Search results for: collective animal behavior algorithm

Authors: Jatindranath Gain, Madhumita DasSarkar, Sudakshina Kundu

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Quantum information is stored in states with multiple quasiparticles, which have a topological degeneracy. Topological quantum computation is concerned with two-dimensional many body systems that support excitations. Anyons are elementary building block of quantum computations. When anyons tunneling in a double-layer system can transition to an exotic non-Abelian state and produce Fibonacci anyons, which are powerful enough for universal topological quantum computation (TQC).Here the exotic behavior of Fibonacci Superlattice is studied by using analytical transfer matrix methods and hence Fibonacci anyons. This Fibonacci anyons can build a quantum computer which is very emerging and exciting field today’s in Nanophotonics and quantum computation.

Keywords: quantum computing, quasicrystals, Multiple Quantum wells (MQWs), transfer matrix method, fibonacci anyons, quantum hall effect, nanophotonics

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Authors: M. Hadji, A. Haddad, Y. Hadji

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The effects of boronizing treatment on the friction coefficient and wear behavior of Ti2AlC were investigated. In order to modify the surface properties of Ti2AlC, boronizing treatment was carried out through powder pack cementation in the 1150-1350 °C temperature range. After boronizing treatment, one mixture layer, composed of TiB2 and SiC, forms on the surface of Ti2AlC. The growth of the coating is processed by inward diffusion of Boron and obeys a linear rule. The Boronizing treatment increases the hardness of Ti2AlC from 6 GPa to 13GPa. In the pin-on-disc test, it was found that the material undergoes a steady-state coefficient of friction of around 0.8 and 0.45 in case of Ti2AlC/Al2O3 tribocouple under 7N load for the non treated and the boronized samples, respectively. The wear resistance of Ti2AlC under Al2O3 ball sliding has been significantly improved, which indicated that the boronizing treatment is a promising surface modification way of Ti2AlC.

Keywords: MAX phase, wear, hardness, boronizing

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: F. Benhamida, A. Graa, L. Benameur, I. Ziane

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The state estimation of the electrical power system operation state is very important for supervising task. With the nonlinearity of the AC power flow model, the state estimation problem (SEP) is a nonlinear mathematical problem with many local optima. This paper treat the mathematical model for the SEP and the monitoring of the nonlinear systems of great dimensions with an application on power electrical system, the modelling, the analysis and state estimation synthesis in order to supervise the power system behavior. in fact, it is very difficult, to see impossible, (for reasons of accessibility, techniques and/or of cost) to measure the excessive number of the variables of state in a large-sized system. It is thus important to develop software sensors being able to produce a reliable estimate of the variables necessary for the diagnosis and also for the control.

Keywords: power system, state estimation, robustness, observability

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Authors: Mai Anh Pham Ho

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Scarf products today is a symbol of fashion to decorate, to make our life more beautiful and bring new features to our living space. It also shows the cultural identity by using the traditional patterns that make easily to introduce the image of Vietnam to other nations all over the world. Therefore, the purpose of this research is to classify Vietnamese traditional patterns according to the era and dynasties. Vietnamese traditional patterns through the dynasties of Vietnamese history are done and classified by five groups of patterns including the geometric patterns, the natural patterns, the animal patterns, the floral patterns, and the character patterns in the Prehistoric times, the Bronze and Iron age, the Chinese domination, the Ngo-Dinh-TienLe-Ly-Tran-Ho dynasty, and the LeSo-Mac-LeTrinh-TaySon-Nguyen dynasty. Besides, there are some special kinds of Vietnamese traditional patterns like buffalo, lotus, bronze-drum, Phuc Loc Tho character, and so on. Extensive research was conducted for modernizing scarf collection applied Vietnamese traditional patterns which the fashion trend is used on creating works. The concept, target, image map, lifestyle map, motif, colours, arrangement and completion of patterns on scarf were set up. The scarf collection is designed and developed by the Adobe Illustrator program with three colour ways for each scarf. Upon completion of the research, digital printing technology is chosen for using on scarf collection which Vietnamese traditional patterns were researched deeply and widely with the purpose of establishment the basic background for Vietnamese culture in order to identify Vietnamese national personality as well as establish and preserve the cultural heritage.

