Search results for: Forward and backward constriction

Authors: Vijay Kumar Kukreja, Ravneet Kaur

Abstract:

In this paper, one dimensional advection diffusion model is analyzed using finite difference method based on Crank-Nicolson scheme. A practical problem of filter cake washing of chemical engineering is analyzed. The model is converted into dimensionless form. For the grid Ω × ω = [0, 1] × [0, T], the Crank-Nicolson spatial derivative scheme is used in space domain and forward difference scheme is used in time domain. The scheme is found to be unconditionally convergent, stable, first order accurate in time and second order accurate in space domain. For a test problem, numerical results are compared with the analytical ones for different values of parameter.

Keywords: Consistency, Crank-Nicolson scheme, Gerschgorin circle, Lax-Richtmyer theorem, Peclet number, stability.

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Authors: Igor Astrov, Mikhail Pikkov, Rein Paluoja

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a flight control procedure to address the dynamics variation and performance requirement difference of flight trajectory for an unmanned helicopter model with vectored thrust configuration. This control strategy for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: Neural network predictive controller, situational awareness, vectored thrust aerial vehicle.

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Authors: Muhammad Abrar, Xiang Gui, Amal Punchihewa

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In this paper we study the resource allocation problem for an OFDMA based cooperative two-way relaying (TWR) network. We focus on amplify and forward (AF) analog network coding (ANC) protocol. An optimization problem for two basic resources namely, sub-carrier and power is formulated for multi-user TWR networks. A joint optimal optimization problem is investigated and two-step low complexity sub-optimal resource allocation algorithm is proposed for multi-user TWR networks with ANC protocol. The proposed algorithm has been evaluated in term of total achievable system sum-rate and achievable individual sum-rate for each userpair. The good tradeoff between system sum-rate and fairness is observed in the two-step proportional resource allocation scheme.

Keywords: Relay Network, Relay Protocols, Resource Allocation, Two –way relaying.

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Authors: Afaf Alharbi, Qianni Zhang

Abstract:

The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper presents a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network-based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation on an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: Attention Multiple Instance Learning, Multiple Instance Learning, transfer learning, histopathological slides, cancer tissue classification.

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Authors: Namal A. Senanayake, A. Beg, Withana C. Prasad

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This paper analyzes the patterns of the Monte Carlo data for a large number of variables and minterms, in order to characterize the circuit path length behavior. We propose models that are determined by training process of shortest path length derived from a wide range of binary decision diagram (BDD) simulations. The creation of the model was done use of feed forward neural network (NN) modeling methodology. Experimental results for ISCAS benchmark circuits show an RMS error of 0.102 for the shortest path length complexity estimation predicted by the NN model (NNM). Use of such a model can help reduce the time complexity of very large scale integrated (VLSI) circuitries and related computer-aided design (CAD) tools that use BDDs.

Keywords: Monte Carlo data, Binary decision diagrams, Neural network modeling, Shortest path length estimation.

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Authors: U. Bottigli, M.Carpinelli, P.L. Fiori, B. Golosio, A. Marras, G. L. Masala, P. Oliva

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The counting process of cell colonies is always a long and laborious process that is dependent on the judgment and ability of the operator. The judgment of the operator in counting can vary in relation to fatigue. Moreover, since this activity is time consuming it can limit the usable number of dishes for each experiment. For these purposes, it is necessary that an automatic system of cell colony counting is used. This article introduces a new automatic system of counting based on the elaboration of the digital images of cellular colonies grown on petri dishes. This system is mainly based on the algorithms of region-growing for the recognition of the regions of interest (ROI) in the image and a Sanger neural net for the characterization of such regions. The better final classification is supplied from a Feed-Forward Neural Net (FF-NN) and confronted with the K-Nearest Neighbour (K-NN) and a Linear Discriminative Function (LDF). The preliminary results are shown.

Keywords: Automatic cell counting, neural network, region growing, Sanger net.

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Authors: Alireza Vakili, Mohsen Danesh Mesgaran, Majid Abdollazade

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In this study four Holstein steers with rumen fistula fed 7 kg of dry matter (DM) of diets differing in concentrate to alfalfa hay ratios as 60:40, 70:30, 80:20, and 90:10 in a 4 × 4 latin square design. The pH of the ruminal fluid was measured before the morning feeding (0.0 h) to 8 h post feeding. In this study, a two-layered feed-forward neural network trained by the Levenberg-Marquardt algorithm was used for modelling of ruminal pH. The input variables of the network were time, concentrate to alfalfa hay ratios (C/F), non fiber carbohydrate (NFC) and neutral detergent fiber (NDF). The output variable was the ruminal pH. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 >0.96). Therefore, we suggest using these model-derived biological values to summarize continuously recorded pH data.

