Search results for: meta heuristic algorithm

Authors: Benmessaoud Hassen

Abstract:

The sensitivity to the desertification map of the south of Aurès region has been elaborated by the crossing of four thematic layers capable to have an impact on the process of desertification. The following step is inspired of MEDALUS (Mediterranean desertification and land Use), which use qualitative index to define the environment zones sensitive to the desertification. The cartographical information of vegetation, the climate, the soil and the socioeconomic state descended from cartographic data transformed to numerical data then seized on, structured and managed by an algorithm dedicated to a geographical information system. In step with information, each layer makes object of 3 or 4 classes, the geometrical median of the four layers used are leaded to sensitivity classes (ISD) of different mapped environment.

Keywords: information systems, thematic layers, the sensitivity to the desertification map, concept MEDALUS, South of Aurès

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Authors: Xiong Xie, Yuhang Chen

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Vulnerability discovery technology is a significant field of the current. In this paper, a fuzzy framework based on concolic execution has been proposed. Fuzzy test and symbolic execution are widely used in the field of vulnerability discovery technology. But each of them has its own advantages and disadvantages. During the path generation stage, path traversal algorithm based on generation is used to get more accurate path. During the constraint solving stage, dynamic concolic execution is used to avoid the path explosion. If there is external call, the concolic based on function summary is used. Experiments show that the framework can effectively improve the ability of triggering vulnerabilities and code coverage.

Keywords: concolic execution, constraint solving, fuzzy test, vulnerability discovery

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Authors: Bharat Singh Om Prakash Vyas

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Now a day’s applications deal with High Dimensional Data have tremendously used in the popular areas. To tackle with such kind of data various approached has been developed by researchers in the last few decades. To tackle with such kind of data various approached has been developed by researchers in the last few decades. One of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, we have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. We have devised an algorithm for initializing the values of the decomposed matrix based on the PSO (Particle Swarm Optimization). Through the experimental result, we will show the proposed method converse very fast in comparison to other row rank approximation like simple NMF multiplicative, and ACLS techniques.

Keywords: ALS, NMF, high dimensional data, RMSE

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Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

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In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

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Authors: Vladimir Hovhannisyan, Chen Yuan Dong

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Recently nanoparticles (NPs) have been introduced in biomedicine as effective agents for cancer-targeted drug delivery and noninvasive tissue imaging. The most important requirements to these agents are their non-toxicity, biocompatibility and stability. In view of these criteria, the zeolite (ZL) nanoparticles (NPs) may be considered as perfect candidates for biomedical applications. ZLs are crystalline aluminosilicates consisting of oxygen-sharing SiO4 and AlO4 tetrahedral groups united by common vertices in three-dimensional framework and containing pores with diameters from 0.3 to 1.2 nm. Generally, the behavior and physical properties of ZLs are studied by SEM, X-ray spectroscopy, and AFM, whereas optical spectroscopic and microscopic approaches are not effective enough, because of strong scattering in common ZL bulk materials and powders. The light scattering can be reduced by using of ZL NPs. ZL NPs have large external surface area, high dispersibility in both aqueous and organic solutions, high photo- and thermal stability, and exceptional ability to adsorb various molecules and atoms in their nanopores. In this report, using multiphoton microscopy and nonlinear spectroscopy, we investigate nonlinear optical properties of clinoptilolite type of ZL micro- and nanoparticles with average diameters of 2200 nm and 240 nm, correspondingly. Multiphoton imaging is achieved using a laser scanning microscope system (LSM 510 META, Zeiss, Germany) coupled to a femtosecond titanium:sapphire laser (repetition rate- 80 MHz, pulse duration-120 fs, radiation wavelength- 720-820 nm) (Tsunami, Spectra-Physics, CA). Two Zeiss, Plan-Neofluar objectives (air immersion 20×∕NA 0.5 and water immersion 40×∕NA 1.2) are used for imaging. For the detection of the nonlinear response, we use two detection channels with 380-400 nm and 435-700 nm spectral bandwidths. We demonstrate that ZL micro- and nanoparticles can produce nonlinear optical response under the near-infrared femtosecond laser excitation. The interaction of hypericine, chlorin e6 and other dyes with ZL NPs and their photodynamic activity is investigated. Particularly, multiphoton imaging shows that individual ZL NPs particles adsorb Zn-tetraporphyrin molecules, but do not adsorb fluorescein molecules. In addition, nonlinear spectral properties of ZL NPs in native biotissues are studied. Nonlinear microscopy and spectroscopy may open new perspectives in the research and application of ZL NP in biomedicine, and the results may help to introduce novel approaches into the clinical environment.

