Search results for: prewitt edge detection algorithm

Authors: Mostafa Firoozabadi, Alireza Foroughi Nematollahi

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Semi autogenous mills are designed for grinding or primary crushed ore, and are the most widely used in concentrators globally. Any defect occurrence in semi autogenous mills can stop the production line. A Gearbox is a significant part of a rotating machine or a mill, so, the gearbox monitoring is a necessary process to prevent the unwanted defects. When a defect happens in a gearbox bearing, this defect can be transferred to the other parts of the equipment like inner ring, outer ring, balls, and the bearing cage. Vibration analysis is one of the most effective and common ways to detect the bearing defects in the mills. Vibration signal in a mill can be made by different parts of the mill including electromotor, pinion girth gear, different rolling bearings, and tire. When a vibration signal, made by the aforementioned parts, is added to the gearbox vibration spectrum, an accurate and on time defect detection in the gearbox will be difficult. In this paper, a new method is proposed to detect the gearbox bearing defects in the semi autogenous mill on time and accurately, using the vibration signal analysis method. In this method, if the vibration values are increased in the vibration curve, the probability of defect occurrence is investigated by comparing the equipment vibration values and the standard ones. Then, all vibration frequencies are extracted from the vibration signal and the equipment defect is detected using the vibration spectrum curve. This method is implemented on the semi autogenous mills in the Golgohar mining and industrial company in Iran. The results show that the proposed method can detect the bearing looseness on time and accurately. After defect detection, the bearing is opened before the equipment failure and the predictive maintenance actions are implemented on it.

Keywords: condition monitoring, gearbox defects, predictive maintenance, vibration analysis

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: M. K. Benhaoua, A. K. Singh, A. E. Benyamina, P. Boulet

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Mapping parallelized tasks of applications onto these MPSoCs can be done either at design time (static) or at run-time (dynamic). Static mapping strategies find the best placement of tasks at design-time, and hence, these are not suitable for dynamic workload and seem incapable of runtime resource management. The number of tasks or applications executing in MPSoC platform can exceed the available resources, requiring efficient run-time mapping strategies to meet these constraints. This paper describes a new Spiral Dynamic Task Mapping heuristic for mapping applications onto NoC-based Heterogeneous MPSoC. This heuristic is based on packing strategy and routing Algorithm proposed also in this paper. Heuristic try to map the tasks of an application in a clustering region to reduce the communication overhead between the communicating tasks. The heuristic proposed in this paper attempts to map the tasks of an application that are most related to each other in a spiral manner and to find the best possible path load that minimizes the communication overhead. In this context, we have realized a simulation environment for experimental evaluations to map applications with varying number of tasks onto an 8x8 NoC-based Heterogeneous MPSoCs platform, we demonstrate that the new mapping heuristics with the new modified dijkstra routing algorithm proposed are capable of reducing the total execution time and energy consumption of applications when compared to state-of-the-art run-time mapping heuristics reported in the literature.

Keywords: multiprocessor system on chip, MPSoC, network on chip, NoC, heterogeneous architectures, run-time mapping heuristics, routing algorithm

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Authors: Sheren H. Salah

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The inverted pendulum is a highly nonlinear and open-loop unstable system. An inverted pendulum (IP) is a pendulum which has its mass above its pivot point. It is often implemented with the pivot point mounted on a cart that can move horizontally and may be called a cart and pole. The characteristics of the inverted pendulum make identification and control more challenging. This paper presents the simulation study of several control strategies for an inverted pendulum system. The goal is to determine which control strategy delivers better performance with respect to pendulum’s angle. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. For controlling the inverted pendulum. The simulation study that sliding mode control (SMC) control produced better response compared to Genetic Algorithm Control (GAs) and proportional-integral-derivative(PID) control.

