Forward Algorithm Research Articles

Seismic Characterization of Swiss Strong-Motion Borehole-Station Sites by Inversion of Full Microtremor Horizontal-to-Vertical Spectral Ratios [H/V(z,f)

ABSTRACT The assessment of the local site amplification during an earthquake requires, among other input information, a reliable estimate of the shear-wave velocity profile, including the contact with engineering and seismic bedrocks. We determine the shear-wave velocity (VS) profiles at two Swiss strong-motion borehole-station sites at Visp (Valais) and Buochs (Nidwalden) by inversion of microtremor horizontal-to-vertical spectral ratio [H/V(z,f)] curves measured at the surface and at different depths. These borehole stations were built to monitor not only the seismic activity in Switzerland and the surrounding areas but also the nonlinear site response, especially liquefaction processes during strong local and regional earthquakes. The boreholes are equipped with accelerometers at various depths, with the deepest borehole located at 102 m below the surface. In the first part, we review the forward modeling algorithm of the full-microtremor H/V(z,f), with a focus on the computational cost and accuracy. In the second part, we perform a temporal analysis of the H/V(z,f) curves obtained from the accelerometers. The results show seasonal variabilities in H/V between summer and winter. The third part presents the inversions of the H/V curves for a single day in summer and winter at both sites. From the full H/V(z,f) inversion, we obtain shear-wave velocities in the upper 30 m (VS30) of 216 and 209 m/s at Visp in winter and summer, respectively. At Buochs, the corresponding VS30 are 269 and 345 m/s. The depths of the seismic bedrock are at 219 and 210 m at Visp, and at Buochs they are at 293 and 213 m. The estimated velocity profiles compare well with independent estimates from array measurements of ambient seismic vibrations, gravimetry, and geological logging information. Finally, we use the obtained seismic velocity profile information to model the theoretical 1D shear wave transfer function. The latter result compares well with amplification function results obtained using earthquake recordings.

P3FA: Unified Unicast/Multicast Forwarding Algorithm for High-Performance Router/Switch

High-performance multicast packet switching technologies are evolving to meet the growing demand for scalability on the Internet and datacenters, etc. Implementing a high-performance switch/router relies on a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether a packet is forwarded through an egress. Among these, Bloom filter (BF)-based and Residue Number System (RNS)-based are being considered as two representatives of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as false-positive probability and time inefficiencies, especially for a carrier-grade PFE with high port-density features. According to similar properties of the RNS, we propose a simplified forwarding algorithm in this paper, named Per-Port Prime Filter Array (P3FA). The simulation results indicate that the P3FA can significantly improve space efficiencies under specific lower egress-diversities conditions. Under the same space constraints, P3FA improves multicast and unicast time efficiency by 1 to 4 orders of magnitude in the port-density 16–1024 range compared to previous works. Although it comes at the expense of hardware cost, it is still acceptable compared to recently improved previous work.

Load2Load: Day-ahead load forecasting at aggregated level

A reliable and accurate short-term load forecasting (STLF) helps utilities and energy providers deal with the challenges posed by supply and demand balance, higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Recent advances in deep learning have been successively utilized to STLF. However, there is no certain study that evaluates the performances of different STLF methods at an aggregated level on different datasets with different numbers of daily measurements.In this study, a deep learning STLF architecture called Load2Load is proposed for day-ahead forecasting. Different forecasting methods have been evaluated and compared on two datasets with different temporal resolutions and features. An additive ensemble method as well as a selective ensemble method that selects the outputs of different forecasters in an hourly manner are proposed. Moreover; a modified sequential forward feature selection algorithm is proposed, resulting in better performance with a much smaller number of features.Numerical results show that the proposed Load2Load architecture has a competing performance compared to other advanced forecasters. When used together with the proposed ensemble methods, Load2Load can significantly improve the forecasting performance. The proposed feature selection algorithm results in better performance for the majority of the cases while reducing the dimensionality. According to the results with two different datasets; the proposed methods are shown to be robust to temporal resolutions, feature types and sequence lengths.