Keywords: multiphoton microscopy, nanoparticles, nonlinear optics, zeolite

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Authors: Abdelaziz Moujane, Abedelali Boulli, Abdellah Ouigmane

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The present work focuses on the assessment of forestlandscape changes in the region of ZaouitAhansal, usingmultitemporal satellite images at high spatial resolution.Severalremotesensingmethodswereappliednamely: The supervised classification algorithm and NDVI whichwerecombined in a GIS environment to quantify the extent and change in density of forest stands (holmoak, juniper, thya, Aleppo pine, crops, and others).The resultsobtainedshowedthat the forest of ZaouitAhansal has undergonesignificantdegradationresulting in a decrease in the area of juniper, cedar, and zeenoak, as well as an increase in the area of baresoil and agricultural land. The remotesensing data providedsatisfactoryresults for identifying and quantifying changes in forestcover. In addition, thisstudycould serve as a reference for the development of management strategies and restoration programs.

Keywords: remote sensing, GIS, satellite image, NDVI, deforestation, zaouit ahansal

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 241

Authors: Minakshi Mainaji Sonawane, Bharti W. Gawali, Sudhir Mendhekar, Ramesh R. Manza

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People's lives and health are severely impacted by skin diseases. A new study proposes an effective method for identifying the different forms of skin diseases. Image denoising is a technique for improving image quality after it has been harmed by noise. The proposed technique is based on the usage of the wavelet transform. Wavelet transform is the best method for analyzing the image due to the ability to split the image into the sub-band, which has been used to estimate the noise ratio at the noisy image. According to experimental results, the proposed method presents the best values for MSE, PSNR, and Entropy for denoised images. we can found in Also, by using different types of wavelet transform filters is make the proposed approach can obtain the best results 23.13, 20.08, 50.7 for the image denoising process

Keywords: MSE, PSNR, entropy, Gaussian filter, DWT

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Authors: Nicholas Burica, Nina Selak

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The ADHCAT (Automated Distribution Hosting Capacity Assessment Tool) was designed to run Hosting Capacity Analysis on the ComEd system via a stochastic DER (Distributed Energy Resource) placement on multiple power flow simulations against a set of violation criteria. The violation criteria in the initial version of the tool captured a limited amount of issues that individual departments design against for DER interconnections. Enhancements were made to the tool to further align with individual department violation and operation criteria, as well as the addition of new modules for use for future load profile analysis. A reporting engine was created for future analytical use based on the simulations and observations in the tool.

Keywords: distributed energy resources, hosting capacity, interconnect, voltage optimization

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Authors: S. Dhivya, M. Abirami, M. Farjana Parveen, M. Keerthiga

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The development of high-speed railway makes people’s lives more and more convenient; meanwhile, handover is the major problem on high-speed railway communication services. In order to overcome that drawback the architecture of Long-Term Evolution (LTE) femtocell networks is used to improve network performance, and the deployment of a femtocell is a key for bandwidth limitation and coverage issues in conventional mobile network system. To increase the handover performance this paper proposed a multiple input multiple output (MIMO) assisted handoff (MAHO) algorithm. It is a technique used in mobile telecom to transfer a mobile phone to a new radio channel with stronger signal strength and improved channel quality.