Keywords: Inverted Pendulum (IP) Proportional-Integral-Derivative (PID), Genetic Algorithm Control (GAs), Sliding Mode Control (SMC)

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Authors: Jiwon Lee, Chanho Jung, Si-Hwan Jang, Kyung-Ill Kim, Sanghyun Joo, Wook-Ho Son

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Since the advances in digital imaging technologies have led to development of high quality digital devices, there are a lot of illegal copies of copyrighted video content on the internet. Thus, we propose a high-quality (HQ) video watermarking scheme that can prevent these illegal copies from spreading out. The proposed scheme is applied spatial and temporal gradient methods to improve the fidelity and detection performance. Also, the scheme duplicates the watermark signal temporally to alleviate the signal reduction caused by geometric and signal-processing distortions. Experimental results show that the proposed scheme achieves better performance than previously proposed schemes and it has high fidelity. The proposed scheme can be used in broadcast monitoring or traitor tracking applications which need fast detection process to prevent illegally recorded video content from spreading out.

Keywords: editing prevention technique, gradient method, luminance change, video watermarking

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Authors: José Luis Lorenzo Bayona, Ljiljana Medic-Pejic, Isabel Amez Arenillas, Blanca Castells Somoza

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The communication presents the study results carried out by the Official Laboratory J. M. Madariaga (LOM) of the Polytechnic University of Madrid to analyze the reliability of methane detection systems used in underground mining. Poor firedamp control in work can cause from production stoppages to fatal accidents and since there is currently a great variety of equipment with different functional characteristics, a study is needed to indicate which measurement principles have the highest degree of confidence. For the development of the project, a series of fixed, transportable and portable methane detectors with different measurement principles have been selected to subject them to laboratory tests following the methods described in the applicable regulations. The test equipment has been the one usually used in the certification and calibration of these devices, subject to the LOM quality system, and the tests have been carried out on detectors accessible in the market. The conclusions establish the main advantages and disadvantages of the equipment according to the measurement principle used; catalytic combustion, interferometry and infrared absorption.

Keywords: ATEX standards, gas detector, methane meter, mining safety

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Authors: Ibrahim Aydogdu, Alper Akin

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In this study, the development of minimizing the cost and the CO2 emission of the RC retaining wall design has been performed by Biogeography Based Optimization (BBO) algorithm. This has been achieved by developing computer programs utilizing BBO algorithm which minimize the cost and the CO2 emission of the RC retaining walls. Objective functions of the optimization problem are defined as the minimized cost, the CO2 emission and weighted aggregate of the cost and the CO2 functions of the RC retaining walls. In the formulation of the optimum design problem, the height and thickness of the stem, the length of the toe projection, the thickness of the stem at base level, the length and thickness of the base, the depth and thickness of the key, the distance from the toe to the key, the number and diameter of the reinforcement bars are treated as design variables. In the formulation of the optimization problem, flexural and shear strength constraints and minimum/maximum limitations for the reinforcement bar areas are derived from American Concrete Institute (ACI 318-14) design code. Moreover, the development length conditions for suitable detailing of reinforcement are treated as a constraint. The obtained optimum designs must satisfy the factor of safety for failure modes (overturning, sliding and bearing), strength, serviceability and other required limitations to attain practically acceptable shapes. To demonstrate the efficiency and robustness of the presented BBO algorithm, the optimum design example for retaining walls is presented and the results are compared to the previously obtained results available in the literature.

Keywords: bio geography, meta-heuristic search, optimization, retaining wall

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Authors: D. Kakulia, L. Shoshiashvili, G. Sapharishvili

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The results of the study are related to the direction of plastic mine detection research using electromagnetic induction, the development of appropriate equipment, and the evaluation of expected results. Electromagnetic induction sensing is effectively used in the detection of metal objects in the soil and in the discrimination of unexploded ordnances. Metal objects interact well with a low-frequency alternating magnetic field. Their electromagnetic response can be detected at the low-frequency range even when they are placed in the ground. Detection of plastic things such as plastic mines by electromagnetic induction is associated with difficulties. The interaction of non-conducting bodies or low-conductive objects with a low-frequency alternating magnetic field is very weak. At the high-frequency range where already wave processes take place, the interaction increases. Interactions with other distant objects also increase. A complex interference picture is formed, and extraction of useful information also meets difficulties. Sensing by electromagnetic induction at the intermediate MHz frequency range is the subject of research. The concept of detecting plastic mines in this range can be based on the study of the electromagnetic response of non-conductive cavity in a low-conductivity environment or the detection of small metal components in plastic mines, taking into account constructive features. The detector node based on the amplitude and phase detector 'Analog Devices ad8302' has been developed for experimental studies. The node has two inputs. At one of the inputs, the node receives a sinusoidal signal from the generator, to which a transmitting coil is also connected. The receiver coil is attached to the second input of the node. The additional circuit provides an option to amplify the signal output from the receiver coil by 20 dB. The node has two outputs. The voltages obtained at the output reflect the ratio of the amplitudes and the phase difference of the input harmonic signals. Experimental measurements were performed in different positions of the transmitter and receiver coils at the frequency range 1-20 MHz. Arbitrary/Function Generator Tektronix AFG3052C and the eight-channel high-resolution oscilloscope PICOSCOPE 4824 were used in the experiments. Experimental measurements were also performed with a low-conductive test object. The results of the measurements and comparative analysis show the capabilities of the simple detector node and the prospects for its further development in this direction. The results of the experimental measurements are compared and analyzed with the results of appropriate computer modeling based on the method of auxiliary sources (MAS). The experimental measurements are driven using the MATLAB environment. Acknowledgment -This work was supported by Shota Rustaveli National Science Foundation (SRNSF) (Grant number: NFR 17_523).