Dependency maximization forward feature selection algorithms based on normalized cross-covariance operator and its approximated form for high-dimensional data

Supervised feature selection (FS) for classification aims at finding a more discriminative subset from original features, to facilitate classifier training, improve classification performance and enhance model interpretability. Primary normalized cross-covariance operator (NOCCO) is a nonlinear kernel-based dependency measure between features and labels, including two inverse matrices, and its approximated version (ANOCCO) is simplified via exploiting linear kernel for features, delta kernel for labels and Moore–Penrose inverse. In this paper, we apply NOCCO and ANOCCO to FS task. According to sequential forward selection, a forward NOCCO-based FS algorithm (i.e., FoNOCCO) is directly implemented via various accelerating strategies, but its computational complexity is extremely high. To this end, we propose its fast version via maximizing ANOCCO (i.e., FoANOCCO), where each candidate feature is evaluated efficiently according to column recursive algorithm for Moore–Penrose inverse. Theoretical analysis shows that evaluating a candidate feature needs a time complexity of O(N2) in FoANOCCO and O(N3) in FoNOCCO, where N is the number of instances. On eight small-size high-dimensional benchmark data sets, our detailed experiments illustrate that our two forward FS algorithms are superior to six state-of-the-art FS methods with four baseline classifiers, and FoANOCCO runs 70 times faster than FoNOCCO with a 1.79 % accuracy drop.

Diagnosis of Asthma Disease and The Levels using Forward Chaining and Certainty Factor

Asthma disease is a major global health issue that affects at least 300 million people worldwide. Even for clinicians working in emergency rooms, predicting the severity of asthma is difficult. Predicting the intensity of an asthma attack is much more challenging because it is dependent on several factors, including the person's illness's features and severity. Forward Chaining and Certainty Factor algorithms can be implemented to diagnose the degree of asthma control, so the consultation process through the system becomes more detailed. The expert system can be used as an initial reference for the diagnosis process. The forward Chaining algorithm is useful for reasoning, starting from a fact to a solution. On the other hand, the Certainty Factor algorithm is used to provide a level of confidence in the conclusions by generating from the Forward Chaining algorithm. The research implemented several phases as follows analysis, data preparation, modeling, and evaluation. On evaluation, this research conduct three stages and tested using 80 medical record data. The result of the study has produced an expert system and generated an accuracy level of 65%, a precision value of 58.3%, and a recall also produced 57.13%. Therefore, the Chaining and Certainty Factor performs reasonably well in the diagnosis of asthma disease.

Analysis of the Importance and Influence of Student Mental Health Based on Neural Networks

With the rapid development of neural network has been widely used in major research areas, this paper will neural network convolution layer structure into the importance of students' mental health and influencing factors. In order to analyze the influencing factors of students' mental health, this paper analyzes the mental health status of different students from the perspective of neural network. Firstly, the main definitions and concepts of students' mental health are put forward, and the methods of evaluating and measuring mental health are analyzed. Secondly, the related structure of neural network and two different neural network model algorithms are described in detail, and the forward propagation and backward propagation algorithms of neural network are proposed (which provide support for the data research through neural network later). Finally, the correlation function of neural network is used to analyze the influencing factors of contemporary students' mental health.

Two Regional Deployment Algorithms of Distributed GNSS Forwarding Spoofer for Multiple Receiver Sensors.

Global navigation satellite system (GNSS) spoofing technology is an effective way to protect sensitive facilities and control foreign objects. To realize effective distributed GNSS forwarding spoofing for multiple receiver sensors in the area, the following research work is carried out: first, the GNSS forwarding spoofing model is established, including a forwarding spoofing mathematical model and an asynchronous traction spoofing method; second, the function scope of forwarding spoofing is fully evaluated; third, two forwarding spoofer area deployment algorithms for multi-spoofer multi-target (MSMT) are proposed—the multi-target same-point spoofing algorithm (MSPSA) is suitable for the same-point spoofing of multiple receiver sensors, and the multi-target different-point spoofing algorithm (MDPSA) is suitable for the different-point spoofing of multiple receiver sensors; and four, the experimental tests of MSPSA and MDPSA for MSMT were carried out. The experimental results show that MSPSA and MDPSA can respectively give the most appropriate deployment scheme of spoofing according to the various needs of the spoofer. For example, the number of spoofing devices is 1, the total distance between each spoofer and each receiver is 40,345.1 m, and the critical spoofing rate (CSR) and spoofing success rate (SSR) are both 100%. The performance of the spoofing deployment scheme can meet the needs of the spoofer.