Keywords: flawless handover, high-speed train, home evolved Node B, LTE, mobile femtocell, RSS

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Authors: Khaled M. El Naggar

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This paper introduces a digital method for fault section identification in transmission lines. The method uses digital set of the measured short circuit current to locate faults in electrical power systems. The digitized current is used to construct a set of overdetermined system of equations. The problem is then constructed and solved using the proposed digital optimization technique to find the fault distance. The proposed optimization methodology is an application of simulated annealing optimization technique. The method is tested using practical case study to evaluate the proposed method. The accurate results obtained show that the algorithm can be used as a powerful tool in the area of power system protection.

Keywords: optimization, estimation, faults, measurement, high voltage, simulated annealing

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Authors: Estelle Ardanouy, Helene Delage, Pascal Zesiger

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Morphological awareness contributes to the acquisition of reading and spelling in typical learners as well as in children with learning disorders. Indeed, the acquisition of phoneme-grapheme correspondences is not sufficient to master spelling, especially in inconsistent orthographic systems such as English or French. Several meta-analyses show the benefit of explicit training in derivational morphology on reading and spelling in old children (who have already learned the main grapheme-phoneme correspondences), but highlight the lack of studies with younger children, particularly in French. In this study, we chose to focus on the efficiency of an intensive training in derivational morphology on spelling skills in French-speaking four-graders (9-10 years of age). The training consisted of 1) learning how to divide words into morphemes (ex: para/pente in French, paraglider in English), as well as 2) working on the meaning of affixes in relation to existing words (ex: para/pente: to protect against – para - the slope -pente). One group of pupils (N = 37, M age = 9.5) received this experimental group training in morphology while an alternative training group (N = 34, M age = 9.6) received a visuo-semantic training based on visual cues to memorize the spelling difficulties of complex words (such as the doubling of “r” in “verre” in French -or "glass" in English-which are represented by the drawing of two glasses). Both trainings lasted a total of 15 hours at a rate of four 45 minutes sessions per week, resulting in five weeks of training in the school setting. Our preliminary results show a significant improvement in the experimental group in the spelling of affixes on the trained (p < 0.001) and untrained word lists (p <0.001), but also in the root of words on the trained (p <0.001) and untrained word lists group (p <0.001). The training effect is also present on both trained and untrained morphologically composed words. By contrast, the alternative training group shows no progress on these previous measures (p >0.15). Further analyses testing the effects of both trainings on other measures such as morphological awareness and reading of morphologically compose words are in progress. These first results support the effectiveness of explicitly teaching derivational morphology to improve spelling in school-aged children. The study is currently extended to a group of children with developmental dyslexia because these children are known for their severe and persistent spelling difficulties.

Keywords: developmental dyslexia, derivational morphology, reading, school-aged children, spelling, training

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Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Chun-Chuan Yang, Jeng-Yueng Chen, Yi-Ting Mai, Chen-Ming Yang

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In this paper, the design of integrated sleep scheduling for relay nodes and user equipments under a Donor eNB (DeNB) in the mode of Time Division Duplex (TDD) in LTE-A is presented. The idea of virtual time is proposed to deal with the discontinuous pattern of the available radio resource in TDD, and based on the estimation of the traffic load, three power saving schemes in the top-down strategy are presented. Associated mechanisms in each scheme including calculation of the virtual subframe capacity, the algorithm of integrated sleep scheduling, and the mapping mechanisms for the backhaul link and the access link are presented in the paper. Simulation study shows the advantage of the proposed schemes in energy saving over the standard DRX scheme.

