Snapshot Model Research Articles

Multivariate uncertainty analysis of fracture problems through model order reduction accelerated SBFEM

An efficient Monte Carlo simulation (MCS) method that analyses the influences of multivariate uncertain parameters on the response of cracked structures is proposed. The scaled boundary finite element method (SBFEM) is used to solve the static and dynamic crack propagation problems and obtain a full-order snapshot model. The combination of a proper orthogonal decomposition with model order reduction and a radial basis function is used to achieve accelerated stochastic analysis based on MC simulations. The proper orthogonal decomposition-radial basis function (POD-RBF) method is more difficult to use directly for multivariate problems. Thus, we proposed an efficient operation suitable for multiple input variables. In crack propagation simulations, an optimized polygon meshing algorithm is used. Numerical results show that the proposed method can stochastically analyse multidimensional input variables and effectively obtain the responses of cracked structures, which verifies the effectiveness of the POD-RBF method.

Organic waste derived bioethanol supply chain network: Multiobjective snapshot model with a real-Korea case study

Bioethanol, a promising biofuel gasoline additive, was recently produced by a new technology using acetic acid derived from organic waste. This study develops a multiobjective mathematical model with two competing minimization objectives: economy and environmental impact. The formulation is based on a mixed integer linear programming approach. The configuration of the organic-waste (OW)-based bioethanol supply chain network is optimized in terms of the number and locations of bioethanol refineries. The flows of acetic acid and bioethanol between the geographical nodes must meet the bioethanol regional demand. The model is validated in three real-scenario case studies with different OW utilization rates (30%, 50%, and 70%) in South Korea in the near future (2030). The multiobjective problem is solved using the ε-constraint method and the selected Pareto solutions balance the trade-off between the economic and environmental objectives. At the “best-choice” solution points, increasing the OW utilization rate from 30% to 70% decreased the total annual cost from 904.2 to 707.3 million $/yr and the total greenhouse emissions from 1087.2 to −15.7 CO2 equiv./yr.

The Snapshot Model and the Atomic Content of Temporal Experiences

The Snapshot Model and the Atomic Content of Temporal Experiences

Deep ResNet-Based Ensemble Model for Short-Term Load Forecasting in Protection System of Smart Grid

Short-term load forecasting is a key digital technology to support urban sustainable development. It can further contribute to the efficient management of the power system. Due to strong volatility of the electricity load in the different stages, the existing models cannot efficiently extract the vital features capturing the change trend of the load series. The above problem limits the forecasting performance and creates the challenge for the sustainability of urban development. As a result, this paper designs the novel ResNet-based model to forecast the loads of the next 24 h. Specifically, the proposed method is composed of a feature extraction module, a base network, a residual network, and an ensemble structure. We first extract the multi-scale features from raw data to feed them into the single snapshot model, which is modeled with a base network and a residual network. The networks are concatenated to obtain preliminary and snapshot labels for each input, successively. Also, the residual blocks avoid the probable gradient disappearance and over-fitting with the network deepening. We introduce ensemble thinking for selectively concatenating the snapshots to improve model generalization. Our experiment demonstrates that the proposed model outperforms exiting ones, and the maximum performance improvement is up to 4.9% in MAPE.

Underdetermined DOA Estimation Using Arbitrary Planar Arrays via Coarray Manifold Separation

Conventional direction-of-arrival (DOA) estimation algorithms like MUSIC only allow localization of fewer number of sources than the number of physical sensors. In this paper, underdetermined azimuth localization (localizing more sources than the number of sensors) using arbitrary planar arrays has been proposed, using only second-order statistics of the received data. To achieve this, we utilize the difference coarray of the actual array and express the elements of the array covariance matrix as the signal received by the virtual sensors of the coarray. We explore the structure and geometry of the difference coarray of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -element planar array and show that the coarray can provide an increased degree-of-freedom (DOF) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N^{2})$</tex-math></inline-formula> which enables underdetermined localization. Then, we extend the manifold separation (MS) technique to the coarray to express the coarray steering matrix in terms of a Vandermonde structured matrix by designing a signal independent coarray characteristic matrix. As the signal model of a coarray is a single snapshot model, the Vandermonde structure enables us to perform a spatial smoothing type operation to restore the rank of the coarray covariance matrix. This allows us to propose a novel subspace-based algorithm, which we call the coarrayMS-MUSIC, to perform underdetermined source localization using arbitrary planar arrays. We have also introduced the polynomial rooting version of our algorithm called the coarrayMS-rootMUSIC. Finally, we have conducted extensive numerical simulations to verify the effectiveness and usefulness of the proposed methods.