Keywords: scarf collection, Vietnamese traditional patterns, printing methods, fabric design

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Authors: Shiuh-Jer Huang, Su-Shean Chen, Sheam-Chyun Lin

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Two-wheel inverted pendulum robot (TWIPR) is designed with two-hub DC motors for human riding and motion control evaluation. In order to measure the tilt angle and angular velocity of the inverted pendulum robot, accelerometer and gyroscope sensors are chosen. The mobile robot’s moving position and velocity were estimated based on DC motor built in hall sensors. The control kernel of this electric mobile robot is designed with embedded Arduino Nano microprocessor. A handle bar was designed to work as steering mechanism. The intelligent model-free fuzzy sliding mode control (FSMC) was employed as the main control algorithm for this mobile robot motion monitoring with different control purpose adjustment. The intelligent controllers were designed for balance control, and moving speed control purposes of this robot under different operation conditions and the control performance were evaluated based on experimental results.

Keywords: balance control, speed control, intelligent controller, two wheel inverted pendulum

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Authors: Gyoutae Park, Seungho Han, Byungduk Kim, Youngdo Jo, Yongsop Shim, Yeonjae Lee, Sangguk Ahn, Hiesik Kim, Jungil Park

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In this paper, we presented not only development technology of an explosion proof type and portable combustible gas leak detector but also algorithm to improve accuracy for measuring gas concentrations. The presented techniques are to apply the flame-proof enclosure and intrinsic safe explosion proof to an infrared gas leak detector at first in Korea and to improve accuracy using linearization recursion equation and Lagrange interpolation polynomial. Together, we tested sensor characteristics and calibrated suitable input gases and output voltages. Then, we advanced the performances of combustible gaseous detectors through reflecting demands of gas safety management fields. To check performances of two company's detectors, we achieved the measurement tests with eight standard gases made by Korea Gas Safety Corporation. We demonstrated our instruments better in detecting accuracy other than detectors through experimental results.

Keywords: accuracy improvement, IR gas sensor, gas leak, detector

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Authors: Shahbaz G. Hassan

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Water quality models are very important to predict the changes in surface water quality for environmental management. The aim of this paper is to give an overview of the water qualities, and to provide directions for selecting models in specific situation. Water quality models include one kind of model based on a mechanistic approach, while other models simulate water quality without considering a mechanism. Mechanistic models can be widely applied and have capabilities for long-time simulation, with highly complexity. Therefore, more spaces are provided to explain the principle and application experience of mechanistic models. Mechanism models have certain assumptions on rivers, lakes and estuaries, which limits the application range of the model, this paper introduces the principles and applications of water quality model based on the above three scenarios. On the other hand, mechanistic models are more easily to compute, and with no limit to the geographical conditions, but they cannot be used with confidence to simulate long term changes. This paper divides the empirical models into two broad categories according to the difference of mathematical algorithm, models based on artificial intelligence and models based on statistical methods.

Keywords: empirical models, mathematical, statistical, water quality

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Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

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Authors: Mnasri Faiza, El Ganaoui Mohammed, Khelifa Mourad, Gabsi Slimane

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The main objective of this paper is to describe the hydrothermal behavior through porous material of construction due to temperature gradient. The construction proposed a bi-layer structure which composed of two different materials. The first is a bio-sourced panel named IBS-AKU (inertia system building), the second is the Neopor material. This system (IBS-AKU Neopor) is developed by a Belgium company (Isohabitat). The study suggests a multi-layer structure of the IBS-AKU panel in one dimension. A numerical method was proposed afterwards, by using the finite element method and a refined mesh area to strong gradients. The evolution of temperature fields and the moisture content has been processed.

Keywords: heat transfer, moisture diffusion, porous media, composite IBS-AKU, simulation

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Authors: Abdelghani Alidra, Mohamed Tahar Kimour

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Genetic algorithm must adapt themselves at design time to cope with the search problem specific requirements and at runtime to balance exploration and convergence objectives. In a previous article, we have shown that modeling and implementing Genetic Algorithms (GA) using the software product line (SPL) paradigm is very appreciable because they constitute a product family sharing a common base of code. In the present article we propose to extend the use of the feature model of the genetic algorithms family to model the potential states of the GA in what is called a Dynamic Software Product Line. The objective of this paper is the systematic generation of a reconfigurable architecture that supports the dynamic of the GA and which is easily deduced from the feature model. The resultant GA is able to perform dynamic reconfiguration autonomously to fasten the convergence process while producing better solutions. Another important advantage of our approach is the exploitation of recent advances in the domain of dynamic SPLs to enhance the performance of the GAs.

