Search results for: mapping algorithm

Authors: Francisco Bulnes

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Through the Fukaya conjecture and the wrapped Floer cohomology, the correspondences between paths in a loop space and states of a wrapping space of states in a Hamiltonian space (the ramification of field in this case is the connection to the operator that goes from TM to T*M) are demonstrated where these last states are corresponding to bosonic extensions of a spectrum of the space-time or direct image of the functor Spec, on space-time. This establishes a distinguished diffeomorphism defined by the mapping from the corresponding loops space to wrapping category of the Floer cohomology complex which furthermore relates in certain proportion D-branes (certain D-modules) with strings. This also gives to place to certain conjecture that establishes equivalences between moduli spaces that can be consigned in a moduli identity taking as space-time the Hitchin moduli space on G, whose dual can be expressed by a factor of a bosonic moduli spaces.

Keywords: Floer cohomology, Fukaya conjecture, Lagrangian submanifolds, quantum topological diffeomorphism

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Authors: Amruta D. S. Pathare, Indira Hinduja, Kusum Zaveri

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Implantation failure (IF) even after the good-quality embryo transfer (ET) in the physiologically normal endometrium is the main obstacle in in vitro fertilization (IVF). Various microarray studies have been performed worldwide to elucidate the genes requisite for endometrial receptivity. These studies have included the population based on different phases of menstrual cycle during natural cycle and stimulated cycle in normal fertile women. Additionally, the literature is also available in recurrent implantation failure patients versus oocyte donors in natural cycle. However, for the first time, we aim to study the genomics of endometrial receptivity in IF patients under controlled ovarian stimulation (COS) during which ET is generally practised in IVF. Endometrial gene expression profiling in IF patients (n=10) and oocyte donors (n=8) were compared during window of implantation under COS by whole genome microarray (using Illumina platform). Enrichment analysis of microarray data was performed to determine dys-regulated biological functions and pathways using Database for Annotation, Visualization and Integrated Discovery, v6.8 (DAVID). The enrichment mapping was performed with the help of Cytoscape software. Microarray results were validated by real-time PCR. Localization of genes related to immune response (Progestagen-Associated Endometrial Protein (PAEP), Leukaemia Inhibitory Factor (LIF), Interleukin-6 Signal Transducer (IL6ST) was detected by immunohistochemistry. The study revealed 418 genes downregulated and 519 genes upregulated in IF patients compared to healthy fertile controls. The gene ontology, pathway analysis and enrichment mapping revealed significant downregulation in activation and regulation of immune and inflammation response in IF patients under COS. The lower expression of Progestagen Associated Endometrial Protein (PAEP), Leukemia Inhibitory Factor (LIF) and Interleukin 6 Signal Transducer (IL6ST) in cases compared to controls by real time and immunohistochemistry suggests the functional importance of these genes. The study was proved useful to uncover the probable reason of implantation failure being imbalance of immune and inflammatory regulation in our group of subjects. Based on the present study findings, a panel of significant dysregulated genes related to immune and inflammatory pathways needs to be further substantiated in larger cohort in natural as well as stimulated cycle. Upon which these genes could be screened in IF patients during window of implantation (WOI) before going for embryo transfer or any other immunological treatment. This would help to estimate the regulation of specific immune response during WOI in a patient. The appropriate treatment of either activation of immune response or suppression of immune response can be then attempted in IF patients to enhance the receptivity of endometrium.

Keywords: endometrial receptivity, immune and inflammatory response, gene expression microarray, window of implantation

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: George R. Puno, Eric N. Bruno

