Search results for: surface runoff network

Authors: Danielle Sandler dos Passos, Helder Coelho, Flávia Mori Sarti

Abstract:

Complexity science seeks the understanding of systems adopting diverse theories from various areas. Network analysis has been gaining space and credibility, namely with the biological, social and economic systems. Significant part of the literature focuses only monolayer representations of connections among agents considering one level of their relationships, and excludes other levels of interactions, leading to simplistic results in network analysis. Therefore, this work aims to demonstrate the advantages of the use of multilayer networks for the representation and analysis of networks. For this, we analyzed an interbank network, composed of 42 banks, comparing the centrality measures of the agents (degree and PageRank) resulting from each method (monolayer x multilayer). This proved to be the most reliable and efficient the multilayer analysis for the study of the current networks and highlighted JP Morgan and Deutsche Bank as the most important banks of the analyzed network.

Keywords: Complexity, interbank networks, multilayer networks, network analysis.

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Authors: Thiang, Handry Khoswanto, Rendy Pangaldus

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Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.

Keywords: Artificial neural network, back propagation, inverse kinematics, manipulator, robot.

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Authors: Yasser k. Ali, Hesham N. Elmahdy, Sanaa El Olla Hanfy Ahmed

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Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.

Keywords: Agent Migration, Decision Tree learning, ID3 algorithm, Naive Bayes Classifier

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Authors: Sirapat Lookrak, Anol Paisal

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Space debris has numerous manifestations including ferro-metalize and non-ferrous. The electric field will induce negative charges to split from positive charges inside the space debris. In this research, we focus only on conducting materials. The assumption is that the electric charge density of a conducting surface is proportional to the electric field on that surface due to Gauss's law. We are trying to find the induced charge density from an external electric field perpendicular to a conducting spherical surface. An object is a sphere on which the external electric field is not uniform. The electric field is, therefore, considered locally. The localised spherical surface is a tangent plane so the Gaussian surface is a very small cylinder and every point on a spherical surface has its own cylinder. The electric field from a circular electrode has been calculated in near-field and far-field approximation and shown Explanation Touchless manoeuvring space debris orbit properties. The electric charge density calculation from a near-field and far-field approximation is done.

Keywords: Near-field approximation, far-field approximation, localized Gauss’s law, electric charge density.

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Authors: Maryamossadat Bozorgtabar, Mohammadreza Rahimipour, Mehdi Salehi, Mohammadreza Jafarpour

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Titanium dioxide coatings were deposited by utilizing atmospheric plasma spraying (APS) system. The agglomerated nanopowder and different spraying parameters were used to determine their influences on the microstructure surface feature and photoabsorption of the coatings. The microstructure of as-sprayed TiO2 coatings were characterized by scanning electron microscope (SEM). Surface characteristics were investigated by Fourier Transform Infrared (FT-IR). The photo absorption was determined by UV-VIS spectrophotometer. It is found that the spray parameters have an influence on the microstructure, surface feature and photo-absorption of the TiO2 coatings.

Keywords: APS, TiO2, Nanostructured Coating, Photoabsorption

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Authors: Anupama Pande, Vishik Goel

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A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.

Keywords: Complex valued neural network, Radial BasisFunction, Image recognition.

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Authors: Che Z. Zulkifli, Nursyahida M. Noor, Siti N. Semunab, Shafawati A. Malek

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Wireless sensors, also known as wireless sensor nodes, have been making a significant impact on human daily life. The Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two complementary technologies; hence, an integrated implementation of these technologies expands the overall functionality in obtaining long-range and real-time information on the location and properties of objects and people. An approach for integrating ZigBee and RFID networks is proposed in this paper, to create an energy-efficient network improved by the benefits of combining ZigBee and RFID architecture. Furthermore, the compatibility and requirements of the ZigBee device and communication links in the typical RFID system which is presented with the real world experiment on the capabilities of the proposed RFID system.

Keywords: Mesh network, RFID, wireless sensor network, zigbee.

