Search results for: input data stream

Authors: Ali Sarosh, Dong Yun-Feng, Muhammad Umer

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Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.

Keywords: TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, aerothermodynamics, mass-modeling, TSTO vehicle.

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Authors: Alae El Alami, Mohamed Bahaj

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This article discusses the passage of RDB to XML documents (schema and data) based on metadata and semantic enrichment, which makes the RDB under flattened shape and is enriched by the object concept. The integration and exploitation of the object concept in the XML uses a syntax allowing for the verification of the conformity of the document XML during the creation. The information extracted from the RDB is therefore analyzed and filtered in order to adjust according to the structure of the XML files and the associated object model. Those implemented in the XML document through a SQL query are built dynamically. A prototype was implemented to realize automatic migration, and so proves the effectiveness of this particular approach.

Keywords: RDB, XML, DTD, semantic enrichment.

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: Spatial Information Network, Traffic prediction, Wavelet decomposition, Time series model.

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Authors: Tiina Brandt, Isaac Wanasika

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Culturally diverse global companies need to understand cultural differences between leaders and employees from different backgrounds. Communication is culturally contingent and has a significant impact on effective execution of leadership goals. The awareness of cultural variations related to communication and interactions will help leaders modify their own behavior, and consequently improve the execution of goals and avoid unnecessary faux pas. Our focus is on young adults that have experienced cultural integration, culturally diverse surroundings in schools and universities, and cultural travels. Our central research problem is to understand the impact of different national cultures on communication. We focus on four countries with distinct national cultures and spatial distribution. The countries are Finland, Indonesia, Russia and USA. Our sample is based on business students (n = 225) from various backgrounds in the four countries. Their responses of communication and leadership styles were analyzed using ANOVA and post-hoc test. Results indicate that culture impacts on communication behavior. Even young culturally-exposed adults with cultural awareness and experience demonstrate cultural differences in their behavior. Apparently, culture is a deeply seated trait that cannot be completely neutralized by environmental variables. Our study offers valuable input for leadership training programs and for expatriates when recognizing specific differences on leaders’ behavior due to culture.

Keywords: Culture, communication, Finland, Indonesia, Russia, USA.

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis.

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Authors: Ali Dehgan Moroozeh, B. Farhadi Bansouleh

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Evapotranspiration is one of the most important components of the hydrological cycle. Evapotranspiration (ETo) is an important variable in water and energy balances on the earth’s surface, and knowledge of the distribution of ET is a key factor in hydrology, climatology, agronomy and ecology studies. Many researchers have a valid relationship, which is a function of climate factors, to estimate the potential evapotranspiration presented to the plant water stress or water loss, prevent. The FAO-Penman method (PM) had been recommended as a standard method. This method requires many data and these data are not available in every area of world. So, other methods should be evaluated for these conditions. When sufficient or reliable data to solve the PM equation are not available then Hargreaves equation can be used. The Hargreaves equation (HG) requires only daily mean, maximum and minimum air temperature extraterrestrial radiation .In this study, Hargreaves method (HG) were evaluated in 12 stations in the North West region of Iran. Results of HG and M.HG methods were compared with results of PM method. Statistical analysis of this comparison showed that calibration process has had significant effect on efficiency of Hargreaves method.

Keywords: Evapotranspiration, Hargreaves equation, FAOPenman method.

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Authors: Iveta Řepková

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The aim of this paper is to estimate the efficiency of the Slovak commercial banks employing the Data Envelopment Analysis (DEA) window analysis approach during the period 2003-2012. The research is based on unbalanced panel data of the Slovak commercial banks. Undesirable output was included into analysis of banking efficiency. It was found that most efficient banks were Postovabanka, UniCredit Bank and Istrobanka in CCR model and the most efficient banks were Slovenskasporitelna, Istrobanka and UniCredit Bank in BCC model. On contrary, the lowest efficient banks were found Privatbanka and CitiBank. We found that the largest banks in the Slovak banking market were lower efficient than medium-size and small banks. Results of the paper is that during the period 2003-2008 the average efficiency was increasing and then during the period 2010-2011 the average efficiency decreased as a result of financial crisis.

Keywords: Data Envelopment Analysis, efficiency, Slovak banking sector, window analysis.

