Forward Algorithm Research Articles

Smart geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia sensor networks

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths.

Prolonged viral shedding prediction on non-hospitalized, uncomplicated SARS-CoV-2 patients using their transcriptome data

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is identified as a highly transmissible coronavirus which threatens the world with this deadly pandemic. WHO reported that it spreads through contact, droplet, airborne, formite, fecal-oral, bloodborne, mother-to-child and animal-to-human. Hence, viral shedding has a huge impact on this pandemic. This study uses transcriptome data of coronavirus disease 2019 (COVID-19) patients to predict the prolonged viral shedding of the corresponding patient. This prediction starts with the transcriptome features which gives the lowest root mean squared value of 16.3±3.3 using top 25 feature selected using forward feature selection algorithm and linear regression algorithm. Then to see the impact of few non-molecular features in this prediction, they were added to the model one by one along with the selected transcriptome features. However, this study shows that those features do not have any impact on prolonged viral shedding prediction. Further this study predicts the day since onset in the same way. Here also top 25 transcriptome features selected using forward feature selection algorithm gives a comparably good accuracy (accuracy value of 0.74±0.1). However, the best accuracy was obtained using the best 20 features from feature importance using SVM (0.78±0.1). Moreover, adding non-molecular features shows a great impact on mutual information selected features in this prediction.

2-D Magnetotelluric Multiparameter Joint Inversion Considering the Induced Polarization Effect

Magnetotelluric (MT) is an important geophysical exploration method that uses natural sources to study the electrical structure of the earth. This method is advantageous owing to its low cost, large exploration depth range, and high resolution for low-resistivity bodies. However, traditional MT modelling approaches can only invert the resistivity parameters of geological bodies, while natural geological bodies also exhibit the induced polarization (IP) effect. Applying the IP effect of geological bodies could be effective for exploring mineral resources, such as polymetallic ores, oil (gas) fields, and coal fields. To incorporate the IP information into MT inversion, we proposed a multi-parameter MT joint inversion algorithm that considers the IP effect. We used the Cole-Cole model to integrate four IP parameters into the forward algorithm: the zero-frequency resistivity ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , chargeability η, frequency exponent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> , and time constant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> . The influence of each IP parameter on the forward response was analyzed by forward simulations, and we concluded that the parameters ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> η should be considered in the inversion. A cross-gradient function was introduced into the objective function of the Occam inversion method to constrain the structural consistency of ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and η. Model testing and practical application results illustrated that the algorithm can not only obtain the subsurface resistivity structure that can be achieved by the traditional MT method but also reveal the distribution of the chargeability parameter. The additional information obtained using this algorithm is conducive to interpreting specific geological structures that cannot be distinguished by the traditional MT method.

Phase Noise Impact and Scalability of Self-Homodyne Short-Reach Coherent Transmission Using DFB Lasers

We investigateon a two-fiber short-reach self-homodyne coherent transmission system without optical amplification,where the same transmission laser is used to generate a modulated signal carrying useful data and a continuous wave signal, which serves as a local oscillator at the receiver side. Target of the work is to determine by experiments and theoretical models under which conditions DFB lasers can be used instead of more expensive ECL lasers. After careful characterization of lasers phase noise in terms of linewidth as a function of the mismatch between the optical paths of the signal and of the local oscillator, the performance of two laser technologies is investigated in the proposed transmission setup, showing that commercial DFB laser can be used, provided that the optical path mismatch between the two fibers is kept below 1.8 m for 28 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$GBaud$</tex-math></inline-formula> PM-QPSK and 0.8 m for PM-16QAM modulation format in combination with a soft-decision forward error correction algorithm. After an experimental demonstration, we theoretically investigate the scalability laws of the proposed systems in different configuration flavours.

Enforcing Full Arc Consistency in Asynchronous Forward Bounding Algorithm

The AFB BJ+ DAC* is the latest variant of asynchronous forward bounding algorithms used to solve Distributed Constraint Optimization Problems (DCOPs). It uses Directional Arc Consistency (DAC*) to remove, from domains of a given DCOP, values that do not belong to its optimal solution. However, in some cases, DAC∗ does not remove all suboptimal values, which causes more unnecessary research to reach the optimal solution. In this paper, to clear more and more suboptimal values from a DCOP, we use a higher level of DAC* called Full Directional Arc Consistency (FDAC*). This level is based on reapplying AC* several times, which gives the possibility of making more deletions and thus quickly reaching the optimal solution. Experiments on some benchmarks show that the new algorithm, AFB BJ+ FDAC*, is better in terms of communication load and computation effort.

