Reliable Algorithm Research Articles

Genetic algorithm-based Wormhole attack detection in WSN

Wireless Sensor Network (WSN) technology has received a lot of attention and has opened new applications. Examining security assaults on the network layer and coming up with a solution for them is one of the difficult topics in modern networks. Therefore, to achieve our goals, we must develop a technique that can identify an attack, prevent the attacker from accessing the network, and do so while consuming the fewest amounts of battery power. This technique must also use a simple, reliable algorithm. The use of a genetic algorithm is suggested in this research as a detection of wormhole attacks in WSNs. The strategy makes use of a genetic algorithm to investigate and identify the ideal group of parameters for a wormhole detection technique. Even in the case of enormous networks, the search for the most effective parameters may be carried out quickly and effectively by using this genetic algorithm.

Neural-Network-Assisted Finite Difference Discretization for Numerical Solution of Partial Differential Equations

A neural-network-assisted numerical method is proposed for the solution of Laplace and Poisson problems. Finite differences are applied to approximate the spatial Laplacian operator on nonuniform grids. For this, a neural network is trained to compute the corresponding coefficients for general quadrilateral meshes. Depending on the position of a given grid point x0 and its neighbors, we face with a nonlinear optimization problem to obtain the finite difference coefficients in x0. This computing step is executed with an artificial neural network. In this way, for any geometric setup of the neighboring grid points, we immediately obtain the corresponding coefficients. The construction of an appropriate training data set is also discussed, which is based on the solution of overdetermined linear systems. The method was experimentally validated on a number of numerical tests. As expected, it delivers a fast and reliable algorithm for solving Poisson problems.

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production.

Transmission reliability evaluation of the wireless mobile ad hoc network considering the routing protocol and randomness of channel capacity

AbstractDue to the characteristics of self‐organization, flexibility, and low cost, Mobile ad hoc networks (MANETs) have been applied in many fields, such as disaster rescues and military actions. However, in reality, the successful route establishment based on routing protocols is the prerequisite of data transmission. Besides, the transmission reliability of MANETs is affected by resource constraints, such as channel capacity and energy, and environmental factors. Hence, how accurately modeling and evaluating the transmission reliability of MANETs is very important to ensure their application. Whereas, existing studies ignore the impact of routing protocols and do not fully consider the influence of some factors on transmission reliability, such as environmental randomness, channel capacity, and energy consumption. To solve the above problems, we first propose a signal‐to‐noise‐ratio‐Capacity connection model to better describe the characteristics of wireless links, and the probability of one hop connection is deduced considering the capacity demand randomness. Then, considering the influence of routing protocol on the MANET link establishment, we take the ad hoc on‐demand distance vector routing protocol as an example, and propose an evaluation algorithm of the MANET transmission reliability based on algebraic graph theory and Monte Carlo simulation. Finally, the effectiveness of the proposed method is demonstrated by a numerical example of an unmanned aerial vehicle search and rescue scene.

Approach to assessing the quality and identifying the naturality of pomegranate juices

The unique chemical composition and exceptional organoleptic properties cause not only the growing demand for pomegranate juice, but also its cost, and, consequently, vulnerability to falsifi cation, especially of high quality. Therefore, the identifi cation of the authenticity of pomegranate juice has remained an important area of scientifi c research. In this case, the most reliable algorithm should be considered the one that combines the study of several indicators: anthocyanins and polyphenolic substances, the concentration of sugars, organic acids, etc. The purpose of the work is a comprehensive study of the chemical composition of pomegranate juices purchased from trade organizations in order to identify juice authenticity. The experiment determined the organoleptic and physicochemical parameters of pomegranate juice samples, including chromatic characteristics. It has been established that, in terms of dry matter content, only sample No. 1 meets the requirements of TR CU 023/2011; it has been shown that in samples No. 2 and 3, there are no natural anthocyanin dyes, while the presence of synthetic dyes in these samples has been established. Pomegranate juice sample No. 1 has been proven to contain all of the organic acids normally found in authentic samples of this product. Meanwhile, samples No. 2 and 3 do not contain tartaric and quinic acids, and sample No. 2 does not contain malic acid either. The conducted studies are aimed at forming a unifi ed methodological approach to assessing the quality and identifying the authenticity of pomegranate juice, which will be developed and tested in further studies.