Keywords: EM induction sensing, detector, plastic mines, remote sensing

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Authors: Lana Dalawr Jalal

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This paper addresses the problem of offline path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively.

Keywords: obstacle avoidance, particle swarm optimization, three-dimensional path planning unmanned aerial vehicles

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Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

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The cultural park of the Ahaggar, occupying a large area of Algeria, is characterized by a rich wetlands area to be preserved and managed both in time and space. The management of a large area, by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information...), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Remote sensing imaging data have been very useful in the last decade in very interesting applications. They can aid in several domains such as the detection and identification of diverse wetland surface targets, topographical details, and geological features... In this work, we try to extract automatically wetlands area using multispectral remotely sensed data on-board the Earth Observing 1 (EO-1) and Landsat satellite. Both are high-resolution multispectral imager with a 30 m resolution. The instrument images an interesting surface area. We have used images acquired over the several area of interesting in the National Park of Ahaggar in the south of Algeria. An Extraction Algorithm is applied on the several spectral index obtained from combination of different spectral bands to extract wetlands fraction occupation of land use. The obtained results show an accuracy to distinguish wetlands area from the other lad use themes using a fine exploitation on spectral index.

Keywords: multispectral data, EO1, landsat, wetlands, Ahaggar, Algeria

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Authors: T. Toathom, M. Munlin, P. Sugunnasil

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The traveling salesman problem (TSP) is one of the best-known problems in optimization problem. There are many research regarding the TSP. One of the most usage tool for this problem is the genetic algorithm (GA). The chromosome of the GA for TSP is normally encoded by the order of the visited city. However, the traditional chromosome encoding scheme has some limitations which are twofold: the large solution space and the inability to encapsulate some information. The number of solution for a certain problem is exponentially grow by the number of city. Moreover, the traditional chromosome encoding scheme fails to recognize the misplaced correct relation. It implies that the tradition method focuses only on exact solution. In this work, we relax some of the concept in the GA for TSP which is the exactness of the solution. The proposed work exploits the relation between cities in order to reduce the solution space in the chromosome encoding. In this paper, a second order GA is proposed to solve the TSP. The term second order refers to how the solution is encoded into chromosome. The chromosome is divided into 2 types: the high order chromosome and the low order chromosome. The high order chromosome is the chromosome that focus on the relation between cities such as the city A should be visited before city B. On the other hand, the low order chromosome is a type of chromosome that is derived from a high order chromosome. In other word, low order chromosome is encoded by the traditional chromosome encoding scheme. The genetic operation, mutation and crossover, will be performed on the high order chromosome. Then, the high order chromosome will be mapped to a group of low order chromosomes whose characteristics are satisfied with the high order chromosome. From the mapped set of chromosomes, the champion chromosome will be selected based on the fitness value which will be later used as a representative for the high order chromosome. The experiment is performed on the city data from TSPLIB.

