonians. On quantum devices, shallow parametrized circuits can be effectively trained to obtain short-range entangled states under the paradigm of variational quantum eigensolver, while deep circuits are generally untrainable due to the barren plateau phenomenon. In this Letter, we give a general lower bound on the variance of circuit gradients for arbitrary quantum circuits composed of local 2-designs. Based on our unified framework, we prove the absence of barren plateaus in training finite local-depth circuits (FLDC) for the ground states of local Hamiltonians. FLDCs are allowed to be deep in the conventional circuit depth to generate long-range entangled ground states, such as topologically ordered states, but their local depths are finite, i.e., there is only a finite number of gates acting on individual qubits. This characteristic sets FLDC apart from shallow circuits: FLDC in general cannot be classically simulated to estimate local observables efficiently by existing tensor network methods in two and higher dimensions. We validate our analytical results with extensive numerical simulations and demonstrate the effectiveness of variational training using the generalized toric code model.

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling.

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.

Large deviations in statistics of the convex hull of passive and active particles: A theoretical study.

We investigate analytically the distribution tails of the area A and perimeter L of a convex hull for different types of planar random walks. For N noninteracting Brownian motions of duration T we find that the large-L and -A tails behave as P(L)∼e^{-b_{N}L^{2}/DT} and P(A)∼e^{-c_{N}A/DT}, while the small-L and -A tails behave as P(L)∼e^{-d_{N}DT/L^{2}} and P(A)∼e^{-e_{N}DT/A}, where D is the diffusion coefficient. We calculated all of the coefficients (b_{N},c_{N},d_{N},e_{N}) exactly. Strikingly, we find that b_{N} and c_{N} are independent of N for N≥3 and N≥4, respectively. We find that the large-L (A) tails are dominated by a single, most probable realization that attains the desired L (A). The left tails are dominated by the survival probability of the particles inside a circle of appropriate size. For active particles and at long times, we find that large-L and -A tails are given by P(L)∼e^{-TΨ_{N}^{per}(L/T)} and P(A)∼e^{-TΨ_{N}^{area}(sqrt[A]/T)}, respectively. We calculate the rate functions Ψ_{N} exactly and find that they exhibit multiple singularities. We interpret these as DPTs of first order. We extended several of these results to dimensions d>2. Our analytic predictions display excellent agreement with existing results that were obtained from extensive numerical simulations.

Affine formation maneuver control of underactuated surface vessels: Guaranteed safety under moving obstacles in narrow channels

This research tackles the challenge of dynamic formation control and collision avoidance in underactuated surface vessels (USVs). Firstly, an affine transformation is employed to implement arbitrary configuration transformations within the multi-USV system. Secondly, by considering both distances and velocities between USVs and obstacles, a novel collision avoidance scheme is proposed based on conventional distance-based artificial potential field methods. This method, building on traditional distance-based artificial potential fields, offers a more precise assessment of collision risks, thereby notably shrinking the potential collision zones. Such refinement leads to reduced energy consumption and minimizes disruption to formation integrity. Thirdly, an anti-perturbation formation maneuver safety control scheme is introduced to achieve a time-varying target formation without collisions, ensuring that local position tracking errors remain bounded. The effectiveness of the proposed approaches is substantiated through comprehensive theoretical analysis and extensive numerical simulations, underscoring their practical applicability.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

From a vortex gas to a vortex crystal in instability-driven two-dimensional turbulence

We study structure formation in two-dimensional turbulence driven by an external force, interpolating between linear instability forcing and random stirring, subject to nonlinear damping. Using extensive direct numerical simulations, we uncover a rich parameter space featuring four distinct branches of stationary solutions: large-scale vortices, hybrid states with embedded shielded vortices (SVs) of either sign, and two states composed of many similar SVs. Of the latter, the first is a dense vortex gas where all SVs have the same sign and diffuse across the domain. The second is a hexagonal vortex crystal forming from this gas when the linear instability is sufficiently weak. These solutions coexist stably over a wide parameter range. The late-time evolution of the system from small-amplitude initial conditions is nearly self-similar, involving three phases: initial inverse cascade, random nucleation of SVs from turbulence and, once a critical number of vortices is reached, a phase of explosive nucleation of SVs, leading to a statistically stationary state. The vortex gas is continued in the forcing parameter, revealing a sharp transition towards the crystal state as the forcing strength decreases. This transition is analysed in terms of the diffusivity of individual vortices using ideas from statistical physics. The crystal can also decay via an inverse cascade resulting from the breakdown of shielding or insufficient nonlinear damping acting on SVs. Our study highlights the importance of the forcing details in two-dimensional turbulence and reveals the presence of non-trivial SV states in this system, specifically the emergence and melting of a vortex crystal.

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

MAB-RP: A Multi-Armed Bandit based workers selection scheme for accurate data collection in crowdsensing

Accurate data collection from workers is crucial for the success of Mobile Crowd Sensing (MCS) applications. However, Current studies exhibit several drawbacks. Firstly, the workers' sensing qualities remain unknown even after the platform acquires the data submitted by the workers, known as the Post Unknown Worker Selection (PUWS) problem. Secondly, systematic deviations between worker data and the Ground Truth Data (GTD) reduce the quality of MCS applications. Thirdly, the data collected by workers for different tasks may vary in accuracy, resulting in low-quality data collection. To address these challenges, we propose a novel Multi-armit-based worker selection scheme with reputation and preference (MAB-RP). The proposed scheme aims to select credible workers for high-quality data collection through trust identification, thus addressing the PUWS issue after worker recruitment. Additionally, the scheme employs a learning-based approach to identify and correct the gaps between the sensed data and the GTD, ultimately improving the accuracy of data collection. Lastly, a matching-based approach is used to identify workers' sensing qualities for different tasks, further enhancing the accuracy of data collection in MCS. Extensive simulations on real-world datasets demonstrate that the proposed MAB-RP scheme outperforms previous strategies in terms of both data quality and cost.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies (GWAS). However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using Bias corrected Estimating Equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