Research on particle collision forward search algorithm and CFD–DEM variable time step coupling calculation method

In CFD–DEM coupling calculation, the calculation accuracy will decrease if the particle calculation time step is too large, while the calculation efficiency will decrease if the particle calculation time step is too small. In this study, first, the displacement of the center particle and the fastest particle in the computational domain is added to construct a search sphere. Subsequently, the particles that may collide are screened to create a search list, and particle collisions are determined using a forward search method. Finally, an improved DEA method is proposed; the time step of particle calculation is automatically adjusted according to the collision time, which solves the contradiction between selecting particle calculation time step, accuracy, and efficiency. The relative error between the particle collision simulation results and the analytical solution obtained by this method is less than 3.00%. In this study, three selected calculation time steps can ensure excellent calculation accuracy and efficiency. In multi-particle and fluid coupling simulation analysis, when the calculation time step is 10–5s, the calculation efficiency is improved by 19.8%.

Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

Computational prediction of Calu-3-based in vitro pulmonary permeability of chemicals

Pulmonary is a potential route for drug delivery and exposure to toxic chemicals. The human bronchial epithelial cell line Calu-3 is generally considered to be a useful in vitro model of pulmonary permeability by calculating the apparent permeability coefficient (Papp) values. Since in vitro experiments are time-consuming and labor-intensive, computational models for pulmonary permeability are desirable for accelerating drug design and toxic chemical assessment. This study presents the first attempt for developing quantitative structure-activity relationship (QSAR) models for addressing this goal. A total of 57 chemicals with Papp values based on Calu-3 experiments was first curated from literature for model development and testing. Subsequently, eleven descriptors were identified by a sequential forward feature selection algorithm to maximize the cross-validation performance of a voting regression model integrating linear regression and nonlinear random forest algorithms. With applicability domain adjustment, the developed model achieved high performance with correlation coefficient values of 0.935 and 0.824 for cross-validation and independent test, respectively. The preliminary results showed that computational models could be helpful for predicting Calu-3-based in vitro pulmonary permeability of chemicals. Future works include the collection of more data for further validating and improving the model.

Multirate Shading with Piecewise Interpolatory Approximation

AbstractEvaluating shading functions on geometry surfaces dominates the rendering computation. A high‐quality but time‐consuming estimate is usually achieved with a dense sampling rate for pixels or sub‐pixels. In this paper, we leverage sparsely sampled points on vertices of dynamically‐generated subdivision surfaces to approximate the ground‐truth shading signal by piecewise linear reconstruction. To control the introduced interpolation error at runtime, we analytically derive an L∞ error bound and compute the optimal subdivision surfaces based on a user‐specified error threshold. We apply our analysis on multiple shading functions including Lambertian, Blinn‐Phong, Microfacet BRDF and also extend it to handle textures, yielding easy‐to‐compute formulas. To validate our derivation, we design a forward multirate shading algorithm powered by hardware tessellator that moves shading computation at pixels to the vertices of subdivision triangles on the fly. We show our approach significantly reduces the sampling rates on various test cases, reaching a speedup ratio of 134% ~ 283% compared to dense per‐pixel shading in current graphics hardware.

A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

The data-driven (DD) is a systematic approach to improve the data and model by deriving/adding features to address the problem identified during the iterative loop of forecasting model development. This article proposes a DD framework for forecasting short-term PV generation and load demand. A framework of three stages with a unique contribution in each stage, such as generalizing data preprocessing steps (stage-1), multivariate feature generation and selection (stage-2), and model hyperparameter tuning (stage-3) for further improvement in forecasting is proposed. It focuses on data as well as forecasting models. The whole process is analyzed using the time-series measured data collected from a real-life demonstration project in Ireland. Data preprocessing is generalized for both generation and demand forecasting under the same framework. The relevant features are selected with the help of the proposed random forest sequential forward feature selection algorithm. Hyperparameters are tuned through tree-structured Parzen estimator algorithm for further improvement. In addition, the performance of the classical autoregressive integrated moving average model is compared with the machine learning-based gate recurrent unit, long short term memory, recurrent neural network, and convolutional neural network models. Results show that the data-driven forecasting model framework systematically improves the model performance. The seasonal variation has also a high impact on the model performances.

