Search results for: degree of accuracy

Authors: L. Mirtskhulava, M. Khunjgurua, N. Lomineishvili, K. Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: Exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability.

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Authors: Akmaluddin, Koichiro Watanabe, Akihiro Kano, Wartono Rahardjo

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Oxygen and carbon isotopes records of multi-species planktonic, benthic foraminifera and bulk carbonate sample from Central Java Indonesia demonstrate that warm sea surface temperature occurred during the Miocene. Planktonic δ18O values from this study consistently lighter (-4 to -3 ‰PDB) than previous studies that indicate sea surface temperature during Miocene in this area was warm than tropical/equatorial localities. A surprising decrease of oxygen isotopic composition was recorded at ±14 Ma where the maximum of δ18O values is -4.87 ‰PDB for Orbulina universa, -5.02 ‰PDB for Globigerinoides sacculifer and -4.30 ‰PDB for Globoquadrina dehiscens, this event we predict as Middle Miocene Optimum. Warming of sea surface temperature we interpret as related to the development of Western Pacific Warm Pool where warm water from Pacific Ocean through the Indonesian seaway appears to remain during Miocene. Our result also show increasing suddenly of oxygen isotope values of planktic, benthic and bulk carbonate sample from ± 12 Ma, the increasing cooled surface water relatively high degree with Late Miocene global cooling climate or we predict that due to closing of Indonesian Gateway.

Keywords: Oxygen isotope, Foraminifera, Miocene, Paleoclimate, Indonesian.

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Authors: Yu-Xiang Zhao, Chien-Hsing Chou, Mu-Chun Su, Yi-Zeng Hsieh

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The purpose of this study is to design a portable virtual piano. By utilizing optical fiber gloves and the virtual piano software designed by this study, the user can play the piano anywhere at any time. This virtual piano consists of three major parts: finger tapping identification, hand movement and positioning identification, and MIDI software sound effect simulation. To play the virtual piano, the user wears optical fiber gloves and simulates piano key tapping motions. The finger bending information detected by the optical fiber gloves can tell when piano key tapping motions are made. Images captured by a video camera are analyzed, hand locations and moving directions are positioned, and the corresponding scales are found. The system integrates finger tapping identification with information about hand placement in relation to corresponding piano key positions, and generates MIDI piano sound effects based on this data. This experiment shows that the proposed method achieves an accuracy rate of 95% for determining when a piano key is tapped.

Keywords: virtual piano, portable, identification, optical fibergloves.

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Authors: Ahmad Aroziki Abdul Aziz, Sakinah Mohd Alauddin, Ruzitah Mohd Salleh, Mohammed Iqbal Shueb

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This paper illustrates the effect of nano Magnesium Hydroxide (MH) loading on the thermal properties of Low Density Polyethylene (LDPE)/Poly (ethylene-co vinyl acetate) (EVA) nano composite. Thermal studies were conducted, as it understanding is vital for preliminary development of new polymeric systems. Thermal analysis of nanocomposite was conducted using thermo gravimetric analysis (TGA), and differential scanning calorimetry (DSC). Major finding of TGA indicated two main stages of degradation process found at (350 ± 25oC) and (480 ± 25oC) respectively. Nano metal filler expressed better fire resistance as it stand over high degree of temperature. Furthermore, DSC analysis provided a stable glass temperature around 51 (±1oC) and captured double melting point at 84 (±2oC) and 108 (±2oC). This binary melting point reflects the modification of nano filler to the polymer matrix forming melting crystals of folded and extended chain. The percent crystallinity of the samples grew vividly with increasing filler content. Overall, increasing the filler loading improved the degradation temperature and weight loss evidently and a better process and phase stability was captured in DSC.

Keywords: Cable and Wire, LDPE/EVA, Nano MH, Nano Particles, Thermal properties.