Opportunities and Challenges of Quantum Computing for Engineering Optimization

Abstract Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be utilized in solving optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.

Machine learning methods to predict the fatigue life of selectively laser melted Ti6Al4V components

AbstractThe aim of the present paper is to predict the fatigue life of Selectively Laser Melted (SLMed) Ti6Al4V components via the process parameters, the thermal treatments, the surface treatments and the stress amplitude, adopting machine learning techniques to reduce the cost of further fatigue testing, and to deliver better predictive fatigue designs. The studies resulted in reliable algorithms capable of predicting trustful fatigue curves. The methods have been trained with experimental data available in the literature and validated on testing sets to assess the extrapolation limits and to compare the different methods. The behavior of the networks has also been mapped by varying one SLM process parameter at the time, highlighting how each one affects the life.

Eye Movement Tracking Using Opencv Python

In this study, we made a simple, low-cost algorithm for tracking eye movements and eye blinks in real-time and non-real-time. Several methods are being used right now. Show parts of the face, like the eyes or the whole face. For this reason, open-source libraries like OpenCV enable high-level programming to implement reliable and accurate detection algorithms like Haar Cascade. Since everything is processed in real-time, payment must be made quickly. Pay attention to how hardware, like a computer, can only use a certain amount of resources (processing power). The system has been proven to work by tests with 15 people of different ages and backgrounds. These tests are done to see how the user and the device work together and ensure everything works correctly. The In the tests done, the system worked between 80% and 100% of the time. The results showed that Haar Cascade had a significant effect by Detection of faces in 100% of cases, while the eyes and pupil where they overlap (light and shade) is less effective. In addition to looking at how well the Through these activities, the demo application also showed that user restrictions shouldn't stop people from enjoying and using a certain type of technology. The program was written in C++, and the OpenCV library makes it work on Windows. This system has many uses in the real world and science. By looking at the data from this algorithm from afar, for example, it can tell if someone has an eye disease or is tired. It can also help people who have physical or mental problems

Polynomial chaos Kriging-based structural reliability analysis via the expected margin volume reduction

Integral learning functions are theoretically sound but suffer from intensive computational complexity induced by the associated double integral. Based on a new notation of limit-state margin volume, a computationally-cheap integral learning function called Expected margin volume reduction (EMVR) is proposed for structural reliability analysis. EMVR has two key contributions. First, closed-form expression for the inner integral is well derived based on Kriging update formulas, due to tractable definition of the margin volume. This gets rid of cumbersome numerical quadrature or drawing massive realizations of Gaussian process. Second, a confined integral domain is rationally defined for the outer integral, by virtue of exploring the locality of its integrand. This bypasses annoying computer memory issue. Moreover, a hybrid stopping condition coupled with two different settings of the associated parameters is deployed, accommodating to reliability problems with different features. Then, the performance of EMVR-based reliability algorithm is illustrated on four numerical examples. The results show that the evaluation time of EMVR is reduced to a level comparable to pointwise learning functions. Moreover, it outperforms those existing learning functions in terms of both computational accuracy and efficiency.

Prioritising paths: An improved cost function for local path planning for UAV in medical applications

AbstractEven the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.

Classification of solutions for guided waves in fluid-loaded viscoelastic composites with large numbers of layers.

Guided ultrasonic waves are used for the inspection of multilayered composite aerospace structures. Calculating the corresponding dispersion diagrams is challenging for thick-walled composites with more than 100 layers, such as in modern rocket booster pressure vessels. The Dispersion Calculator (DC) is an open source software for calculating such dispersion diagrams and mode shapes of guided waves. Attenuation caused by viscoelasticity and fluid-loading makes the dispersion curve tracing much more difficult than in the nonattenuated case because the modal solutions are sought in the complex wavenumber plane. The tracing problem is mastered by a reliable algorithm. Whereas leaky Lamb and Scholte waves in coupled and decoupled cases are modeled using the stiffness matrix method, shear horizontal (SH) waves are traced using the transfer matrix method without facing the numerical instability. Through implementation of mode family specific dispersion equations in both matrix techniques for nonattenuated and attenuated cases, symmetric, antisymmetric, and nonsymmetric leaky Lamb, Scholte, and SH waves can be traced separately with better efficiency and robustness. The capabilities of DC are demonstrated by calculating dispersion diagrams and mode shapes for a viscoelastic composite with 400 layers immersed in water. Results are compared against DISPERSE (Imperial College London, London, UK) for selected cases.