Organic-waste-derived butyric acid-to-biodiesel supply-chain network: Strategic planning design using a deterministic snapshot model

An integrated optimization model for an organic-waste-derived butyric acid-to-butanol supply-chain network (BABSCN) is proposed to minimize the total network cost by simultaneously optimizing both strategic biodiesel production and waste management planning decisions. This model is useful for ensuring effective organic-waste provision for large-scale biodiesel production and waste management. The proposed mixed-integer linear-programming model optimizes the activities ranging from organic-waste preprocessing to butyric acid (BA), transportation of BA to biorefinery, butanol (BuOH) production and mixing with diesel to the distribution of biodiesel. This model is useful for forecasting organic-waste management biodiesel supply chains in South Korea in 2030. The case study results show that a total network cost of $US 3.16/gallon of B3 contains 3% BuOH from organic waste products combined with diesel. The biorefinery-related cost accounts for 98.3% of the total network cost, followed by the organic waste procurement cost (1.1%) and biodiesel distribution cost (0.6%). A scenario-based analysis shows that a 7%-BuOH increase in biodiesel increases the total network cost by 18.8%.

Memorable meals: The memory-experience gap in day-to-day experiences.

Research shows that retrospective memory is often more extreme than in-the-moment experiences. While investigations into this phenomenon have mostly focused on distinct, one-time experiences, we examined it with respect to recurring day-to-day experiences in the eating domain, focusing on variables of the snapshot model—i.e., the most intense and the final experience. We used a smartphone-based Ecological Momentary Assessment to assess the food intake and eating happiness of 103 participants (82.52% female, Mage = 21.97 years) over eight days, and then calculated their best (positive peak), worst (negative peak) and final experiences. Remembered eating happiness was assessed immediately after the study (immediate recall) and after four weeks (delayed recall). A significant memory-experience gap was revealed at immediate recall (d = .53). Remembered eating happiness was predicted by the worst eating experience (β = .41, p < .001), but not by the best or final eating experience. Analyzing changes over time did not show a significant memory-experience gap at delayed recall, but did reveal a similar influence of the worst eating experience (β = .39, p < .001). Findings indicate that, in the domain of eating, retrospective memory is mainly influenced by negative experiences. Overall, the results indicate that the snapshot model is a valid conceptualization to explain recall of both outstanding and day-to-day experiences.

이미지 특징점 가중치 학습을 통한 내비게이션

Visual navigation is a challenging subject in robotics, which is involved estimating the target position and direction at an arbitrary location. In this study, we follow the snapshot model, a bio-inspired model to determine the target direction with the snapshots taken at the current location and the target location. From the snapshots, we collect landmarks with three different features, the corner landmarks with SURF (Speeded Up Robust Features), the vertical edge landmarks with HOG (Histogram of Gradient) and the Haar-like feature landmarks. Those methods can play significant roles in finding appropriate visual features depending on the environment. A linear combination of those landmarks, that is, weighted feature landmarks are more suitable to find homing vector than landmarks found with one method alone. We propose that the gradient-descent method should be applied to the weighted feature landmarks to improve the homing performance. The homing results with ALV (Average Landmark Vector) model are demonstrated to show the effectiveness of the method.

Ensemble Residual Networks for Short-Term Load Forecasting

In this paper, we propose a new ensemble residual network model for short-term load forecasting (STLF). This model improves the accuracy of short-term load forecasting (24 hours in advance). The model has a two-stage network structure. First, the different fully-connected layers are combined, and the combined structure is similar to a recurrent neural network (RNN). Features obtained from historical load data are input to the first stage of the model to get preliminary prediction results. The second stage of the model is a modified residual network, and the final predictions are output from here. We use the ensemble snapshot model with learning rate decay to improve the generalization capability of the model. The model proposed in this paper was trained and tested on two public datasets. Numerical testing shows that the proposed model can get better forecasting results in comparison with other methods, and the ensemble method adopted effectively improves the generalization ability of the model.