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Authors: Azmi Ibrahim, Nor Azan Mat Zin, Noraidah Sahari Ashaari

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Dengue fever is prevalent in Malaysia with numerous cases including mortality recorded over the years. Public education on the prevention of the desease through various means has been carried out besides the enforcement of legal means to eradicate Aedes mosquitoes, the dengue vector breeding ground. Hence, other means need to be explored, such as predicting the seasonal peak period of the dengue outbreak and identifying related climate factors contributing to the increase in the number of mosquitoes. Simulation model can be employed for this purpose. In this study, we created a simulation of system dynamic to predict the spread of dengue outbreak in Hulu Langat, Selangor Malaysia. The prototype was developed using STELLA 9.1.2 software. The main data input are rainfall, temperature and denggue cases. Data analysis from the graph showed that denggue cases can be predicted accurately using these two main variables- rainfall and temperature. However, the model will be further tested over a longer time period to ensure its accuracy, reliability and efficiency as a prediction tool for dengue outbreak.

Keywords: dengue fever, prediction, system dynamic, simulation

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Authors: Mohamed S. El-Mahallawy, Attalah Hashad, Hazem Hassan Ali, Heba Sami Zaky

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This paper presents an evaluation for a wavelet-based digital watermarking technique used in estimating the quality of video sequences transmitted over Additive White Gaussian Noise (AWGN) channel in terms of a classical objective metric, such as Peak Signal-to-Noise Ratio (PSNR) without the need of the original video. In this method, a watermark is embedded into the Discrete Wavelet Transform (DWT) domain of the original video frames using a quantization method. The degradation of the extracted watermark can be used to estimate the video quality in terms of PSNR with good accuracy. We calculated PSNR for video frames contaminated with AWGN and compared the values with those estimated using the Watermarking-DWT based approach. It is found that the calculated and estimated quality measures of the video frames are highly correlated, suggesting that this method can provide a good quality measure for video frames transmitted over AWGN channel without the need of the original video.

Keywords: AWGN, DWT, PSNR, Watermarking, VideoQuality.

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Authors: Abdelgawad, Salah El-Tahawy

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This paper aimed to establish econometrical equation models for the Nile delta region in Egypt, which will represent a basement for future predictions of Lumpy skin disease outbreaks and its pathway in relation to climate change. Data of lumpy skin disease (LSD) outbreaks were collected from the cattle farms located in the provinces representing the Nile delta region during 1 January, 2015 to December, 2015. The obtained results indicated that there was a significant association between the degree of the LSD outbreaks and the investigated climate factors (temperature, wind speed, and humidity) and the outbreaks peaked during the months of June, July, and August and gradually decreased to the lowest rate in January, February, and December. The model obtained depicted that the increment of these climate factors were associated with evidently increment on LSD outbreaks on the Nile Delta of Egypt. The model validation process was done by the root mean square error (RMSE) and means bias (MB) which compared the number of LSD outbreaks expected with the number of observed outbreaks and estimated the confidence level of the model. The value of RMSE was 1.38% and MB was 99.50% confirming that this established model described the current association between the LSD outbreaks and the change on climate factors and also can be used as a base for predicting the of LSD outbreaks depending on the climatic change on the future.

Keywords: LSD, climate factors, econometric models, Nile Delta.

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc.).

Keywords: Defuzzification, floating search, fuzzy clustering, Zernike moments.

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Authors: S. Forughi, A. De Zoysa, S. Bhati

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The present study focuses on the environmental performance of the companies in the electricity-producing sector and its relationship with their financial performance. We will review the major studies that examined the relationship between the environmental and financial performance of firms in various industries. While the classical economic debates consider the environmental friendly activities costly and harmful to a firm’s profitability, it is claimed that firms will be rewarded with higher profitability in long run through the investments in environmental friendly activities. In this context, prior studies have examined the relationship between the environmental and financial performance of firms operating in different industry sectors. Our study will employ an environmental indicator to increase the accuracy of the results and be employed as an independent variable in our developed econometric model to evaluate the impact of the financial performance of the firms on their environmental friendly activities in the context of companies operating in the Australian electricity-producing sector. As a result, we expect our methodology to contribute to the literature and the findings of the study will help us to provide recommendations and policy implications to the electricity producers.

Keywords: Australian electricity sector, efficiency measurement, environmental-financial performance interaction, environmental index.

