Nonlinear Method Research Articles

Seismic Monitoring near Ithaca, New York, Reveals Nonuniform Distribution of Microseismicity in an Intraplate Region

Abstract Cornell University intends to use a deep direct-use geothermal system to heat its Ithaca, New York, campus. In preparation for this project, the Cornell Seismic Network has been monitoring the background seismicity in this intraplate region since 2019. From January 2020 to June 2023, 95 events were detected within 20 km of the proposed geothermal well site, with local magnitudes ranging from −1.02 to 0.56. None of these events appear in regional or national catalogs. Events locate in a narrow geographic band, with one-fourth exhibiting multimodal hypocentral probability peaks both near the surface and at 1–4 km depth. We relocate events with a joint hypocenter and 1D velocity model inversion, in addition to a fully nonlinear method, and then compare observations with synthetic waveforms. Together, these approaches provide strong evidence for >95% of events locating at the surface or within the 3-km-thick sedimentary sequence. We explore how anthropogenic activity and regional topographic stress may contribute to frequent surficial events. This information is critical for characterizing the background microseismicity for comparison during future geothermal operations. Ithaca’s geology of Paleozoic sediments overlying Precambrian crystalline basement is typical of many continental interiors, so these results also provide insight into intraplate microseismicity patterns.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Abstract Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

The Ascendancy of Video Games: Can They Eclipse Literary Works?

This article investigates the factors contributing to the rise of video games and their potential to surpass literature in narrative expression. By examining extensive research on the intersection of video games and literature, the study dissects their complex interrelationships, mutual impacts, and shared evolution. Video game narratives, often inspired by literary predecessors, have become increasingly complex and engaging, drawing on literary techniques and non-linear storytelling methods from postmodernism and metafiction. These narratives challenge players to actively construct meaning, enhancing emotional resonance through character development, evocative prose, and carefully crafted dialogue. Technological advancements have played a crucial role in the evolution and rising popularity of video games, offering immersive, interactive experiences that empower players to shape the narrative and storyline, contrasting with literature, which relies on the reader's imagination and interpretation. The future of storytelling is likely to be shaped by the ongoing interplay between video games and literature. The integration of literary techniques and interactive elements across these mediums will continue to evolve, resulting in innovative hybrid entertainment forms that leverage the strengths of both literature and video games. This dynamic interaction ensures a diverse and vibrant future for storytelling, reflecting the multidimensionality of human creativity and imagination. As video games continue to incorporate more sophisticated literary techniques and storytelling methods, they become more capable of delivering rich, nuanced narratives that rival those found in traditional literature. This evolution opens up new possibilities for hybrid forms of storytelling that combine the best elements of both video games and literature. By embracing these hybrid forms, creators can push the boundaries of narrative expression and create new, immersive experiences that engage audiences in ways previously unimaginable. The article concludes that the future of storytelling lies in the continued convergence of video games and literature, promising a diverse and dynamic landscape that celebrates human creativity and imagination in all its forms.

Modeling and Control of Multi‐Stack Fuel Cell Air System based on Nonlinear Model Predictive Control Method

Hydrogen is crucial for achieving SDGs by driving energy transition and combating climate change. Proton exchange membrane fuel cell technology, leveraging hydrogen, faces challenges in meeting high‐power demands. The multistack fuel cell system (MFCS) tackles this by integrating multiple substacks, yet its air supply needs meticulous control. Proportional integral derivative (PID) decoupling from single‐stack falls short of MFCS. This article proposes nonlinear model predictive control (NMPC) for optimized air flow and pressure decoupling. Modeling MFCS's air system and designing a predictive model, it is aimed to ensuring precise control of air flow and pressure in each substack. The decoupling experiments show that NMPC outperforms PID, accurately managing air flow and pressure and reducing load fluctuations. For air mass flow, NMPC cuts mean‐absolute error (MAE) by 64.56% and root‐mean‐square error (RMSE) by 81.36%. For pressure, MAE drops 81.23% and RMSE 83.59%. Comprehensive step load tests confirm NMPC's precise, dynamic regulation too, compared to PID, NMPC lowers average MAE for air mass by 20.67%, pressure by 32.22%. RMSE improvements of 31.08% and 33.23% highlight NMPC's strength. NMPC's quick response mitigates coupling issues, enhancing vehicle load adaptability.