A snapshot neural ensemble method for cancer-type prediction based on copy number variations

An accurate diagnosis and prognosis for cancer are specific to patients with particular cancer types and molecular traits, which needs to address carefully. The discovery of important biomarkers is becoming an important step toward understanding the molecular mechanisms of carcinogenesis in which genomics data and clinical outcomes need to be analyzed before making any clinical decision. Copy number variations (CNVs) are found to be associated with the risk of individual cancers and hence can be used to reveal genetic predispositions before cancer develops. In this paper, we collect the CNVs data about 8000 cancer patients covering 14 different cancer types from The Cancer Genome Atlas. Then, two different sparse representations of CNVs based on 578 oncogenes and 20,308 protein-coding genes, including genomic deletions and duplication across the samples, are prepared. Then, we train Conv-LSTM and convolutional autoencoder (CAE) networks using both representations and create snapshot models. While the Conv-LSTM can capture locally and globally important features, CAE can utilize unsupervised pretraining to initialize the weights in the subsequent convolutional layers against the sparsity. Model averaging ensemble (MAE) is then applied to combine the snapshot models in order to make a single prediction. Finally, we identify most significant CNVs biomarkers using guided-gradient class activation map plus (GradCAM++) and rank top genes for different cancer types. Results covering several experiments show fairly high prediction accuracies for the majority of cancer types. In particular, using protein-coding genes, Conv-LSTM and CAE networks can predict cancer types correctly at least 72.96% and 76.77% of the cases, respectively. Contrarily, using oncogenes gives moderately higher accuracies of 74.25% and 78.32%, whereas the snapshot model based on MAE shows overall 2.5% of accuracy improvement.

The Sustainable Development of Choronymic Cultural Landscapes in China Based on Geo-Informatic Tupu

As a part of cultural landscapes, toponyms with abundant cultural connotations and a long history are valuable cultural heritage assets. Choronyms not only reflect natural and social phenomena but also help with relevant management and naming work. In order to explore the historical development sequence of choronymic cultural landscape evolution, we analyze the spatial–temporal pattern evolution, spatial–temporal variation, spatial association, and semantic evolution of choronymic cultural landscapes since the Qin dynasty. We adopt the sequent snapshot model and an event-based state amendment model to establish a spatio-temporal database. That can provide decision supports and theoretical reference for the sustainable development of toponymic landscapes. Results indicate the following: (1) Spatial distribution of toponym density has been different since the Qin dynasty. The cores of toponym density spread from the middle-lower reaches of the Yellow River to Yangtze Plain, Chengdu Plain, Pearl River Delta Plain. (2) Spatial distribution of choronyms is agglomerative since the Qin dynasty and uneven at national and provincial scales since the Yuan dynasty. Temporal distribution of toponyms at different levels is centralized. (3) Spatial agglomeration phenomena of toponyms are positively clustered in nine periods. The Ming dynasty presents the largest degree of spatial aggregation. (4) Words relevant to blessings, orientation, and hydrological features have high proportions in the top 20 words. (5) Spatial distribution of county-level choronyms named over the last 1000 years and “Millennium Ancient Counties” are unbalanced at national and provincial scales.