Keywords: Ruminal pH, Artificial Neural Network (ANN), Non Fiber Carbohydrate, Neutral Detergent Fiber.

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Authors: J. Erina, I. Erins

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The goals of the present research are to estimate Six Sigma implementation in Latvian commercial banks and to identify the perceived benefits of its implementation. To achieve the goals, the authors used sequential explanatory method. To obtain empirical data, the authors have developed the questionnaire and adapted it for the employees of Latvian commercial banks. The questions are related to Six Sigma implementation and its perceived benefits. The questionnaire mainly consists of closed questions, the evaluation of which is based on 5 point Likert scale. The obtained empirical data has shown that of the two hypotheses put forward in the present research – Hypothesis 1 – has to be rejected, while Hypothesis 2 has been partially confirmed. The authors have also faced some research limitations related to the fact that the participants in the questionnaire belong to different rank of the organization hierarchy.

Keywords: Six Sigma, Quality, Commercial banking sector, Latvia.

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Authors: Michal Martinovič, Jaroslav Polec, Kvetoslava Kotuliaková

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In this paper we proposed multistage adaptive ARQ/HARQ/HARQ scheme. This method combines pure ARQ (Automatic Repeat reQuest) mode in low channel bit error rate and hybrid ARQ method using two different Reed-Solomon codes in middle and high error rate conditions. It follows, that our scheme has three stages. The main goal is to increase number of states in adaptive HARQ methods and be able to achieve maximum throughput for every channel bit error rate. We will prove the proposal by calculation and then with simulations in land mobile satellite channel environment. Optimization of scheme system parameters is described in order to maximize the throughput in the whole defined Signal-to- Noise Ratio (SNR) range in selected channel environment.

Keywords: Signal-to-noise ratio, throughput, forward error correction (FEC), pure and hybrid automatic repeat request (ARQ).

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Authors: Roza Dastres, Mohsen Soori

Abstract:

Digital Signal Processing (DSP) is the use of digital processing systems by computers in order to perform a variety of signal processing operations. It is the mathematical manipulation of a digital signal's numerical values in order to increase quality as well as effects of signals. DSP can include linear or nonlinear operators in order to process and analyze the input signals. The nonlinear DSP processing is closely related to nonlinear system detection and can be implemented in time, frequency and space-time domains. Applications of the DSP can be presented as control systems, digital image processing, biomedical engineering, speech recognition systems, industrial engineering, health care systems, radar signal processing and telecommunication systems. In this study, advanced methods and different applications of DSP are reviewed in order to move forward the interesting research filed.

Keywords: Digital signal processing, advanced telecommunication, nonlinear signal processing, speech recognition systems.

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Authors: Yannick Verbelen, Tim Dekegel, Ann Peeters, Klara Stinders, Niek Blondeel, Sam De Winne, An Braeken, Abdellah Touhafi

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Matching an embedded electronic application with a cantilever vibration energy harvester remains a difficult endeavour due to the large number of factors influencing the output power. In the presented work, complementary balanced energy harvester parametrization is used as a methodology for simplification of harvester integration in electronic applications. This is achieved by a dual approach consisting of an adaptation of the general parametrization methodology in conjunction with a straight forward harvester benchmarking strategy. For this purpose, the design and implementation of a suitable user friendly cantilever energy harvester benchmarking platform is discussed. Its effectiveness is demonstrated by applying the methodology to a commercially available Mide V21BL vibration energy harvester, with excitation amplitude and frequency as variables.

Keywords: Energy harvesting, vibrations, piezoelectric transducers, embedded systems, harvester parametrization.

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Authors: Wang Ye, Wei Wei

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Residential block construction of big cities in China began in the 1950s, and four models had far-reaching influence on modern residential block in its development process, including unit compound and residential district in 1950s to 1980s, and gated community and open community in 1990s to now. Based on analysis of the four models’ fabric, the article takes residential blocks in Hangzhou west area as an example and carries on the studies from urban structure level and block spacial level, mainly including urban road network, land use, community function, road organization, public space and building fabric. At last, the article puts forward “Semi-open Sub-community” strategy to improve the current fabric.