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The natural landscape of the Philippine archipelago plus the current realities of climate change make the country vulnerable to flood hazards. Flooding becomes the recurring natural disaster in the country resulting to lose of lives and properties. Musimusi is among the rivers which exhibited inundation particularly at the inhabited floodplain portion of its watershed. During the event, rescue operations and distribution of relief goods become a problem due to lack of high resolution flood maps to aid local government unit identify the most affected areas. In the attempt of minimizing impact of flooding, hydrologic modelling with high resolution mapping is becoming more challenging and important. This study focused on the analysis of flood extent as a function of different geomorphologic characteristics of Musimusi watershed. The methods include the delineation of morphometric parameters in the Musimusi watershed using Geographic Information System (GIS) and geometric calculations tools. Digital Terrain Model (DTM) as one of the derivatives of Light Detection and Ranging (LiDAR) technology was used to determine the extent of river inundation involving the application of Hydrologic Engineering Center-River Analysis System (HEC-RAS) and Hydrology Modelling System (HEC-HMS) models. The digital elevation model (DEM) from synthetic Aperture Radar (SAR) was used to delineate watershed boundary and river network. Datasets like mean sea level, river cross section, river stage, discharge and rainfall were also used as input parameters. Curve number (CN), vegetation, and soil properties were calibrated based on the existing condition of the site. Results showed that the drainage density value of the watershed is low which indicates that the basin is highly permeable subsoil and thick vegetative cover. The watershed’s elongation ratio value of 0.9 implies that the floodplain portion of the watershed is susceptible to flooding. The bifurcation ratio value of 2.1 indicates higher risk of flooding in localized areas of the watershed. The circularity ratio value (1.20) indicates that the basin is circular in shape, high discharge of runoff and low permeability of the subsoil condition. The heavy rainfall of 167 mm brought by Typhoon Seniang last December 29, 2014 was characterized as high intensity and long duration, with a return period of 100 years produced 316 m3s-1 outflows. Portion of the floodplain zone (1.52%) suffered inundation with 2.76 m depth at the maximum. The information generated in this study is helpful to the local disaster risk reduction management council in monitoring the affected sites for more appropriate decisions so that cost of rescue operations and relief goods distribution is minimized.

Keywords: flooding, geomorphology, mapping, watershed

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Authors: Tan Zhang, Zhongjian Wang, Jack Xin, Zhiwen Zhang

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We aim to efficiently compute the spreading speeds of reaction-diffusion-advection (RDA) fronts in divergence-free random flows under the Kolmogorov-Petrovsky-Piskunov (KPP) nonlinearity. We study a stochastic interacting particle method (IPM) for the reduced principal eigenvalue (Lyapunov exponent) problem of an associated linear advection-diffusion operator with spatially random coefficients. The Fourier representation of the random advection field and the Feynman-Kac (FK) formula of the principal eigenvalue (Lyapunov exponent) form the foundation of our method implemented as a genetic evolution algorithm. The particles undergo advection-diffusion and mutation/selection through a fitness function originated in the FK semigroup. We analyze the convergence of the algorithm based on operator splitting and present numerical results on representative flows such as 2D cellular flow and 3D Arnold-Beltrami-Childress (ABC) flow under random perturbations. The 2D examples serve as a consistency check with semi-Lagrangian computation. The 3D results demonstrate that IPM, being mesh-free and self-adaptive, is simple to implement and efficient for computing front spreading speeds in the advection-dominated regime for high-dimensional random flows on unbounded domains where no truncation is needed.

Keywords: KPP front speeds, random flows, Feynman-Kac semigroups, interacting particle method, convergence analysis

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Authors: Elham Allahmoradi, Bahman Allahmoradi, Ali Bonyadi Naeini

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Complex systems have many aspects. A variety of methods have been developed to analyze these systems. The most efficient of these methods should not only be simple, but also provide useful and comprehensive information about many aspects of the system. Matrix methods are considered the most commonly methods used to analyze and design systems. Each matrix method can examine a particular aspect of the system. If these methods are combined, managers can access to more comprehensive and broader information about the system. This study was conducted in four steps. In the first step, a process model of a real project has been extracted through IDEF3. In the second step, activity levels have been attained by writing a process model in the form of a design structure matrix (DSM) and sorting it through triangulation algorithm (TA). In the third step, sub-processes have been obtained by writing the process model in the form of an interface structure matrix (ISM) and clustering it through cluster identification algorithm (CIA). In the fourth step, a mixed model has been developed to provide a unified picture of the project structure through the simultaneous presentation of activities and sub-processes. Finally, the paper is completed with a conclusion.