The diagnostic potential of the weak field approximation for investigating the quiet Sun magnetism: the Si I 10 827 Å line

Aims. We aim to investigate the validity of the weak field approximation (WFA) for determining magnetic fields in quiet regions of the solar photosphere using the polarization caused by the Zeeman effect in the Si I10 827 Å line.Methods. We solved the NLTE line formation problem by means of multilevel radiative transfer calculations in a three-dimensional (3D) snapshot model taken from a state-of-the-art magneto-convection simulation of the small-scale magnetic activity in the quiet solar photosphere. The 3D model used is characterized by a surface mean magnetic field strength of about 170 G. The calculated Stokes profiles were degraded because of the atmospheric turbulence of Earth and light diffraction by the telescope aperture. We apply the WFA to the StokesI,Q,U,Vprofiles calculated for different seeing conditions and for the apertures of the VTT, GREGOR, EST and DKIST telescopes. We compare the inferred longitudinal and transverse components of the magnetic field with the original vertical and horizontal fields of the 3D model.Results. We find that with a spatial resolution significantly better than 0.5″ the surface maps of the magnetic field inferred from the Stokes profiles of the Si I10 827 Å line applying the WFA are close to the magnetic field of the model on the corrugated surface, corresponding to line optical depth unity at Δλ ≈ 0.1 Å for a disk-center line of sight. The correlation between them is relatively high, except that the inferred longitudinal and transverse components of the magnetic field turn out to be lower than in the 3D model.Conclusions. The use of the WFA for interpreting high-spatial-resolution spectropolarimetric observations of the Si I10 827 Å line obtained with telescopes like GREGOR, EST, and DKIST allows the longitudinal and transverse components of the magnetic field to be retrieved with reasonable precision over the whole quiet solar photosphere, the result being worse for telescopes of lower aperture.

Smart Roaming: How Operator Cooperation Can Increase Spectrum Usage Efficiency at Practically No Cost

We propose smart roaming (SR), a cooperation technique between cellular telcos operating in the same region, that enables users to roam for performance reasons (even if they are covered by their operator). Within a region, base stations of different operators are sometimes co-located but, in that case, the sectors are rarely aligned. SR leverages spatial diversity to enhance spectrum usage efficiency. Simply put, an edge user of an operator might be a “good user” for another one. This paper answers the following research questions: 1) Can significant gain be obtained with SR? 2) What are the factors that affect the gain? 3) How to manage operator heterogeneity to avoid that a large operator cross subsidizes a smaller one? and 4) How to implement and manage SR in an online fashion while keeping the signaling information manageable? We answer the first three questions by proposing a snapshot model for the downlink that shows that SR can indeed provide significant gain without yielding cross-subsidies if done properly. We then propose two schemes to implement and manage SR online and evaluate them via extensive simulations.

Minima sensibilia: Against the dynamic snapshot model of temporal experience

AbstractIn our wakeful conscious lives, the experience of time and dynamic temporal phenomena—such as continuous motion and change—appears to be ubiquitous. How is it that temporality is woven into our conscious experience? Is it through perceptual experience presenting a series of instantaneous states of the world, which combine together—in a sense which would need to be specified—to give us experience of dynamic temporal phenomena? In this paper, I argue that this is not the case. Several authors have recently proposed dynamic snapshot models of temporal experience—such as Prosser and Arstila, building upon Le Poidevin—according to which, perceptual experience has no temporal content of a non‐zero extent. I argue that there is an absence of motivation for such a view; I develop and defend the claim that perceptual experience minimally presents something of some non‐zero temporal extent as such.

Robust Method for Determination of Magnetic Field Strength in the Solar Photosphere

The classical method for determining the magnetic field strength from the distance between the peaks of blue and red wings of the Stokes V profile of a magnetically sensitive spectral line is modified. To reduce the influence of noise and to more accurately measure the distance between these peaks, the observed Stokes V profile was approximated by a modified wavelet-function. The parameters of the best fitted approximation function were determined by multidimensional optimization. Following such an approach, the magnetic field strength can be found analytically using such an approximation. We investigate the modified method by means of calculations of the Fe I λ 1564.8 nm Stokes V and I profiles in a three-dimensional snapshot model atmosphere. Magneto-convection snapshot model with small-scale dynamo action performed by Rempel was used. It was found that the method proposed is less sensitive to noise and the shape of the observed V-signal of the line. This makes it possible to conclude that the approach of determining of the magnetic field strength from the observed splitting of the Fe I λ 1564.8 nm Stokes V profile is more reliable in comparison with the classical one.