Hyperchaotic System with Application to Image Encryption

This paper introduces a novel hyperchaotic circuit system that is extremely sensitive to circuit parameters and system initial values. The typical dynamical behaviors of the hyperchaotic system such as one-parameter bifurcation diagrams, two-parameter bifurcation diagrams and Lyapunov exponent spectrum are discussed in detail. Then an image encryption algorithm is designed by using the hyperchaotic sequences. In this algorithm, Zigzag algorithm is used to scramble pixel positions, and backward diffusion and forward diffusion algorithms are used to diffuse pixel values. The results indicate that the proposed algorithm can improve the security of the image encryption algorithm. In addition, some circuit simulation experiments are performed to verify the sensitivity of the hyperchaotic circuit to parameters by Multisim. Finally, the hyperchaotic circuit is implemented by using physical elements. The simulation results are consistent with the experimental results of hardware circuit, which effectively verifies the existence of hyperchaotic attractors.

Versatile optical beam routers based on inversely designed supercell metagratings

Metagratings manipulating the wavefront with a subwavelength volume have been widely explored in recent years for their potential applications in optical beam routers, such as laser beam splitters and combiners. Current metagratings are hampered from practical beam rerouting due to inhibitive computational load of engineering design and expensive nanofabrication cost. In this work, we apply the emerging concept of supercell metagratings, which arranges random binary gratings of a unit cell in a one-dimensional periodicity, to simplify such design and fabrication processes. A high-performance inverse design framework for the supercell gratings is proposed. In such a framework, we use coupled wave algorithm as a fast-converging electromagnetic solver for the forward calculation and particle swarm optimization algorithm for the backward optimization. By manipulating the building blocks of a unit cell, specific diffraction orders of supercell gratings can be intentionally strengthened or eliminated. We finally realize high-efficiency, broadband and multi-functionality optical beam splitters and combiners both in visible and near-infrared regimes. We envision that the proposed method offers an avenue towards the mass production realization of flat optics.

ECG segmentation algorithm based on bidirectional hidden semi-Markov model

Accurate segmentation of electrocardiogram (ECG) waves is crucial for cardiovascular diseases (CVDs). In this study, a bidirectional hidden semi-Markov model (BI-HSMM) based on the probability distributions of ECG waveform duration was proposed for ECG wave segmentation. Four feature-vectors of ECG signals were extracted as the observation sequence of the hidden Markov model (HMM), and the statistical probability distribution of each waveform duration was counted. Logistic regression (LR) was used to train model parameters. The starting and ending positions of the QRS wave were first detected, and thereafter, bidirectional prediction was employed for the other waves. Forwardly, ST segment, T wave, and TP segment were predicted. Backwardly, P wave and PQ segments were detected. The Viterbi algorithm was improved by integrating the recursive formula of the forward prediction and backward backtracking algorithms. In the QT database, the proposed method demonstrated excellent performance (Acc = 97.98%, F1 score of P wave = 98.37%, F1 score of QRS wave = 97.60%, F1 score of T wave = 97.79%). For the wearable dynamic electrocardiography (DCG) signals collected by the Shandong Provincial Hospital (SPH), the detection accuracy was 99.71% and the F1 of each waveform was above 99%. The experimental results and real DCG signal validation confirmed that the proposed new BI-HSMM method exhibits significant ability to segment the resting and DCG signals; this is conducive to the detection and monitoring of CVDs.

Traffic Prediction and Fast Uplink for Hidden Markov IoT Models

In this work, we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the Age of Information (AoI) while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models, such as time-division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and AoI.

IoT botnet detection with feature reconstruction and interval optimization

The existing botnet detection methods have the problems of uneven sampling, poor feature selection, and weak generalization ability, resulting in low detection and classification results and poor adaptability to the internet of things (IoT) environment with limited computing and storage resources. This paper proposes an IoT botnet detection method using feature reconstruction and interval optimization to solve the above problems. Through the designed address triple and time window-based IP aggregation and feature reconstruction method (ATTW-IP-FR), the network traffic samples obtained from the IoT gateway are integrated, and the flow features are reconstructed to attain the reconstructed sample set. The proposed self-corrected hybrid weighted sampling algorithm balances the normal and botnet flow samples in the reconstructed sample set to get the resampling sample set. The introduced multiattribute decision-making and adjacency relation chain-based sequential forward selection algorithm is applied to eliminate the redundant features in the resampling sample set, and the optimal feature subset is obtained. The resampling sample set filtered by the optimal feature subset is detected and classified through the designed two-stage hybrid heterogeneous model optimized by the intermittent chaos and bald eagle search algorithm-based interval optimization algorithm. The experimental results show that the proposed method effectively detects the botnet in two real IoT scenarios. The detection accuracy is 99.17 % $ \% $ , the Matthews correlation coefficient is 98.35 % $ \% $ , the false positive rate is 0.25 % $ \% $ , and the false negative rate is 1.27 % $ \% $ , which are better than the existing methods. This method can effectively reduce sampling and feature selection time and space overhead and better adapt to the resource-constrained IoT environment.