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Authors: Souvik Bhattacharyya, Gautam Sanyal

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Over last two decades, due to hostilities of environment over the internet the concerns about confidentiality of information have increased at phenomenal rate. Therefore to safeguard the information from attacks, number of data/information hiding methods have evolved mostly in spatial and transformation domain.In spatial domain data hiding techniques,the information is embedded directly on the image plane itself. In transform domain data hiding techniques the image is first changed from spatial domain to some other domain and then the secret information is embedded so that the secret information remains more secure from any attack. Information hiding algorithms in time domain or spatial domain have high capacity and relatively lower robustness. In contrast, the algorithms in transform domain, such as DCT, DWT have certain robustness against some multimedia processing.In this work the authors propose a novel steganographic method for hiding information in the transform domain of the gray scale image.The proposed approach works by converting the gray level image in transform domain using discrete integer wavelet technique through lifting scheme.This approach performs a 2-D lifting wavelet decomposition through Haar lifted wavelet of the cover image and computes the approximation coefficients matrix CA and detail coefficients matrices CH, CV, and CD.Next step is to apply the PMM technique in those coefficients to form the stego image. The aim of this paper is to propose a high-capacity image steganography technique that uses pixel mapping method in integer wavelet domain with acceptable levels of imperceptibility and distortion in the cover image and high level of overall security. This solution is independent of the nature of the data to be hidden and produces a stego image with minimum degradation.

Keywords: Cover Image, Pixel Mapping Method (PMM), StegoImage, Integer Wavelet Tranform.

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: Semantic links, data mining, linked data, SKOS.

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the CPU, RAM, and ROM memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles.

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Authors: Rossella Arcucci, Luisa D’Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: Data Assimilation, Parallel Algorithm, GPU architectures, Ocean Models.

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8.

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Authors: Yibo Fan, Takeshi Ikenaga, Yukiyasu Tsunoo, Satoshi Goto

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This paper proposes a low-cost reconfigurable architecture for AES algorithm. The proposed architecture separates SubBytes and MixColumns into two parallel data path, and supports different bit-width operation for this two data path. As a result, different number of S-box can be supported in this architecture. The throughput and power consumption can be adjusted by changing the number of S-box running in this design. Using the TSMC 0.18μm CMOS standard cell library, a very low-cost implementation of 7K Gates is obtained under 182MHz frequency. The maximum throughput is 360Mbps while using 4 S-Box simultaneously, and the minimum throughput is 114Mbps while only using 1 S-Box

Keywords: AES, Reconfigurable architecture, low cost

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Authors: Ju-Hong Lee, Ching-Wei Liao, Kun-Che Lee

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This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

Keywords: Adaptive beamforming, antenna array, linearly constrained minimum variance, robustness, steering vector.

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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: Hussain Jiffry, Kypros Pilakoutas, Reyes Garcia

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This study examines analytically the effect of tsunami loads on reinforced concrete (RC) frame buildings. The impact of tsunami wave loads and waterborne objects are analyzed using a typical substandard full-scale two-story RC frame building tested as part of the EU-funded Ecoleader project. The building was subjected to shake table tests in bare condition, and subsequently strengthened using Carbon Fiber Reinforced Polymers (CFRP) composites and retested. Numerical models of the building in both bare and CFRP-strengthened conditions are calibrated in DRAIN-3DX software to match the test results. To investigate the response of wave loads and impact forces, the numerical models are subjected to nonlinear dynamic analyses using force time-history input records. The analytical results are compared in terms of displacements at the floors and at the “impact point” of a boat. The results show that the roof displacement of the CFRP-strengthened building reduced by 63% when compared to the bare building. The results also indicate that strengthening only the mid-height of the impact column using CFRP is more effective at reducing damage when compared to strengthening other parts of the column. Alternative solutions to mitigate damage due to tsunami loads are suggested.

Keywords: Tsunami loads, hydrodynamic load, impact load, waterborne objects, RC buildings.

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Authors: Rajesh Kumar, Puneet Aggarwal

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In context of understanding problems faced by undergraduate students while carrying out laboratory experiments dealing with high voltages, it was found that most of the students are hesitant to work directly on machine. The reason is that error in the circuitry might lead to deterioration of machine and laboratory instruments. So, it has become inevitable to include modern pedagogic techniques for undergraduate students, which would help them to first carry out experiment in virtual system and then to work on live circuit. Further advantages include that students can try out their intuitive ideas and perform in virtual environment, hence leading to new research and innovations. In this paper, virtual environment used is of MATLAB/Simulink for three-phase induction machines. The performance analysis of three-phase induction machine is carried out using virtual environment which includes Direct Current (DC) Test, No-Load Test, and Block Rotor Test along with speed torque characteristics for different rotor resistances and input voltage, respectively. Further, this paper carries out computer aided teaching of basic Voltage Source Inverter (VSI) drive circuitry. Hence, this paper gave undergraduates a clearer view of experiments performed on virtual machine (No-Load test, Block Rotor test and DC test, respectively). After successful implementation of basic tests, VSI circuitry is implemented, and related harmonic distortion (THD) and Fast Fourier Transform (FFT) of current and voltage waveform are studied.

