Exact Input Research Articles

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation. By classifying historical water demand into fuzzy clusters, the framework allows for certain linguistic inputs (e.g., "high" or "low" demand) to be used in water quality simulations. The framework also incorporates representative hourly demand patterns and temperature-dependent chlorine decay constants based on historical data correlations. Validations were conducted on the Anytown network using WNTR-EPANET, comparing simulated chlorine residuals with Validation data from 181 steady-state simulations. The simulation through the framework achieved a Jensen-Shannon Divergence (JSD) of <0.008 across all demand clusters, indicating high similarity between predicted and actual probability distributions . In comparison to other simulation scenarios tested, which exhibited increased variability (JSD > 0.18), the proposed framework demonstrated improved accuracy in representing chlorine residual distributions. The methodology is adaptable to other systems, if similar historical datasets containing key variables, such as flow rates and temperature, are provided. While the framework offers a more flexible and accurate approach to handling uncertainties in WDS, its effectiveness is contingent upon the availability of robust historical demand and temperature data for decay constant calibration.

Coronal underspecification as an emerging property in the development of speech processing.

Is the developing lexicon phonologically detailed or are representations underspecified? Experimental results from toddlers suggest phonological specificity. By contrast, the featurally underspecified lexicon theory (Lahiri, 2018; Lahiri & Reetz, 2010), motivated by evidence such as the cross-linguistic prevalence of phenomena such as coronal assimilation (rainbow → rai[m]bow), proposes that coronal sounds are unspecified for place of articulation even in the adult lexicon. The featurally underspecified lexicon, therefore, predicts that asymmetries in mispronunciation sensitivity are also present in the developing lexicon. Recent research (Ren et al., 2019) has rejected this, reporting similar sensitivity to mispronunciation of coronals and noncoronals at 19 months. Using a more sensitive experimental paradigm, we provide new evidence demonstrating a lack of asymmetries at 18 months, but mispronunciation sensitivity for coronals disappears by 24 months. In an intermodal preferential looking study, growth curve analysis shows that 18-month-olds are sensitive to mispronunciations of words with a coronal (e.g., duck vs. *buck) and noncoronal (e.g., bird vs. *dird) onset. At 24 months, mispronunciations of coronal-onset words were treated just like the accurate pronunciations. We conclude that coronals are underspecified in the developing lexicon at 24 months. We propose a model under which initial representations are phonetic in nature and require exact acoustic input, whereas phonological coronal underspecification at the lexical level emerges gradually as a result of exposure to variation in the input such as coronal assimilations that only become detectable patterns with growing lexical and segmentation skills. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Minimally Distorted Adversarial Images with a Step-Adaptive Iterative Fast Gradient Sign Method

The safety and robustness of convolutional neural networks (CNNs) have raised increasing concerns, especially in safety-critical areas, such as medical applications. Although CNNs are efficient in image classification, their predictions are often sensitive to minor, for human observers, invisible modifications of the image. Thus, a modified, corrupted image can be visually equal to the legitimate image for humans but fool the CNN and make a wrong prediction. Such modified images are called adversarial images throughout this paper. A popular method to generate adversarial images is backpropagating the loss gradient to modify the input image. Usually, only the direction of the gradient and a given step size were used to determine the perturbations (FGSM, fast gradient sign method), or the FGSM is applied multiple times to craft stronger perturbations that change the model classification (i-FGSM). On the contrary, if the step size is too large, the minimum perturbation of the image may be missed during the gradient search. To seek exact and minimal input images for a classification change, in this paper, we suggest starting the FGSM with a small step size and adapting the step size with iterations. A few decay algorithms were taken from the literature for comparison with a novel approach based on an index tracking the loss status. In total, three tracking functions were applied for comparison. The experiments show our loss adaptive decay algorithms could find adversaries with more than a 90% success rate while generating fewer perturbations to fool the CNNs.

Speeded classification of visual events is sensitive to crossmodal intensity correspondence.