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Authors: John Kabuba, Antoine Mulaba-Bafubiandi, Kim Battle

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In this study a neural network (NN) was proposed to predict the sorption of binary mixture of copper-cobalt ions into clinoptilolite as ion-exchanger. The configuration of the backpropagation neural network giving the smallest mean square error was three-layer NN with tangent sigmoid transfer function at hidden layer with 10 neurons, linear transfer function at output layer and Levenberg-Marquardt backpropagation training algorithm. Experiments have been carried out in the batch reactor to obtain equilibrium data of the individual sorption and the mixture of coppercobalt ions. The obtained modeling results have shown that the used of neural network has better adjusted the equilibrium data of the binary system when compared with the conventional sorption isotherm models.

Keywords: Adsorption isotherm, binary system, neural network; sorption

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Authors: Jaswinder Kaur, Satwinder Singh, Karanjeet Singh Kahlon

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Accurate software cost estimates are critical to both developers and customers. They can be used for generating request for proposals, contract negotiations, scheduling, monitoring and control. The exact relationship between the attributes of the effort estimation is difficult to establish. A neural network is good at discovering relationships and pattern in the data. So, in this paper a comparative analysis among existing Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model and Neural Network Based Model is performed. Neural Network has outperformed the other considered models. Hence, we proposed Neural Network system as a soft computing approach to model the effort estimation of the software systems.

Keywords: Effort Estimation, Neural Network, Halstead Model, Walston-Felix Model, Bailey-Basili Model, Doty Model.

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City.

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Authors: M.H. Ahmad Fadzil, Esa Prakasa, Hurriyatul Fitriyah, Hermawan Nugroho, Azura Mohd Affandi, S.H. Hussein

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Psoriasis is a widespread skin disease affecting up to 2% population with plaque psoriasis accounting to about 80%. It can be identified as a red lesion and for the higher severity the lesion is usually covered with rough scale. Psoriasis Area Severity Index (PASI) scoring is the gold standard method for measuring psoriasis severity. Scaliness is one of PASI parameter that needs to be quantified in PASI scoring. Surface roughness of lesion can be used as a scaliness feature, since existing scale on lesion surface makes the lesion rougher. The dermatologist usually assesses the severity through their tactile sense, therefore direct contact between doctor and patient is required. The problem is the doctor may not assess the lesion objectively. In this paper, a digital image analysis technique is developed to objectively determine the scaliness of the psoriasis lesion and provide the PASI scaliness score. Psoriasis lesion is modelled by a rough surface. The rough surface is created by superimposing a smooth average (curve) surface with a triangular waveform. For roughness determination, a polynomial surface fitting is used to estimate average surface followed by a subtraction between rough and average surface to give elevation surface (surface deviations). Roughness index is calculated by using average roughness equation to the height map matrix. The roughness algorithm has been tested to 444 lesion models. From roughness validation result, only 6 models can not be accepted (percentage error is greater than 10%). These errors occur due the scanned image quality. Roughness algorithm is validated for roughness measurement on abrasive papers at flat surface. The Pearson-s correlation coefficient of grade value (G) of abrasive paper and Ra is -0.9488, its shows there is a strong relation between G and Ra. The algorithm needs to be improved by surface filtering, especially to overcome a problem with noisy data.

Keywords: psoriasis, roughness algorithm, polynomial surfacefitting.

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Authors: Jana Novakova, Lenka Petrkovska, Josef Brychta, Robert Cep, Lenka Ocenasova

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The contribution is dealing with the influence of high speed parameters on the quality of machined surface. In general the principle of high speed cutting lies in achieving faster machine times with concurrent increase in accuracy and quality of the machined areas in largely irregular, mathematically hard to define shapes. High speed machining is a highly effective method of machining with the following goals: increasing of machining productivity, increasing of quality of the machined surface, improving of machining economy, improving of ecological aspects of machining. This article is based on an experiment performed by the Department of Machining and Assembly of the Faculty of Mechanical Engineering of VŠBTechnical University of Ostrava.