Keywords: Hangzhou West Area, residential block model, residential block fabric, “Semi-open Sub-community” strategy.

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Authors: Kul Pun, F. A. Hamad, T. Ahmed, J. O. Ugwu, J. Eyers, G. Lawson, P. A. Russell

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Determining droplet size and distribution is essential when determining the separation efficiency of a two/three-phase separator. This paper investigates the effect of liquid flow and oil pad thickness on the droplet size at the lab scale. The findings show that increasing the inlet flow rates of the oil and water results in size reduction of the droplets and increasing the thickness of the oil pad increases the size of the droplets. The data were fitted with a simple Gaussian model, and the parameters of mean, standard deviation, and amplitude were determined. Trends have been obtained for the fitted parameters as a function of the Reynolds number, which suggest a way forward to better predict the starting parameters for population models when simulating separation using CFD packages. The key parameter to predict to fix the position of the Gaussian distribution was found to be the mean droplet size.

Keywords: Two-phase separator, average bubble droplet, bubble size distribution, liquid-liquid phase.

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Authors: Laveen Sundararaj, Palanisamy Vellaiyan

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Delay and Disruption Tolerant Networking is part of the Inter Planetary Internet with primary application being Deep Space Networks. Its Terrestrial form has interesting research applications such as Alagappa University Delay Tolerant Water Monitoring Network which doubles as test beds for improvising its routing scheme. DTNs depend on node mobility to deliver packets using a store-carry-and forward paradigm. Throwboxes are small and inexpensive stationary devices equipped with wireless interfaces and storage. We propose the use of Throwboxes to enhance the contact opportunities of the nodes and hence improve the Throughput. The enhancement is evaluated using Alunivdtnsim, a desktop simulator in C language and the results are graphically presented.

Keywords: Alunivdtnsim – Alagappa University Delay TolerantNetwork Simulator, AUDTWMN- Alagappa University DelayTolerant Water Monitoring Network, DTN - Delay and DisruptionTolerant Networking, LTP – Lick Lider Transmission Protocol.

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Authors: Hemashree Bordoloi, Kandarpa Kumar Sarma

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Rotation or tilt present in an image capture by digital means can be detected and corrected using Artificial Neural Network (ANN) for application with a Face Recognition System (FRS). Principal Component Analysis (PCA) features of faces at different angles are used to train an ANN which detects the rotation for an input image and corrected using a set of operations implemented using another system based on ANN. The work also deals with the recognition of human faces with features from the foreheads, eyes, nose and mouths as decision support entities of the system configured using a Generalized Feed Forward Artificial Neural Network (GFFANN). These features are combined to provide a reinforced decision for verification of a person-s identity despite illumination variations. The complete system performing facial image rotation detection, correction and recognition using re-enforced decision support provides a success rate in the higher 90s.

Keywords: Rotation, Face, Recognition, ANN.

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Authors: Ji Nyong Jang, Min Woo Lee, Eun Kyung Kim, Ki Keun Kim, Jae Sung Lim

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Since DVB-RCS has been successively implemented, the mobile communication on the multi-beam satellite communication is attractive attention. And the DVB-RCS standard sets up to support mobility of a RCST. In the case of the spot-beam satellite system, the received signal strength does not differ largely between the center and the boundary of the beam. Thus, the RSS based handoff detection algorithm is not benefit to the satellite system as a terrestrial system. Therefore we propose an Adaptive handoff detection algorithm based on RCST mobility information. Our handoff detection algorithm not only can be used as centralized handoff detection algorithm but also removes uncertainties of handoff due to the variation of RSS. Performances were compared with RSS based handoff algorithm. Simulation results show that the proposed handoff detection algorithm not only achieved better handoff and link degradation rate, but also achieved better forward link spectral efficiency.

Keywords: DVB-RCS, satellite multi-beam handoff, mobility information, handover.

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Authors: S. Hariharan, P. Muthuchidambaranathan

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In this paper, we consider a multi user multiple input multiple output (MU-MIMO) based cooperative reporting system for cognitive radio network. In the reporting network, the secondary users forward the primary user data to the common fusion center (FC). The FC is equipped with linear equalizers and an energy detector to make the decision about the spectrum. The primary user data are considered to be a digital video broadcasting - terrestrial (DVB-T) signal. The sensing channel and the reporting channel are assumed to be an additive white Gaussian noise and an independent identically distributed Raleigh fading respectively. We analyzed the detection probability of MU-MIMO system with linear equalizers and arrived at the closed form expression for average detection probability. Also the system performance is investigated under various MIMO scenarios through Monte Carlo simulations.