Keywords: Block rotor test, DC test, no-load test, virtual environment, VSI.

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

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Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: Sayyed R Mousavi

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The Far From Most Strings Problem (FFMSP) is to obtain a string which is far from as many as possible of a given set of strings. All the input and the output strings are of the same length, and two strings are said to be far if their hamming distance is greater than or equal to a given positive integer. FFMSP belongs to the class of sequences consensus problems which have applications in molecular biology. The problem is NP-hard; it does not admit a constant-ratio approximation either, unless P = NP. Therefore, in addition to exact and approximate algorithms, (meta)heuristic algorithms have been proposed for the problem in recent years. On the other hand, in the recent years, hybrid algorithms have been proposed and successfully used for many hard problems in a variety of domains. In this paper, a new metaheuristic algorithm, called Constructive Beam and Local Search (CBLS), is investigated for the problem, which is a hybridization of constructive beam search and local search algorithms. More specifically, the proposed algorithm consists of two phases, the first phase is to obtain several candidate solutions via the constructive beam search and the second phase is to apply local search to the candidate solutions obtained by the first phase. The best solution found is returned as the final solution to the problem. The proposed algorithm is also similar to memetic algorithms in the sense that both use local search to further improve individual solutions. The CBLS algorithm is compared with the most recent published algorithm for the problem, GRASP, with significantly positive results; the improvement is by order of magnitudes in most cases.

Keywords: Bioinformatics, Far From Most Strings Problem, Hybrid metaheuristics, Matheuristics, Sequences consensus problems.

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Authors: D. B. L. Bong, K.C. Lai, A. Joseph

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The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm was developed to extract roads automatically from satellite-taken images. In order to extract the road network accurately, the satellite image must be analyzed prior to the extraction process. The characteristics of these elements are analyzed and consequently the relationships among them are determined. In this study, the road regions are extracted based on colour space elements and edge details of roads. Besides, edge detection method is applied to further filter out the non-road regions. The extracted road regions are validated by using a segmentation method. These results are valuable for building road map and detecting the changes of the existing road database. The proposed Hybrid Simple Colour Space Segmentation and Edge Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks fully automatic, where the user only needs to input a high-resolution satellite image and wait for the result. Moreover, this system can work on complex road network and generate the extraction result in seconds.

Keywords: Road Network Recognition, Colour Space, Edge Detection, Urban Planning.

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Authors: C. Slim

Abstract:

This paper presents an integrated model, which hybridized data envelopment analysis (DEA) and support vector machine (SVM) together, to class countries according to their efficiency and performance. This model takes into account aspects of multi-dimensional indicators, decision-making hierarchy and relativity of measurement. Starting from a set of indicators of performance as exhaustive as possible, a process of successive aggregations has been developed to attain an overall evaluation of a country’s competitiveness.

Keywords: Artificial neural networks, support vector machine, data envelopment analysis, aggregations, indicators of performance.

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: Concrete, FEM, pavement, sensitivity, simulation.

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Authors: Ime R. Udotong, Justina I. R. Udotong

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Mobil Producing Nigeria Unlimited (MPNU), a subsidiary of ExxonMobil and the highest crude oil & condensate producer in Nigeria has its operational base and an oil terminal, the Qua Iboe terminal (QIT) located at Ibeno, Nigeria. Other oil companies like Network Exploration and Production Nigeria Ltd, Frontier Oil Ltd; Shell Petroleum Development Company Ltd; Elf Petroleum Nigeria Ltd and Nigerian Agip Energy, a subsidiary of the Italian ENI E&P operate onshore, on the continental shelf and in deep offshore of the Atlantic Ocean, respectively with the coastal waters of Ibeno, Nigeria as the nearest shoreline. This study was designed to delineate the oil-polluted sites in Ibeno, Nigeria using microbiological and physico-chemical characterization of soils, sediments and ground and surface water samples from the study area. Results obtained revealed that there have been significant recent hydrocarbon inputs into this environment as observed from the high counts of hydrocarbonoclastic microorganisms in excess of 1% at all the stations sampled. Moreover, high concentrations of THC, BTEX and heavy metals contents in all the samples analyzed corroborate the high recent crude oil input into the study area. The results also showed that the pollution of the different environmental media sampled were of varying degrees, following the trend: ground water > surface water > sediments > soils.

Keywords: Microbiological characterization, oil-polluted sites, physico-chemical analyses, total hydrocarbon content.

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: Situation-awareness, Smart home, IoT, Machine learning, Classifier.

