Search results for: Reliability prediction.

Authors: Mehdi Moayyedian, Kazem Abhary, Romeo Marian

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This paper presents an evaluation of a plastic defect in injection molding before it occurs in the process; it is known as the short shot defect. The evaluation of different parameters which affect the possibility of short shot defect is the aim of this paper. The analysis of short shot possibility is conducted via SolidWorks Plastics and Taguchi method to determine the most significant parameters. Finite Element Method (FEM) is employed to analyze two circular flat polypropylene plates of 1 mm thickness. Filling time, part cooling time, pressure holding time, melt temperature and gate type are chosen as process and geometric parameters, respectively. A methodology is presented herein to predict the possibility of the short-shot occurrence. The analysis determined melt temperature is the most influential parameter affecting the possibility of short shot defect with a contribution of 74.25%, and filling time with a contribution of 22%, followed by gate type with a contribution of 3.69%. It was also determined the optimum level of each parameter leading to a reduction in the possibility of short shot are gate type at level 1, filling time at level 3 and melt temperature at level 3. Finally, the most significant parameters affecting the possibility of short shot were determined to be melt temperature, filling time, and gate type.

Keywords: Injection molding, plastic defects, short shot, Taguchi method.

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: Classifier ensemble, breast cancer survivability, data mining, SEER.

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression.

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Authors: Yuki Hara

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Entrepreneurs are welcomed to the landscaping industry, conserving practically and theoretically biological diversity in landscaping construction, although there are limited reports on corporative trials making a market with a new logistics system of native plants (NP) between landscaping companies and nurserymen. This paper explores the entrepreneurial process of a landscaping company, “5byMidori” for NP marketing. This paper employs a case study design. Data are collected in interviews with the manager and designer of 5byMidori, 2 scientists, 1 organization, and 18 nurserymen, fieldworks at two nurseries, observations of marketing activities in three years, and texts from published documents about the business concept and marketing strategy with NP. These data are analyzed by qualitative methods. The results show that NP is suitable for the vision of 5byMidori improving urban desertified environment with closer urban-rural linkage. Professional landscaping team changes a forestry organization into NP producers conserving a large nursery of a mountain. Multifaceted PR based on the entrepreneurial context and personal background of a landscaping venture can foster team members' businesses and help customers and users to understand the biodiversity value of the product. Wider partnerships with existing nurserymen at other sites in many regions need socio-economic incentives and environmental reliability. In conclusion, the entrepreneurial marketing of a landscaping company needs to add more meanings and a variety of merits in terms of ecosystem services, as NP tends to be in academic definition and independent from the cultures like nurseryman and forestry.

Keywords: Biological diversity, landscaping industry, marketing, native plants.

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Authors: Noor Ayuni Che Zakaria, Takashi Komeda, Cheng Yee Low, Kaoru Inoue, Fazah Akhtar Hanapiah

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Previous studies have shown that there are arguments regarding the reliability and validity of the Ashworth and Modified Ashworth Scale towards evaluating patients diagnosed with upper limb disorders. These evaluations depended on the raters’ experiences. This initiated us to develop an upper limb disorder part-task trainer that is able to simulate consistent upper limb disorders, such as spasticity and rigidity signs, based on the Modified Ashworth Scale to improve the variability occurring between raters and intra-raters themselves. By providing consistent signs, novice therapists would be able to increase training frequency and exposure towards various levels of signs. A total of 22 physiotherapists and occupational therapists participated in the study. The majority of the therapists agreed that with current therapy education, they still face problems with inter-raters and intra-raters variability (strongly agree 54%; n = 12/22, agree 27%; n = 6/22) in evaluating patients’ conditions. The therapists strongly agreed (72%; n = 16/22) that therapy trainees needed to increase their frequency of training; therefore believe that our initiative to develop an upper limb disorder training tool will help in improving the clinical education field (strongly agree and agree 63%; n = 14/22).

Keywords: Upper limb disorders, Clinical education tool, Inter/intra-raters variability, Spasticity, Modified Ashworth Scale.