Study on Mechanical Properties of Tunnel Flame Retardant Asphalt Pavement Considering the Ablative Effect

With the widespread utilization of composite pavements in highway tunnel projects, the flammability issue of asphalt layers has garnered increasing attention. Previous research has demonstrated that the addition of flame retardants effectively reduces the occurrence of pavement fires. However, there are few studies combining asphalt flame retardant technology with tunnel composite pavement structures. Therefore, this paper, considering the context of actual engineering projects, uses ABAQUS software to analyze and calculate the mechanical response of the tunnel composite pavement, considering the thickness of various asphalt layers under the ablation depth. Key design indexes such as shear stress and lower tensile strain of the asphalt layer, the bottom tensile stress of the cement concrete subgrade, and interlayer shear stress between the asphalt layer and cement concrete subgrade of composite pavements are analyzed using the nonlinear finite element analysis method. The findings enable the determination of the optimal thickness of the composite pavement asphalt layer combination after ablation, thus contributing to enhancing the safety of tunnel pavements.

Ship autonomous collision avoidance decision from the perspective of navigation practice

Navigation safety is an eternal theme for ships, and it is an inherent strategic requirement for both transportation powers and maritime powers. With the development of intelligent ships, how to make intelligent decision-making results consistent with maritime practice is the key to navigation safety. This paper constructs a complex water area collision avoidance decision-making model that conforms to maritime practice, providing autonomous collision avoidance decision-making schemes for manned or unmanned ships. Firstly, based on the nonlinear velocity obstacle method, dynamic constraints such as ship maneuverability, target ship motion uncertainty, Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and good seamanship are fully considered, and a safe collision avoidance action plan is solved in the velocity domain; Then, by introducing an overshoot coefficient to improve the line of sight method, a feasible collision avoidance decision plan is derived by searching for a resumption navigation plan based on the safe avoidance action plan; Finally, collision avoidance decision plan is optimized to achieve autonomous collision avoidance decision-making for ships. To verify the effectiveness of the proposed model, a series of case studies, which focus on different encounter situations are designed and executed, the results indicate that the outcome of the model consistently aligns with real-world scenarios, which is in line with maritime practice.

Application of the high-resolution layer-stripping constrained seismic inversion method in thin reservoirs: a case study of Triassic reservoir prediction in Lunnan, Tarim Basin, NW China

The Lunnan Oilfield’s Triassic reservoir is a braided river delta deposition, exhibiting notable characteristics such as rapid lateral sand body changes, thin reservoir thickness, and deep reservoir burial. However, the conventional seismic inversion technique used in this geological context is hindered by the low accuracy of the low-frequency model, resulting in inversion outcomes with limited vertical resolution. Consequently, accurately characterizing the reservoir distribution becomes challenging, posing a significant obstacle for the subsequent exploration and development. In this study, we introduce an enhanced seismic inversion approach using high-resolution layer-stripping constraints. The process begins with the creation of a fine-grained layer sequence grid on wells through Integrated Prediction Error Filter Analysis (INPEFA) technology. Subsequently, this grid aids in interpreting high-resolution sequence layers on the seismic volume, obtained through a frequency division RGB (Red, Green, and Blue) fusion technique. Finally, a novel nonlinear stochastic inversion method is formulated, utilizing the high-resolution sequence to refine both the initial model and inverted results. This innovative seismic inversion technique significantly enhances accuracy, particularly in predicting thin reservoirs, approximately 3 meters thick. The application of this method in a case study on Triassic reservoir prediction in Lunnan, Tarim Basin, NW China, demonstrates its promising future applications.

