Search results for: industrial machine

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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Authors: A. P. Kulkarni, P. Randive, A. R. Mache

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In today-s era of plasma and laser cutting, machines using oxy-acetylene flame are also meritorious due to their simplicity and cost effectiveness. The objective to devise a Computer controlled Oxy-Fuel profile cutting machine arose from the increasing demand for metal cutting with respect to edge quality, circularity and lesser formation of redeposit material. The System has an 8 bit micro controller based embedded system, which assures stipulated time response. A new window based Application software was devised which takes a standard CAD file .DXF as input and converts it into numerical data required for the controller. It uses VB6 as a front end whereas MS-ACCESS and AutoCAD as back end. The system is designed around AT89C51RD2, powerful 8 bit, ISP micro controller from Atmel and is optimized to achieve cost effectiveness and also maintains the required accuracy and reliability for complex shapes. The backbone of the system is a cleverly designed mechanical assembly along with the embedded system resulting in an accuracy of about 10 microns while maintaining perfect linearity in the cut. This results in substantial increase in productivity. The observed results also indicate reduced inter laminar spacing of pearlite with an increase in the hardness of the edge region.

Keywords: Computer-Control, Profile, Oxy-Fuel.

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Authors: G. Renuka Devi

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This paper presents design and analysis of fault tolerate feature of n-phase induction motor drive. The n-phase induction motor (more than 3-phases) has a number of advantages over conventional 3-phase induction motor, it has low torque pulsation with increased torque density, more fault tolerant feature, low current ripple with increased efficiency. When increasing the number of phases, it has reduced current per phase without increasing per phase voltage, resulting in an increase in the total power rating of n-phase motors in the same volume machine. In this paper, the theory of operation of a multi-phase induction motor is discussed. The detailed study of d-q modeling of n-phase induction motors is elaborated. The d-q model of n-phase (5, 6, 7, 9 and 12) induction motors is developed in a MATLAB/Simulink environment. The steady state and dynamic performance of the multi-phase induction motor is studied under varying load conditions. Comparison of 5-phase induction is presented under normal and fault conditions.

Keywords: d-q model, dynamic Response, fault tolerant feature, matlab/simulink, multi-phase induction motor, transient response.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: B. Dora Arul Selvi, .N. Kamaraj

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Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.

Keywords: Fuzzy Support Vector Machine (FSVM), Incremental Cost, Preventive Control, Transient stability

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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Average temperatures worldwide are expected to continue to rise. At the same time, major cities in developing countries are becoming increasingly populated and polluted. Governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of a model, which is able to estimate the occupant exposure to extreme temperatures and high air pollution within domestic buildings. Building physics simulations were performed using the EnergyPlus building physics software. An accurate metamodel is then formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) have been compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: Neural Networks, Radial Basis Functions, Metamodelling, Python machine learning libraries.

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Authors: Wan Hasrulnizzam Wan Mahmood, Mohd Razali Muhamad, Nurulain Mat Tahar

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This paper is prepared to provide a review of how an automotive manufacturer, ISUZU HICOM Malaysia Co. Ltd. sustained the supply chain management after business process reengineering in 2007. One of the authors is currently undergoing industrial attachment and has spent almost 6 months researching in the production and operation management system of the company. This study was carried out as part of the tasks in the attachment program. The result shows that delivery lateness and outsourcing are the main barriers that affected productivity. From the gap analysis, the authors found that new business process operation had improved suppliers delivery performance.

Keywords: Supply Chain Management, Business Process Re- Engineering, Delivery, Outsourcing, Automotive Manufacturer.

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Authors: Amir-Massoud Bidgoli, Mehdi Naseri Parsa

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In this paper a combined feature selection method is proposed which takes advantages of sample domain filtering, resampling and feature subset evaluation methods to reduce dimensions of huge datasets and select reliable features. This method utilizes both feature space and sample domain to improve the process of feature selection and uses a combination of Chi squared with Consistency attribute evaluation methods to seek reliable features. This method consists of two phases. The first phase filters and resamples the sample domain and the second phase adopts a hybrid procedure to find the optimal feature space by applying Chi squared, Consistency subset evaluation methods and genetic search. Experiments on various sized datasets from UCI Repository of Machine Learning databases show that the performance of five classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First Decision Tree and JRIP) improves simultaneously and the classification error for these classifiers decreases considerably. The experiments also show that this method outperforms other feature selection methods.

Keywords: feature selection, resampling, reliable features, Consistency Subset Evaluation.