Keywords: self-adaptive genetic algorithms, software engineering, dynamic software product lines, reconfigurable architecture

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Authors: Amir Sharifi Miavaghi

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It is believed recombination mechnisms in graphene-bonded perovskite solar cells based on numerical model in which doped-graphene structures are employed as anode/cathode bonding semiconductor. Moreover, th‌‌‌‌e da‌‌‌‌‌rk-li‌‌‌‌‌ght c‌‌‌‌urrent d‌‌‌‌ens‌‌‌‌ity-vo‌‌‌‌‌‌‌ltage density-voltage cu‌‌‌‌‌‌‌‌‌‌‌rves are investigated by regression analysis. L‌‌‌oss m‌‌‌‌echa‌‌‌‌nisms suc‌‌‌h a‌‌‌‌‌‌s ba‌‌‌‌ck c‌‌‌ontact b‌‌‌‌‌arrier, d‌‌‌‌eep surface defect i‌‌‌‌n t‌‌‌‌‌‌‌he adsorbent la‌‌‌yer is det‌‌‌‌‌ermined b‌‌‌y adapting th‌‌‌e sim‌‌‌‌‌ulated ce‌‌‌‌‌ll perfor‌‌‌‌‌mance to t‌‌‌‌he measure‌‌‌‌ments us‌‌‌‌ing the diffe‌‌‌‌‌‌rential evolu‌‌‌‌‌tion of th‌‌‌‌e global optimization algorithm. T‌‌‌‌he performance of t‌‌‌he c‌‌‌‌ell i‌‌‌‌n the connection proc‌‌‌‌‌ess incl‌‌‌‌‌‌udes J-V cur‌‌‌‌‌‌ves that are examined at di‌‌‌‌‌fferent tempe‌‌‌‌‌‌‌ratures an‌‌‌d op‌‌‌‌en cir‌‌‌‌cuit vol‌‌‌‌tage (V) und‌‌‌‌er differ‌‌‌‌‌ent light intensities as a function of temperature. Ba‌‌‌‌sed o‌‌‌n t‌‌‌he prop‌‌‌‌osed nu‌‌‌‌‌merical mod‌‌‌‌el a‌‌‌‌nd the acquired lo‌‌‌‌ss mecha‌‌‌‌‌‌nisms, our approach can be used to improve the efficiency of the solar cell further. Due to the high demand for alternative energy sources, solar cells are good alternatives for energy storage using the photovoltaic phenomenon.

Keywords: numerical model, recombination mechanism, graphen, perovskite solarcell

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Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

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This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

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Authors: G. Usha, S. Kannimuthu, K. Mahalakshmi

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Mobile Adhoc Network (MANET) is one of the most promising technologies that have applications ranging from various portable devices to military networks. MANET has no fixed infrastructure and the security of such network is a big concern. Therefore, in order to operate MANET’s securely, the misbehavior and intrusions should be detected before the attackers affect the network communication. In this article, we make a comprehensive survey against black hole attack that is a serious threat against MANET that exploits the routing behavior of the MANET. We have given broad survey solutions that detect black hole attacks in MANET. This is achieved by analyzing the techniques involved in detecting the attacks in each scheme. Furthermore, we examine about the challenges to the researchers for constructing an in-depth solution against black hole attack.

Keywords: AODV, cross layer security, mobile Adhoc network (MANET), packet delivery ratio, single layer security

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Authors: Houssam A. Toutanji, Meng Han

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This study focuses on the development of a fracture mechanics based-model that predicts the debonding behavior of FRP strengthened RC beams. In this study, a database includes 351 concrete prisms bonded with FRP plates tested in single and double shear were prepared. The existing fracture-mechanics-based models are applied to this database. Unfortunately the properties of adhesive layer, especially a soft adhesive layer, used on the specimens in the existing studies were not always able to found. Thus, the new model’s proposal was based on fifteen newly conducted pullout tests and twenty four data selected from two independent existing studies with the application of a soft adhesive layers and the availability of adhesive properties.