A Nonuniform Clustering Routing Algorithm Based on a Virtual Gravitational Potential Field in Underwater Acoustic Sensor Network

Abstract-Due to the harsh deployment environment of the underwater coustic sensor networks (UASNs), a reliable and energy-saving routing algorithm has always been an important challenge and a hot topic. A Non-uniform Clustering (NC) algorithm is designed first in which clusters are genetated according to different node densities. Based on NC, the backbone of the underwater acoustic sensor network is formed in UASNs. To guarantee the reliability of data transmission of the backbone network, a Non-uniform Clustering Routing algorithm based on Virtual Gravitational Potential Field (NC_RVGPF) is proposed. This algorithm (1) establishes a virtual gravitational potential field model to allow data transmission by three-dimensional underwater nodes, (2) designs the virtual gravitational potential energy by combining the transmission distance between the nodes, the residual energy of the nodes, and other parameters, and (3) selects the path of the highest average potential energy as being the optimal path of data transmission. The simulation results show that compared with the classical routing algorithm, NC_RVGPF algorithm has higher transmission efficiency, less energy consumption, and can more effectively extend the network’s lifetime.

A NON-LINEAR OPTIMISATION APPROACH TO IN-PIT HAUL ROAD DESIGN

Truck haul costs, as one of the predominant operational costs for mining and quarrying operations, are known to be heavily dependent on the design parameters of haul roads. Furthermore, in-pit haul road design parameters determine the pit limits and therefore, the potential feasibility of the mining operation. Thus, when in search of an optimal solution in terms of in-pit haul roads, one should primarily consider the location of the in-pit haul road, its design features and the deriving operational costs regarding extraction and haul costs. A suitable objective function in this case may be the undiscounted profit for the ultimate pit design. However, for each considered scenario, truck and excavator operational costs can be calculated using simulation techniques for better accuracy. Furthermore, finding an optimal solution requires the execution of a reliable and efficient algorithm, depending on the shape of the objective function. Hence, a non-linear optimisation approach was proposed in this paper for solving the in-pit haul road optimisation problem, based on a simulation of the materials allocation, which was used for calculating the objective function. Design parameters were assumed to be predetermined, while the only variable used for finding an optimal solution was the location of the in-pit haul road inside the pit contour. In addition, two 1-D algorithms were compared for finding the optimal solution (Search with accelerated step size and 1-D Simplex method). Furthermore, two regression models are proposed (Multiple Linear Regression – MLR and Non-Linear Regression - NLR), which could identify the more feasible region for the in-pit haul road location and reduce the number of iterations required for convergence.

Defining a Continuous Glucose Baseline to assess the impact of nutritional interventions.

Accurate and robust estimation of individuals' basal glucose level is a crucial measure in nutrition research but is typically estimated from one or more morning fasting samples. The use of Continuous Glucose Monitoring (CGM) devices presents an opportunity to define more robust basal glucose levels, which estimates can be generalized to any time of the day. However, to date, no standardized method has been delineated. The current paper seeks to define a reliable algorithm to characterize the individual's basal glucose level over 24 h from CGM measurements. Data drawn from four nutritional intervention studies performed on adults free from chronic diseases were used to define that basal glucose levels were optimally estimated using the 40th percentile of the previous 24 h CGM data. This simple algorithm provides a Continuous Glucose Baseline over 24 h (24 h-CGB) that is an unbiased and highly correlated estimator (r = 0.86, p-value < 0.01) of standard fasting glucose. We conclude that 24-CGB can provide reliable basal glucose estimates across the day while being more robust to interference than standard fasting glucose, adaptable to evolving daily routines and providing useful reference values for free-living nutritional intervention research in non-diabetic individuals.

Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks.

Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.