Keywords: LTE-A, relay, TDD, power saving

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Authors: Nagaratna Rajur, J. D. Mallapur, Y. B. Kirankumar

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The security in mobile communication is very different from the internet or telecommunication, because of its poor user interface and limited processing capacity, as well as combination of complex network protocols. Hence, it poses a challenge for less memory usage and low computation speed based security system. Security involves all the activities that are undertaken to protect the value and on-going usability of assets and the integrity and continuity of operations. An effective network security strategies requires identifying threats and then choosing the most effective set of tools to combat them. Cryptography is a simple and efficient way to provide security in communication. RSA is an asymmetric key approach that is highly reliable and widely used in internet communication. However, it has not been efficiently implemented in mobile communication due its computational complexity and large memory utilization. The proposed algorithm modifies the current RSA to be useful in mobile communication by reducing its computational complexity and memory utilization.

Keywords: M-RSA, sensor networks, sensor applications, security

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Authors: Andrey V. Timofeev, Viktor M. Denisov

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A robust sequential nonparametric method is proposed for adaptation to background noise parameters for real-time. The distribution of background noise was modelled like to Huber contamination mixture. The method is designed to operate as an adaptation-unit, which is included inside a detection subsystem of an integrated multichannel monitoring system. The proposed method guarantees the given size of a nonasymptotic confidence set for noise parameters. Properties of the suggested method are rigorously proved. The proposed algorithm has been successfully tested in real conditions of a functioning C-OTDR monitoring system, which was designed to monitor railways.

Keywords: guaranteed estimation, multichannel monitoring systems, non-asymptotic confidence set, contamination mixture

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Authors: Carolina S. Correia, Thalita V. Thomé, Andersen Boniolo, Dhayana I. Veiga

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Evidence-based practices (EBP) in education is a set of principles and practices used to raise educational policy, it involves the integration of professional expertise in education with the best empirical evidence in making decisions about how to deliver instruction. The purpose of this presentation is to describe and characterize studies about EBP in education in elementary classroom setting. Data here presented is part of an ongoing systematic review research. Articles were searched and selected from four academic databases: ProQuest, Scielo, Science Direct and Capes. The search terms were evidence-based practices or program effectiveness, and education or teaching or teaching practices or teaching methods. Articles were included according to the following criteria: The studies were explicitly described as evidence-based or discussed the most effective practices in education, they discussed teaching practices in classroom context in elementary school level. Document excerpts were extracted and recorded in Excel, organized by reference, descriptors, abstract, purpose, setting, participants, type of teaching practice, study design and main results. The total amount of articles selected were 1.185, 569 articles from Proquest Research Library; 216 from CAPES; 251 from ScienceDirect and 149 from Scielo Library. The potentially relevant references were 178, from which duplicates were removed. The final number of articles analyzed was 140. From 140 articles, are 47 theoretical studies and 93 empirical articles. The following research design methods were identified: longitudinal intervention study, cluster-randomized trial, meta-analysis and pretest-posttest studies. From 140 articles, 103 studies were about regular school teaching and 37 were on special education teaching practices. In several studies, used as teaching method: active learning, content acquisition podcast (CAP), precision teaching (PT), mediated reading practice, speech therapist programs and peer-assisted learning strategies (PALS). The countries of origin of the studies were United States of America, United Kingdom, Panama, Sweden, Scotland, South Korea, Argentina, Chile, New Zealand and Brunei. The present study in is an ongoing project, so some representative findings will be discussed, providing further acknowledgment on the best teaching practices in elementary classroom setting.

Keywords: best practices, children, evidence-based education, elementary school, teaching methods

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Authors: Shen Jian, Hu Jie, Zhu Guo Niu, Peng Ying Hong

Abstract:

There are two issues exited in the traditional visual prosthesis: lacking systematic method and the low level of humanization. To tackcle those obstacles, a visual prosthesis design method based on biologically inspired design is proposed. Firstly, a constrained FBS knowledge cell model is applied to construct the functional model of visual prosthesis in biological field. Then the clustering results of engineering domain are ob-tained with the use of the cross-domain knowledge cell clustering algorithm. Finally, a prototype system is designed to support the bio-logically inspired design where the conflict is digested by TRIZ and other tools, and the validity of the method is verified by the solution scheme