A nonlinear interval finite element method for elastic–plastic problems with spatially uncertain parameters

This paper proposes a nonlinear interval finite element method for elastic–plastic analysis of structures with spatially uncertain parameters. The spatially uncertain parameters are described by the interval field, and the variation bounds of the elastic–plastic structural responses can be calculated effectively. Quantified by the interval field, the spatially uncertain parameters are represented by the interval Karhunen–Loève (K-L) expansion, based on which the nonlinear interval finite element equilibrium equation is formulated. An interval iterative method is then presented to solve the equilibrium equation and obtain an outer solution of the variation bounds of structural responses such as displacement. In this method, the Newton-Raphson iterative method is used to transform the nonlinear problem into a linear one, and then the interval iterative method is introduced to solve the interval linear equations. Three numerical examples are employed to illustrate the feasibility and accuracy of the proposed method.

Simulation study on the thermal effect of continuous laser heating quartz materials.

The continuous development and application of laser technology, and the increasing energy and power of laser output have promoted the development of various types of laser optical systems. The optical components based on quartz materials are key components of high-power laser systems, and their quality directly affects the load capacity of the system. Due to the photothermal effect when the laser interacts with the quartz material and generates extremely high temperatures in a short period of time, it is impossible to experimentally solve the phenomena and physical mechanisms under extreme conditions. Therefore, it is very important to select a suitable method to investigate the thermal effect of intense laser interaction with quartz materials and explain the related physical mechanism. In this study, a three-dimensional quarter-symmetric laser heating quartz material geometry model by using nonlinear transient finite element method was established, and its transient temperature field distribution of the quartz material after being heated by a 1,064nm continuous laser was investigated. In addition, the influence of different laser parameters (laser spot radius, heat flux and irradiation time), material parameters (material thickness, material absorption rate of laser) on the thermal effect of heating quartz material were also studied. When the laser heat flux is 20W/cm2, the diameter of the laser spot is 10cm, the irradiation time is 600s and the thickness is 4cm, the temperature after laser heating can reach 940.18°C, which is far lower than the melting point. In addition, the temperature maximum probes were set at the overall model, spot edge and rear surface respectively, and their temperature rise curves with time were obtained. It is also found that there is a significant hysteresis period for the rear surface temperature change of the quartz material compared with the overall temperature change due to heat conduction. Finally, the method proposed can also be applied to the laser heating of other non-transparent materials.

Identification, uncertainty quantification and validation of orthotropic material properties for additively manufactured polymers

The mechanical behavior of polymeric parts manufactured by fused filament fabrication is often characterized as orthotropic because of the layer-wise extrusion of the material. To perform reliable simulations of the mechanical response of these parts subjected to certain loads, the material parameters have to be known. This leads to the task of material parameter identification from suitable experimental data. In this work, the identification of the material parameters for orthotropic material behavior is studied in detail with a focus on the specific characteristics of additively manufactured parts. This comprises working out the differences that arise compared to the parameter identification for classical orthotropic composite materials. Moreover, the material parameters are identified from tensile and shear test information together with full-field displacement data from digital image correlation measurements. Then, the obtained parameters are compared for analytical and numerical approaches, where a non-linear least-squares method with finite elements is drawn on. The parameters are not only identified, but also an uncertainty analysis is carried out and, subsequently, the different methods are validated. Further, a clear derivation of commonly applied analytical equations is provided with a special focus on the underlying assumptions. It turns out that the analytical parameter identification procedures cannot be easily transferred to numerical procedures, especially when using full-field data, because of correlations between the parameters. Moreover, the identified parameters are employed to prove the reasonability of orthotropic material behavior for specimens manufactured by fused filament fabrication. Finally, during validation it turns out that all parameter identification methods provide a good agreement with experimental data, showing only small differences between the results of the different calibration strategies.

Gaussian bistable cascade double feedback stochastic resonance weak signal enhancement detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

The urban desirability paradox: U.K. urban-rural differences in well-being, social satisfaction, and economic satisfaction.

As the majority of the global population resides in cities, it is imperative to understand urban well-being. While cities offer concentrated social and economic opportunities, the question arises whether these benefits translate to equitable levels of satisfaction in these domains. Using a robust and objective measure of urbanicity on a sample of 156,000 U.K. residents aged 40 and up, we find that urban living is associated with lower scores across seven dimensions of well-being, social satisfaction, and economic satisfaction. In addition, these scores exhibit greater variability within urban areas, revealing increased inequality. Last, we identify optimal distances in the hinterlands of cities with the highest satisfaction and the least variation. Our findings raise concern for the psychological well-being of urban residents and show the importance of nonlinear methods in urban research.