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Authors: Hsing-Kuo Pao, John Case

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Biological sequences from different species are called or-thologs if they evolved from a sequence of a common ancestor species and they have the same biological function. Approximations of Kolmogorov complexity or entropy of biological sequences are already well known to be useful in extracting similarity information between such sequences -in the interest, for example, of ortholog detection. As is well known, the exact Kolmogorov complexity is not algorithmically computable. In prac-tice one can approximate it by computable compression methods. How-ever, such compression methods do not provide a good approximation to Kolmogorov complexity for short sequences. Herein is suggested a new ap-proach to overcome the problem that compression approximations may notwork well on short sequences. This approach is inspired by new, conditional computations of Kolmogorov entropy. A main contribution of the empir-ical work described shows the new set of entropy-based machine learning attributes provides good separation between positive (ortholog) and nega-tive (non-ortholog) data - better than with good, previously known alter-natives (which do not employ some means to handle short sequences well).Also empirically compared are the new entropy based attribute set and a number of other, more standard similarity attributes sets commonly used in genomic analysis. The various similarity attributes are evaluated by cross validation, through boosted decision tree induction C5.0, and by Receiver Operating Characteristic (ROC) analysis. The results point to the conclu-sion: the new, entropy based attribute set by itself is not the one giving the best prediction; however, it is the best attribute set for use in improving the other, standard attribute sets when conjoined with them.

Keywords: compression, decision tree, entropy, ortholog, ROC.

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Authors: K. V. Pagalthivarthi, R. J. Visintainer

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Two-dimensional Eulerian (volume-averaged) continuity and momentum equations governing multi-size slurry flow through pump casings are solved by applying a penalty finite element formulation. The computational strategy validated for multi-phase flow through rectangular channels is adapted to the present study.   The flow fields of the carrier, mixture and each solids species, and the concentration field of each species are determined sequentially in an iterative manner. The eddy viscosity field computed using Spalart-Allmaras model for the pure carrier phase is modified for the presence of particles. Streamline upwind Petrov-Galerkin formulation is used for all the momentum equations for the carrier, mixture and each solids species and the concentration field for each species. After ensuring mesh-independence of solutions, results of multi-size particulate flow simulation are presented to bring out the effect of bulk flow rate, average inlet concentration, and inlet particle size distribution. Mono-size computations using (1) the concentration-weighted mean diameter of the slurry and (2) the D50 size of the slurry are also presented for comparison with multi-size results.

Keywords: Eulerian-Eulerian model, Multi-size particulate flow, Penalty finite elements, Pump casing, Spalart-Allmaras.

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Authors: R. S. Quintal, J. L. Tesch Santos, M. D. Davis, E. C. de Santana, M. de F. Bandeira dos Santos

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The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government.

Keywords: Process Management, Management Control, Business Intelligence.

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Authors: Brian H. Marsh

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Nitrogen fertilizer is the most used and often the most mismanaged nutrient input. Nitrogen management has tremendous implications on crop productivity, quality and environmental stewardship. Sufficient nitrogen is needed to optimum yield and quality. Soil and in-season plant tissue testing for nitrogen status are a time consuming and expensive process. Real time sensing of plant nitrogen status can be a useful tool in managing nitrogen inputs. The objectives of this project were to assess the reliability of remotely sensed non-destructive plant nitrogen measurements compared to wet chemistry data from sampled plant tissue, develop in-season nitrogen recommendations based on remotely sensed data for improved nitrogen use efficiency and assess the potential for determining yield and quality from remotely sensed data. Very good correlations were observed between early-season remotely sensed crop nitrogen status and plant nitrogen concentrations and subsequent in-season fertilizer recommendations. The transmittance/absorbance type meters gave the most accurate readings. Early in-season fertilizer recommendation would be to apply 40 kg nitrogen per hectare plus 15 kg nitrogen per hectare for each unit difference measured with the SPAD meter between the crop and reference area or 25 kg plus 13 kg per hectare for each unit difference measured with the CCM 200. Once the crop was sufficiently fertilized meter readings became inconclusive and were of no benefit for determining nitrogen status, silage yield and quality and grain yield and protein.

Keywords: Wheat, nitrogen fertilization, chlorophyll meter.