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Authors: Elise Stephanie Mvodo Meyo, Dapeng Liang

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Recently, Cassava has been the driving force of many developing countries- economic progress. To attain this level, prerequisites were put in place enabling cassava sector to become an industrial and a highly competitive crop. Cameroon can achieve the same results. Moreover, it can upgrade the living conditions of both rural and urban dwellers and stimulate the development of the whole economy. Achieving this outcome calls for agricultural policy reforms. The adoption and implementation of adequate policies go along with efficient strategies. To choose effective strategies, an indepth investigation of the sector-s problems is highly recommended. This paper uses gap analysis method to evaluate cassava sector in Cameroon. It studies the present situation (where it is now), interrogates the future (where it should be) and finally proposes solutions to fill the gap.

Keywords: Cameroon, cassava sector, drivers of agricultural growth, gap analysis.

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Authors: Jana Petrů, Tomáš Zlámal, Robert Čep, Lenka Čepová

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This article is focused on the thermal spray coating machining issue. Those are irreplaceable in many areas of nowadays industrial branches such as aerospace industry, mostly thanks to their excellent qualities in production and also in renovation of machinery parts. The principals of thermal spraying and elementary diversification are described in introduction. Plasma coating method of composite materials – cermets – is described more thoroughly. The second part describes thermal spray coating machining and grinding in detail. This part contains suggestion of appropriate grinding tool and assessment of cutting conditions used for grinding a given part. Conclusion describes a problem which occurred while grinding a cermet thermal spray coating with a specially designed grindstone and a way to solve this problem.

Keywords: Coating, aerospace, plasma, grinding.

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Authors: Samat Turganbekov, Zhakan Mol

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This article examines the emergence and development of the Kazakhstan species of humanism. The biggest challenge for Kazakhstan in terms of humanism is connected with advocating human values in parallel to promoting national interests; preserving the continuity of traditions in various spheres of life, business and culture. This should be a common goal for the entire society, the main direction for a national intelligence, and a platform for the state policy. An idea worth considering is a formation of national humanist tradition model; the challenges are adapting people to live in the context of new industrial and innovative economic conditions, keeping the balance during intensive economic development of the country, and ensuring social harmony in the society.

Keywords: Kazakh humanism, humanist tradition, national culture, spiritual and moral priority, national interest.

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Authors: S. Bangphan, P. Bangphan, T. Boonkang

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Quality control helps industries in improvements of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that turning practice in bringing a department of industrial engineering process under control. In this research, the process control of a turning manufactured at workshops machines. The varying measurements have been recorded for a number of samples of a rice polished cylinder obtained from a number of trials with the turning practice. SPC technique has been adopted by the process is finally brought under control and process capability is improved.

Keywords: Rice polished cylinder, statistical process control, control charts, process capability.

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Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

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Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the workpiece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: Dexel, process stability, material removal, milling.

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Authors: M. Samaka

Abstract:

This paper presents the development of a software application for Off-line robot task programming and simulation. Such application is designed to assist in robot task planning and to direct manipulator motion on sensor based programmed motion. The concept of the designed programming application is to use the power of the knowledge base for task accumulation. In support of the programming means, an interactive graphical simulation for manipulator kinematics was also developed and integrated into the application as the complimentary factor to the robot programming media. The simulation provides the designer with useful, inexpensive, off-line tools for retain and testing robotics work cells and automated assembly lines for various industrial applications.

Keywords: Robot programming, task-level programming, robot languages, robot simulation, robotics software.

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Authors: Hiraku Nunomiya, Takuo Ichiju, Yoshiyuki Higuchi

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In the management of industrial waste, conversion from the use of paper invoices to electronic forms is currently under way in developed countries. Difficulties in such computerization include the lack of synchronization between the actual goods and the corresponding data managed by the server. Consequently, a system which utilizes the incorporation of a QR code in connection with the waste material has been developed. The code is read at each stage, from discharge until disposal, and progress at each stage can be easily reported. This system can be linked with Japanese public digital authentication service of waste, taking advantage of its good points, and can be used to submit reports to the regulatory authorities. Its usefulness was confirmed by a verification test, and put into actual practice.

Keywords: Infectious Waste, Electronic Manifest, Real Time Management, QR code.

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Authors: Ghaffar Tari

Abstract:

The aim of this paper is to investigate the effect of mean size of industry on survival of new firms in East-Azarbaijan province through 1981-2006 using hazard function. So the effect of two variables including mean employment of industry and mean capital of industry are investigated on firm's survival. The Industry & Mine Ministry database has used for data gathering and the data are analyzed using the semi-parametric cox regression model. The results of this study shows that there is a meaningful negative relationship between mean capital of industry and firm's survival, but the mean employment of industry has no meaningful effect on survival of new firms.