Quadrotor Trajectory Tracking Via SMC-Embedded Wild Horse Optimization

This study provides an integrated control strategy with proportional derivative (PD) and sliding mode control (SMC) for a quadrotor. For inner loop control, the SMC, a robust nonlinear control method, is used to increase the system's resistance to uncertainty and shocks. By using high-speed switching in variable structure control, it reduces tracking mistakes. In the meantime, the PD control concentrates on tracking a helical path while controlling the outer loop for trajectory tracking. The PD and SMC parameters (k1, k2, and k3) could not be manually adjusted to produce acceptable results at first. As a result, this work presents an improvement by optimizing these control parameters using the Wild Horse Optimization (WHO) algorithm, a modern and cutting-edge optimization technique. This improvement shows good results in improving the quadrotor's tracking precision and stability, and it also greatly increases system performance.All simulations were performed in MATLAB, which facilitated detailed analysis and validation of the proposed control strategy.

Frailty Modeling using Machine Learning Methodologies: A Systematic Review with Discussions on Outstanding Questions.

Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic techniques rather than available advanced machine learning methods, which may be sub-optimal. There is no large-scale systematic review on applications of machine learning methods on frailty modeling. In this study we explore the use of machine learning methods to predict or classify frailty in older persons in routinely collected data. We reviewed 181 research articles, and categorised analytic methods into three categories: generalised linear models, survival models, and non-linear models. These methods have a moderate agreement with existing frailty scores and predictive validity for adverse outcomes. Limited evidence suggests that non-linear methods outperform generalised linear methods. The top-three predictor/input variables are specific diagnosis or groups of diagnoses, functional performance (e.g., ADLs), and impaired cognition. Mortality, hospital admissions and prolonged hospital stay are the mainly predicted outcomes. Most studies utilise classical machine learning methods with cross-sectional data. Longitudinal data collected by wearable sensors have been used for frailty modeling. We also discuss the opportunities to use more advanced machine learning methods with high dimensional longitudinal data for more personalised and accessible frailty tools.

Full waveform inversion of cross-hole radio frequency electromagnetic data

Summary We consider application of full waveform inversion (FWI) to radio-frequency electromagnetic (EM) data. Radio-frequency imaging (RIM) is a cross-borehole technique to image electromagnetic subsurface properties from measurements of transmitted radio-frequency waves. It is used in coal seam imaging, ore exploration and various engineering and civil engineering applications. RIM operates at frequencies from 50 kHz to several tens of MHz. It differs from other geophysical EM methods, because the frequency band includes the transition between the wave propagation and diffusion regimes. RIM data are acquired in two-dimensional cross-hole sections in a reciprocal manner. Traditionally, radio-frequency data are inverted by straight ray tomography because it is inexpensive and easy to implement. It is argued that due to attenuation, the sensitivity of the transmitted electric field is the strongest within the first Fresnel zone of the ray connecting the transmitter and receiver. While straight ray tomography is a simple to implement and fast method, the non-linearity in the relationship between model parameters and data is often strong enough to warrant non-linear inversion techniques. FWI is an iterative high resolution technique, in which the physical properties are updated to minimize the misfit between the measured and modelled wavefields. Full waveform techniques have been used and extensively studied for the inversion of seismic data, and more recently, they have been applied to the inversion of GPR data. Non-linear inversion methods for RIM data are less advanced. Their use has been hindered by the high cost of full wave modelling and the high conductivity contrasts of many RIM targets, and, to some extent, by the limitations of the measuring instruments. We present the first application of this methodology to simultaneous conductivity and permittivity inversion of RIM data. We implement the inversion in the frequency domain in two dimensions using L-BFGS optimization. We analyze the sensitivity of the data to the model parameters and the parameter trade-off and validate the proposed methodology on a synthetic example with moderate conductivity variations and localized highly conductive targets. We then apply the FWI methodology to a field data set from Sudbury, Canada. For the field data set, we determine the most appropriate preprocessing steps that take into account specific peculiarities of RIM: the insufficient prior information about the subsurface and the limitations of the measuring equipment. We show that FWI is applicable under the conditions of RIM and is robust to imperfect prior knowledge: we obtain satisfactory model recoveries starting from homogeneous initial models in all of our examples. Just as other methods, FWI underestimates large conductivity contrasts due to the loss of sensitivity of the transmitted electric field to the conductivity variations as the conductivity increases above a certain level. The permittivity inside high conductors can not be recovered, however, recovering permittivity variations in the resistive zones helps obtain better focused conductivity images with fewer artifacts. Overall, FWI produces cleaner, less noisy and higher resolution reconstructions than the methods currently used in practice.