2D-DOA Estimation for Multiple FH Signals Based on Unitary ESPRIT

In order to use the two-dimensional direction-of-arrival(2D-DOA) of the frequency hopping (FH) signals for network sorting, an FH signal 2D-DOA estimation algorithm based on Unitary ESPRIT is proposed in this paper. Firstly, the snapshot model of FH signal is established based on the structure of the planar antenna array. Then using the morphological filtering method to correct the time-frequency map of FH signals after the SPWVD transform to complete the effective hop extraction, so FH signals can be simplified as a narrow-band signal when studying one hop. Thirdly, the Unitary ESPRIT is exploited to estimate the 2D-DOA of FH signals, which transforms the received data from the complex field to the real domain by the unitary transformation to decrease the calculation complexity and reuses the data during constructing the real matrix to improve the accuracy of the estimation. Finally, we did three experiments to measure the performance of proposed algorithm from three aspects: signal-to-noise ratio, snapshot count and time-consuming. The simulation results show that the proposed algorithm has good performance.

Visual Homing Navigation With Haar-Like Features in the Snapshot

Visual homing navigation has been a challenging issue in indoor localization and navigation. Inspired by insect navigation, the snapshot model was introduced for homing navigation, where a pair of snapshots at the current location and at the nest are compared to guide the homing direction. We investigate Haar-like features in vision to extract visual cues, based on the snapshot model. The Haar-like features consist of masks randomly generated over the snapshot image at the home location, and later, their matching scores at the snapshot available at the current location are calculated for the correspondence measure. We draw landmark vectors using the correspondence measure of Haar-like features at their angular positions. Interestingly, a collection of Haar-like features provide visual characteristics to reflect a pair of snapshot images, which can determine the homing direction. In this paper, we propose two types of homing methods based on the image difference using Haar-like features, the Haar-like landmark vector model and the Haar-like image distance model. We demonstrate the effectiveness of the methods in several environments.

High-Order Moment Models of Landmark Distribution for Local Homing Navigation

For local homing navigation, a mobile robot is supposed to return home using snapshots of the surrounding environment. It basically follows the snapshot model, comparing the home snapshot and the current view to determine the homing direction. In this paper, we suggest a high-order moment potential to describe the landmark feature distribution for local homing navigation. The moment potential function calculates the sum of products of the feature and the distance of landmark particles as a holistic view, allowing a high order of the distance. It effectively combines the range sensor values of landmarks in the current view and the visual features. By analogy with the moment in physics, the center of the moment is estimated as the reference point, which is the unique convergence point in the convex moment potential from any view, and using the property, the gradient of moment potential at the current position and home location can derive the homing vector. We provide a proof of convergence for any moment potential with order greater than or equal to one. Also, we demonstrate homing performances with various moment models in real environments to validate our models. The suggested moment models combining both landmark distance and visual feature have better performances than the visual information alone, and high-order moment potentials can be searched to obtain a better description of landmark distribution for a given environment.

Local Homing Navigation Based on the Moment Model for Landmark Distribution and Features.

For local homing navigation, an agent is supposed to return home based on the surrounding environmental information. According to the snapshot model, the home snapshot and the current view are compared to determine the homing direction. In this paper, we propose a novel homing navigation method using the moment model. The suggested moment model also follows the snapshot theory to compare the home snapshot and the current view, but the moment model defines a moment of landmark inertia as the sum of the product of the feature of the landmark particle with the square of its distance. The method thus uses range values of landmarks in the surrounding view and the visual features. The center of the moment can be estimated as the reference point, which is the unique convergence point in the moment potential from any view. The homing vector can easily be extracted from the centers of the moment measured at the current position and the home location. The method effectively guides homing direction in real environments, as well as in the simulation environment. In this paper, we take a holistic approach to use all pixels in the panoramic image as landmarks and use the RGB color intensity for the visual features in the moment model in which a set of three moment functions is encoded to determine the homing vector. We also tested visual homing or the moment model with only visual features, but the suggested moment model with both the visual feature and the landmark distance shows superior performance. We demonstrate homing performance with various methods classified by the status of the feature, the distance and the coordinate alignment.

Landmark-Based Homing Navigation Using Omnidirectional Depth Information.

A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation.