Search results for: genetic algorithm

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Atif Zafar, Fan Haijun

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A sequence of different Reservoir Engineering methods and tools in reservoir characterization and field development are presented in this paper. The real data of Jin Gas Field of L-Basin of Pakistan is used. The basic concept behind this work is to enlighten the importance of well test analysis in a broader way (i.e. reservoir characterization and field development) unlike to just determine the permeability and skin parameters. Normally in the case of reservoir characterization we rely on well test analysis to some extent but for field development plan, the well test analysis has become a forgotten tool specifically for locations of new development wells. This paper describes the successful implementation of well test analysis in Jin Gas Field where the main uncertainties are identified during initial stage of field development when location of new development well was marked only on the basis of G&G (Geologic and Geophysical) data. The seismic interpretation could not encounter one of the boundary (fault, sub-seismic fault, heterogeneity) near the main and only producing well of Jin Gas Field whereas the results of the model from the well test analysis played a very crucial rule in order to propose the location of second well of the newly discovered field. The results from different methods of well test analysis of Jin Gas Field are also integrated with and supported by other tools of Reservoir Engineering i.e. Material Balance Method and Volumetric Method. In this way, a comprehensive way out and algorithm is obtained in order to integrate the well test analyses with Geological and Geophysical analyses for reservoir characterization and field development. On the strong basis of this working and algorithm, it was successfully evaluated that the proposed location of new development well was not justified and it must be somewhere else except South direction.

Keywords: field development plan, reservoir characterization, reservoir engineering, well test analysis

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Authors: Shraddha Rane, Richard Warren, Stephen Eyre

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L-23 is a pro-inflammatory molecule that signals T cells to release cytokines such as IL-17A and IL-22. Psoriasis is driven by a dysregulated immune response, within which IL-23 is now thought to play a key role. Genome-wide association studies (GWAS) have identified a number of genetic risk loci that support the involvement of IL-23 signalling in psoriasis; in particular a robust susceptibility locus at a gene encoding a subunit of the IL-23 receptor (IL23R) (Stuart et al., 2015; Tsoi et al., 2012). The lead psoriasis-associated SNP rs9988642 is located approximately 500 bp downstream of IL23R but is in tight linkage disequilibrium (LD) with a missense SNP rs11209026 (R381Q) within IL23R (r2 = 0.85). The minor (G) allele of rs11209026 is present in approximately 7% of the population and is protective for psoriasis and several other autoimmune diseases including IBD, ankylosing spondylitis, RA and asthma. The psoriasis-associated missense SNP R381Q causes an arginine to glutamine substitution in a region of the IL23R protein between the transmembrane domain and the putative JAK2 binding site in the cytoplasmic portion. This substitution is expected to affect the receptor’s surface localisation or signalling ability, rather than IL23R expression. Recent studies have also identified a psoriatic arthritis (PsA)-specific signal at IL23R; thought to be independent from the psoriasis association (Bowes et al., 2015; Budu-Aggrey et al., 2016). The lead PsA-associated SNP rs12044149 is intronic to IL23R and is in LD with likely causal SNPs intersecting promoter and enhancer marks in memory CD8+ T cells (Budu-Aggrey et al., 2016). It is therefore likely that the PsA-specific SNPs affect IL23R function via a different mechanism compared with the psoriasis-specific SNPs. It could be hypothesised that the risk allele for PsA located within the IL23R promoter causes an increase IL23R expression, relative to the protective allele. An increased expression of IL23R might then lead to an exaggerated immune response. The independent genetic signals identified for psoriasis and PsA in this locus indicate that different mechanisms underlie these two conditions; although likely both affecting the function of IL23R. It is very important to further characterise these mechanisms in order to better understand how the IL-23 receptor and its downstream signalling is affected in both diseases. This will help to determine how psoriasis and PsA patients might differentially respond to therapies, particularly IL-23 biologics. To investigate this further we have developed an in vitro model using CD4 T cells which express either wild type IL23R and IL12Rβ1 or mutant IL23R (R381Q) and IL12Rβ1. Model expressing different isotypes of IL23R is also underway to investigate the effects on IL23R expression. We propose to further investigate the variants for Ps and PsA and characterise key intracellular processes related to the variants.

Keywords: IL23R, psoriasis, psoriatic arthritis, SNP

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Authors: Muhammad Bilal, Zhongfeng Qiu

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Aerosols, suspended particles in the atmosphere, play an important role in the earth energy budget, climate change, degradation of atmospheric visibility, urban air quality, and human health. To fully understand aerosol effects, retrieval of aerosol optical properties such as aerosol optical depth (AOD) at high spatiotemporal resolution is required. Therefore, in the present study, hourly AOD observations at 500 m resolution were retrieved from the geostationary ocean color imager (GOCI) using the simplified aerosol retrieval algorithm (SARA) over the urban area of Beijing for the year 2016. The SARA requires top-of-the-atmosphere (TOA) reflectance, solar and sensor geometry information and surface reflectance observations to retrieve an accurate AOD. For validation of the GOCI retrieved AOD, AOD measurements were obtained from the aerosol robotic network (AERONET) version 3 level 2.0 (cloud-screened and quality assured) data. The errors and uncertainties were reported using the root mean square error (RMSE), relative percent mean error (RPME), and the expected error (EE = ± (0.05 + 0.15AOD). Results showed that the high spatiotemporal GOCI AOD observations were well correlated with the AERONET AOD measurements with a correlation coefficient (R) of 0.92, RMSE of 0.07, and RPME of 5%, and 90% of the observations were within the EE. The results suggested that the SARA is robust and has the ability to retrieve high-resolution spatiotemporal AOD observations over the urban area using the geostationary satellite.

Keywords: AEORNET, AOD, SARA, GOCI, Beijing

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Authors: Hashem Shariatmadar, Mozhgansadat Momtazdargahi

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The purpose of control structure against earthquake is to dissipate earthquake input energy to the structure and reduce the plastic deformation of structural members. There are different methods for control structure against earthquake to reduce the structure response that they are active, semi-active, inactive and hybrid. In this paper two different combined control systems are used first system comprises base isolator and multi tuned mass dampers (BI & MTMD) and another combination is hybrid base isolator and multi tuned mass dampers (HBI & MTMD) for controlling an eight story isolated benchmark steel structure. Active control force of hybrid isolator is estimated by fuzzy logic algorithms. The influences of the combined systems on the responses of the benchmark structure under the two near-field earthquake (Newhall & Elcentro) are evaluated by nonlinear dynamic time history analysis. Applications of combined control systems consisting of passive or active systems installed in parallel to base-isolation bearings have the capability of reducing response quantities of base-isolated (relative and absolute displacement) structures significantly. Therefore in design and control of irregular isolated structures using the proposed control systems, structural demands (relative and absolute displacement and etc.) in each direction must be considered separately.

Keywords: base-isolated benchmark structure, multi-tuned mass dampers, hybrid isolators, near-field earthquake, fuzzy algorithm

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Authors: Abiodun Olayinka, Daniel Dzidzienyo, Pangirayi Tongoona, Samuel Offei, Edwige Gaby Nkouaya Mbanjo, Chiedozie Egesi, Ismail Yusuf Rabbi

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Cassava (Manihot esculenta Crantz) is a major source of starch for various industrial applications. However, the traditional cultivation and harvesting methods of cassava are labour-intensive and inefficient, limiting the supply of fresh cassava roots for industrial starch production. To achieve improved productivity and quality of fresh cassava roots through mechanized cultivation, cassava cultivars with compact plant architecture and moderate plant height are needed. Plant architecture-related traits, such as plant height, harvest index, stem diameter, branching angle, and lodging tolerance, are critical for crop productivity and suitability for mechanized cultivation. However, the genetics of cassava plant architecture remain poorly understood. This study aimed to identify the genetic bases of the relationships between plant architecture traits and productivity-related traits, particularly starch content. A panel of 453 clones developed at the International Institute of Tropical Agriculture, Nigeria, was genotyped and phenotyped for 18 plant architecture and productivity-related traits at four locations in Nigeria. A genome-wide association study (GWAS) was conducted using the phenotypic data from a panel of 453 clones and 61,238 high-quality Diversity Arrays Technology sequencing (DArTseq) derived Single Nucleotide Polymorphism (SNP) markers that are evenly distributed across the cassava genome. Five significant associations between ten SNPs and three plant architecture component traits were identified through GWAS. We found five SNPs on chromosomes 6 and 16 that were significantly associated with shoot weight, harvest index, and total yield through genome-wide association mapping. We also discovered an essential candidate gene that is co-located with peak SNPs linked to these traits in M. esculenta. A review of the cassava reference genome v7.1 revealed that the SNP on chromosome 6 is in proximity to Manes.06G101600.1, a gene that regulates endodermal differentiation and root development in plants. The findings of this study provide insights into the genetic basis of plant architecture and yield in cassava. Cassava breeders could leverage this knowledge to optimize plant architecture and yield in cassava through marker-assisted selection and targeted manipulation of the candidate gene.

Keywords: manihot esculenta crantz, plant architecture, dartseq, snp markers, genome-wide association study

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Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

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Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

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Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

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Authors: Masoumeh Mofarrah, Amir NahanMoghadam

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Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise, and improve EOR methods and their application. Recently, Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic, and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization infeasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

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Authors: Ananchai Ukaew, Choopong Chauypen

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Design concepts of real-time embedded system can be realized initially by introducing novel design approaches. In this literature, model based design approach and in-the-loop testing were employed early in the conceptual and preliminary phase to formulate design requirements and perform quick real-time verification. The design and analysis methodology includes simulation analysis, model based testing, and in-the-loop testing. The design of conceptual drive-by-wire, or DBW, algorithm for electronic control unit, or ECU, was presented to demonstrate the conceptual design process, analysis, and functionality evaluation. The concepts of DBW ECU function can be implemented in the vehicle system to improve electric vehicle, or EV, conversion drivability. However, within a new development process, conceptual ECU functions and parameters are needed to be evaluated. As a result, the testing system was employed to support conceptual DBW ECU functions evaluation. For the current setup, the system components were consisted of actual DBW ECU hardware, electric vehicle models, and control area network or CAN protocol. The vehicle models and CAN bus interface were both implemented as real-time applications where ECU and CAN protocol functionality were verified according to the design requirements. The proposed system could potentially benefit in performing rapid real-time analysis of design parameters for conceptual system or software algorithm development.

Keywords: drive-by-wire ECU, in-the-loop testing, model-based design, real-time embedded system

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Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

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Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Dmitrii Starikov, Evgeny Rybakov, Denis Zhuravlev

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Plastic for 3D printing is very necessary material part for printers. But plastic production is technological process, which implies application of different control algorithms. Possible algorithms of providing set diameter of plastic fiber are proposed and described in the article. Results of research were proved by existing unit of filament production.

Keywords: ABS-plastic, automation, control system, extruder, filament, PID-algorithm

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Authors: Lakdar Sadi-Haddad

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The past few years study of very high speed electrical drives have seen a resurgence of interest. An inventory of the number of scientific papers and patents dealing with the subject makes it relevant. In fact democratization of magnetic bearing technology is at the origin of recent developments in high speed applications. These machines have as main advantage a much higher power density than the state of the art. Nevertheless particular attention should be paid to the design of the inverter as well as control and command. Surface mounted permanent magnet synchronous machine is the most appropriate technology to address high speed issues. However, it has the drawback of using a carbon sleeve to contain magnets that could tear because of the centrifugal forces generated in rotor periphery. Carbon fiber is well known for its mechanical properties but it has poor heat conduction. It results in a very bad evacuation of eddy current losses induce in the magnets by time and space stator harmonics. The three-phase inverter is the main harmonic source causing eddy currents in the magnets. In high speed applications such harmonics are harmful because on the one hand the characteristic impedance is very low and on the other hand the ratio between the switching frequency and that of the fundamental is much lower than that of the state of the art. To minimize the impact of these harmonics a first lever is to use strategy of modulation producing low harmonic distortion while the second is to introduce a sinus filter between the inverter and the machine to smooth voltage and current waveforms applied to the machine. Nevertheless, in very high speed machine the interaction of the processes mentioned above may introduce particular harmonics that can irreversibly damage the system: harmonics at the resonant frequency, harmonics at the shaft mode frequency, subharmonics etc. Some studies address these issues but treat these phenomena with separate solutions (specific strategy of modulation, active damping methods ...). The purpose of this paper is to present a complete new active harmonic compensation algorithm based on an improvement of the standard vector control as a global solution to all these issues. This presentation will be based on a complete theoretical analysis of the processes leading to the generation of such undesired harmonics. Then a state of the art of available solutions will be provided before developing the content of a new active harmonic compensation algorithm. The study will be completed by a validation study using simulations and practical case on a high speed machine.