Keywords: integrated definition for process description capture (IDEF3) method, design structure matrix (DSM), interface structure matrix (ism), mixed matrix model, activity level, sub-process

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Authors: Sue Hershberger Yoder, Jon Yoder

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“The appearance does not hide the essence, it reveals it; it is the essence.”—Jean-Paul Sartre, Being and Nothingness Three decades ago, transarchitect Marcos Novak developed an early form of algorithmic animation he called “liquid architecture.” In that project, digitally floating forms morphed seamlessly in cyberspace without claiming to evolve or improve. Change itself was seen as inevitable. And although some imagistic moments certainly stood out, none was hierarchically privileged over another. That project challenged longstanding assumptions about creativity and artistic genius by posing infinite parametric possibilities as inviting alternatives to traditional notions of stability, originality, and evolution. Through ephemeral processes of printing, milling, and projecting, the exhibition “Liquid Illumination” destabilizes the solid foundations of fashion and architecture. The installation is neither worn nor built in the conventional sense, but—like the sensual art forms of fashion and architecture—it is still radically embodied through the logics and techniques of design. Appearances are everything. Surface pattern and color are no longer understood as minor afterthoughts or vapid carriers of dubious content. Here, they become essential but ever-changing aspects of precisely fabricated images. Fourteen silk “colorways” (a term from the fashion industry) are framed selections from ongoing experiments with intricate pattern and complex color configurations. Whether these images are printed on fabric, milled in foam, or illuminated through projection, they explore and celebrate the untapped potentials of the surficial and superficial. Some components of individual prints appear to float in front of others through stereoscopic superimpositions; some figures appear to melt into others due to subtle changes in hue without corresponding changes in value; and some layers appear to vibrate via moiré effects that emerge from unexpected pattern and color combinations. The liturgical atmosphere of Liquid Illumination is intended to acknowledge that, like the simultaneously sacred and superficial qualities of rose windows and illuminated manuscripts, artistic and religious ideologies are also always malleable. The intellectual provocation of this paper pushes the boundaries of current thinking concerning viable applications for fashion print designs and architectural images—challenging traditional boundaries between fine art and design. The opportunistic installation of digital printing, CNC milling, and video projection mapping in a gallery that is normally reserved for fine art exhibitions raises important questions about cultural/commercial display, mass customization, digital reproduction, and the increasing prominence of surface effects (color, texture, pattern, reflection, saturation, etc.) across a range of artistic practices and design disciplines.

Keywords: fashion, print design, architecture, projection mapping, image, fabrication

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Authors: Elmabruk Laias

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IEEE Standard 802.16 provides QoS (Quality of Service) for the applications such as Voice over IP, video streaming and high bandwidth file transfer. With the ability of broadband wireless access of an IEEE 802.16 system, a WiMAX TDD frame contains one downlink subframe and one uplink subframe. The capacity allocated to each subframe is a system parameter that should be determined based on the expected traffic conditions. a proper resource allocation scheme for packet transmissions is imperatively needed. In this paper, we present a new resource allocation scheme, called additional bandwidth yielding (ABY), to improve transmission efficiency of an IEEE 802.16-based network. Our proposed scheme can be adopted along with the existing scheduling algorithms and the multi-priority scheme without any change. The experimental results show that by using our ABY, the packet queuing delay could be significantly improved, especially for the service flows of higher-priority classes.

Keywords: IEEE 802.16, WiMAX, OFDMA, resource allocation, uplink-downlink mapping

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Authors: Marcela de Oliveira, Guilherme Giacomini, Allan Felipe Fattori Alves, Ana Luiza Menegatti Pavan, Maria Eugenia Dela Rosa, Fernando Antonio Bacchim Neto, Diana Rodrigues de Pina

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It is estimated that each year one death every 10 seconds (about 2 million deaths) in the world is attributed to tuberculosis (TB). Even after effective treatment, TB leaves sequelae such as, for example, pulmonary fibrosis, compromising the quality of life of patients. Evaluations of the aforementioned sequel are usually performed subjectively by radiology specialists. Subjective evaluation may indicate variations inter and intra observers. The examination of x-rays is the diagnostic imaging method most accomplished in the monitoring of patients diagnosed with TB and of least cost to the institution. The application of computational algorithms is of utmost importance to make a more objective quantification of pulmonary impairment in individuals with tuberculosis. The purpose of this research is the use of computer algorithms to quantify the pulmonary impairment pre and post-treatment of patients with pulmonary TB. The x-ray images of 10 patients with TB diagnosis confirmed by examination of sputum smears were studied. Initially the segmentation of the total lung area was performed (posteroanterior and lateral views) then targeted to the compromised region by pulmonary sequel. Through morphological operators and the application of signal noise tool, it was possible to determine the compromised lung volume. The largest difference found pre- and post-treatment was 85.85% and the smallest was 54.08%.