Keywords: knowledge-based engineering, visual prosthesis, biologically inspired design, biomedical engineering

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Authors: Young-Min Ko, Seung-Jun Yu, Seongjoo Lee, Hyoung-Kyu Song

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The Internet of things (IoT) system has attracted attention. IoT is a technology to connect all the objects to the internet as well as computer. IoT makes it possible for providing more data interoperability methods for an application purpose. Among the IoT technology, the research of devices so that they can communicate without power supply has been actively conducted. Batteryless system permits us to communicate without power supply devices. In this paper, batteryless backscatter system is used as a tag. And mobile devices which are embedded wireless fidelity (Wi-Fi) chipset are used as a reader. The backscatter tag can be obtained Internet connectivity from the reader. Conventional Wi-Fi backscatter system has limitation in the transmission range. In this paper, the proposed algorithm can be obtained improved reliability as well as overcoming the limitation about transmission range.

Keywords: Ambient RF, Backscatter, Batteryless communication, Energy-harvesting, IoT, RFID, Tag, Wi-Fi

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Authors: Akram Khaleghei Ghosheh Balagh, Viliam Makis, Leila Jafari

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In this paper, we present a new maintenance model for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model. A cost-optimal Bayesian control policy is developed for maintaining the system. The control problem is formulated in the semi-Markov decision process framework. An effective computational algorithm is developed and illustrated by a numerical example.

Keywords: partially observable system, hidden Markov model, competing risks, multivariate Bayesian control

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Authors: Yingxu Wang

Abstract:

This work presents basic research on the recursive structures and dual sequential patterns of primes for the formal proof of the Twin-Prime Conjecture (TPC). A rigorous methodology of Twin-Prime Decomposition (TPD) is developed in MATLAB to inductively verify potential twins in the dual sequences of primes. The key finding of this basic study confirms that the dual sequences of twin primes are not only symmetric but also infinitive in the unique base 6 cycle, except a limited subset of potential pairs is eliminated by the lack of dual primality. Both theory and experiments have formally proven that the infinity of twin primes stated in TPC holds in the P x P space.

Keywords: number theory, primes, twin-prime conjecture, dual primes (P x P), twin prime decomposition, formal proof, algorithm

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Authors: P. Kooche Baghy, S. Eskandari, E.javanmard

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Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.

Keywords: artificial neural network, Bayesian, cold rolling, force evaluation

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Authors: Sukryool Kang, James McConnaughey, Robin Doroshow, Raj Shekhar

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Still’s murmur, a vibratory heart murmur, is the most common normal innocent murmur of childhood. Many children with this murmur are unnecessarily referred for cardiology consultation and testing, which exacts a high cost financially and emotionally on the patients and their parents. Pediatricians to date are not successful at distinguishing Still’s murmur from murmurs of true heart disease. In this paper, we present a new algorithmic approach to distinguish Still’s murmur from pathological murmurs in children. We propose two distinct features, spectral width and signal power, which describe the sharpness of the spectrum and the signal intensity of the murmur, respectively. Seventy pediatric heart sound recordings of 41 Still’s and 29 pathological murmurs were used to develop and evaluate our algorithm that achieved a true positive rate of 97% and false positive rate of 0%. This approach would meet clinical standards in recognizing Still’s murmur.

Keywords: AR modeling, auscultation, heart murmurs, Still's murmur

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Authors: Somaiyeh Dehghan, Mohammad Reza Kheirkhahan Haghighi

Abstract:

The rapid growth of domains that have data with a large number of features, while the number of samples is limited has caused difficulty in constructing strong classifiers. To reduce the dimensionality of the feature space becomes an essential step in classification task. Random subspace method (or attribute bagging) is an ensemble classifier that consists of several classifiers that each base learner in ensemble has subset of features. In the present paper, we introduce Random Subspace Ensemble of CMAC neural network (RSE-CMAC), each of which has training with subset of features. Then we use this model for classification task. For evaluation performance of our model, we compare it with bagging algorithm on 36 UCI datasets. The results reveal that the new model has better performance.