Keywords: active harmonic compensation, eddy current losses, high speed machine

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Authors: Vipan Kumar Sohpal, Rajesh K Sharma

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Diminishing of conventional fuels and hysterical vehicles emission leads to deterioration of the environment, which emphasize the research to work on biofuels. Biofuels from different sources attract the attention of research due to low emission and biodegradability. Emission of carbon monoxide, carbon dioxide and H-C reduced drastically using Biofuels (B-20) combustion. Contrary to the conventional fuel, engine emission results indicated that nitrous oxide emission is higher in Biofuels. So this paper examines and compares the nitrogen oxide emission of Jatropha Curcus (JCO) B-20% blends with the vegetable oil. In addition to that computational analysis of crude non edible oil performed to assess the impact of composition on emission quality. In conclusion, JCO have the potential feedstock for the biodiesel production after the genetic modification in the plant.

Keywords: jatropha curcus, computational analysis, emissions, NOx biofuels

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Authors: Mohamed Abdelwahed

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We propose an optimization algorithm for the geometric control of fluid flow. The used approach is based on the topological sensitivity analysis method. It consists in studying the variation of a cost function with respect to the insertion of a small obstacle in the domain. Some theoretical and numerical results are presented in 2D and 3D.

Keywords: sensitivity analysis, topological gradient, shape optimization, stokes equations

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Jiahao Tian, Michael D. Porter

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Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

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Authors: Ali A. Ukasha, Majdi F. Elbireki, Mohammad F. Abdullah

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Data security needed in data transmission, storage, and communication to ensure the security. This paper is divided into two parts. This work interests with the color image which is decomposed into red, green and blue channels. The blue and green channels are compressed using 3-levels discrete wavelet transform. The Arnold transform uses to changes the locations of red image channel pixels as image scrambling process. Then all these channels are encrypted separately using the key image that has same original size and are generating using private keys and modulo operations. Performing the X-OR and modulo operations between the encrypted channels images for image pixel values change purpose. The extracted contours from color images recovery can be obtained with accepted level of distortion using single step parallel contour extraction (SSPCE) method. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Color images and completely reconstructed without any distortion. Also shown that the analyzed algorithm has extremely large security against some attacks like salt and pepper and Jpeg compression. Its proof that the color images can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services.

Keywords: SSPCE method, image compression and salt and peppers attacks, bitplanes decomposition, Arnold transform, color image, wavelet transform, lossless image encryption

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Ajay B. C., Meena H. N., Dagla M. C., Narendra Kumar, Makwana A. D., Bera S. K., Kalariya K. A., Singh A. L.

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The high-energy value, protein content and minerals makes peanut a rich source of nutrition at comparatively low cost. Basic information on genetics and inheritance of these mineral elements is very scarce. Hence, in the present study inheritance (using additive-dominance model) and association of mineral elements was studied in two peanut crosses. Dominance variance (H) played an important role in the inheritance of P, K, Fe and Zn in peanut pods. Average degree of dominance for most of the traits was greater than unity indicating over dominance for these traits. Significant associations were also observed among mineral elements both in F2 and F3 generations but pod yield had no associations with mineral elements (with few exceptions). Di-allele/bi-parental mating could be followed to identify high yielding and mineral dense segregates.