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Authors: G. H. Senussi

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The present work is concerned with the effect of turning process parameters (cutting speed, feed rate, and depth of cut) and distance from the center of work piece as input variables on the chip micro-hardness as response or output. Three experiments were conducted; they were used to investigate the chip micro-hardness behavior at diameter of work piece for 30[mm], 40[mm], and 50[mm]. Response surface methodology (R.S.M) is used to determine and present the cause and effect of the relationship between true mean response and input control variables influencing the response as a two or three dimensional hyper surface. R.S.M has been used for designing a three factor with five level central composite rotatable factors design in order to construct statistical models capable of accurate prediction of responses. The results obtained showed that the application of R.S.M can predict the effect of machining parameters on chip micro-hardness. The five level factorial designs can be employed easily for developing statistical models to predict chip micro-hardness by controllable machining parameters. Results obtained showed that the combined effect of cutting speed at it?s lower level, feed rate and depth of cut at their higher values, and larger work piece diameter can result increasing chi micro-hardness.

Keywords: Machining Parameters, Chip Micro-Hardness, CNCMachining, 304-Austenic Stainless Steel.

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

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A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: Transportation network, critical path, connectivity reliability, network model, Neo4J application, optimal path, critical path, edge betweenness centrality index, node betweenness centrality index, Yen’s k-shortest paths.

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Authors: M. Arefi, A.M. Amini, K. Fallahi

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One of the basic concepts in marketing is the concept of meeting customers- needs. Since customer satisfaction is essential for lasting survival and development of a business, screening and observing customer satisfaction and recognizing its underlying factors must be one of the key activities of every business. The purpose of this study is to recognize the drivers that effect customer satisfaction in a business-to-business situation in order to improve marketing activities. We conducted a survey in which 93 business customers of a manufacturer of Diesel Generator in Iran participated and they talked about their ideas and satisfaction of supplier-s services related to its products. We developed the measures for drivers of satisfaction first by as investigative research (by means of feedback from executives and customers of sponsoring firm). Then based on these measures, we created a mail survey, and asked the respondents to explain their opinion about the sponsoring firm which was a supplier of diesel generator and similar products. Furthermore, the survey required the participants to mention their functional areas and their company features. In Conclusion we found that there are three drivers for customer satisfaction, which are reliability, information about product, and commercial features. Buyers/users from different functional areas attribute different degree of importance to the last two drivers. For instance, people from buying and management areas believe that commercial features are more important than information about products. But people in engineering, maintenance and production areas believe that having information about products is more important than commercial aspects. Marketing experts should consider the attribute of customers regarding information about the product and commercial features to improve market share.

Keywords: B2B, Customer satisfaction, Commercial, Industry.

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Authors: B. Arzhanov, A. Correia, P. Delgado, J. Meireles

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Due to the higher power loss levels in electronic components, the thermal design of PCBs (Printed Circuit Boards) of an assembled device becomes one of the most important quality factors in electronics. Nonetheless, some of leading causes of the microelectronic component failures are due to higher temperatures, the leakages or thermal-mechanical stress, which is a concern, is the reliability of microelectronic packages. This article presents an experimental approach to measure the junction temperature of exposed pad packages. The implemented solution is in a prototype phase, using a temperature-sensitive parameter (TSP) to measure temperature directly on the die, validating the numeric results provided by the Mechanical APDL (Ansys Parametric Design Language) under same conditions. The physical device-under-test is composed by a Thermal Test Chip (TTC-1002) and assembly in a QFN cavity, soldered to a test-board according to JEDEC Standards. Monitoring the voltage drop across a forward-biased diode, is an indirectly method but accurate to obtain the junction temperature of QFN component with an applied power range between 0,3W to 1.5W. The temperature distributions on the PCB test-board and QFN cavity surface were monitored by an infra-red thermal camera (Goby-384) controlled and images processed by the Xeneth software. The article provides a set-up to monitorize in real-time the junction temperature of ICs, namely devices with the exposed pad package (i.e. QFN). Presenting the PCB layout parameters that the designer should use to improve thermal performance, and evaluate the impact of voids in solder interface in the device junction temperature.

Keywords: Quad Flat No-Lead packages, exposed pads, junction temperature, thermal management, measurements.

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Authors: Shang-En Yu

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Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.

Keywords: Heterogeneity, residential mortgage loans, foreclosure.