Keywords: genetic algorithm, traveling salesman problem, initial population, chromosomes encoding

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Authors: Azam Zare

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Separation flow control for performance enhancement over airfoils at high incidence angle has become an increasingly important topic. This work details the characteristics of an efficient feedback control of the turbulent subsonic flow over NACA23012 airfoil using forced reduced‐order model based on the proper orthogonal decomposition/Galerkin projection and perturbation method on the compressible Reynolds Averaged Navier‐Stokes equations. The forced reduced‐order model is used in the optimal control of the turbulent separated flow over a NACA23012 airfoil at Mach number of 0.2, Reynolds number of 5×106, and high incidence angle of 24° using blowing/suction controlling jets. The Spallart-Almaras turbulence model is implemented for high Reynolds number calculations. The main shortcoming of the POD/Galerkin projection on flow equations for controlling purposes is that the blowing/suction controlling jet velocity does not show up explicitly in the resulting reduced order model. Combining perturbation method and POD/Galerkin projection on flow equations introduce a forced reduced‐order model that can predict the time-varying influence of the blowing/suction controlling jet velocity. An optimal control theory based on forced reduced‐order system is used to design a control law for a nonlinear reduced‐order model, which attempts to minimize the vorticity content in the turbulent flow field over NACA23012 airfoil. Numerical simulations were performed to help understand the behavior of the controlled suction jet at 12% to 18% chord from leading edge and a pair of blowing/suction jets at 15% to 18% and 24% to 30% chord from leading edge, respectively. Analysis of streamline profiles indicates that the blowing/suction jets are efficient in removing separation bubbles and increasing the lift coefficient up to 22%, while the perturbation method can predict the flow field in an accurate Manner.

Keywords: flow control, POD, Galerkin projection, separation

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Authors: Cheng-Yang Lee, Petrus Tang, Tzu-Hao Chang

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Copy number variations (CNVs) have played an important role in many kinds of human diseases, such as Autism, Schizophrenia and a number of cancers. Many diseases are found in genome coding regions and whole exome sequencing (WES) is a cost-effective and powerful technology in detecting variants that are enriched in exons and have potential applications in clinical setting. Although several algorithms have been developed to detect CNVs using WES and compared with other algorithms for finding the most suitable methods using their own samples, there were not consistent datasets across most of algorithms to evaluate the ability of CNV detection. On the other hand, most of algorithms is using command line interface that may greatly limit the analysis capability of many laboratories. We create a series of simulated WES datasets from UCSC hg19 chromosome 22, and then evaluate the CNV detective ability of 19 algorithms from OMICtools database using our simulated WES datasets. We compute the sensitivity, specificity and accuracy in each algorithm for validation of the exome-derived CNVs. After comparison of 19 algorithms from OMICtools database, we construct a platform to install all of the algorithms in a virtual machine like VirtualBox which can be established conveniently in local computers, and then create a simple script that can be easily to use for detecting CNVs using algorithms selected by users. We also build a table to elaborate on many kinds of events, such as input requirement, CNV detective ability, for all of the algorithms that can provide users a specification to choose optimum algorithms.

Keywords: whole exome sequencing, copy number variations, omictools, pipeline

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Authors: Krishnendra Shekhawat

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Given a graph G = (V, E), a rectangular dual of G represents the vertices of G by a set of interior-disjoint rectangles such that two rectangles touch if and only if there is an edge between the two corresponding vertices in G. Rectangular duals do not exist for every graph, so we can define maximal rectangular duals. A maximal rectangular dual is a rectangular dual of a graph G such that there exists no graph G ′ with a rectangular dual where G is a subgraph of G ′. In this paper, we enumerate all maximal rectangular duals (or, to be precise, the corresponding planar graphs) up to six nodes and presents a necessary condition for the existence of a rectangular dual. This work allegedly has applications in integrated circuit design and architectural floor plans.

Keywords: adjacency, degree sequence, dual graph, rectangular dual

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Authors: Z. Yousefniayejahr, S. Bolognini, A. Bonini, C. Campobasso, N. Poma, F. Vivaldi, M. Di Luca, A. Tavanti, F. Di Francesco