Keywords: Firm's Survival, Hazard Function, Mean Capital of Industry, Mean Employment of Industry.

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Authors: Ambra Giovannelli, Erika Maria Archilei

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The amount of the electrical power required by refrigeration systems is relevant worldwide. It is evaluated in the order of 15% of the total electricity production taking refrigeration and air-conditioning into consideration. For this reason, in the last years several energy saving techniques have been proposed to reduce the power demand of such plants. The paper deals with the development of an innovative internal recovery system for cryogenic cooling plants. Such a system consists in a Compressor-Expander Group (CEG) designed on the basis of the automotive turbocharging technology. In particular, the paper is focused on the design of the expander, the critical component of the CEG system. Due to the low volumetric flow entering the expander and the high expansion ratio, a commercial turbocharger expander wheel was strongly modified. It was equipped with a transonic nozzle, designed to have a radially inflow full admission. To verify the performance of such a machine and suggest improvements, two different set of nozzles have been designed and modelled by means of the commercial Ansys-CFX software. steady-state 3D CFD simulations of the second-generation prototype are presented and compared with the initial ones.

Keywords: Energy saving, organic fluids, radial turbine, refrigeration plant, vapor compression systems.

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Authors: Yong Ho Shim, Ki Youn Kim, Ji Yeon Cho, Jin Kyung Park, Bong Gyou Lee

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This study considers priorities of primary goals to increase policy efficiency of Green ICT. Recently several studies have been published that address how IT is linked to climate change. However, most of the previous studies are limited to Green ICT industrial statute and policy directions. This paper present Green ICT policy making processes systematically. As a result of the analysis of Korean Green ICT policy, the following emerged as important to accomplish for Green ICT policy: eco-friendliness, technology evolution, economic efficiency, energy efficiency, and stable supply of energy. This is an initial study analyzing Green ICT policy, which provides an academic framework that can be used a guideline to establish Green ICT policy.

Keywords: AHP(Analytic Hierarchy Process), Case Study, Green ICT, Policy Priority

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: Distillation, machine learning, neural networks, quantization.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Bachir Chemani, Rachid Halfaoui

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The aim of study was to evaluate pressure distribution characteristics of the elastic textile bandages using two instrumental techniques: a prototype Instrument and a load Transference. The prototype instrument which simulates shape of real leg has pressure sensors which measure bandage pressure. Using this instrument, the results show that elastic textile bandages presents different pressure distribution characteristics and none produces a uniform distribution around lower limb.

The load transference test procedure is used to determine whether a relationship exists between elastic textile bandage structure and pressure distribution characteristics. The test procedure assesses degree of load, directly transferred through a textile when loads series are applied to bandaging surface. A range of weave fabrics was produced using needle weaving machine and a sewing technique. A textile bandage was developed with optimal characteristics far superior pressure distribution than other bandages. From results, we find that theoretical pressure is not consistent exactly with practical pressure. It is important in this study to make a practical application for specialized nurses in order to verify the results and draw useful conclusions for predicting the use of this type of elastic band.

Keywords: Textile, cotton, pressure, venous ulcers, elastic.

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Authors: Fekkak Bouazza, Menaa Mohamed, Loukriz Abdelhamid, Krim Mohamed L.

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This paper presents modeling and control strategy for the grid connected wind turbine system based on Permanent Magnet Synchronous Generator (PMSG). The considered system is based on back-to-back converter topology. The Grid Side Converter (GSC) achieves the DC bus voltage control and unity power factor. The Machine Side Converter (MSC) assures the PMSG speed control. The PMSG is used as a variable speed generator and connected directly to the turbine without gearbox. The pitch angle control is not either considered in this study. Further, Optimal Tip Speed Ratio (OTSR) based MPPT control strategy is used to ensure the most energy efficiency whatever the wind speed variations. A filter (L) is put between the GSC and the grid to reduce current ripple and to improve the injected power quality. The proposed grid connected wind system is built under MATLAB/Simulink environment. The simulation results show the feasibility of the proposed topology and performance of its control strategies.

Keywords: Wind, grid, PMSG, MPPT, OTSR.

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Authors: S.H. Khoon, G.I. Issabayeva, L.W. Lee

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Air quality in Setapak district of Kuala Lumpur was studied by analysing the rainwater chemical composition using ion chromatography method. Twelve sampling sites were selected and 120 rainwater samples were collected in the period of 10 weeks. The results of this study were compared to the earlier published data and the evaluation showed that the NO3 - ion concentration increased from 0.41 to 3.32 ppm, while SO4 2- ion concentration increased from 0.39 to 3.26 ppm over the past two decades that is mostly due to rapid urban development of the city. However, it was found that the chemical composition for both residential and industrial areas does not have significant difference. Most of the rainwater samples showed alkaline pH (pH > 5.6). The possible factors for such alkaline pH in rainwater samples are assumed to be the marine sources, biomass burning and alkaline character of soil particles.