Crossmodal correspondences refer to systematic associations between stimulus attributes encountered in different sensory modalities. These correspondences can be probed in the speeded classification task where they tend to produce congruency effects. This study aimed to replicate and extend previous work conducted by Marks (1987, Experiment 3, Journal of Experimental Psychology: Human Perception and Performance, Vol. 13, No. 3, 384-394) which demonstrated a crossmodal correspondence between auditory and visual intensity attributes. Experiment 1 successfully replicates Marks' original finding that performance in a brightness classification task is affected by whether the loudness of a concurrently presented auditory distractor matches the brightness of the visual target. Furthermore, in line with the original study, we found that this effect was absent in a lightness classification task. In Experiment 2, we demonstrate that loudness-brightness correspondence is robust even when the exact stimulus input changes. This finding suggests that there is a context-dependent mapping between loudness and brightness levels, rather than an absolute mapping between any particular intensity levels. Finally, exploratory analysis using the diffusion model for conflict tasks indicated that evidence from the task-irrelevant modality generates a burst of weak, short-lived automatic activation that can bias decision-making in difficult tasks, but not in easy tasks. Our results provide further evidence for the existence of a flexible crossmodal correspondence between brightness and loudness, which might be helpful in determining one's distance to a stimulus source during the early stages of multisensory integration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Augmented reality guided autonomous assembly system: A novel framework for assembly sequence input validations and creation of virtual content for AR instructions development

In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user’s view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.

Fractionation of neural reward processing into independent components by novel decoding principle

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

(2+1)-dimensional modulated nonlinear ion-acoustic soliton packets in magnetized electronegative plasma

The dynamics of propagation of (2+1)-dimensional ion-acoustic wave (IAW) packets in magnetized electronegative plasma made of negative ions with electrons, both in Boltzmann distribution and cold mobile positive ions is studied. For this purpose, the standard reductive perturbation method is used to derive the (2+1)-dimensional nonlinear Schrödinger (NLS) equation, which governs the modulation of positive IAW packets. The coefficients of the obtained NLS equation do not only depend on the system parameters specially the electronegative parameters such as the negative ion concentration ratio and the electron-to-negative ion temperature ratio (α and σn, respectively), but also on the magnetic field (ωp). The modulational instability is studied to analyze the areas of the emergence of waves. In order to describe the main characteristics of IAWs, the variational method is performed involving trial function solution of NLS equation and the exact (input profile) and numerical (output profile) solutions are also discussed.

Affordable method for measuring fluorescence using Gaussian distributions and bounded MESE.

We present an accurate and low-cost method for measuring fluorescence in materials. Our method outputs an estimate of the material's Donaldson matrix, which is a commonly used two-dimensional spectral characterization of its fluorescence and reflectance properties. To find the estimate, only a few measurements of the material's reflectance under a few illuminants are needed, which we demonstrate using low-cost optical components. Internally, our algorithm is based on representing each Donaldson matrix with a multivariate Gaussian mixture model and its diagonal with a bounded MESE (maximum entropy spectral estimate). It parametrizes and constrains the estimate in a robust and simple way, allowing the use of gradient-descent optimization. We evaluate our algorithm on a combination of real and synthetic data, and four examples of distinct optical components. We reach significantly lower errors than the current state of the art on the exact same inputs, our estimates do not suffer from artifacts such as oscillations of the spectra, and they are stable and robust.

Strong Left Inversion of Linear Systems and Input Reconstruction

Left inversion of linear time-invariant systems aims at identifying the input acting on a system, for the zero initial state, from partial information on the input and the state. In the present contribution, we propose to generalize such left inversion for linear systems in various directions, that take arbitrary initial state into consideration to address both exact and asymptotic input reconstruction. Necessary and sufficient algebraic conditions are given to achieve such strong left inversion properties. Complete characterizations of introduced concepts in terms of system zeros are provided. Relationship between inversion and input reconstruction is therefore investigated, with emphasis on causal realization. Conditions for input observer existence are proposed, and a constructive causal design for an asymptotically convergent input observer is presented.