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Authors: Mohamed Omar

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Superelastic Shape Memory Alloy (SMA) is accepted when it used as connection in steel structures. The seismic behaviour of steel frames with SMA is being assessed in this study. Three eightstorey steel frames with different SMA systems are suggested, the first one of which is braced with diagonal bracing system, the second one is braced with nee bracing system while the last one is which the SMA is used as connection at the plastic hinge regions of beams. Nonlinear time history analyses of steel frames with SMA subjected to two different ground motion records have been performed using Seismostruct software. To evaluate the efficiency of suggested systems, the dynamic responses of the frames were compared. From the comparison results, it can be concluded that using SMA element is an effective way to improve the dynamic response of structures subjected to earthquake excitations. Implementing the SMA braces can lead to a reduction in residual roof displacement. The shape memory alloy is effective in reducing the maximum displacement at the frame top and it provides a large elastic deformation range. SMA connections are very effective in dissipating energy and reducing the total input energy of the whole frame under severe seismic ground motion. Using of the SMA connection system is more effective in controlling the reaction forces at the base frame than other bracing systems. Using SMA as bracing is more effective in reducing the displacements. The efficiency of SMA is dependant on the input wave motions and the construction system as well.

Keywords: Finite element analysis, seismic response, shapesmemory alloy, steel frame, superelasticity

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Authors: Hee-Chang Lim

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The study aims to understand the surface pressure distribution around the bodies such as the suction pressure in the leading edge on the top and side-face when the aspect ratio of bodies and the wind direction are changed, respectively. We carried out the wind tunnel measurement and numerical simulation around a series of rectangular bodies (40^d×80^w×80^h, 80^d×80^w×80^h, 160^d×80^w×80^h, 80^d×40^w×80^h and 80^d×160^w×80^h in mm³) placed in a deep turbulent boundary layer. Based on a modern numerical platform, the Navier-Stokes equation with the typical 2-equation (k-ε model) and the DES (Detached Eddy Simulation) turbulence model has been calculated, and they are both compared with the measurement data. Regarding the turbulence model, the DES model makes a better prediction comparing with the k-ε model, especially when calculating the separated turbulent flow around a bluff body with sharp edged corner. In order to observe the effect of wind direction on the pressure variation around the cube (e.g., 80^d×80^w×80^h in mm), it rotates at 0º, 10º, 20º, 30º, and 45º, which stands for the salient wind directions in the tunnel. The result shows that the surface pressure variation is highly dependent upon the approaching wind direction, especially on the top and the side-face of the cube. In addition, the transverse width has a substantial effect on the variation of surface pressure around the bodies, while the longitudinal length has little or no influence.

Keywords: Rectangular bodies, wind direction, aspect ratio, surface pressure distribution, wind-tunnel measurement, k-ε model, DES model, CFD.

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Authors: Astiaj Khoramshahi, Hossein Ahmadi Danesh Ashtiani, Ahmad Khoshgard, Hamidreza Damghani, Leila Damghani

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Rural electrification rates are generally low in Iran and many parts of the world that lack sustainable renewable energy resources. Many homes are based on polluting solutions such as crude oil and diesel generators for lighting, heating, and charging electrical gadgets. Small-scale portable solar battery packs are accessible to the public; however, they have low capacity and are challenging to be distributed in developing countries. To design a battery-based microgrid power systems, the load profile is one of the key parameters. Additionally, the reliability of the system should be taken into account. A conventional microgrid system can be either AC or coupling DC. Both AC and DC microgrids have advantages and disadvantages depending on their application and can be either connected to the main grid or perform independently. This article proposes a tool for optimal sizing of microgrid-independent systems via respective analysis. To show such an analysis, the type of power generation, number of panels, battery capacity, microgrid size, and group of available consumers should be considered. Therefore, the optimization of different design scenarios is based on number of solar panels and super saving sources, ranges of the depth of discharges, to calculate size and estimate the overall cost. Generally, it is observed that there is an inverse relationship between the depth spectrum of discharge and the solar microgrid costs.

Keywords: Storage, super-storage, grid-independent, economic factors, microgrid.

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Authors: Seema Chopra, R. Mitra, Vijay Kumar

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A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.

Keywords: Fuzzy control, neuro-fuzzy techniques, fuzzy subtractive clustering, extraction of rules, and optimization of membership functions.