Keywords: algorithm, radiology, tuberculosis, x-rays exam

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

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This paper aims at developing a multi-objective model for supplier selection and order allocation problem in stochastic environment, where purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. In this regard, dependent chance programming is used which maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. The abovementioned stochastic multi-objective programming problem is then transformed into a stochastic single objective programming problem using minimum deviation method. In the next step, the further problem is solved applying a genetic algorithm, which performs a simulation process in order to calculate the stochastic objective function as its fitness function. Finally, the impact of stochastic parameters on the given solution is examined via a sensitivity analysis exploiting coefficient of variation. The results show that whatever stochastic parameters have greater coefficients of variation, the value of the objective function in the stochastic single objective programming problem is deteriorated.

Keywords: supplier selection, order allocation, dependent chance programming, genetic algorithm

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Authors: Abdullah Mohamed, Raipat Vaidehi

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With increasing immigration to urban areas, every city is experiencing shortage of housing. Vertical habitats are the only solution to this problem, it is hence important to make sure that these habitats are environmentally, socially and economically sustainable. A lot of work on vertical habitats has already been carried out in terms of environmental and economic sustainability, hence this research aims to study the aspects of social sustainability of the vertical habitats. It being the least studied topic, opens many reals of novelty and uniqueness. In this Research, user perception survey and various mapping methods have been used to study the social sustainability of the existing vertical habitats in the selected cities. The various aspects that can be used to define social sustainability of any place include; safety, equity, accessibility, legibility, imagibility, readability, memorability and ease of movement. This research would help to evolve new strategies in form of design and/or guidelines to make the existing vertical habitats socially sustainable.

Keywords: user lifestyle, user perception, social sustainability, vertical habitats

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: John K. Avor, Choong-Koo Chang

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The transfer of electrical loads at power generation stations from Standby Auxiliary Transformer (SAT) to Unit Auxiliary Transformer (UAT) and vice versa is through a fast bus transfer scheme. Fast bus transfer is a time-critical application where the transfer process depends on various parameters, thus transfer schemes apply advance algorithms to ensure power supply reliability and continuity. In a nuclear power generation station, supply continuity is essential, especially for critical class 1E electrical loads. Bus transfers must, therefore, be executed accurately within 4 to 10 cycles in order to achieve safety system requirements. However, the main problem is that there are instances where transfer schemes scrambled due to inaccurate interpretation of key parameters; and consequently, have failed to transfer several critical loads from UAT to the SAT during main generator trip event. Although several techniques have been adopted to develop robust transfer schemes, a combination of Artificial Neural Network and Fuzzy Systems (Neuro-Fuzzy) has not been extensively used. In this paper, we apply the concept of Neuro-Fuzzy to determine plant operating mode and dynamic prediction of the appropriate bus transfer algorithm to be selected based on the first cycle of voltage information. The performance of Sequential Fast Transfer and Residual Bus Transfer schemes was evaluated through simulation and integration of the Neuro-Fuzzy system. The objective for adopting Neuro-Fuzzy approach in the bus transfer scheme is to utilize the signal validation capabilities of artificial neural network, specifically the back-propagation algorithm which is very accurate in learning completely new systems. This research presents a combined effect of artificial neural network and fuzzy systems to accurately interpret key bus transfer parameters such as magnitude of the residual voltage, decay time, and the associated phase angle of the residual voltage in order to determine the possibility of high speed bus transfer for a particular bus and the corresponding transfer algorithm. This demonstrates potential for general applicability to improve reliability of the auxiliary power distribution system. The performance of the scheme is implemented on APR1400 nuclear power plant auxiliary system.

Keywords: auxiliary power system, bus transfer scheme, fuzzy logic, neural networks, reliability

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Authors: Aziza Barrek, Mohamed Haythem Msaddek, Ismail Chenini

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Excessive groundwater pumping due to the increasing water demand, especially in the agricultural sector, causes groundwater scarcity. Groundwater recharge is the most important process that contributes to the water's durability. This paper is based on the Analytic Hierarchy Process multicriteria analysis to establish a groundwater recharge susceptibility map. To delineate aquifer suitability for groundwater recharge, eight parameters were used: soil type, land cover, drainage density, lithology, NDVI, slope, transmissivity, and rainfall. The impact of each factor was weighted. This method was applied to the El Fahs plain shallow aquifer. Results suggest that 37% of the aquifer area has very good and good recharge suitability. The results have been validated by the Receiver Operating Characteristics curve. The accuracy of the prediction obtained was 89.3%.