A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Context: Epilepsy is a neurological disease that affects more than 50 million people worldwide, causing recurrent seizures, with a significant impact on patients' quality of life due to abnormally synchronized neuronal activity. Method: This article discusses three methods used for signal analysis in patients diagnosed with epilepsy. Conventional signal decomposition methods, such as the fast Fourier transform, widely used in signal analysis based on time series techniques, have some issues when analyzing nonlinear and non-stationary signals, in addition to difficulties in detecting low-order frequencies. Results: To overcome these limitations, alternatives such as empirical mode decomposition and one of its variants, called ensemble empirical mode decomposition, have been developed. These techniques allow observing different oscillation modes through intrinsic mode functions and instantaneous frequencies. Conclusions: In this study, the results obtained through the aforementioned techniques were compared, revealing the impact of nonlinear methods on the reconstruction of brain activity.

Operational stability study of lactate biosensors: modeling, parameter identification, and stability analysis.

This paper investigates the operational stability of lactate biosensors, crucial devices in various biomedical and biotechnological applications. We detail the construction of an amperometric transducer tailored for lactate measurement and outline the experimental setup used for empirical validation. The modeling framework incorporates Brown and Michaelis-Menten kinetics, integrating both distributed and discrete delays to capture the intricate dynamics of lactate sensing. To ascertain model parameters, we propose a nonlinear optimization method, leveraging initial approximations from the Brown model's delay values for the subsequent model with discrete delays. Stability analysis forms a cornerstone of our investigation, centering on linearization around equilibrium states and scrutinizing the real parts of quasi-polynomials. Notably, our findings reveal that the discrete delay model manifests marginal stability, occupying a delicate balance between asymptotic stability and instability. We introduce criteria for verifying marginal stability based on characteristic quasi-polynomial roots, offering practical insights into system behavior. Qalitative examination of the model elucidates the influence of delay on dynamic behavior. We observe a transition from stable focus to limit cycle and period-doubling phenomena with increasing delay values, as evidenced by phase plots and bifurcation diagrams employing Poincaré sections. Additionally, we identify limitations in model applicability, notably the loss of solution positivity with growing delays, underscoring the necessity for cautious interpretation when employing delayed exponential function formulations. This comprehensive study provides valuable insights into the design and operational characteristics of lactate biosensors, offering a robust framework for understanding and optimizing their performance in diverse settings.

Research on an Active Adjustment Mechanism Based on Non-Singular Terminal Sliding Mode and Finite-Time Disturbance Observer

With the continuous development of synchrotron radiation light sources, higher requirements have been put forward for the stability of double-crystal monochromators in synchrotron radiation facilities. This paper designs an active adjustment mechanism for a double-crystal monochromator to improve its stability. Firstly, three spatial degrees of freedom are designed based on the active adjustment mechanism of flexible leaf spring parallel coupling, and the prototype of the mechanism is fabricated. Secondly, system identification experiments are carried out and the system transfer function curve is fitted by the nonlinear least squares method. Thirdly, the controller based on non-singular terminal sliding modes and a finite-time disturbance observer was designed for stability control and disturbance compensation. Finally, the effectiveness of the controller is verified by a model-in-the-loop approach based on the performance of the real-time target machine. The results show that the non-singular terminal sliding mode + finite-time disturbance observer control strategy can reduce the RMS value of the vibration displacement of Axis-1/Axis-2/Axis-3 by 81.25%, 78.53%, and 71.82%.