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Pathogenic bacteria represent a threat to healthcare systems and the food industry because their rapid detection remains challenging. Electrochemical biosensors are gaining prominence as a novel technology for the detection of pathogens due to intrinsic features such as low cost, rapid response time, and portability, which make them a valuable alternative to traditional methodologies. These sensors use biorecognition elements that are crucial for the identification of specific bacteria. In this context, bacteriophages are promising tools for their inherent high selectivity towards bacterial hosts, which is of fundamental importance when detecting bacterial pathogens in complex biological samples. In this study, we present the development of a low-cost and portable sensor based on the Zeno phage for the rapid detection of Staphylococcus aureus. Screen-printed gold electrodes functionalized with the Zeno phage were used, and electrochemical impedance spectroscopy was applied to evaluate the change of the charge transfer resistance (Rct) as a result of the interaction with S. aureus MRSA ATCC 43300. The phage-based biosensor showed a linear range from 101 to 104 CFU/mL with a 20-minute response time and a limit of detection (LOD) of 1.2 CFU/mL under physiological conditions. The biosensor’s ability to recognize various strains of staphylococci was also successfully demonstrated in the presence of clinical isolates collected from different geographic areas. Assays using S. epidermidis were also carried out to verify the species-specificity of the phage sensor. We only observed a remarkable change of the Rct in the presence of the target S. aureus bacteria, while no substantial binding to S. epidermidis occurred. This confirmed that the Zeno phage sensor only targets S. aureus species within the genus Staphylococcus. In addition, the biosensor's specificity with respect to other bacterial species, including gram-positive bacteria like Enterococcus faecium and the gram-negative bacterium Pseudomonas aeruginosa, was evaluated, and a non-significant impedimetric signal was observed. Notably, the biosensor successfully identified S. aureus bacterial cells in a complex matrix such as a nasal swab, opening the possibility of its use in a real-case scenario. We diluted different concentrations of S. aureus from 108 to 100 CFU/mL with a ratio of 1:10 in the nasal swap matrices collected from healthy donors. Three different sensors were applied to measure various concentrations of bacteria. Our sensor indicated high selectivity to detect S. aureus in biological matrices compared to time-consuming traditional methods, such as enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and radioimmunoassay (RIA), etc. With the aim to study the possibility to use this biosensor to address the challenge associated to pathogen detection, ongoing research is focused on the assessment of the biosensor’s analytical performances in different biological samples and the discovery of new phage bioreceptors.

Keywords: electrochemical impedance spectroscopy, bacteriophage, biosensor, Staphylococcus aureus

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Authors: Navdeep Singh Randhawa, Shally Sharma

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Wireless Sensor Network is an autonomous technology emanating in the current scenario at a fast pace. This technology faces a number of defiance’s and energy management is one of them, which has a huge impact on the network lifetime. To sustain energy the different types of routing protocols have been flourished. The classical routing protocols are no more compatible to perform in complicated environments. Hence, in the field of routing the intelligent algorithms based on nature systems is a turning point in Wireless Sensor Network. These nature-based algorithms are quite efficient to handle the challenges of the WSN as they are capable of achieving local and global best optimization solutions for the complex environments. So, the main attention of this paper is to develop a routing algorithm based on some swarm intelligent technique to enhance the performance of Wireless Sensor Network.

Keywords: wireless sensor network, routing, swarm intelligence, MPRSO

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Authors: Kumar Dron Shrivastav, Ankan Mukherjee Das, Arti Taneja, Harpreet Singh, Priya Ranjan, Rajiv Janardhanan

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Cervical cancer is one of the most common cancer among women worldwide which can be cured if detected early. Manual pathology which is typically utilized at present has many limitations. The current gold standard for cervical cancer diagnosis is exhaustive and time-consuming because it relies heavily on the subjective knowledge of the oncopathologists which leads to mis-diagnosis and missed diagnosis resulting false negative and false positive. To reduce time and complexities associated with early diagnosis, we require an interactive diagnostic tool for early detection particularly in developing countries where cervical cancer incidence and related mortality is high. Incorporation of digital pathology in place of manual pathology for cervical cancer screening and diagnosis can increase the precision and strongly reduce the chances of error in a time-specific manner. Thus, we propose a robust and interactive cervical cancer image analysis and diagnostic tool, which can categorically process both histopatholgical and cytopathological images to identify abnormal cells in the least amount of time and settings with minimum resources. Furthermore, incorporation of a set of specific parameters that are typically referred to for identification of abnormal cells with the help of open source software -’R’ is one of the major highlights of the tool. The software has the ability to automatically identify and quantify the morphological features, color intensity, sensitivity and other parameters digitally to differentiate abnormal from normal cells, which may improve and accelerate screening and early diagnosis, ultimately leading to timely treatment of cervical cancer.