Hippocampal output profoundly impacts the interpretation of tactile input patterns in SI cortical neurons

Due to continuous state variations in neocortical circuits, individual somatosensory cortex (SI) neurons invivo display a variety of intracellular responses to the exact same spatiotemporal tactile input pattern. To manipulate the internal cortical state, we here used brief electrical stimulation of the output region of the hippocampus, which preceded the delivery of specific tactile afferent input patterns to digit 2 of the anesthetized rat. We find that hippocampal output had a diversified, remarkably strong impact on the intracellular response types displayed by each neuron in the primary SI to each given tactile input pattern. Qualitatively, this impact was comparable to that previously described for cortical output, which was surprising given the widely assumed specific roles of the hippocampus, such as in cortical memory formation. The findings show that hippocampal output can profoundly impact the state-dependent interpretation of tactile inputs and hence influence perception, potentially with affective and semantic components.

Development of a Motion Control for a highly dynamic, self-propelled driving simulator

The new concept of the self-propelled driving simulator comprises a hexapod, a yaw joint and a wheel-based motion platform with four individually steerable wheels. This concept provides a theoretically unlimited motion range, which especially enables highly dynamic drive maneuvers. To ensure an omnidirectional motion, the motion platform has to accelerate instantly in any direction. This requirement leads to the main challenges in the control system of the simulator: taking into account the nonlinear and transient tire characteristics and generating the target accelerations as expected by the driver. According to these requirements, the Motion Control is only for controlling the horizontal dynamics of the motion platform. The Motion Control presented in this paper includes various model definitions, especially regarding the essential tire characteristics considered within an extended HSRI (Highway Safety Research Institute) tire model. The Motion Control as Two-Degrees-of-Freedom control contains a Feedforward for generating target body forces, a Control Allocation for an optimal force distribution to the wheels, a Single Wheel Control as a specific control of the tire forces, and a Compensation Control on acceleration level. Investigation of this control by simulation, using a simplified reference model, already revealed a high controller performance regarding accuracy and quality. The optimal force distribution leads to an equal adhesion utilization and the Compensation Control compensates the remaining Single Wheel Control deviations. Difficulties only occur for the steering angle in the case of low velocity up to a standstill. Due to the exact input–output linearization, the Single Wheel Control leads to a singularity and instability. Therefore, the steering angle requires exceptional control in this case.

A new efficiency evaluation approach with rough data: An application to Indian fertilizer

&lt;p style='text-indent:20px;'&gt;In the world of chaos, nothing is certain. In such an unpredictable world, measuring the efficiency of any individual is inevitable. In a conventional data envelopment analysis (DEA) model, exact input and output quantity data are needed to measure the relative efficiencies of homogeneous decision-making units (DMUs). However, in many real-world applications, the exact knowledge of data might not be available. The rough set theory allows for handling this type of situation. This paper tries to construct a rough DEA model by combining conventional DEA and rough set theory using optimistic and pessimistic &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \beta $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; confidence values of rough variables, all of which help provide a way to quantify uncertainty. In the proposed method, the same set of constraints (production possibility sets) is employed to build a unified production frontier for all DMUs that can be used to properly assess each DMU's performance in the presence of rough input and output data. Besides, a ranking system is presented based on the approaches that have been proposed. In the presence of uncertain conditions, this article investigates the efficiency of the Indian fertilizer supply chain for over a decade. The results of the proposed models are compared to the existing DEA models, demonstrating how decision-makers can increase the supply chain performance of Indian fertilizer industries.&lt;/p&gt;

Enhancement of nonlinear signal-based control to estimate earthquake excitations from absolute acceleration responses of nonlinear structures

This study constructs an inverse method for multi-storey structures with nonlinear hysteretic characteristics commonly used in earthquake and structural engineering. Constructing the inverse methods, which accurately reconstruct an input signal from a response and structural information, has been challenging owing to the severity of the nonlinear characteristics as well as structural complexity. Nonlinear signal-based control (NSBC) can achieve the exact input estimation from a nonlinear structural response when its linear model is exactly invertible. However, its estimation accuracy decreases when the exact inversion of the linear model is impossible, and an approximation is required. This study introduces a controller design consisting of three enhancements of NSBC to realise accurate input estimation even when the inversion requires an approximation; the estimation of an earthquake excitation from acceleration in the absolute coordinates of nonlinear structures is a typical example. The first enhancement is the improvement of a differentiator used to convert an improper inverse model into a proper one. The second is the zero-stiffness design wherein the stiffness of all storeys in the linear model is intentionally set to zero. The third is the addition of inner-error feedback actions to conventional NSBC controllers. The effectiveness of these enhancements is examined via numerical simulations of the input estimations of nonlinear 2/4-degree-of-freedom (2/4DOF) systems shaken by an earthquake excitation. The enhanced NSBC successfully estimates the earthquake excitation with 100% accuracy from one known absolute acceleration response of the nonlinear 2/4DOF systems. Its potential application for estimating earthquake excitations by realising a predetermined response on nonlinear structures is also analysed.