Innovative Surface-Borehole Transient Electromagnetic Method for Sensing the Coal Seam Roof Grouting Effect

With the purpose of monitoring coal seam roof grouting, an innovative surface–borehole transient electromagnetic (TEM) method to compare the Earth resistivity distribution of the coal seam roof before grouting and after grouting is proposed. The TEM secondary field time–decay curve of the proposed surface–borehole TEM method is compared with that of the traditional surface TEM method. It is found that the time–decay curve of the former is very different from that of the latter. For the first time, forward modeling algorithm has been proposed to evaluate the TEM response characteristics of Earth, with an inversion algorithm developed to invert the measured data into resistivity distribution. A field work has been implemented with the customized equipment to evaluate the surface–borehole TEM method. In the field work, the proposed surface–borehole TEM measurement is performed twice, i.e., before and after the coal seam roof grouting. The analysis results demonstrated that the resistivity distribution had a distinct change near the depth of grouting with low resistivity distribution replaced by high resistivity distribution, which validates the effectiveness of the proposed surface–borehole TEM method.

Refined choice set generation and the investigation of multi-criteria transit route choice behavior

To best make informed public transportation decisions, agencies must be aware of how individuals interact with the transit system. The key question to consider is how do passengers get from Point A to Point B and which attributes of the transit trip have the largest influence on that choice? To more accurately understand passengers’ interaction with transit systems, a new method for transit route choice estimation is proposed. This method modifies and builds upon a schedule-based forward label setting multi-criteria shortest path algorithm by introducing an iterative trip elimination methodology. This new methodology yields high quality transit path choice sets with exact regeneration of 70.5% of the observed paths. Moreover, detailed temporal information on all types of network links (in-vehicle, walking, and waiting) are produced with reasonable computational time of 0.5 s per path calculation and 6.9 s per individual’s choice set generation. This increased specificity, in turn, heightens the validity and accuracy of the route choice model. Passenger information is sampled from a transit on-board survey containing origin–destination locations, demographic details, and trip-specific attributes. A multinomial logit model with stop-level path size correction term is estimated. This estimation yields a 67% recovery rate between the path with the highest estimated likelihood and the surveyed (taken) transit path. Furthermore, a transfer penalty of 28.8 min was estimated while the marginal rates of substitution for the waiting and walking time coefficients are in close alignment with similar values in the literature. Express bus routes were found to have a statistically significant negative impact on path utility for the lowest income thresholds while transitways (light rail, bus rapid transit, or commuter rail) had a positive associated perception for the highest household income class. Thus, support is found for the claim that transitways can potentially attract higher-income “choice” riders to the transit network. The results of this research can be used to improve ridership projections and highlight areas for policy improvements that could have the largest impact on retaining and attracting new passengers to the transit system.

3-D Inversion of Z-Axis Tipper Electromagnetic Data Using Finite-Element Method With Unstructured Tetrahedral Grids

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> -axis tipper electromagnetic (ZTEM) technique is an airborne electromagnetic method that detects anomalies in the deep earth that are induced by natural sources. Conventional ZTEM forward modeling is generally conducted using structured grids that have limited accuracy and cannot be used to invert complex underground structures and topography. However, unstructured grids can accurately model complex underground structures with fewer cells. Therefore, to effectively model and recover the complex underground structures, we developed a 3-D framework with unstructured tetrahedral grids for ZTEM forward modeling and inversions. The forward problem was formulated using the finite-element method with unstructured tetrahedral grids. To solve the inverse problem, we used a limited-memory quasi-Newton algorithm (L-BFGS) with a parallel direct solver for optimization in order to avoid the explicit calculation of the Hessian matrix and save the memory and computational time. To validate our forward and inversion algorithms, we conducted numerical experiments on three synthetic models and inverted a survey dataset acquired for a tunnel project in Tibet, China. The experimental results demonstrate the effectiveness of our unstructured finite-element and L-BFGS method for modeling and inverting ZTEM data.