Keywords: cervical cancer, early detection, digital Pathology, screening

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Authors: C. Hamamura, V. Gialluca

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Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan.

Keywords: image pattern recognition, trees pruning, trees recognition, neural network

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Authors: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

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The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

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Authors: Terence Soule, Tami Al Ghamdi

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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: transfer learning, genetic algorithm, evolutionary computation, source and target

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Raquel Amanda Villamizar Gallardo, Oscar Orlando Ortíz Rodríguez

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Enteric viruses are cosmopolitan agents present in several environments including water. These viruses can cause different diseases including gastroenteritis, hepatitis, conjunctivitis, respiratory problems among others. Although in Colombia there are not regulations concerning to routine viral analysis of drinking water, an enhanced understanding of viral pollution and resistance to treatments is desired in order to assure pure water to the population. Viral detection is often complex due to the need of specialized and time-consuming procedures. In addition, viruses are highly diluted in water which is a drawback from the analytical point of view. To this end, a fast and selective detection method for detection enteric viruses (i.e. Hepatitis A and Rotavirus) were applied. Micro- magnetic particles were functionalized with monoclonal antibodies anti-Hepatitis and anti-Rotavirus and they were used to capture, concentrate and separate whole viral particles in raw water and drinking water samples from four treatment plants identified as CAR-01, MON-02, POR-03, TON-04 and located in the Northeastern Colombia. Viruses were molecularly by using RT-PCR One Step Superscript III. Each plant was analyzed at the entry and exit points, in order to determine the initial presence and eventual reduction of Hepatitis A and Rotavirus after disinfection. The results revealed the presence of both enteric viruses in a 100 % of raw water analyzed in all plants. This represents a potential health hazard, especially for those people whose use this water for agricultural purposes. However, in drinking water analysis, enteric viruses was only positive in CAR-01, where was found the presence of Rotavirus. As a conclusion, the results confirm Rotavirus as the best indicator to evaluate the efficacy of potable treatment plant in eliminating viruses. CAR potable water plant should improve their disinfection process in order to remove efficiently enteric viruses.

Keywords: drinking water, hepatitis A, rotavirus, virus removal

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Authors: Alexandre Marin, Alexandra Bac, Laurent Astart

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New numerical methods for PDE resolution (such as Finite Volumes (FV) or Virtual Elements Method (VEM)) open new needs in terms of meshing of domains of interest, and in particular, polyhedral meshes have many advantages. One way to build such meshes consists of constructing Restricted Voronoi Diagrams (RVDs) whose boundaries respect the domain of interest. By minimizing a function defined for RVDs, the shapes of cells can be controlled, e.g., elongated according to user-defined directions or adjusted to comply with given aspect ratios (anisotropy) and density variations. In this paper, our contribution is threefold: First, we introduce a new gradient formula for the Voronoi tessellation energy under a continuous anisotropy field. Second, we describe a meshing algorithm based on the optimisation of this function that we validate against state-of-the-art approaches. Finally, we propose a hierarchical approach to speed up our meshing algorithm.

Keywords: anisotropic Voronoi diagrams, meshes for numerical simulations, optimisation, volumic polyhedral meshing

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

Abstract:

Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

Abstract:

Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Jae-Hyun Ro, Jong-Kwang Kim, Chang-Hee Kang, Hyoung-Kyu Song

Abstract:

In multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, QR decomposition-M algorithm (QRD-M) has suboptimal error performance. However, the QRD-M has still high complexity due to many calculations at each layer in tree structure. To reduce the complexity of the QRD-M, proposed QRD-M modifies existing tree structure by eliminating unnecessary candidates at almost whole layers. The method of the elimination is discarding the candidates which have accumulated squared Euclidean distances larger than calculated threshold. The simulation results show that the proposed QRD-M has same bit error rate (BER) performance with lower complexity than the conventional QRD-M.

Keywords: complexity, MIMO-OFDM, QRD-M, squared Euclidean distance

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Authors: Saeid Jalilzadeh

Abstract:

PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK.

Keywords: controller, GA, optimization, PID, PSO

Procedia PDF Downloads 546

Authors: Chen Guo, Heng Tang, Ben Niu

Abstract:

Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

Procedia PDF Downloads 149