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Authors: Ibrahim Abdel Gadir Malik, Dafalla Siddig Dafalla, Abdelazim Ibrahim

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Jabal Omar is located in the western side of Makkah city in Saudi Arabia. The proposed Jabal Omar Development project includes several multi-storey buildings, roads, bridges and below ground structures founded at various depths. In this study, geological mapping and site inspection which covered pre-selected areas were carried out within the easily accessed parts. Geological features; including rock types, structures, degree of weathering, and geotechnical hazards were observed and analyzed with specified software and also were documented in form of photographs. The presence of joints and fractures in the area made the rock blocks small and weak. The site is full of jointing; it was observed that, the northern side consists of 3 to 4 jointing systems with 2 random fractures associated with dykes. The southern part is affected by 2 to 3 jointing systems with minor fault and shear zones. From the field measurements and observations, it was concluded that, the Jabal Omar intruded by andesitic and basaltic dykes of different thickness and orientation. These dykes made the outcrop weak, highly deformed and made the rock masses sensitive to weathering.

Keywords: Rock, slope, stabilization, protection, Makkah.

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Authors: Rujijan Vichivanives, Kittiya Poonsilp, Canasanan Wanavijit

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This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy.

Keywords: Rice disease, analysis system, mobile application, iOS operating system.

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Authors: Ben Richard Hughes, Hassam Nasarullah Chaudhry

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The main aim of this work is to establish the capabilities of new green buildings to ascertain off-grid electricity generation based on the integration of wind turbines in the conceptual model of a rotating tower [2] in Dubai. An in depth performance analysis of the WinWind 3.0MW [3] wind turbine is performed. Data based on the Dubai Meteorological Services is collected and analyzed in conjunction with the performance analysis of this wind turbine. The mathematical model is compared with Computational Fluid Dynamics (CFD) results based on a conceptual rotating tower design model. The comparison results are further validated and verified for accuracy by conducting experiments on a scaled prototype of the tower design. The study concluded that integrating wind turbines inside a rotating tower can generate enough electricity to meet the required power consumption of the building, which equates to a wind farm containing 9 horizontal axis wind turbines located at an approximate area of 3,237,485 m2 [14].

Keywords: computational fluid dynamics, green building, horizontal axis wind turbine, rotating tower, velocity gradient.

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Authors: S. X. Sun, Q. Wang

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With the rapid development of industrialization, countries in different stages of development in the world have gradually begun to pay attention to the impact of air pollution on health and the environment. Air control in developed countries is an effective reference for air control in developing countries. Artificial intelligence and other technologies also play a positive role in the prediction of air pollution. By comparing the annual changes of pollution in London and Beijing, this paper concludes that the pollution in developed cities is relatively low and stable, while the pollution in Beijing is relatively heavy and unstable, but is clearly improving. In addition, by analyzing the changes of major pollutants in Beijing in the past eight years, it is concluded that all pollutants except O3 show a significant downward trend. In addition, all pollutants except O3 have certain correlation. For example, PM10 and PM2.5 have the greatest influence on air quality index (AQI). Python, which is commonly used by artificial intelligence, is used as the main software to establish two models, support vector machine (SVM) and linear regression. By comparing the two models under the same conditions, it is concluded that SVM has higher accuracy in pollution prediction. The results of this study provide valuable reference for pollution control and prediction in developing countries.

Keywords: Air pollution, particulate matter, AQI, correlation coefficient, air pollution prediction.

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Authors: Mohammad Reza Karami Nejad

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A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.

Keywords: Routing, Quality of Service, Multicaset, Learning Automata, Genetic, Next Generation Networks.

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Authors: Robert Koch, Mario Golling, Gabi Dreo Rodosek