Keywords: correlation, dominance variance, mineral elements, peanut

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Authors: Mustafa Sengul, Enes Sahin, Sertac Arslan

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Pumpjets are one of the marine propulsion systems frequently used in underwater vehicles nowadays. The reasons for frequent use of pumpjet as a propulsion system are that it has higher relative efficiency at high speeds, better cavitation, and acoustic performance than its rivals. Pumpjets are composed of rotor, stator, and duct, and there are two different types of pumpjet configurations depending on the desired hydrodynamic characteristic, which are with accelerating and decelerating duct. Pumpjet with an accelerating channel is used at cargo ships where it works at low speeds and high loading conditions. The working principle of this type of pumpjet is to maximize the thrust by reducing the pressure of the fluid through the channel and throwing the fluid out from the channel with high momentum. On the other hand, for decelerating ducted pumpjets, the main consideration is to prevent the occurrence of the cavitation phenomenon by increasing the pressure of the fluid about the rotor region. By postponing the cavitation, acoustic noise naturally falls down, so decelerating ducted systems are used at noise-sensitive vehicle systems where acoustic performance is vital. Therefore, duct design becomes a crucial step during pumpjet design. This study, it is aimed to optimize the duct geometry of a decelerating ducted pumpjet for a highly speed underwater vehicle by using proper optimization tools. The target output of this optimization process is to obtain a duct design that maximizes fluid pressure around the rotor region to prevent from cavitation and minimizes drag force. There are two main optimization techniques that could be utilized for this process which are parameter-based optimization and gradient-based optimization. While parameter-based algorithm offers more major changes in interested geometry, which makes user to get close desired geometry, gradient-based algorithm deals with minor local changes in geometry. In parameter-based optimization, the geometry should be parameterized first. Then, by defining upper and lower limits for these parameters, design space is created. Finally, by proper optimization code and analysis, optimum geometry is obtained from this design space. For this duct optimization study, a commercial codedparameter-based optimization algorithm is used. To parameterize the geometry, duct is represented with b-spline curves and control points. These control points have x and y coordinates limits. By regarding these limits, design space is generated.

Keywords: pumpjet, decelerating duct design, optimization, underwater vehicles, cavitation, drag minimization

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Authors: Rubin Dan, Xingcai Wang, Ziyang Chen

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A chaotic time series noise reduction method based on the fusion of the local projection method, wavelet transform, and particle swarm algorithm (referred to as the LW-PSO method) is proposed to address the problem of false recognition due to noise in the recognition process of chaotic time series containing noise. The method first uses phase space reconstruction to recover the original dynamical system characteristics and removes the noise subspace by selecting the neighborhood radius; then it uses wavelet transform to remove D1-D3 high-frequency components to maximize the retention of signal information while least-squares optimization is performed by the particle swarm algorithm. The Lorenz system containing 30% Gaussian white noise is simulated and verified, and the phase space, SNR value, RMSE value, and K value of the 0-1 test method before and after noise reduction of the Schreiber method, local projection method, wavelet transform method, and LW-PSO method are compared and analyzed, which proves that the LW-PSO method has a better noise reduction effect compared with the other three common methods. The method is also applied to the classical system to evaluate the noise reduction effect of the four methods and the original system identification effect, which further verifies the superiority of the LW-PSO method. Finally, it is applied to the Chengdu rainfall chaotic sequence for research, and the results prove that the LW-PSO method can effectively reduce the noise and improve the chaos recognition rate.