Keywords: carbon fiber composite materials, interface response, fracture characteristics, maximum shear stress, ultimate transferable load

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Authors: Guanyu Jiang, Donghai Xu, Huanteng Liu

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After the Fukushima-Daiichi accident, the development of accident tolerant fuel cladding materials to improve reactor safety has become a hot topic in the field of nuclear industry. ZrO₂ has a satisfactory neutron economy and can guarantee the fission chain reaction process, which enables it to be a promising coating for zirconium alloy cladding. Maintaining a good corrosion resistance in primary coolant loop during normal operations of Pressurized Water Reactors is a prerequisite for ZrO₂ as a protective coating on zirconium alloy cladding. Research on the corrosion performance of ZrO₂ coating in nuclear water chemistry is relatively scarce, and existing reports failed to provide an in-depth explanation for the failure causes of ZrO₂ coating. Herein, a detailed corrosion process of ZrO₂ coating in lithiated water at 360 °C and 18.5 MPa was proposed based on experimental research and molecular dynamics simulation. Lithiated water with different LiOH solutions in the present work was deaerated and had a dissolved oxygen concentration of < 10 ppb. The concentration of Li (as LiOH) was determined to be 2.3 ppm, 70 ppm, and 500 ppm, respectively. Corrosion tests were conducted in a static autoclave. Modeling and corresponding calculations were operated on Materials Studio software. The calculation of adsorption energy and dynamics parameters were undertaken by the Energy task and Dynamics task of the Forcite module, respectively. The protective effect and failure mechanism of ZrO₂ coating on Zry-4 under varied LiOH concentrations was further revealed by comparison with the coating corrosion performance in pure water (namely 0 ppm Li). ZrO₂ coating provided a favorable corrosion protection with the occurrence of localized corrosion at low LiOH concentrations. Factors influencing corrosion resistance mainly include pitting corrosion extension, enhanced Li+ permeation, short-circuit diffusion of O²⁻ and ZrO₂ phase transformation. In highly-concentrated LiOH solutions, intergranular corrosion, internal oxidation, and perforation resulted in coating failure. Zr ions were released to coating surface to form flocculent ZrO₂ and ZrO₂ clusters due to the strong diffusion and dissolution tendency of α-Zr in the Zry-4 substrate. Considering that primary water of Pressurized Water Reactors usually includes 2.3 ppm Li, the stability of ZrO₂ make itself a candidate fuel cladding coating material. Under unfavorable conditions with high Li concentrations, more boric acid should be added to alleviate caustic corrosion of ZrO₂ coating once it is used. This work can provide some references to understand the service behavior of nuclear coatings under variable water chemistry conditions and promote the in-pile application of ZrO₂ coating.

Keywords: ZrO₂ coating, Zry-4, corrosion behavior, failure mechanism, LiOH concentration

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Sonia Anand Knowlton

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There are some cases where we as a society feel deeply uncomfortable with the use of Artificial Intelligence (AI) tools in the judicial decision-making process, and justifiably so. A perfect example is COMPAS, an algorithmic model that predicts recidivism rates of offenders to assist in the determination of their bail conditions. COMPAS turned out to be extremely racist: it massively overpredicted recidivism rates of Black offenders and underpredicted recidivism rates of white offenders. At the same time, there are certain uses of AI in the judicial decision-making process that many would feel more comfortable with and even support. Take, for example, a “super-breathalyzer,” an (albeit imaginary) tool that uses AI to deliver highly detailed information about the subject of the breathalyzer test to the legal decision-makers analyzing their drunk-driving case. This article evaluates the point at which a judge’s use of AI tools begins to undermine the public’s trust in the administration of justice. It argues that the answer to this question depends on whether the AI tool is in a role in which it must perform a moral evaluation of a human being.

Keywords: artificial intelligence, judicial reasoning, morality, technology, algorithm

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Authors: Eric Huang, Coleman DeLude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

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High performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from electromagnetic (EM) simulations is often times cumbersome leading to large storage requirement. This paper proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. This paper solves this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.

Keywords: RADAR, RCS, high performance computing, point scatterer model

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Authors: Xuan Zhou, Litian Zhang, Shuixiang Li

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Mesh deformation using radial basis function interpolation method has been demonstrated to produce quality meshes with relatively little computational cost using a concise algorithm. However, it still suffers from the limited deformation ability, especially in large deformation. In this paper, a pre-displacement improvement is proposed to improve the problem that illegal meshes always appear near the moving inner boundaries owing to the large relative displacement of the nodes near inner boundaries. In this improvement, nodes near the inner boundaries are first associated to the near boundary nodes, and a pre-displacement based on the displacements of associated boundary nodes is added to the nodes near boundaries in order to make the displacement closer to the boundary deformation and improve the deformation capability. Several 2D and 3D numerical simulation cases have shown that the pre-displacement improvement for radial basis function (RBF) method significantly improves the mesh quality near inner boundaries and deformation capability, with little computational burden increasement.