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Tracing and locating the geographical location of users (Geolocation) is used extensively in todays Internet. Whenever we, e.g., request a page from google we are - unless there was a specific configuration made - automatically forwarded to the page with the relevant language and amongst others, dependent on our location identified, specific commercials are presented. Especially within the area of Network Security, Geolocation has a significant impact. Because of the way the Internet works, attacks can be executed from almost everywhere. Therefore, for an attribution, knowledge of the origination of an attack - and thus Geolocation - is mandatory in order to be able to trace back an attacker. In addition, Geolocation can also be used very successfully to increase the security of a network during operation (i.e. before an intrusion actually has taken place). Similar to greylisting in emails, Geolocation allows to (i) correlate attacks detected with new connections and (ii) as a consequence to classify traffic a priori as more suspicious (thus particularly allowing to inspect this traffic in more detail). Although numerous techniques for Geolocation are existing, each strategy is subject to certain restrictions. Following the ideas of Endo et al., this publication tries to overcome these shortcomings with a combined solution of different methods to allow improved and optimized Geolocation. Thus, we present our architecture for improved Geolocation, by designing a new algorithm, which combines several Geolocation techniques to increase the accuracy.

Keywords: IP geolocation, prosecution of computer fraud, attack attribution, target-analysis

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: Big data, building-value analysis, machine learning, price prediction.

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Authors: Lalita Gupta, R. P. Singh

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Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.

Keywords: Array Signal Processing, Beamforming, ULA, Direction of Arrival, MUSIC

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Authors: J. H. Park, S. K. Lee, Y. W. Kim, D. C. Ko

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A large variety of pipe flange is required in marine and construction industry. Pipe flanges are usually welded or screwed to the pipe end and are connected with bolts. This approach is very simple and widely used for a long time; however, it results in high development cost and low productivity, and the productions made by this approach usually have safety problem at the welding area. In this research, a new approach of forming pipe flange based on cold forging and floating die concept is presented. This innovative approach increases the effectiveness of the material usage and save the time cost compared with conventional welding method. To ensure the dimensional accuracy of the final product, the finite element analysis (FEA) was carried out to simulate the process of cold forging, and the orthogonal experiment methods were used to investigate the influence of four manufacturing factors (pin die angle, pipe flange angle, rpm, pin die distance from clamp jig) and predicted the best combination of them. The manufacturing factors were obtained by numerical and experimental studies and it shows that the approach is very useful and effective for the forming of pipe flange, and can be widely used later.

Keywords: Cold forging, FEA, finite element analysis, Forge- 3D, rotating forming, tubes.

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Authors: Pranee Pathomchaiwat

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This research aims to study tourism data and behavior of foreign tourists visited Wat Phrachetuponwimolmangkalaram (Wat Po) Sample groups are tourists who visited inside the temple, during February, March, April and May 2013. Tools used in the research are questionnaires constructed by the researcher, and samples are dawn by Convenience sampling. There are 207 foreign tourists who are willing to be respondents. Statistics used are percentage, average mean and standard deviation. The results of the research reveal that: A. General Data of Respondents The foreign tourists who visited the temple are mostly female (57.5 %), most respondents are aged between 20-29 years (37.2%). Most respondents live in Europe (62.3%), most of them got the Bachelor’s degree (40.1%), British are mostly found (16.4%), respondents who are students are also found (23.2%), and Christian are mostly found (60.9%). B. Tourists’ Behavior While Visiting the Temple Compound. The result shows that the respondents came with family (46.4%), have never visited the temples (40.6%), and visited once (42 %). It is found that the foreign tourists’ inappropriate behavior are wearing revealing attires (58.9%), touching or getting closed to the monks (55.1%), and speaking loudly (46.9%) respectively. The respondents’ outstanding objectives are to visit inside the temple (57.5%), to pay respect to the Reclining Buddha Image in the Viharn (44.4%) and to worship the Buddha image in the Phra Ubosod (37.7%) respectively. C. The Respondents’ Self-evaluation of Performance It is found that over all tourists evaluated themselves in the highest level averaged 4.40. When focusing on each item, it is shown that they evaluated themselves in the highest level on obeying the temple staff averaged 4.57, and cleanness concern of the temple averaged 4.52, well-behaved performance during the temple visit averaged 4.47 respectively.