COMPATIVE ANALYSIS OF DEEP LEARNING MODELS FOR DDOS ATTACKS DETECTION

Recently, Distributed Denial of Service(DDOS) attacks have been on the rise and come in very many forms costing many technology firms a lot of time and money. In this study, deep learning models were compared in terms of performance, to solve the problem of detecting these attacks. The first step to mitigating DDOS attacks is by first identifying them, which serves as a toll order. This report used two deep learning models: the Deep Feed Forward (DFF) algorithm and a hybrid containing a CNN with BiLSTM (bidirectional long short-term memory). To compare these algorithms, the “DDoS Botnet Attack on IoT a71a0b42-4” dataset available on Kaggle was chosen. The dataset was undergone various evaluations to find out the performance metrics between the two algorithms. From the simulations conducted, DFF was found to have an accuracy of 87.2% with detecting the time of 0.8 seconds, while the CNN-Bi-LSTM was found to have an accuracy of 94.6% with detecting the time of 1.4 seconds.

ANN model for detection and classification of sleep and non-sleep stages

Disorders related to sleep has become one of the prime issues in human life, which may become the major reasons for health consequences. This work proposes an efficient approach to discriminate sleep stage from non-sleep stage by analysing electroencephalogram (EEG) signals from frontal lobes of brain from a single channel. Second order FIR filter is designed to separate Delta and Theta waves from the collected EEG data, empirical mode decomposition decomposes the EEG signals into small definite components which extracts distinct features like Kurtosis, median absolute deviation (MAD) and inter quartile range (IQR). An artificial neural network model is developed to classify the features of sleep and non-sleep classes which combine the advantages of feed forward and backpropagation algorithms. The experimental results obtained determine that the features extracted proved to be good discriminators of sleep and non-sleep stage with an accuracy of 92%, sensitivity 100% and specificity 98%.

Optimal Intermittent Particle Filter

The problem of the optimal allocation (in the expected mean square error sense) of a measurement budget for particle filtering is addressed. We propose three different optimal intermittent filters, whose optimality criteria depend on the information available at the time of decision making. For the first, the stochastic program filter, the measurement times are given by a policy that determines whether a measurement should be taken based on the measurements already acquired. The second, called the offline filter, determines all measurement times at once by solving a combinatorial optimization program before any measurement acquisition. For the third one, which we call online filter, each time a new measurement is received, the next measurement time is recomputed to take all the information that is then available into account. We prove that in terms of expected mean square error, the stochastic program filter outperforms the online filter, which itself outperforms the offline filter. However, these filters are generally intractable. For this reason, the filter estimate is approximated by a particle filter. Moreover, the mean square error is approximated using a Monte-Carlo approach, and different optimization algorithms are compared to approximately solve the combinatorial programs (a random trial algorithm, greedy forward and backward algorithms, a simulated annealing algorithm, and a genetic algorithm). Finally, the performance of the proposed methods is illustrated on two examples: a tumor motion model and a common benchmark for particle filtering.

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage

Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells. However, they are challenged by overcomplexity, coupled with a strong desire for new materials discovery at high speed and high precision. Herein, we propose a new approach involving a combination of extreme feature engineering and automated machine learning to adaptively learn the structure-composition-property relationships of perovskite oxide materials for energy conversion and storage. Structure-composition-property relationships between stability and other features of perovskites are investigated. Extreme feature engineering is used to construct a great quantity of fresh descriptors, and a crucial subset of 23 descriptors is acquired by sequential forward selection algorithm. The best descriptor for stability of perovskites is determined with linear regression. The results demonstrate a high-efficient and non-priori-knowledge investigation of structure-composition-property relationships for perovskite materials, providing a new road to discover advanced energy materials.

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network’s performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm’s learnings. The AI agent’s ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance.

Variable Selection Based on Profile Forward Selection of Partial Linear Models with Interactive Terms

Due to the rapid development of information technology and data acquisition technology, the model which only considers the linear main effect can not provide accurate prediction results, and the interaction between the predictor and response variables can not be ignored, so the variable selection problem of the model with interaction terms has become an important research topic in the statistical analysis today. In this paper, we discuss the problem of variable selection for a partially linear model with interaction terms using the profile forward selection method under high dimensional data. We propose the two-stage interactive selection algorithm (iPFST) under strong genetic condition and the profile forward selection algorithm (iPFSM) under marginality principle respectively. Theoretically, we use the consistency of profile estimators to prove that profile estimators have uniform convergence rate, and use the screening consistency to prove that iPFST algorithm and iPFSM algorithm can uniformly identify all important linear main effect terms and important interaction effect terms with probability 1. Seven regularization conditions for the theorem are given. Numerical simulation shows the superiority of iPFST and iPFSM in variable selection, and the two algorithms are compared, then iPFST algorithm is better than iPFSM algorithm. Finally, we give detailed technical proof.