Keywords: Schreiber noise reduction, wavelet transform, particle swarm optimization, 0-1 test method, chaotic sequence denoising

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Authors: Kamand Bagherian, Nariman Niknejad

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A vibration isolation technology for precise position control of a rotary system powered by two permanent magnet DC (PMDC) motors is proposed, where this system is mounted on an oscillatory frame. To achieve vibration isolation for this system, active damping and disturbance rejection (ADDR) technology is presented which introduces a cooperation of a main and an auxiliary PMDC, controlled by discrete-time sliding mode control (DTSMC) based schemes. The controller of the main actuator tracks a desired position and the auxiliary actuator simultaneously isolates the induced vibration, as its controller follows a torque trend. To determine this torque trend, a combination of two algorithms is introduced by the ADDR technology. The first torque-trend producing algorithm rejects the disturbance by counteracting the perturbation, estimated using a model-based observer. The second torque trend applies active variable damping to minimize the oscillation of the output shaft. In this practice, the presented technology is implemented on a rotary system with a pendulum attached, mounted on a linear actuator simulating an oscillation-transmitting structure. In addition, the obtained results illustrate the functionality of the proposed technology.

Keywords: active damping, discrete-time nonlinear controller, disturbance tracking algorithm, oscillation transmitting support, position control, stability robustness, vibration isolation

Procedia PDF Downloads 80

Authors: Masoumeh Mofarrah, Amir NahanMoghadam

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Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise and improve EOR methods and their application. Recently Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization in feasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

Procedia PDF Downloads 382

Authors: Sadaf Salek

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Introduction: Cats are susceptible to various health conditions, and one such ailment that can affect their oral health is epulis in their lips. Epulis refers to a benign tumor or growth that can develop in different areas of a cat's mouth, including the lips. While epulis is not life-threatening, it can still cause discomfort and affect a cat's overall quality of life. This essay aims to delve into the causes, symptoms, and treatment options for epulis in cat's lips, shedding light on this lesser-known oral condition. Causes: Epulis in a cat's lips can have several causes. Firstly, genetic predisposition plays a significant role, with certain breeds being more prone to developing these growths. Secondly, chronic irritation to the mouth, such as from dental diseases or foreign objects, can also contribute to the development of epulis. Lastly, hormonal imbalances, specifically an excess of estrogen, have been associated with the occurrence of these tumors in cats. Understanding these causes can help cat owners take preventive measures to reduce the risk of epulis in their feline companions. Symptoms: Identifying the symptoms of epulis in a cat's lips is vital for early intervention and effective treatment. The most common symptoms include swelling, redness, and the presence of a visible growth or lump on the lip. Cats with epulis may also exhibit drooling, difficulty eating, and a reluctance to groom themselves. Any change in eating habits or oral behavior should not be overlooked and prompt a visit to the veterinarian for a thorough examination. Treatment ptions: When it comes to treating epulis in a cat's lips, various options are available, depending on the size, location, and characteristics of the growth. The primary treatment involves surgical removal of the tumor. This procedure should be performed by a qualified veterinarian, ensuring complete excision of the mass while preserving as much healthy tissue as possible. In some cases, radiation therapy may be necessary, especially if the tumor is large or aggressive. Additionally, a veterinarian may recommend oral hygiene care and regular dental cleaning to prevent further growths and maintain the cat's oral health. Prevention and Care: Preventing epulis in a cat's lips is not always possible, especially if genetic factors are involved. However, certain preventive measures can minimize the risk of these growths. Maintaining good oral hygiene through regular brushing and the use of appropriate dental products can help prevent chronic irritation and dental diseases. Routine veterinary check-ups should also include thorough oral examinations to detect any abnormal growths or changes in the mouth at an early stage. Pet owners should be observant and seek veterinary care promptly for any signs of discomfort or changes in eating habits. Conclusion: Epulis in a cat's lips is a condition that requires attention and proper treatment. Understanding the causes, identifying symptoms, and exploring treatment options are of utmost importance to help improve a cat's oral health and overall well-being.