Keywords: mesh deformation, mesh quality, background mesh, radial basis function

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Authors: S. Belgherras Bekkouche, T. Benouaz, S. M. A. Bekkouche

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Tokamak plasma is far from having a stable background. The study of turbulent transport is an important part of the current research and advanced scenarios were devised to minimize it. To do this, we used a three-wave interaction model which allows to investigate the occurrence drift-wave turbulence driven by pressure gradients in the edge plasma of a tokamak. In order to simulate the energy redistribution among different modes, the growth/decay rates for the three waves was added. After a numerical simulation, we can determine certain aspects of the temporal dynamics exhibited by the model. Indeed for a wide range of the wave decay rate, an intermittent transition from periodic behavior to chaos is observed. Then, a control strategy of chaos was introduced with the aim of reducing or eliminating the weak turbulence.

Keywords: wave interaction, plasma drift waves, wave turbulence, tokamak, edge plasma, chaos

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Noppadon Sangwalpetch

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The objective of this research is to use Rhizophora wood to design a walking stick for elderly by applying its properties on strength and toughness. The research was conducted by studying the behavior and the type of walking sticks used by 70 elderly aged between 60-80 years in Pragnamdaeng Sub-District, Ampawa District, Samudsongkram Province. Questionnaires were used to collect data which were calculated to find percentage, mean, and standard deviation. The results are as follows: 1) most elderly use walking sticks due to the Osteoarthritis of the knees. 2) Most elderly need to use walking sticks because the walking sticks help to balance their positioning and prevent from stumble. 3) Most elderly agree that Rhizophora wood is suitable to make a walking stick because of its strength and toughness. In addition, it is a local plant which is available and cheap. 4) The design of the walking stick should be fine and practical with comfortable handle and the tip of the stick must not be slippery.

Keywords: rhizophora wood, the design of a walking stick, elderly, visual arts

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Authors: Mahmoud M. Othman, Almoataz Y. Abdelaziz, Yasser G. Hegazy

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This paper presents a load control strategy based on modification of the Big Bang Big Crunch optimization method. The proposed strategy aims to determine the optimal load to be controlled and the corresponding time of control in order to minimize the energy purchased from substation. The presented strategy helps the distribution network operator to rely on the renewable energy sources in supplying the system demand. The renewable energy sources used in the presented study are modeled using the diagonal band Copula method and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithm.

Keywords: big bang big crunch, distributed generation, load control, optimization, planning

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Authors: Jae Moon Lee

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Generally the predator will continuously aim to attack the prey, while the prey will maintain a safe distance from the predator in order to avoid it . If the predator has enough energy to chase a certain amount of distance, it will begin to attack the prey. The prey needs to approach the predator for various reasons such as getting food. However, it will also try to keep a safe distance because of the threat of predators. The safe distance is dependent on the amount of the energy of predator, and the behaviors of prey is changed according to the size of the safe distance. This paper is to simulate the behaviors of preys to avoid the pursuit of predator based on the safe distance. The simulations will be executed experimentally under single predator and multiple preys. The results of the simulations show that the amount of energy of predator gives a great influence on the behavior of the prey.

Keywords: predator, prey, energy, safe distance, simulation

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Authors: Abdulatif Abdulsalam Mohamed Shaban

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This paper states that the art of Procedure Entry Array (PEA) plan with a focus on control system applications. This paper begins with an impression of PEA technology development, followed by an arrangement of design technologies, and the use of programmable description languages and system-level design tools. They allow a practical approach based on a unique model for complete engineering electronics systems. There are three main design rules are implemented in the system. These are algorithm based fine-tuning, modularity, and the control act and the architectural constraints. An overview of contributions and limits of PEAs is also given, followed by a short survey of PEA-based gifted controllers for recent engineering systems. Finally, two complete and timely case studies are presented to illustrate the benefits of a PEA implementation when using the proposed system modelling and devise attitude. These consist of the direct control for training motor drives and the control of a diesel-driven stand-alone generator with the help of logical design.