Keywords: High speed cutting, measurement, surface integrity, surface roughness, residual stress/

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Authors: Kasun Karunanayaka, Sanath Siriwardana, Chamari Edirisinghe, Ryohei Nakatsu, PonnampalamGopalakrishnakone

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In this paper, we are presenting a new type of pointing interface for computers which provides mouse functionalities with near surface haptic feedback. Further, it can be configured as a haptic display where users may feel the basic geometrical shapes in the GUI by moving the finger on top of the device surface. These functionalities are achieved by tracking three dimensional positions of the neodymium magnet using Hall Effect sensors grid and generating like polarity haptic feedback using an electromagnet array. This interface brings the haptic sensations to the 3D space where previously it is felt only on top of the buttons of the haptic mouse implementations.

Keywords: Pointing interface, near surface haptic feedback, tactile display, tangible user interface.

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Authors: Toshihiko Sasama, Kentaro Kishida, Kazunori Sugahara, Hiroshi Masuyama

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A mobile ad hoc network is a network of mobile nodes without any notion of centralized administration. In such a network, each mobile node behaves not only as a host which runs applications but also as a router to forward packets on behalf of others. Clustering has been applied to routing protocols to achieve efficient communications. A CH network expresses the connected relationship among cluster-heads. This paper discusses the methods for constructing a CH network, and produces the following results: (1) The required running costs of 3 traditional methods for constructing a CH network are not so different from each other in the static circumstance, or in the dynamic circumstance. Their running costs in the static circumstance do not differ from their costs in the dynamic circumstance. Meanwhile, although the routing costs required for the above 3 methods are not so different in the static circumstance, the costs are considerably different from each other in the dynamic circumstance. Their routing costs in the static circumstance are also very different from their costs in the dynamic circumstance, and the former is one tenths of the latter. The routing cost in the dynamic circumstance is mostly the cost for re-routing. (2) On the strength of the above results, we discuss new 2 methods regarding whether they are tolerable or not in the dynamic circumstance, that is, whether the times of re-routing are small or not. These new methods are revised methods that are based on the traditional methods. We recommended the method which produces the smallest routing cost in the dynamic circumstance, therefore producing the smallest total cost.

Keywords: cluster, mobile ad hoc network, re-routing cost, simulation

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

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Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: Surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations.

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Authors: A.Qaderi, A. Heydarinasab, M. Ardjmand

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Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Keywords: Kinetic Modeling, Poly-β-Hydroxybutyrate (PHB), Hydrogenophaga Pseudoflava, Artificial Neural Network, Leudeking-Piret

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: Neural network, rule extraction, rule insertion, self-organizing map.

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Authors: Yuanyuan Zhang, Guang Sun, Li Su, Depeng Jin, Lieguang Zeng

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Since Network-on-Chip (NoC) uses network interfaces (NIs) to improve the design productivity, by now, there have been a few papers addressing the design and implementation of a NI module. However, none of them considered the difference of address encoding methods between NoC and the traditional bus-shared architecture. On the basis of this difference, in the paper, we introduce a transmit mechanism to solve such a problem for global asynchronous locally synchronous (GALS) NoC. Furthermore, we give the concrete implementation of the NI module in this transmit mechanism. Finally, we evaluate its performance and area overhead by a VHDL-based cycle-accurate RTL model and simulation results confirm the validity of this address-oriented transmit mechanism.

Keywords: Network-on-Chip, Network Interface, Open CoreProtocol, Address.

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Authors: Jain-Shing Liu

Abstract:

In this paper, we present a matrix game-theoretic cross-layer optimization formulation to maximize the network lifetime in wireless ad hoc networks with network coding. To this end, we introduce a cross-layer formulation of general NUM (network utility maximization) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlinks and transmission modes, and design the payoffs specific to the lifetime. In particular, with the inherit merit that matrix game can be solved with linear programming, our cross-layer programming formulation can benefit from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical example demonstrates its performance results on a well-known wireless butterfly network to verify the cross-layer optimization scheme.

Keywords: Cross-layer design, Lifetime maximization, Matrix game, Network coding

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Authors: Kyoung-Jae Kim, Hyunchul Ahn

Abstract:

This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.