Keywords: Cooperative MU-MIMO, DVB-T, Linear Equalizers.

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

Abstract:

An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: Retrieval, generative, deep learning, response generation, knowledge.

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Authors: Tainchi Lu, Chochih Lin

Abstract:

With a rapid growth in 3D graphics technology over the last few years, people are desired to see more flexible reacting motions of a biped in animations. In particular, it is impossible to anticipate all reacting motions of a biped while facing a perturbation. In this paper, we propose a three-level tracking method for animating a 3D humanoid character. First, we take the laws of physics into account to attach physical attributes, such as mass, gravity, friction, collision, contact, and torque, to bones and joints of a character. The next step is to employ PD controller to follow a reference motion as closely as possible. Once the character cannot tolerate a strong perturbation to prevent itself from falling down, we are capable of tracking a desirable falling-down action to avoid any falling condition inaccuracy. From the experimental results, we demonstrate the effectiveness and flexibility of the proposed method in comparison with conventional data-driven approaches.

Keywords: Character Animation, Forward Dynamics, Motion Tracking, PD Control.

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Authors: S. A. Ahmadi, S. Sharif, R. Jamshidi

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In this article, the flow behavior around a NACA 0012 airfoil which is oscillating with different Reynolds numbers and in various amplitudes has been investigated numerically. Numerical simulations have been performed with ANSYS software. First, the 2- D geometry has been studied in different Reynolds numbers and angles of attack with various numerical methods in its static condition. This analysis was to choose the best turbulent model and comparing the grids to have the optimum one for dynamic simulations. Because the analysis was to study the blades of wind turbines, the Reynolds numbers were not arbitrary. They were in the range of 9.71e5 to 22.65e5. The angle of attack was in the range of -41.81° to 41.81°. By choosing the forward wind speed as the independent parameter, the others like Reynolds and the amplitude of the oscillation would be known automatically. The results show that the SST turbulent model is the best choice that leads the least numerical error with respect the experimental ones. Also, a dynamic stall phenomenon is more probable at lower wind speeds in which the lift force is less.

Keywords: Dynamic stall, Numerical simulation, Wind turbine, Turbulent Model

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: Artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt.

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Authors: Alaoui Hamza, Moutacalli Mohamed Tarik, Chebak Ahmed

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The number of people suffering from hemiplegia is growing each year. This lower limb disability affects all the aspects of their lives by taking away their autonomy. This implicates their close relatives, as well as the health system to provide the necessary care they need. The integration of exoskeletons in the medical field became a promising solution to resolve this issue. This paper presents an exoskeleton designed to help hemiplegic people get back the sensation and ability of normal walking. For this purpose, three step models have been created. The first step allows a simple forward movement of the leg. The second method is designed to overcome some obstacles in the patient path, and finally the third step model gives the patient total control over the device. Each of the control methods was designed to offer a solution to the challenges that the patients may face during the walking process.

Keywords: Ability of normal walking, exoskeleton, hemiplegic patients, lower limb motion, mechatronics.

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Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh

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This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.

Keywords: Modeling, Neural Networks, Phenol, Soil media

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Authors: Nichanant Sermsri, Chonmapat Torasa

Abstract:

The objective of this research was to investigate the efficiency of the light emitting diode (LED) tube in various color lights used to lure the adult coconut hispine beetle. The research was conducted by setting the forward bias on LED tubes, and the next step was to test luminous efficacy and quantity of electricity used to power each LED tube in different color lights. Finally, the researcher examined the efficiency of each color-light LED tube to lure the adult coconut hispine beetle.

The results showed that the ultraviolet LED tubes had the most capacity to allure the adult coconut hispine beetles with the percentage of 82.92, followed by the blue LED tubes with the percentage of 59.76. Whereas the yellow, pink, red and warm white LED tubes had no influence to the adult coconut hispine beetles.

Keywords: Coconut Hispine Beetle, Color Lights, Lighting Efficiency, LED Tubes.