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Authors: Marco Raciti Castelli, Alberto Castelli, Ernesto Benini

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The construction of a civil structure inside a urban area inevitably modifies the outdoor microclimate at the building site. Wind speed, wind direction, air pollution, driving rain, radiation and daylight are some of the main physical aspects that are subjected to the major changes. The quantitative amount of these modifications depends on the shape, size and orientation of the building and on its interaction with the surrounding environment.The flow field over a flat roof model building has been numerically investigated in order to determine two-dimensional CFD guidelines for the calculation of the turbulent flow over a structure immersed in an atmospheric boundary layer. To this purpose, a complete validation campaign has been performed through a systematic comparison of numerical simulations with wind tunnel experimental data.Several turbulence models and spatial node distributions have been tested for five different vertical positions, respectively from the upstream leading edge to the downstream bottom edge of the analyzed model. Flow field characteristics in the neighborhood of the building model have been numerically investigated, allowing a quantification of the capabilities of the CFD code to predict the flow separation and the extension of the recirculation regions.The proposed calculations have allowed the development of a preliminary procedure to be used as a guidance in selecting the appropriate grid configuration and corresponding turbulence model for the prediction of the flow field over a twodimensional roof architecture dominated by flow separation.

Keywords: CFD, roof, building, wind.

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Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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In general, state-of-the-art Data Acquisition Systems (DAQ) in high energy physics experiments must satisfy high requirements in terms of reliability, efficiency and data rate capability. This paper presents the development and deployment of a debugging tool named DAQ Debugger for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. Utilizing a hardware event builder, the iFDAQ is designed to be able to readout data at the average maximum rate of 1.5 GB/s of the experiment. In complex softwares, such as the iFDAQ, having thousands of lines of code, the debugging process is absolutely essential to reveal all software issues. Unfortunately, conventional debugging of the iFDAQ is not possible during the real data taking. The DAQ Debugger is a tool for identifying a problem, isolating the source of the problem, and then either correcting the problem or determining a way to work around it. It provides the layer for an easy integration to any process and has no impact on the process performance. Based on handling of system signals, the DAQ Debugger represents an alternative to conventional debuggers provided by most integrated development environments. Whenever problem occurs, it generates reports containing all necessary information important for a deeper investigation and analysis. The DAQ Debugger was fully incorporated to all processes in the iFDAQ during the run 2016. It helped to reveal remaining software issues and improved significantly the stability of the system in comparison with the previous run. In the paper, we present the DAQ Debugger from several insights and discuss it in a detailed way.

Keywords: DAQ debugger, data acquisition system, FPGA, system signals, Qt framework.

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Authors: Nuri Yucel, Muhammed Ilter Berberoglu, Salih Karaaslan, Nureddin Dinler

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The objective of this study is to investigate fire behaviors, experimentally and numerically, in a scaled version of an underground station. The effect of ventilation velocity on the fire is examined. Fire experiments are simulated by burning 10 ml isopropyl alcohol fuel in a fire pool with dimensions 5cm x 10cm x 4 mm at the center of 1/100 scaled underground station model. A commercial CFD program FLUENT was used in numerical simulations. For air flow simulations, k-ω SST turbulence model and for combustion simulation, non-premixed combustion model are used. This study showed that, the ventilation velocity is increased from 1 m/s to 3 m/s the maximum temperature in the station is found to be less for ventilation velocity of 1 m/s. The reason for these experimental result lies on the relative dominance of oxygen supply effect on cooling effect. Without piston effect, maximum temperature occurs above the fuel pool. However, when the ventilation velocity increased the flame was tilted in the direction of ventilation and the location of maximum temperature moves along the flow direction. The velocities measured experimentally in the station at different locations are well matched by the CFD simulation results. The prediction of general flow pattern is satisfactory with the smoke visualization tests. The backlayering in velocity is well predicted by CFD simulation. However, all over the station, the CFD simulations predicted higher temperatures compared to experimental measurements.

Keywords: Fire, underground station, flame propagation, CFDsimulation, k-ω SST turbulence model, non-premixed combustionmodel.

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. Several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature, and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control.

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Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Mujeeb Ur Rehman, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes, it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity, and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to effect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth

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Keywords: K-Nearest Neighbour, Support Vector Regression, Random Forest Regression, Long Short-Term Memory Network, earthquakes, solar activity, sunspot number, solar wind, solar flares.