Keywords: Social network analysis, Recommender systems, Collaborative filtering, Customer relationship management

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Authors: S. Ganesh

Abstract:

Power distribution systems typically have tie and sectionalizing switches whose states determine the topological configuration of the network. The aim of network reconfiguration of the distribution network is to minimize the losses for a load arrangement at a particular time. Thus the objective function is to minimize the losses of the network by satisfying the distribution network constraints. The various constraints are radiality, voltage limits and the power balance condition. In this paper the status of the switches is obtained by using Artificial Bee Colony (ABC) algorithm. ABC is based on a particular intelligent behavior of honeybee swarms. ABC is developed based on inspecting the behaviors of real bees to find nectar and sharing the information of food sources to the bees in the hive. The proposed methodology has three stages. In stage one ABC is used to find the tie switches, in stage two the identified tie switches are checked for radiality constraint and if the radilaity constraint is satisfied then the procedure is proceeded to stage three otherwise the process is repeated. In stage three load flow analysis is performed. The process is repeated till the losses are minimized. The ABC is implemented to find the power flow path and the Forward Sweeper algorithm is used to calculate the power flow parameters. The proposed methodology is applied for a 33–bus single feeder distribution network using MATLAB.

Keywords: Artificial Bee Colony (ABC) algorithm, Distribution system, Loss reduction, Network reconfiguration.

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Authors: Tarik Rashid, B. Q. Huang, M-T. Kechadi, B. Gleeson

Abstract:

this paper presents an auto-regressive network called the Auto-Regressive Multi-Context Recurrent Neural Network (ARMCRN), which forecasts the daily peak load for two large power plant systems. The auto-regressive network is a combination of both recurrent and non-recurrent networks. Weather component variables are the key elements in forecasting because any change in these variables affects the demand of energy load. So the AR-MCRN is used to learn the relationship between past, previous, and future exogenous and endogenous variables. Experimental results show that using the change in weather components and the change that occurred in past load as inputs to the AR-MCRN, rather than the basic weather parameters and past load itself as inputs to the same network, produce higher accuracy of predicted load. Experimental results also show that using exogenous and endogenous variables as inputs is better than using only the exogenous variables as inputs to the network.

Keywords: Daily peak load forecasting, neural networks, recurrent neural networks, auto regressive multi-context neural network.

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Authors: D. Satish Kumar, S. Karthik

Abstract:

A dual tiered network model is designed to overcome the problem of energy alert and fault tolerance. This model minimizes the delay time and overcome failure of links. Performance analysis of the dual tiered network model is studied in this paper where the CA and LS schemes are compared with DEO optimal. We then evaluate  the Integrated Network Topological Control and Key Management (INTK) Schemes, which was proposed to add security features of the wireless sensor networks. Clustering efficiency, level of protections, the time complexity is some of the parameters of INTK scheme that were analyzed. We then evaluate the Cluster based Energy Competent n-coverage scheme (CEC n-coverage scheme) to ensure area coverage for wireless sensor networks.

Keywords: CEC n-coverage scheme, Clustering efficiency, Dual tired network, Wireless sensor networks.

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Authors: Pawel Glibowski, Agnieszka Glibowska

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The rheological properties, structure and potential synergistic interactions of whey proteins (1-6%) and inulin (20%) in mixed gels in the presence of CaCl2 was the aim of this study. Whey proteins have a strong influence on inulin gel formation. At low concentrations (2%) whey proteins did not impair in inulin gel formation. At higher concentration (4%) whey proteins impaired inulin gelation and inulin impaired the formation of a Ca2+-induced whey protein network. The presence of whey proteins at a level allowing for protein gel network formation (6%) significantly increased the rheological parameters values of the gels. SEM micrographs showed that whey protein structure was coated by inulin moieties which could make the mixed gels firmer. The protein surface hydrophobicity measurements did not exclude synergistic interactions between inulin and whey proteins, however. The use of an electrophoretic technique did not show any stable inulin-whey protein complexes.

Keywords: gels, hydrophobicity, inulin, SEM, whey proteins.