Keywords: AHP, El Fahs aquifer, empirical formula, groundwater recharge zone, remote sensing, semi-arid region

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Authors: Anees K. Ahamed, Aakash Kumar R. G., Raj M. Mohan

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The main objective is to apply lean tools to identify and eliminate waste in and among the work stations so as to improve the process speed and quality. From the top seven wastes in the lean concept, we consider the movement of materials, defects, and inventory for the improvement since these cause the major impact on the performance measures. The layout was improved to reduce the movement of materials. It also quantifies the reduction in movement among the work stations. Value stream mapping has been used for identification of waste. Cause and effect diagram and 5W analysis are used to identify the reasons for defects and to provide the counter measures. Some cycle time reduction techniques also proposed to improve the productivity. Lean Audit check sheet was also used to identify the current position of the industry and to identify the gap to make the industry Lean.

Keywords: cause and effect diagram, cycle time reduction, defects, lean, waste reduction

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Authors: Shinji Kawakura, Ryosuke Shibasaki

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We have been grouping and developing various kinds of practical, promising sensing applied systems concerning agricultural advancement and technical tradition (guidance). These include advanced devices to secure real-time data related to worker motion, and we analyze by methods of various advanced statistics and human dynamics (e.g. primary component analysis, Ward system based cluster analysis, and mapping). What is more, we have been considering worker daily health and safety issues. Targeted fields are mainly common farms, meadows, and gardens. After then, we observed and discussed time-line style, changing data. And, we made some suggestions. The entire plan makes it possible to improve both the aforementioned applied systems and farms.

Keywords: advanced statistical analysis, wearable sensing system, tradition of skill, supporting for workers, detecting crisis

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Exa Heydemans, Jessica Sjah, Dwinanti Rika Marthanty

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This paper presents numerical simulations using an open boundary algorithm with modified dynamic boundary condition (mDBC) for weakly compressible smoothed particle hydrodynamics models from particle-based code Dualsphysics. The problems of piping erosion in dams and dikes are aimed for studying the algorithm. The case 2D model of unsteady fluid flow past around a fixed cylinder is simulated, where various values of Reynold’s numbers (Re40, Re60, Re80, and Re100) and different model’s resolution are considered. A constant velocity with different values of viscosity for generating various Reynold’s numbers and different numbers of particles over a cylinder for the resolution are modeled. The interaction between solid particles of the cylinder and fluid particles is concerned. The cylinder is affected by the hydrodynamics force caused by the flow of fluid particles. The solid particles of the cylinder are the observation points to obtain force and pressure due to the hydrodynamics forces. As results of the simulation, which is to show the capability to model 2D unsteady with various Reynold’s numbers, the pressure coefficient, drag coefficient, lift coefficient, and Strouhal number are compared to the previous work from literature.

Keywords: hydrodynamics, internal erosion, dualsphysics, viscous fluid flow

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Authors: Hailang Yang

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To ensure the confidentiality and integrity of speech signals in communication transmission, this paper proposes a voice signal encryption scheme based on chaotic theory. Firstly, the scheme utilizes chaotic mapping to generate a key stream and then employs the key stream to perform bitwise exclusive OR (XOR) operations for encrypting the speech signal. Additionally, the scheme utilizes a chaotic hash function to generate a Message Authentication Code (MAC), which is appended to the encrypted data to verify the integrity of the data. Subsequently, we analyze the security performance and encryption efficiency of the scheme, comparing and optimizing it against existing solutions. Finally, experimental results demonstrate that the proposed scheme can resist common attacks, achieving high-quality encryption and speed.

Keywords: chaotic theory, XOR encryption, chaotic hash function, Message Authentication Code (MAC)

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Authors: Tohid Rahimi, Yahya Naderi, Babak Yousefi, Seyed Hossein Hoseini

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Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation.