Attribute reduction with personalized information granularity of nearest mutual neighbors

Neighborhood-based attribute reduction plays a vital role in pattern recognition, for selecting a series of informative and relevant attributes from data sets. The increase in dimensionality and complexity of data along with superfluous and noisy attributes, results in significant and tricky issues when reasoning data. To address the ineffectiveness of classical attribute reduction within the neighborhood framework, a competent attribute reduction scheme based on a novel concept of nearest mutual neighbor-based personalized information granularity is proposed in this paper. This study comprises three phases. First, we intend to integrate the strategies of personalized radius and two-sided neighbors into the modeling of information granularity, and both personalized and mutual neighborhood rough sets are developed. In the second phase, the concept of the belief function from evidence theory is introduced to construct an adaptive neighborhood information granule with the guidance of the principle of justifiable granularity; in this sense, the search for a justifiable granularity is transformed into an optimization issue. In the third phase, a new neighborhood homogeneous evidence-based attribute significance evaluation is discussed to avoid the inadequacy of voting-based attribute measures. In addition, an enhanced forward greedy algorithm is proposed to address non-monotonic circumstances. For the experimental evaluation, fifteen benchmark data sets, involving six high-dimensional microarray data sets, are chosen to display the performance of the method and algorithm. The experimental findings reveal the superiority of our work from different perspectives.

Improving Air Pollution Prediction Modelling Using Wrapper Feature Selection

Feature selection is considered as one of the essential steps in data pre-processing. However, all of the previous studies on predicting PM10 concentration in Malaysia have been limited to statistical method feature selection, and none of these studies used machine-learning approaches. Therefore, the objective of this research is to investigate the influence variables of the PM10 prediction model by using wrapper feature selection to compare the prediction model performance of different wrapper feature selection and to predict the concentration of PM10 for the next day. This research uses 10 years of daily data on pollutant concentrations from two stations (Klang and Shah Alam) obtained from the Department of Environment Malaysia (DOE) from 2009 until 2018. Six wrapper methods (forward selection, backward elimination, stepwise, brute-force, weight-guided and genetic algorithm evolution and the predictive analytics multiple linear regression (MLR) and artificial neural network (ANN)) were implemented in this study. This study found that brute-force is the dominant wrapper method in most of the best models in selecting important features for MLR. Moreover, compared to MLR, ANN provides more advantages regarding model accuracy and permits feature selection in predicting PM10. The overall results revealed that the RMSE value for next day prediction in Klang is 20.728, while the AE value is 15.69. Furthermore, the RMSE value for next day prediction in Shah Alam is 10.004, while the AE value is 7.982. Finally, all of the predicted models in Klang and Shah Alam can be used to predict the PM10 concentrations. This proposed model can be used as a tool for an early warning system in giving air quality information to local authorities in order to formulate air-quality-improvement strategies.

Development of a mobile expert system for the diagnosis on motorcycle damage using forward chaining algorithm

Indonesia is an <span>ASEAN country with the most motorcycle users, where one-third of its population own motorcycles. The automatic motorcycle is the most widely used type due to its agility and fuel-efficient abilities. Sudden motorcycle damage could hamper the users' activities. However, most of these users do not know the reason for the damage. This paper presents the development of expert system for diagnosing the damage of motorcycle using forward chaining method. This system was implemented in mobile application. Through a mobile application, a solution for these users can be obtained. This application immediately discovers the damaged location and repair process. Furthermore, it acts as the first solution before motorcycle repair is carried out in a shop. In this study, the forward chaining method was implemented. It is based on a pattern-matching algorithm whose primary objective is to match facts (input data) with appropriate rules from the rule base. Various test results showed that the diagnostic application used for automatic motorcycle damages 100% worked.</span>