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Authors: Roza Dastres, Mohsen Soori

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In order to electronically exchange information between network users in the web of data, different software such as outlook is presented. So, the traffic of users on a site or even the floors of a building can be decreased as a result of applying a secure and reliable data sharing software. It is essential to provide a fast, secure and reliable network system in the data sharing webs to create an advanced communication systems in the users of network. In the present research work, different encoding methods and algorithms in data sharing systems is studied in order to increase security of data sharing systems by preventing the access of hackers to the transferred data. To increase security in the networks, the possibility of textual conversation between customers of a local network is studied. Application of the encryption and decryption algorithms is studied in order to increase security in networks by preventing hackers from infiltrating. As a result, a reliable and secure communication system between members of a network can be provided by preventing additional traffic in the website environment in order to increase speed, accuracy and security in the network and web systems of data sharing.

Keywords: Secure Socket Layer, Security of networks.

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Authors: S. Niksarlioglu, F. Kulahci

Abstract:

Earthquakes are natural phenomena that occur with influence of a lot of parameters such as seismic activity, changing in the ground waters' motion, changing in the water-s temperature, etc. On the other hand, the radon gas concentrations in soil vary as nonlinear generally with earthquakes. Continuous measurement of the soil radon gas is very important for determination of characteristic of the seismic activity. The radon gas changes as continuous with strain occurring within the Earth-s surface during an earthquake and effects from the physical and the chemical processes such as soil structure, soil permeability, soil temperature, the barometric pressure, etc. Therefore, at the modeling researches are notsufficient to knowthe concentration ofradon gas. In this research, we determined relationships between radon emissions based on the environmental parameters and earthquakes occurring along the East Anatolian Fault Zone (EAFZ), Turkiye and predicted magnitudes of some earthquakes with the artificial neural network (ANN) model.

Keywords: Earthquake, Modeling, Prediction, Radon.

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Authors: Pankaj Chandna, Dinesh Kumar

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The present work analyses different parameters of end milling to minimize the surface roughness for AISI D2 steel. D2 Steel is generally used for stamping or forming dies, punches, forming rolls, knives, slitters, shear blades, tools, scrap choppers, tyre shredders etc. Surface roughness is one of the main indices that determines the quality of machined products and is influenced by various cutting parameters. In machining operations, achieving desired surface quality by optimization of machining parameters, is a challenging job. In case of mating components the surface roughness become more essential and is influenced by the cutting parameters, because, these quality structures are highly correlated and are expected to be influenced directly or indirectly by the direct effect of process parameters or their interactive effects (i.e. on process environment). In this work, the effects of selected process parameters on surface roughness and subsequent setting of parameters with the levels have been accomplished by Taguchi’s parameter design approach. The experiments have been performed as per the combination of levels of different process parameters suggested by L9 orthogonal array. Experimental investigation of the end milling of AISI D2 steel with carbide tool by varying feed, speed and depth of cut and the surface roughness has been measured using surface roughness tester. Analyses of variance have been performed for mean and signal-to-noise ratio to estimate the contribution of the different process parameters on the process.

Keywords: D2 Steel, Orthogonal Array, Optimization, Surface Roughness, Taguchi Methodology.

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Authors: Hyung T. Kwak, Jun Gao, Yao An, Alfred Kleinhammes, Yue Wu

Abstract:

Surface wettability is a crucial factor in oil recovery. In oil industry, the rock wettability involves the interplay between water, oil, and solid surface. Therefore, studying the interplay between adsorptions of water and hydrocarbon molecules on solid surface would be very informative for understanding rock wettability. Here we use the in-situ Nuclear Magnetic Resonance (NMR) gas isotherm technique to study competitive adsorptions of water and isopropanol, an intermediate step from hydrocarbons. This in-situ NMR technique obtains information on thermodynamic properties such as the isotherm, molecular dynamics via spin relaxation measurements, and adsorption kinetics such as how fast the system can reach thermal equilibrium after changes of vapor pressures. Using surfaces of silica glass beads, which can be modified from hydrophilic to hydrophobic, we obtained information on the influence of surface hydrophilicity on the state of surface water via obtained thermodynamic and dynamic properties.

Keywords: Competitive adsorption, nuclear magnetic resonance, wettability.

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Authors: Kexin Jia, Youxin Lu

Abstract:

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).

Keywords: Modulation classification, combined classifier, fuzzy neural network, interclass distance.

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