Tidal turbine hydrofoil design and optimization based on deep learning

The optimal design of hydrofoils is critical to improve the hydrodynamic performance of the tidal turbine. However, the global optimization of hydrofoils is limited by the high dimensionality of the design space, which requires extensive computational fluid dynamics simulations. This paper proposes an interactive framework for hydrofoil design and optimization based on deep learning. Generative adversarial networks are used to parameterize the hydrofoil design, which automatically learns representations from existing hydrofoils and controls new hydrofoil generation using fewer variables to reduce optimization dimensions. Moreover, the surrogate model based on convolutional neural networks is constructed, which realizes the mapping of hydrofoil design and operating parameters to hydrodynamic performance parameters. The framework can generate a large number of smooth and realistic hydrofoils with three design variables and quickly predict the performance, enabling effective optimization design of hydrofoils. The results show that the optimized hydrofoil shapes have larger lift-to-drag ratios than those of the common hydrofoils. Furthermore, the optimized hydrofoil is applied to the design of 3D horizontal axis tidal turbine blades. The simulation results show that the framework is effective and stable, which can facilitate the design of tidal turbine rotors and provide hydrofoils with higher power coefficients.

Direction-of-Arrival Estimation via Sparse Bayesian Learning Exploiting Hierarchical Priors with Low Complexity.

For direction-of-arrival (DOA) estimation problems in a sparse domain, sparse Bayesian learning (SBL) is highly favored by researchers owing to its excellent estimation performance. However, traditional SBL-based methods always assign Gaussian priors to parameters to be solved, leading to moderate sparse signal recovery (SSR) effects. The reason is Gaussian priors play a similar role to l2 regularization in sparsity constraint. Therefore, numerous methods are developed by adopting hierarchical priors that are used to perform better than Gaussian priors. However, these methods are in straitened circumstances when multiple measurement vector (MMV) data are adopted. On this basis, a block-sparse SBL method (named BSBL) is developed to handle DOA estimation problems in MMV models. The novelty of BSBL is the combination of hierarchical priors and block-sparse model originating from MMV data. Therefore, on the one hand, BSBL transfers the MMV model to a block-sparse model by vectorization so that Bayesian learning is directly performed, regardless of the prior independent assumption of different measurement vectors and the inconvenience caused by the solution of matrix form. On the other hand, BSBL inherited the advantage of hierarchical priors for better SSR ability. Despite the benefit, BSBL still has the disadvantage of relatively large computation complexity caused by high dimensional matrix operations. In view of this, two operations are implemented for low complexity. One is reducing the matrix dimension of BSBL by approximation, generating a method named BSBL-APPR, and the other is embedding the generalized approximate message passing (GAMB) technique into BSBL so as to decompose matrix operations into vector or scale operations, named BSBL-GAMP. Moreover, BSBL is able to suppress temporal correlation and handle wideband sources easily. Extensive simulation results are presented to prove the superiority of BSBL over other state-of-the-art algorithms.

Mechanistic insights into G-protein activation via phosphorylation mediated non-canonical pathway

Activation of heterotrimeric G-proteins (Gαβγ) downstream to receptor tyrosine kinases (RTKs) is a well-established crosstalk between the signaling pathways mediated by G-protein coupled receptors (GPCRs) and RTKs. While GPCR serves as a guanine exchange factor (GEF) in the canonical activation of Gα that facilitates the exchange of GDP for GTP, the mechanism through which RTK phosphorylations induce Gα activation remains unclear. Recent experimental studies revealed that the epidermal growth factor receptor (EGFR), a well-known RTK, phosphorylates the helical domain tyrosine residues Y154 and Y155 and accelerates the GDP release from the Gαi3, a subtype of Gα-protein. Using well-tempered metadynamics and extensive unbiased molecular dynamics simulations, we captured the GDP release event and identified the intermediates between bound and unbound states through Markov state models. In addition to weakened salt bridges at the domain interface, phosphorylations induced the unfolding of helix αF, which contributed to increased flexibility near the hinge region, facilitating a greater distance between domains in the phosphorylated Gαi3. Although the larger domain separation in the phosphorylated system provided an unobstructed path for the nucleotide, the accelerated release of GDP was attributed to increased fluctuations in several conserved regions like P-loop, switch 1, and switch 2. Overall, this study provides atomistic insights into the activation of G-proteins induced by RTK phosphorylations and identifies the specific structural motifs involved in the process. The knowledge gained from the study could establish a foundation for targeting non-canonical signaling pathways and developing therapeutic strategies against the ailments associated with dysregulated G-protein signaling.

Active fault control based on interval observer for discrete-time linear switched systems

This paper presents a comprehensive investigation on active fault tolerant control (AFTC) for discrete-time switched systems subject to disturbances and known switching signals. The proposed approach employs interval observers in a two-step implementation strategy. Firstly, a fault detection and isolation (FDI) module based on interval observers is introduced to estimate actuator faults affecting the switched system. In order to enhance the accuracy of this module, an L∞ criterion is utilized. Secondly, a feedback control law is designed to compensate the impact of actuator faults. The controller parameters are determined by solving linear matrix inequalities (LMI). The effectiveness of the proposed AFTC scheme is demonstrated through extensive analysis and simulation studies.