Keywords: acid deposition; atmospheric pollution; deposition fluxes; trajectories

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Authors: Yi-Ping Lo, Shi-Yao Wei, Chih-Chun Ma

Abstract:

Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.

Keywords: Machine learning, user interface, user experience, Internet of things, health promotion.

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Authors: M. Elhaj, M. Almrabet, M. Rgeai, I. Ehtiwesh

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A Comparison and evaluation of the different condition monitoring (CM) techniques was applied experimentally on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous Angular Speed (IAS), for the detection and diagnosis of valve faults in a two - stage reciprocating compressor for a programme of condition monitoring which can successfully detect and diagnose a fault in machine. Leakage in the valve plate was introduced experimentally into a two-stage reciprocating compressor. The effect of the faults on compressor performance was monitored and the differences with the normal, healthy performance noted as a fault signature been used for the detection and diagnosis of faults. The paper concludes with what is considered to be a unique approach to condition monitoring. First, each of the two most useful techniques is used to produce a Truth Table which details the circumstances in which each method can be used to detect and diagnose a fault. The two Truth Tables are then combined into a single Decision Table to provide a unique and reliable method of detection and diagnosis of each of the individual faults introduced into the compressor. This gives accurate diagnosis of compressor faults.

Keywords: Condition Monitoring, Dynamic Pressure, Instantaneous Angular Speed, Reciprocating Compressor.

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Authors: A. Ghaffari, A. S. Mostafavi

Abstract:

Complex assemblies of interacting proteins carry out most of the interesting jobs in a cell, such as metabolism, DNA synthesis, mitosis and cell division. These physiological properties play out as a subtle molecular dance, choreographed by underlying regulatory networks that control the activities of cyclin-dependent kinases (CDK). The network can be modeled by a set of nonlinear differential equations and its behavior predicted by numerical simulation. In this paper, an innovative approach has been proposed that uses genetic algorithms to mine a set of behavior data output by a biological system in order to determine the kinetic parameters of the system. In our approach, the machine learning method is integrated with the framework of existent biological information in a wiring diagram so that its findings are expressed in a form of system dynamic behavior. By numerical simulations it has been illustrated that the model is consistent with experiments and successfully shown that such application of genetic algorithms will highly improve the performance of mathematical model of the cell division cycle to simulate such a complicated bio-system.

Keywords: Cell cycle, Cyclin-dependent kinase, Fission yeast, Genetic algorithms, Mathematical modeling, Wiring diagram

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy.

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Authors: Hannu Nurmesniemi, Mikko Mäkelä, Risto Pöykiö, Olli Dahl

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Due to the low heavy metal concentrations, the bottom ash from a 32 MW municipal district heating plant was determined to be a potential forest fertilizer as such. However, additional Ca would be needed, because its Ca concentration of 1.9- % (d.w.) was lower than the statutory Finnish minimum limit value of 6.0-% (d.w.) for Ca in forest fertilizer. Due to the elevated As concentration (53.0 mg/kg; d.w.) in the fly ash from the 32 MW municipal district heating plant, and Cr concentration (620 mg/kg; d.w.) in the ash fraction (i.e. mixture of the bottom ash and fly ash) from the 6 MW municipal district heating plant, which exceed the limit values of 30 mg/kg (d.w.) and 300 mg/kg (d.w.) for As and Cr, respectively, these residues are not suitable as forest fertilizers. Although these ash fractions cannot be used as a forest fertilizer as such, they can be used for the landscaping of landfills or in industrial and other areas that are closed to the public. However, an environmental permit is then needed.

Keywords: Ash, fertilizer, peat, forest residue, waste

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Authors: P. Chaijak, M. Lertworapreecha, C. Sukkasem

Abstract:

A huge of dark color palm oil mill effluent (POME) cannot pass the discharge standard. It has been identified as the major contributor to the pollution load into ground water. Here, lignin-degrading yeast isolated from a termite nest was tested to treat the POME. Its lignin-degrading and decolorizing ability was determined. The result illustrated that Galactomyces sp. was successfully grown in POME. The decolorizing test demonstrated that 40% of Galactomyces sp. could reduce the color of POME (50% v/v) about 74-75% in 5 days without nutrient supplement. The result suggested that G. reessii has a potential to apply for decolorizing the dark wastewater like POME and other industrial wastewaters.

Keywords: Decolorization, palm oil mill effluent, ligninolytic enzyme, yeast, termite.

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