Keywords: fibroma, cat, lip, epulis

Procedia PDF Downloads 26

Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

Procedia PDF Downloads 120

Authors: Beata Lachtara, Renata Szewczyk, Katarzyna Bielinska, Kinga Wieczorek, Jacek Osek

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Listeria monocytogenes is an important human and animal pathogen that causes foodborne outbreaks. The bacteria may be present in different types of food: cheese, raw vegetables, sliced meat products and vacuum-packed sausages, poultry, meat, fish. The most common method, which has been used for the investigation of genetic diversity of L. monocytogenes, is PFGE. This technique is reliable and reproducible and established as gold standard for typing of L. monocytogenes. The aim of the study was characterization by molecular serotyping and PFGE analysis of L. monocytogenes strains isolated from fresh fish and fish products in Poland. A total of 301 samples, including fresh fish (n = 129) and fish products (n = 172) were, collected between January 2014 and March 2016. The bacteria were detected using the ISO 11290-1 standard method. Molecular serotyping was performed with PCR. The isolates were tested with the PFGE method according to the protocol developed by the European Union Reference Laboratory for L. monocytogenes with some modifications. Based on the PFGE profiles, two dendrograms were generated for strains digested separately with two restriction enzymes: AscI and ApaI. Analysis of the fingerprint profiles was performed using Bionumerics software version 6.6 (Applied Maths, Belgium). The 95% of similarity was applied to differentiate the PFGE pulsotypes. The study revealed that 57 of 301 (18.9%) samples were positive for L. monocytogenes. The bacteria were identified in 29 (50.9%) ready-to-eat fish products and in 28 (49.1%) fresh fish. It was found that 40 (70.2%) strains were of serotype 1/2a, 14 (24.6%) 1/2b, two (4.3%) 4b and one (1.8%) 1/2c. Serotypes 1/2a, 1/2b, and 4b were presented with the same frequency in both categories of food, whereas serotype 1/2c was detected only in fresh fish. The PFGE analysis with AscI demonstrated 43 different pulsotypes; among them 33 (76.7%) were represented by only one strain. The remaining 10 profiles contained more than one isolate. Among them 8 pulsotypes comprised of two L. monocytogenes isolates, one profile of three isolates and one restriction type of 5 strains. In case of ApaI typing, the PFGE analysis showed 27 different pulsotypes including 17 (63.0%) types represented by only one strain. Ten (37.0%) clusters contained more than one strain among which four profiles covered two strains; three had three isolates, one with five strains, one with eight strains and one with ten isolates. It was observed that the isolates assigned to the same PFGE type were usually of the same serotype (1/2a or 1/2b). The majority of the clusters had strains of both sources (fresh fish and fish products) isolated at different time. Most of the strains grouped in one cluster of the AscI restriction was assigned to the same groups in ApaI investigation. In conclusion, PFGE used in the study showed a high genetic diversity among L. monocytogenes. The strains were grouped into varied clonal clusters, which may suggest different sources of contamination. The results demonstrated that 1/2a serotype was the most common among isolates from fresh fish and fish products in Poland.

Keywords: Listeria monocytogenes, molecular characteristic, PFGE, serotyping

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Authors: M. V. Belubekyan, S. R. Martirosyan

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On the example of an elastic rectangular plate streamlined by a supersonic gas flow, we have investigated the phenomenon of divergence and of panel flatter of the overrunning of the gas flow at a free edge under assumption of the presence of concentrated inertial masses and moments at the free edge. We applied a new approach of finding of solution of these problems, which was developed based on the algorithm for an analytical solution finding. This algorithm is easy to use for theoretical studies for the wides circle of nonconservative problems of linear elastic stability. We have established the relation between the characteristics of natural vibrations of the plate and velocity of the streamlining gas flow, which enables one to draw some conclusions on the stability of disturbed motion of the plate depending on the parameters of the system plate-flow. Its solution shows that either the divergence or the localized divergence and the flutter instability are possible. The regions of the stability and instability in space of parameters of the problem are identified. We have investigated the dynamic behavior of the disturbed motion of the panel near the boundaries of region of the stability. The safe and dangerous boundaries of region of the stability are found. The transition through safe boundary of the region of the stability leads to the divergence or localized divergence arising in the vicinity of free edge of the rectangular plate. The transition through dangerous boundary of the region of the stability leads to the panel flutter. The deformations arising at the flutter are more dangerous to the skin of the modern aircrafts and rockets resulting to the loss of the strength and appearance of the fatigue cracks.

Keywords: stability, elastic plate, divergence, localized divergence, supersonic panels flutter

Procedia PDF Downloads 433