Keywords: Deportment, Traveler

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Authors: Cheng-Hong Yang, Yu-Huei Cheng, Hsueh-Wei Chang, Li-Yeh Chuang

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Before performing polymerase chain reactions (PCR), a feasible primer set is required. Many primer design methods have been proposed for design a feasible primer set. However, the majority of these methods require a relatively long time to obtain an optimal solution since large quantities of template DNA need to be analyzed. Furthermore, the designed primer sets usually do not provide a specific PCR product. In recent years, evolutionary computation has been applied to PCR primer design and yielded promising results. In this paper, a particle swarm optimization (PSO) algorithm is proposed to solve primer design problems associated with providing a specific product for PCR experiments. A test set of the gene CYP1A1, associated with a heightened lung cancer risk was analyzed and the comparison of accuracy and running time with the genetic algorithm (GA) and memetic algorithm (MA) was performed. A comparison of results indicated that the proposed PSO method for primer design finds optimal or near-optimal primer sets and effective PCR products in a relatively short time.

Keywords: polymerase chain reaction (PCR), primer design, evolutionary computation, particle swarm optimization (PSO).

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Authors: M. Sedighizadeh, A. Rezazadeh

Abstract:

Movable power sources of proton exchange membrane fuel cells (PEMFC) are the important research done in the current fuel cells (FC) field. The PEMFC system control influences the cell performance greatly and it is a control system for industrial complex problems, due to the imprecision, uncertainty and partial truth and intrinsic nonlinear characteristics of PEMFCs. In this paper an adaptive PI control strategy using neural network adaptive Morlet wavelet for control is proposed. It is based on a single layer feed forward neural networks with hidden nodes of adaptive morlet wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. The proposed method is applied to a typical 1 KW PEMFC system and the results show the proposed method has more accuracy against to MLP (Multi Layer Perceptron) method.

Keywords: Adaptive Control, Morlet Wavelets, PEMFC.

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Authors: Julien Romieux, Fabio Verdicchio

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Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: Cooperative signal processing, power management, signal representation, signal approximation, wireless sensor networks.

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Authors: Khampheth Bounnady, Boontee Kruatrachue, Somkiat Wangsiripitak

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This paper proposed high level feature for online Lao handwritten recognition. This feature must be high level enough so that the feature is not change when characters are written by different persons at different speed and different proportion (shorter or longer stroke, head, tail, loop, curve). In this high level feature, a character is divided in to sequence of curve segments where a segment start where curve reverse rotation (counter clockwise and clockwise). In each segment, following features are gathered cumulative change in direction of curve (- for clockwise), cumulative curve length, cumulative length of left to right, right to left, top to bottom and bottom to top ( cumulative change in X and Y axis of segment). This feature is simple yet robust for high accuracy recognition. The feature can be gather from parsing the original time sampling sequence X, Y point of the pen location without re-sampling. We also experiment on other segmentation point such as the maximum curvature point which was widely used by other researcher. Experiments results show that the recognition rates are at 94.62% in comparing to using maximum curvature point 75.07%. This is due to a lot of variations of turning points in handwritten.

Keywords: Handwritten feature, chain code, Lao handwritten recognition.

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Authors: Bobby Anak John, Zamri Noranai, Md. Norrizam Mohmad Jaat, Hamidon Salleh, Mohammad Zainal Md Yusof

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Air conditioning is mainly use as human comfort cooling medium. It use more in high temperatures are country such as Malaysia. Proper estimation of cooling load will archive ideal temperature. Without proper estimation can lead to over estimation or under estimation. The ideal temperature should be comfort enough. This study is to develop a program to calculate an ideal cooling load demand, which is match with heat gain. Through this study, it is easy to calculate cooling load estimation. Objective of this study are to develop user-friendly and easy excess cooling load program. This is to insure the cooling load can be estimate by any of the individual rather than them using rule-of-thumb. Developed software is carryout by using Matlab-GUI. These developments are only valid for common building in Malaysia only. An office building was select as case study to verify the applicable and accuracy of develop software. In conclusion, the main objective has successfully where developed software is user friendly and easily to estimate cooling load demand.

Keywords: Cooling Load, Heat Gain, Building and GUI.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

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Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.