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Authors: Abdollah Hosseini, Abbas Maleki, Khalil Fasihi, Rahim Naseri

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In order to investigate the effect of Plant Growth Promoting Rhizobacteria (PGPR) and rhizobium bacteria on grain yield and some agronomic traits of mungbean (Vigna radiate L.), an experiment was carried out based on randomized complete block design with three replications in Malekshahi, Ilam province, Iran during 2012-2013 cropping season. Experimental treatments consisted of control treatment, inoculation with rhizobium bacteria, rhizobium bacteria and Azotobacter, rhizobium bacteria and Azospirillum, rhizobium bacteria and Pseudomonas, rhizobium bacteria, Azotobacter and Azospirillum, rhizobium bacteria, Azotobacter and Pseudomonas, rhizobium bacteria, Azospirillum and Pseudomonas and rhizobium bacteria, Azotobacter, Azospirillum and Pseudomonas. The results showed that the effect of PGPR and rhizobium bacteria were significant affect on grain and its components in mungbean plant. Grain yield significantly increased by PGPR and rhizobium bacteria, so that the maximum grain yield was obtained from rhizobium bacteria + Azospirillum + Pseudomonas with the amount of 2287 kg.ha-1 as compared to control treatment. Excessive application of chemical fertilizers causes environmental and economic problems. That is, the overfertilization of P and N leads to pollution due to soil erosion and runoff water, so the use of PGPR and rhizobium bacteria can be justified due to reduce input costs, increase in grain yield and environmental friendly.

Keywords: Azotobacter, Mungbean, Pseudomonas, Rhizobium bacteria.

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Authors: Nurcan Tuncbag, Turkan Haliloglu, Ozlem Keskin

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Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.

Keywords: Pair-wise protein interactions, DIP database, functional correlations, biclustering.

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Authors: César Estébanez, Ricardo Aler, José M. Valls

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In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.

Keywords: Classification, genetic programming, projections.

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Authors: Jungho Moon, Lae-Jeong Park

Abstract:

This paper introduces a signal monitoring program developed with a view to helping electrical engineering students get familiar with sensors with digital output. Because the output of digital sensors cannot be simply monitored by a measuring instrument such as an oscilloscope, students tend to have a hard time dealing with digital sensors. The monitoring program runs on a PC and communicates with an MCU that reads the output of digital sensors via an asynchronous communication interface. Receiving the sensor data from the MCU, the monitoring program shows time and/or frequency domain plots of the data in real time. In addition, the monitoring program provides a serial terminal that enables the user to exchange text information with the MCU while the received data is plotted. The user can easily observe the output of digital sensors and configure the digital sensors in real time, which helps students who do not have enough experiences with digital sensors. Though the monitoring program was programmed in the Matlab programming language, it runs without the Matlab since it was compiled as a standalone executable.

Keywords: Digital sensor, MATLAB, MCU, signal monitoring program.

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Authors: Adrian O’Hagan, Robert McLoughlin

Abstract:

Considerable focus in the world of insurance risk quantification is placed on modeling loss values from lines of business (LOBs) that possess upper tail dependence. Copulas such as the Joe, Gumbel and Student-t copula may be used for this purpose. The copula structure imparts a desired level of tail dependence on the joint distribution of claims from the different LOBs. Alternatively, practitioners may possess historical or simulated data that already exhibit upper tail dependence, through the impact of catastrophe events such as hurricanes or earthquakes. In these circumstances, it is not desirable to induce additional upper tail dependence when modeling the joint distribution of the loss values from the individual LOBs. Instead, it is of interest to accurately assess the degree of tail dependence already present in the data. The empirical copula and its associated upper tail dependence coefficient are presented in this paper as robust, efficient means of achieving this goal.

Keywords: Empirical copula, extreme events, insurance loss reserving, upper tail dependence coefficient.

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Authors: T. Santiboon

Abstract:

This research were investigated, determined, and analyzed of the climate characteristically change in the provincial Udon Thani in the period of 60 surrounding years from 1951 to 2010 A.D. that it-s transferred to effects of climatologically data for determining global warming. Statistically significant were not found for the 60 years- data (R2<0.81). Statistically significant were found after adapted data followed as the Sun Spot cycle in 11 year periods, at the level 0.001 (R2= 1.00). These results indicate the Udon Thani-s weather are affected change; temperatures and evaporation were increased, but rainfall and number days of rainfall, cyclone storm, wind speed, and humidity, forest assessment were decreased. The effects of thermal energy from the sun radiation energy and human activities that they-re followed as the sunspot cycle are able to be predicted from the last to the future of the uniformitarian-s the climate change and global warming effect of the world.

Keywords: Climate Change, Global Warming, Udon Thani Province Weather

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