Finite time stable inversion in discrete frequency domain: Accuracy analysis, improvement and application to wafer stage

Stable inversion represents a classic approach to achieve the exact inverse input for non-minimum-phase (NMP) systems. Solutions deduced based on state–space equations and transfer functions have been frequently proposed, however they are basically developed in the infinite-time horizon and are inherently time-domain computation methods. Considering the practical finite-time tracking tasks, this paper investigates the finite-time stable inversion problem. In particular, the discrete-frequency-domain solution which enables frequency-domain computation is studied. As the main contribution of the paper, the accuracy issues of the discrete-frequency-domain solution are revealed and an easy-to-use procedure is provided to improve the inversion accuracy by utilizing pre-actuation and post-actuation methods. Simulation and experiment both verify the effectiveness of the developed discrete-frequency-domain stable inversion technique.

Model Agnostic Defence Against Backdoor Attacks in Machine Learning

Machine learning (ML) has automated a multitude of our day-to-day decision-making domains, such as education, employment, and driving automation. The continued success of ML largely depends on our ability to trust the model we are using. Recently, a new class of attacks called backdoor attacks have been developed. These attacks undermine the user’s trust in ML models. In this article, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> , a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models. For a given image classification model, our approach analyzes the inputs it receives and determines if the model is backdoored. In addition to this feature, we also mitigate these attacks by determining the correct predictions of the poisoned images. We have implemented <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> and evaluated it against three state-of-the-art poisoned models. In our evaluation, we show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> can detect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 88% of the poisoned inputs on average and it is as fast as 4.4 ms per input image. We also compare our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> approach with the state-of-the-art defence methodologies proposed for backdoor attacks. Our evaluation reveals that despite being a blackbox approach, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neo</small> is more effective in thwarting backdoor attacks than the existing techniques. Finally, we also reconstruct the exact poisoned input for the user to effectively test their systems.

Learn traffic as a signal: Using ensemble empirical mode decomposition to enhance short-term passenger flow prediction in metro systems

Due to the complex temporal dependency and various external factors, it is challenging to capture its nonlinear and unsteady trends accurately. In addition, there are several inevitable errors in the traffic sensor record, including bias and noise. However, most recent works regard the record data as exact input ignoring the effect of unknown errors. In this research, a novel framework that integrates Ensemble Empirical Mode Decomposition (EEMD) method and the Bidirectional Gate Recurrent Units (BiGRU) model was proposed to eliminate noise and enhance short-term prediction. The proposed model is mainly divided into three stages. Firstly, the EEMD algorithm adaptively decomposes the nonlinear and non-steady passenger flow signal into several sub-signals, which share more straightforward fluctuation trends and higher correlation coefficients in the preprocessing stage. Secondly, in the feature recognition and extraction stage, knowledge of the transportation field and statistical theories are applied to analyze and extract the critical decomposed components. Finally, in the prediction stage, the stacked BiGRU can learn and extract information from the input features in both directions and use a multi-step prediction to output the final prediction result. A real dataset of the Chengdu metro system is included in our experiments. The experimental results reveal that the proposed EEMD-BiGRU model's prediction performance exceeds all benchmark models. The Root Mean Square Error (RMSE) of the proposed model is reduced by up to 28.29% compared to a single GRU model without EEMD preprocessing. Also, experiments show the effectiveness and robustness of the proposed method for predicting short-term passenger flow in metro systems.

Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model

AbstractData‐driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational‐Multiscale (VMS) Artificial Neural Network (ANN) discretization which makes no a priori assumptions on the model form, and is only restricted in its accuracy by the precision of the ANN. Using a simplified problem, we demonstrate that good predictions of the required closure terms can be obtained with relatively compact ANN architectures. We then turn our attention to the stability of the VMS‐ANN discretization in the context of a single implicit time step. It is demonstrated that the ANN parameterization introduces nonphysical solutions to the governing equations that can significantly affect or prevent convergence. We show that enriching the training data with nonphysical states from intra‐time step iterations is an effective remedy. This indicates that the lack of representative ANN‐induced errors in our original, exact training inputs underpin the observed instabilities. In turn, this suggests that data set enrichment might aid in resolving instabilities that develop over several time steps.

EXACT CALCULATIONS FOR ONE PHOTON MICROMASER WITH COHERENT INPUTS I

We report a numerical simulation of the action of the one 85 Rb atom micromaser which shows that the micromaser field evolves towards a steady state equilibrium when the condition that Tp t int satisfies. In this steady state regime the cavity field reaches a steady state after a sufficient number of atoms have passed through the cavity. The cavity temperature T and the cavity quality factor Q can produce a quantitive change when one of them or the two are changed but they are not responsible for the qualitative changes in the micromaser field. Evidently the only responsible parameter for these qualitative changes is the repetition time T p namely if T p t int then the micromaser field evolution is towards a steady state equilibrium. In contrast when T p reduced to the order of t int then the micromaser field evolution is controlled by the trapping state dynamics – no steady state is apparent in this case. The first going-up trapping state at n=3 plays a significant role in the early dynamics of the micromaser field. Moreover the state of the field ultimately evolves towards a mixed state rather than a pure state.

Visco-elastic material characterization by means of full field Lamb waves

In structural design and structural health monitoring (SHM), the identification of the visco-elastic stiffness tensor of composite materials is considered fundamental, however, the reconstruction of the stiffness coefficients is not straightforward due to the inherent presence of anisotropy and heterogeneity. In the current study, an inverse procedure is proposed based on full field wave dispersion curves to characterize the (18) visco-elastic stiffness parameters of an orthotropic composite plate. The procedure is illustrated by means of finite element method simulations conducted with broadband signals. First, the FEM simulation data, consisting of the in-plane and out-of-plane velocities on the 2D top surface of a composite plate, were converted into the frequency-complex wavenumber domain, including information on elasticity and viscosity, by using the matrix pencil method. The converted data set was then considered as input to a surrogate optimization inversion algorithm, using the semi analytical finite element method (SAFE) as the forward model owing to its accuracy, efficiency and robustness. The full tensorial visco-elastic stiffness parameters were calculated and updated by minimizing the error between the simulated and the calculated complex wavenumbers. The final reconstructed stiffness properties in case of an aluminum plate and of a unidirectional composite laminate show good agreement with the exact input stiffness values, with average errors below 5% for all 18 visco-elastic stiffness parameters.

A Road Map to Various Pathways for Calculating the Memory Kernel of the Generalized Quantum Master Equation.

The generalized quantum master equation (GQME) provides a powerful framework for simulating electronic energy, charge, and coherence transfer dynamics in molecular systems. Within this framework, the effect of the nuclear degrees of freedom on the time evolution of the electronic reduced density matrix is fully captured by a memory kernel superoperator. However, the actual memory kernel depends on the choice of projection operator and is therefore not unique. Furthermore, calculating the memory kernel can be done in multiple ways that use different forms of projection-free inputs. Although the electronic dynamics is invariant to those choices when quantum-mechanically exact projection-free inputs are used, this is not the case when they are obtained via more feasible semiclassical or mixed quantum-classical approximate methods. Furthermore, the accuracy and numerical stability of the resulting electronic dynamics has been observed to be sensitive to the above-mentioned choices when approximate methods are used to calculate the projection-free inputs. In this article, we provide a systematic road map to 30 possible pathways for calculating the memory kernel and highlight how they are related as well as the ways in which they differ. We also compare the performance of different pathways in the context of the spin-boson benchmark model, with the projection-free inputs obtained via a mapping Hamiltonian linearized semiclassical method. In this case, we find that expressing the memory kernel with an exponential operator where the projection operator precedes the Liouvillian yields the most accurate and most numerically stable results.