Analysis of Unbalanced Radial Distribution System with SVR and Impact of Phase Shifter Angle considering Time Varying Loads

ABSTRACT Distribution network experiences unbalancing due to random switching of loads, low voltage profile, high R/X ratio, and unequal loading in the different phases, which inturn causes more losses compared to transmission line. The voltage profiles, active, and reactive power in each phase are distinct due to unbalancing. In the present work, the authors analyzed unbalanced distribution network in presence of Series Voltage Regulator (SVR) considering the reduction in the losses and improvement in the voltage profile. The main highlights of the paper are: (i) placement and analysis of unbalance radial network with SVR at various phase angles; (ii) reduction in active, reactive power loss of the network and improvement in voltage profile; (iii) saving in cost of energy loss at different phase angle of SVR; and (iv) impact analysis of seasonal time varying load model. The placement of SVR at candidate node by considering real, reactive power loss reduction, and percentage rate of under voltage mitigated problem. Before and after SVR placements, the forward and backward sweep algorithm is used for the load flow calculation for analysis. The analysis is carried out on IEEE 25-bus radial distribution network with SVR phase shifter considering time varying seasonal load variations.

On the Transformation Optimization for Stencil Computation

Stencil computation optimizations have been investigated quite a lot, and various approaches have been proposed. Loop transformation is a vital kind of optimization in modern production compilers and has proved successful employment within compilers. In this paper, we combine the two aspects to study the potential benefits some common transformation recipes may have for stencils. The recipes consist of loop unrolling, loop fusion, address precalculation, redundancy elimination, instruction reordering, load balance, and a forward and backward update algorithm named semi-stencil. Experimental evaluations of diverse stencil kernels, including 1D, 2D, and 3D computation patterns, on two typical ARM and Intel platforms, demonstrate the respective effects of the transformation recipes. An average speedup of 1.65× is obtained, and the best is 1.88× for the single transformation recipes we analyze. The compound recipes demonstrate a maximum speedup of 1.92×.

Integrated line planning and train timetabling through price-based cross-resolution feedback mechanism

Railway line planning and train timetabling are two key planning steps that determine the operating cost and passenger service quality of a railway operator under the infrastructure capacity limitations. Traditionally, the line planning and train timetabling problems are solved sequentially at the strategic and tactical level, respectively. In this study, by introducing two types of binary decision variables, we first propose a unified integer linear programming (ILP) model for the integrated optimization of line planning and train timetabling. The line planning problem is modeled using ILP to satisfy passenger demand, whereas the cyclic train timetabling problem is formulated as a multi-commodity network flow model with a side track capacity constraint. The two types of binary decision variables are coupled by a cross-resolution consistency constraint, which ensures the conformity of the line planning and train timetabling decisions. Furthermore, a dual decomposition mechanism based on the Alternating Direction Method of Multipliers (ADMM) is developed to dualize the cross-resolution consistency and track capacity constraints, such that the original ILP model is decomposed into a line planning sub-problem and a set of train-specific sub-problems. After the linearization of the quadratic penalty terms in the ADMM, each sub-problem contains the Lagrangian relaxation price information based on the cross-resolution consistency constraint. Moreover, the primal and dual solutions are obtained by iteratively and efficiently solving the line planning sub-problem using a commercial solver, and each train-specific sub-problem through a tailored forward dynamic programming algorithm. Furthermore, a real-life case study is conducted based on the Beijing–Shanghai high-speed railway corridor to verify the efficiency and effectiveness of the proposed model and algorithm. The results of the numerical experiments demonstrate that the ADMM can achieve significantly smaller optimality gaps than Lagrangian relaxation, and the integrated optimization approach can improve the objective value by 5.78% on average compared with the sequential optimization approach.

Design and Development of an Intelligent System Based on the Internet of Things for the Early Detection of Forest Fires

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem

Reverse logistics planning plays a crucial role in supply chain management. Stochasticity in different parameters along with time horizon can be a challenge in solving reverse logistics problems. This paper proposes a multi-stage, multi-period reverse logistics with lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, and return quality. Moment matching method was adopted to generate a discrete set of scenarios to represent the original continuous distribution of stochastic parameters. Fast forward selection algorithm was employed to select the most representative scenarios and facilitate computational tractability. A case study was conducted and optimal solution of the recursive problem obtained by solving extensive form. Sensitivity analysis was implemented on different elements of stochastic solution. Results sow that solution of recursive problem (RP) outperforms the solution obtained from the problem with expected values of uncertain parameters (EEV).