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Authors: Heval Serhat Uluk, Sam M. Dakka, Kuldeep Singh, Richard Jefferson-Loveday

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This paper will focus on the suitability of a trapped vortex combustor as a candidate for gas turbine combustor objectives to minimize pressure drop across the combustor and investigate aerodynamic performance. Non-reacting simulation of axisymmetric cavity trapped vortex combustors was run to investigate the pressure drop for various cavity aspect ratios of 0.3, 0.6 and 1 and for air mass flow rates of 14 m/s, 28 m/s and 42 m/s. A numerical study of an axisymmetric trapped vortex combustor was carried out by using two-dimensional and three-dimensional computational domains. A comparison study was conducted between Reynolds Averaged Navier Stokes (RANS) k-ε Realizable with enhanced wall treatment and RANS k-ω Shear Stress Transport (SST) models to find the most suitable turbulence model. It was found that the k-ω SST model gives relatively close results to experimental outcomes. The numerical results were validated and showed good agreement with the experimental data. Pressure drop rises with increasing air mass flow rate, and the lowest pressure drop was observed at 0.6 cavity aspect ratio for all air mass flow rates tested, which agrees with the experimental outcome. A mixing enhancement study showed that 30-degree angle air injectors provide improved fuel-air mixing.

Keywords: Aerodynamic, Computational Fluid Dynamics, Propulsion, Trapped Vortex Combustor.

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Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

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Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: Watershed simulation, WetSpa, stream flow, flood prediction.

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Authors: Yiqin Lin, Wenbo Li

Abstract:

Dimensionality reduction and feature extraction are of crucial importance for achieving high efficiency in manipulating the high dimensional data. Two-dimensional discriminant locality preserving projection (2D-DLPP) and two-dimensional discriminant supervised LPP (2D-DSLPP) are two effective two-dimensional projection methods for dimensionality reduction and feature extraction of face image matrices. Since 2D-DLPP and 2D-DSLPP preserve the local structure information of the original data and exploit the discriminant information, they usually have good recognition performance. However, 2D-DLPP and 2D-DSLPP only employ single-sided projection, and thus the generated low dimensional data matrices have still many features. In this paper, by combining the discriminant supervised LPP with the bidirectional projection, we propose the bidirectional discriminant supervised LPP (BDSLPP). The left and right projection matrices for BDSLPP can be computed iteratively. Experimental results show that the proposed BDSLPP achieves higher recognition accuracy than 2D-DLPP, 2D-DSLPP, and bidirectional discriminant LPP (BDLPP).

Keywords: Face recognition, dimension reduction, locality preserving projection, discriminant information, bidirectional projection.

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Authors: A. Pereira, S. Haffner, L. V. Gasperin

Abstract:

This paper presents two simplified models to determine nodal voltages in power distribution networks. These models allow estimating the impact of the installation of reactive power compensations equipments like fixed or switched capacitor banks. The procedure used to develop the models is similar to the procedure used to develop linear power flow models of transmission lines, which have been widely used in optimization problems of operation planning and system expansion. The steady state non-linear load flow equations are approximated by linear equations relating the voltage amplitude and currents. The approximations of the linear equations are based on the high relationship between line resistance and line reactance (ratio R/X), which is valid for power distribution networks. The performance and accuracy of the models are evaluated through comparisons with the exact results obtained from the solution of the load flow using two test networks: a hypothetical network with 23 nodes and a real network with 217 nodes.

Keywords: Distribution network models, distribution systems, optimization, power system planning.

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Authors: Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh

Abstract:

Although face recognition seems as an easy task for human, automatic face recognition is a much more challenging task due to variations in time, illumination and pose. In this paper, the influence of time-lapse on visible and thermal images is examined. Orthogonal moment invariants are used as a feature extractor to analyze the effect of time-lapse on thermal and visible images and the results are compared with conventional Principal Component Analysis (PCA). A new triangle square ratio criterion is employed instead of Euclidean distance to enhance the performance of nearest neighbor classifier. The results of this study indicate that the ideal feature vectors can be represented with high discrimination power due to the global characteristic of orthogonal moment invariants. Moreover, the effect of time-lapse has been decreasing and enhancing the accuracy of face recognition considerably in comparison with PCA. Furthermore, our experimental results based on moment invariant and triangle square ratio criterion show that the proposed approach achieves on average 13.6% higher in recognition rate than PCA.

Keywords: Infrared Face recognition, Time-lapse, Zernike moment invariants

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