Keywords: control (DC), engineering electronics systems, training motor drives, procedure entry array

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Weikang Gong, Chunhua Li

Abstract:

Dynamics plays an essential role in function exertion of proteins. Elastic network model (ENM), a harmonic potential-based and cost-effective computational method, is a valuable and efficient tool for characterizing the intrinsic dynamical properties encoded in biomacromolecule structures and has been widely used to detect the large-amplitude collective motions of proteins. Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. In recent years, many ENM variants have been proposed. Here, we propose a small but effective modification (denoted as modified mENM) to the multiscale ENM (mENM) where fitting weights of Kirchhoff/Hessian matrixes with the least square method (LSM) is modified since it neglects the details of pairwise interactions. Then we perform its comparisons with the original mENM, traditional ENM, and parameter-free ENM (pfENM) on reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM achieves the best performance among the four ENM models. Additionally, it is noted that with the weights of the multiscale Kirchhoff/Hessian matrixes modified, interestingly, the modified mGNM/mANM still has a much better performance than the corresponding traditional ENM and pfENM models. As to dynamical cross-correlation map (DCCM) calculation, taking the data obtained from MD trajectories as the standard, mENM performs the worst while the results produced by the modified mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Generally, ANMs perform better than the corresponding GNMs except for the mENM. Thus, pfANM and the modified mANM, especially the former, have an excellent performance in dynamical cross-correlation calculation. Compared with GNMs (except for mGNM), the corresponding ANMs can capture quite a number of positive correlations for the residue pairs nearly largest distances apart, which is maybe due to the anisotropy consideration in ANMs. Furtherly, encouragingly the modified mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while mANM fails in all the cases. This suggests that the consideration of long-range interactions is critical for ANM models to produce protein functional motions. Based on the analyses, the modified mENM is a promising method in capturing multiple dynamical characteristics encoded in protein structures. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Keywords: elastic network model, ENM, multiscale ENM, molecular dynamics, parameter-free ENM, protein structure

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

Abstract:

With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: neural networks, radial basis functions, metamodelling, python machine learning libraries

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Authors: John Njikem

Abstract:

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most commonly diagnosed behavioral disorders in children, associated with this disorder are core symptoms of inattention, hyperactivity and impulsivity. This study was purposely to enlighten and inform teachers, policy makers and other professionals concern in the education of this group of learners in inclusive schools in Buea, Cameroon. The major purpose of this study was to identify children with ADHD in elementary schools practicing inclusive education and to investigate the effect of classroom based intervention on their academic performance. The research problem stems from the fact that majority of children with ADHD in our school mostly have problems with classroom tasks like paying attention, easily distracted, and difficulties in organization and very little has been done to manage this numerous conditions, therefore it was necessary for the researcher to identify them and implement some inclusive strategies that teachers can better use in managing the behavior of this group of learners. There were four research questions and the study; the sample population used for the study was 27 pupils (3-7years old) formally identified with key symptoms of ADHD from primary 3-6 from four primary inclusive schools in Buea. Two sub-types of ADHD children were identified by using the recent DSM-IV behavioral checklist in recording their behavior after teacher and peer nomination they were later subjected to three groups for classroom intervention. Data collection was done by using interviews and other supportive methods such as document consultation, field notes and informal talks as additional sources was also used to gather information. Classroom Intervention techniques were carried out by the teachers themselves for 8 weeks under the supervision of the researcher, results were recorded for the 27 children's academic performance in the areas of math’s, writing and reading. Descriptive Statistics was applied in analyzing the data in percentages while tables and diagrams were used to represent the results. Findings obtained indicated that there was significant increase in the level of attention and organization on classroom tasks in the areas of reading, writing and mathematics. Finding also show that there was a more significant improvement made on their academic performance using the combined intervention approach which was proven to be the most effective intervention technique for pupils with ADHD in the study. Therefore it is necessary that teachers in inclusive primary schools in Buea understand the needs of these children and learn how to identify them and also use this intervention approaches to accommodate them in classroom task in order to encourage inclusive educational classroom practices in the country. Recommendations were based on each research objective and suggestions for further studies centered on other methods of classroom intervention for ADHD children in inclusive settings.

Keywords: attention deficit hyperactivity disorder, inclusive classrooms, academic performance, impulsivity

Procedia PDF Downloads 234