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Authors: Jing Zhu, Liancun Zheng, Xinxin Zhang

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This work is focused on the steady boundary layer flow near the forward stagnation point of plane and axisymmetric bodies towards a stretching sheet. The no slip condition on the solid boundary is replaced by the partial slip condition. The analytical solutions for the velocity distributions are obtained for the various values of the ratio of free stream velocity and stretching velocity, slip parameter, the suction and injection velocity parameter, magnetic parameter and dimensionality index parameter in the series forms with the help of homotopy analysis method (HAM). Convergence of the series is explicitly discussed. Results show that the flow and the skin friction coefficient depend heavily on the velocity slip factor. In addition, the effects of all the parameters mentioned above were more pronounced for plane flows than for axisymmetric flows.

Keywords: slip flow, axisymmetric flow, homotopy analysismethod, stagnation-point.

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Authors: T. Hacib, M. R. Mekideche, N. Ferkha

Abstract:

This paper presents an approach which is based on the use of supervised feed forward neural network, namely multilayer perceptron (MLP) neural network and finite element method (FEM) to solve the inverse problem of parameters identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the finite element method (FEM). Both variations in relative magnetic permeability and electrical conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of MLP neural network. Finally, the obtained neural network is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. Noisy data, added to the probe measurements is used to enhance the robustness of the method. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Keywords: Inverse problem, MLP neural network, parametersidentification, FEM.

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Authors: Oi Mean Foong, Tan Jung Low, Satrio Wibowo

Abstract:

Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by manufacturers around the globe but they are neither flexible nor cost-effective for the end users. This paper presents a system prototype that is able to automatically recognize sign language to help normal people to communicate more effectively with the hearing or speech impaired people. The Sign to Voice system prototype, S2V, was developed using Feed Forward Neural Network for two-sequence signs detection. Different sets of universal hand gestures were captured from video camera and utilized to train the neural network for classification purpose. The experimental results have shown that neural network has achieved satisfactory result for sign-to-voice translation.

Keywords: Hand gesture detection, neural network, signlanguage, sequence detection.

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Authors: Shattyk Aliyev, Zhakypbek Altayev, Pirimbek Suleimenov, Asset Kuranbek, Zhamila Amirkulova

Abstract:

Modern civilization has come in recent decades into a new phase in its development, called the information society. The concept of "information society" has become one of the most common. Therefore, the attempt to understand what exactly the society we live in, what are its essential features, and possible future scenarios, is important to the social and philosophical analysis. At the heart of all these deep transformations is more increasing, almost defining role knowledge and information as play substrata of «information society». The mankind opened for itself and actively exploits a new resource – information. Information society puts forward on the arena new type of the power, at the heart of which activity – mastering by a new resource: information and knowledge. The password of the new power – intelligence as synthesis of knowledge, information and communications, the strength of mind, fundamental sociocultural values. In a postindustrial society, the power of knowledge and information is crucial in the management of the company, pushing into the background the influence of money and state coercion.

Keywords: Information society, philosophy of power, management, globalization and innovation.

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Authors: Mohammad Reza Ghasemi, Amin Ghorbani

Abstract:

The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the development of wavelet neural networks. Wavelet neural networks are feed-forward networks using wavelet as activation function. Wavelets are mathematical functions within suitable inner parameters, which help them to approximate arbitrary functions. WNN was used to predict the frequency of the structures. In WNN a RAtional function with Second order Poles (RASP) wavelet was used as a transfer function. It is shown that the convergence speed was faster than other neural networks. Also comparisons of WNN with the embedded Artificial Neural Network (ANN) and with approximate techniques and also with analytical solutions are available in the literature.

Keywords: Weight Minimization, Frequency Constraints, Steel Frames, ANN, WNN, RASP Function.

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Authors: M. Jändel

Abstract:

A new conceptual architecture for low-level neural pattern recognition is presented. The key ideas are that the brain implements support vector machines and that support vectors are represented as memory patterns in competitive queuing memories. A binary classifier is built from two competitive queuing memories holding positive and negative valence training examples respectively. The support vector machine classification function is calculated in synchronized evaluation cycles. The kernel is computed by bisymmetric feed-forward networks feed by sensory input and by competitive queuing memories traversing the complete sequence of support vectors. Temporary summation generates the output classification. It is speculated that perception apparatus in the brain reuses structures that have evolved for enabling fluent execution of prepared action sequences so that pattern recognition is built on internalized motor programmes.

Keywords: Competitive queuing model, Olfactory system, Pattern recognition, Support vector machine, Thalamus

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