Keywords: classification, random subspace, ensemble, CMAC neural network

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Authors: Felipe M. de Freitas, Icaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

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The SPICE-based simulators are quite robust and widely used for simulation of electronic circuits, their algorithms support linear and non-linear lumped components and they can manipulate an expressive amount of encapsulated elements. Despite the great potential of these simulators based on SPICE in the analysis of quasi-static electromagnetic field interaction, that is, at low frequency, these simulators are limited when applied to microwave hybrid circuits in which there are both lumped and distributed elements. Usually the spatial discretization of the FDTD (Finite-Difference Time-Domain) method is done according to the actual size of the element under analysis. After spatial discretization, the Courant Stability Criterion calculates the maximum temporal discretization accepted for such spatial discretization and for the propagation velocity of the wave. This criterion guarantees the stability conditions for the leapfrogging of the Yee algorithm; however, it is known that for the field update, the stability of the complete FDTD procedure depends on factors other than just the stability of the Yee algorithm, because the FDTD program needs other algorithms in order to be useful in engineering problems. Examples of these algorithms are Absorbent Boundary Conditions (ABCs), excitation sources, subcellular techniques, grouped elements, and non-uniform or non-orthogonal meshes. In this work, the influence of the stability of the FDTD method in the modeling of concentrated elements such as resistive sources, resistors, capacitors, inductors and diode will be evaluated. In this paper is proposed, therefore, the electromagnetic modeling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-wide frequencies. The models of the resistive source, the resistor, the capacitor, the inductor, and the diode will be evaluated, among the mathematical models for lumped components in the LE-FDTD method (Lumped-Element Finite-Difference Time-Domain), through the parametric analysis of Yee cells size which discretizes the lumped components. In this way, it is sought to find an ideal cell size so that the analysis in FDTD environment is in greater agreement with the expected circuit behavior, maintaining the stability conditions of this method. Based on the mathematical models and the theoretical basis of the required extensions of the FDTD method, the computational implementation of the models in Matlab® environment is carried out. The boundary condition Mur is used as the absorbing boundary of the FDTD method. The validation of the model is done through the comparison between the obtained results by the FDTD method through the electric field values and the currents in the components, and the analytical results using circuit parameters.

Keywords: hybrid circuits, LE-FDTD, lumped element, parametric analysis

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Authors: S. Som-Am, B. Grechuk

Abstract:

Moser’s worm problem is the unsolved problem in geometry which asks for the minimal area of a convex region on the plane which can cover all curves of unit length, assuming that curves may be rotated and translated to fit inside the region. We study a version of this problem asking for a minimal convex cover for closed unit curves. By combining geometric methods with numerical box’s search algorithm, we show that any such cover should have an area at least 0.0975. This improves the best previous lower bound of 0.096694. In fact, we show that the minimal area of convex hull of circle, equilateral triangle, and rectangle of perimeter 1 is between 0.0975 and 0.09763.

Keywords: Moser’s worm problem, closed arcs, convex cover, minimal-area cover

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Authors: Slimane Ouhmad, Abdellah Halimi

Abstract:

In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: classification, data mining, evaluation measures, groundwater

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Authors: Kyoung-Jin Joo, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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Recently, the permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) that can be substituted for the induction motor has been studying because of the needs of the development of the premium high efficiency motor for the minimum energy performance standard (MEPS). PMa-SynRM is required to the speed and position information for motor speed and torque controls. However, to apply the sensors has many problems that are sensor mounting space shortage and additional cost, etc. Therefore, in this paper, speed-sensorless control based on model reference adaptive system (MRAS) is introduced to eliminate the sensor. The sensorless method is constructed in a reference model as standard and an adaptive model as the state observer. The proposed algorithm is verified by the simulation.

Keywords: PMa-SynRM, sensorless control, robust estimation, MRAS method

Procedia PDF Downloads 386