An image encryption algorithm based on cascade chaotic map and DNA coding

AbstractThe 2D‐Cascade‐Transeform‐Logistic‐Sine Map (2D‐CTLSM) with good randomness and large parameter chaos range is obtained by improving the Logistic and Sine map in a cascade manner. By combining the chaotic map with DNA coding, an efficient encryption algorithm based on a chaotic map with chunking dislocation and alternate row diffusion is designed. The shortcomings of zigzag itself are overcome by performing different zigzag transformations in chunks while combining DNA encoding to accomplish the diffusion operation. Due to the excellent properties of DNA encoding, the diffusion operation is performed by the DNA computation method with alternate row diffusion so that each value can complete the diffusion operation twice. The simulation experimental results and security analysis show that the algorithm can effectively resist various typical attacks, such as the information entropy is close to the expected value of 8, the correlation coefficient is close to 0, and the NPCR and UACI values can meet the critical value range, and so on. This is a safe and reliable image encryption algorithm.

McPrA - A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution

Air pollutants represent an environmental and health risk, and the methods for their effective assessment are of the greatest importance. The MAX-DOAS method is a reliable retrieval algorithm, enabling a vertical gas profile analysis. However, the current MAX-DOAS retrieval algorithm heavily relies on the a priori profile, limiting its accuracy. To address this issue, we introduced a novel MAX-DOAS trace gas profile inversion algorithm called McPrA, which is less dependent on the a priori profile. It employs the Monte Carlo method to resolve the problem of optimal estimation of trace gases. The gas vertical column density is obtained from the air mass factor calculated by SCIATRAN. Afterward, the trace gas vertical distribution is retrieved by combining the weight function with the a priori profile. A normalization process is also included to improve the matching of the weight function and the a priori profile. The McPrA algorithm enables greater flexibility in grid modification to achieve a higher vertical resolution of up to 50 m, while sensitivity experiments contribute to determining the optimal configuration of retrieval parameters, with a degree of freedom of over 3.0. Comparative verification experiments indicate that the McPrA algorithm accurately retrieves gas profiles, with a correlation coefficient of over 0.89 for NO2 in the first layer compared to in situ data. Furthermore, comparisons with WRF-Chem and the simulation of synthetic data demonstrate the effectiveness of the McPrA algorithm in accurately retrieving gas profiles.

Efficient variable cell shape geometry optimization

A fast and reliable geometry optimization algorithm is presented that optimizes atomic positions and lattice vectors simultaneously. Using a series of benchmarks, it is shown that the method presented in this paper outperforms in most cases the standard optimization methods implemented in popular codes such as Quantum ESPRESSO and VASP. To motivate the variable cell shape optimization method presented in here, the eigenvalues of the lattice Hessian matrix are investigated thoroughly. It is shown that they change depending on the shape of the cell and the number of particles inside the cell. For certain cell shapes the resulting condition number of the lattice matrix can grow quadratically with respect to the number of particles. By a coordinate transformation, which can be applied to all variable cell shape optimization methods, the undesirable conditioning of the lattice Hessian matrix is eliminated.

An arbitrarily high order unfitted finite element method for elliptic interface problems with automatic mesh generation

We consider the reliable implementation of high-order unfitted finite element methods on Cartesian meshes with hanging nodes for elliptic interface problems. We construct a reliable algorithm to merge small interface elements with their surrounding elements to automatically generate the finite element mesh whose elements are large with respect to both domains. We propose new basis functions for the interface elements to control the growth of the condition number of the stiffness matrix in terms of the finite element approximation order, the number of elements of the mesh, and the interface deviation which quantifies the mesh resolution of the geometry of the interface. Numerical examples are presented to illustrate the competitive performance of the method.

The efficient conversion between linear wave function expansions and nonlinear graphically contracted function expansions

Several algorithms are examined for the transformation of a graphically contracted function (GCF) to a configuration state function (CSF) vector, denoted , and for the reverse transformation from CSF vector to GCF, denoted . The initial implementation of these algorithms is applied to a sequence of full-CI wave functions of similar molecules. The most efficient transformation is based on a depth-first search (DFS) and scales as where is an average facet count for the GCF wave function, is the dimension of the vector x, and is the dimension of the GCF basis. Two different transformations are proposed, one based on a pairwise recursive merge algorithm and the other based on a least-squares fitting algorithm. The recursive merge is a reliable algorithm, but our initial implementation does not display the expected scaling behaviour with respect to . The iterative least-squares fitting algorithm is based on an efficient DFS procedure to compute the error ε and its gradient g. However, the resulting iterative least-squares optimisation is slowly convergent, requiring excessive iterations to converge to a target error tolerance. For most of the calculations in this case study, the recursive merge procedure is found to be more efficient than the least-squares optimisation procedure.