Keywords: power oscillation damping (POD), frequency oscillation damping (FOD), Static synchronous series compensator (SSSC), Genetic Algorithm (GA)

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Authors: Elsaed Elsaed

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With the constant progress in our daily lives through information and communication technology and the presence of abundant in research activities in the hardware and software associated with them, and develop and improve their performance, but still there is a need to provide all combined solutions in one business. A systematic mapping study was conducted to investigate the contributions that have been prepared, and the areas of knowledge that are explored further, and any aspects of the research used to divide the common understanding of the latest technology in software engineering education. Which, we have categorized into a well-defined engineering framework. An overview of current research topics and trends and their distribution by type of research and scope of application. In addition, the topics were grouped into groups and a list of proposed methods and frameworks and tools was used. The map shows that the current research impact is limited to a few areas of knowledge are needed to map a future path to fill the gaps in the instruction activities.

Keywords: methodological maps, engineering education program, literature survey, communication technology

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Authors: Sabri Arik

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In this paper, we study the global asymptotic robust stability of delayed neural networks with norm-bounded uncertainties. By employing the Lyapunov stability theory and Homeomorphic mapping theorem, we derive some new types of sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slopebounded activation functions. An important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties symmetric matrices associated with the uncertainty sets of network parameters. The obtained results are shown to be generalization of some of the previously published corresponding results. Some comparative numerical examples are also constructed to compare our results with some closely related existing literature results.

Keywords: neural networks, delayed systems, lyapunov functionals, stability analysis

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.

Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: F. Yildiz, S. Y. Oturanc

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Advances technology in the field of photogrammetry replaces analog cameras with reflection on aircraft GPS/IMU system with a digital aerial camera. In this system, when determining the position of the camera with the GPS, camera rotations are also determined by the IMU systems. All around the world, digital aerial cameras have been used for the photogrammetry applications in the last ten years. In this way, in terms of the work done in photogrammetry it is possible to use time effectively, costs to be reduced to a minimum level, the opportunity to make fast and accurate. Geo-referencing techniques that are the cornerstone of the GPS / INS systems, photogrammetric triangulation of images required for balancing (interior and exterior orientation) brings flexibility to the process. Also geo-referencing process; needed in the application of photogrammetry targets to help to reduce the number of ground control points. In this study, the use of direct and indirect geo-referencing techniques on the accuracy of the points was investigated in the production of photogrammetric mapping.

Keywords: photogrammetry, GPS/IMU systems, geo-referecing, digital aerial camera

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Authors: M. S. Sukumar

Abstract:

Our objective is to develop a full-fledged system for exploring and studying nature of fossils and to extend this to underwater archaeology and mineral mapping. This includes aerial surveying, imaging techniques, artefact extraction and spectrum analysing techniques. These techniques help in regular monitoring of fossils and also the sensing system. The ROV was designed to complete several tasks which simulate collecting data and samples. Given the time constraints, the ROV was engineered for efficiency and speed in performing tasks. Its other major design consideration was modularity, allowing the team to distribute the building process, to easily test systems as they were completed and troubleshoot and replace systems as necessary. Our design itself had several challenges of on-board waterproofed sensor mounting, waterproofing of motors, ROV stability criteria, camera mounting and hydrophone sound acquisition.

Keywords: remotely operated vehicle (ROV) dragonair, underwater archaeology, full-fledged system, aerial imaging and detection

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Authors: Sagir M. Yusuf, Chris Baber

Abstract:

In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Nasser M. Amaitik, Ngwan F. Elsagzli

Abstract:

The Lean technique is very important in the service and industrial fields. It is defined as an effective tool to eliminate the wastes. In lean the wastes are defined as anything which does not add value to the end product. There are wastes that can be avoided, but some are unavoidable to many reasons. The present study aims to apply the principles of lean in two different sectors, healthcare, and industry. Two case studies have been selected to apply the experimental work. The first case was Al-Jalaa Hospital while the second case study was the Technical Company of Aluminum Sections in Benghazi, Libya. In both case studies the Value Stream Map (VSM) of the current state has been constructed. The proposed plans have been implemented by merging or eliminating procedures or processes. The results obtained from both case studies showed improvement in capacity, idle time and utilized time.

Keywords: healthcare service delivery, idle time, lean principles, utilized time, value stream mapping, wastes

Procedia PDF Downloads 287

Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

Abstract:

Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

Procedia PDF Downloads 345