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Authors: Wafa Ghonaim

Abstract:

Though quite fast on growth, Islamic financing at large, and its diverse instruments, is a controversial matter among scholars. This is evident from the ongoing debates on its Shariah compliance. Arguments, however, are inciting doubts and concerns among clients about its credibility, which is harming this lucrative sector. The work here investigates, particularly, some issues related to the Tawarruq instrument. The work examines the issues of linking Murabaha and Wakala contracts, the reselling of commodities to same traders, and the transfer of ownerships. The work affirms that a multi-agent smart electronic market design would facilitate Shariah compliance. The smart market exploits the rational decision-making capabilities of autonomous proxy agents that enable the clients, traders, brokers, and the bank buy and sell commodities, and manage transactions and cash flow. The smart electronic market design delivers desirable qualities that terminate the need for Wakala contracts and the reselling of commodities to the same traders. It also resolves the ownership transfer issues by allowing stakeholders to trade independently. The bank administers the smart electronic market and assures reliability of trades, transactions and cash flow. A multi-agent simulation is presented to validate the concept and processes. We anticipate that the multi-agent smart electronic market design would deliver Shariah compliance of personal financing to the aspiration of scholars, banks, traders and potential clients.

Keywords: Islamic finance, Shariah compliance, smart electronic markets design, multi-agent systems.

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Authors: Salem Alsanusi, Loubna Bentaher

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Response Surface Methods (RSM) provide statistically validated predictive models that can then be manipulated for finding optimal process configurations. Variation transmitted to responses from poorly controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates ‘finding the flats’ on the surfaces generated by RSM. The dual response approach to RSM captures the standard deviation of the output as well as the average. It accounts for unknown sources of variation. Dual response plus propagation of error (POE) provides a more useful model of overall response variation. In our case, we implemented this technique in predicting compressive strength of concrete of 28 days in age. Since 28 days is quite time consuming, while it is important to ensure the quality control process. This paper investigates the potential of using design of experiments (DOE-RSM) to predict the compressive strength of concrete at 28th day. Data used for this study was carried out from experiment schemes at university of Benghazi, civil engineering department. A total of 114 sets of data were implemented. ACI mix design method was utilized for the mix design. No admixtures were used, only the main concrete mix constituents such as cement, coarseaggregate, fine aggregate and water were utilized in all mixes. Different mix proportions of the ingredients and different water cement ratio were used. The proposed mathematical models are capable of predicting the required concrete compressive strength of concrete from early ages.

Keywords: Mix proportioning, response surface methodology, compressive strength, optimal design.

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Authors: Suttipong Boonphadung

Abstract:

The research on ARCS for critical information retrieval development aimed to (1) investigate conditions of critical information retrieval skill of the Mathematics pre-service teachers before applying ARCS model in learning activities, (2) study and analyze the development of critical information retrieval skill of the Mathematics pre-service teachers after utilizing ARCS model in learning activities, and (3) evaluate the Mathematics pre-service teachers’ satisfaction on using ARCS model in learning activities as a tool to development critical information retrieval skill. Forty-one of 4th year Mathematics pre-service teachers who have enrolled in the subject of Research for Learning Development of semester 2 in 2012 were purposively selected as the research cohort. The research tools were self-report and interview questionnaire that was approved as content validity and reliability (IOC=.66-1.00, α =.834). The research found that critical information retrieval skill of the research samples before using ARCS model in learning activities was in the normal high level. According to the in-depth interview and focus group, the result however showed that the pre-service teachers still lack inadequate and effective knowledge in information retrieval. Additionally, critical information retrieval skill of the research cohort after applying ARCS model in learning activities appeared to be high level. The result revealed that the pre-service teachers are able to explain the method of searching, extraction, and selecting information as well as evaluating quality of information, and effectively making decision in accepting information. Moreover, the research discovered that the pre-service teachers showed normal high to highest level of satisfaction on using ARCS model in learning activities as a tool to development their critical information retrieval skill.

Keywords: Critical information retrieval skill, ARCS model, Satisfaction.

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: Information Gain (IG), Intrusion Detection System (IDS), K-means Clustering, Weka.

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Authors: Li Xu, Huanbao Jiang, Zhenfei Huang, Lailai Zhang

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In recent years, as global warming, the sea-ice extent of North Arctic undergoes an evident decrease and Arctic channel has attracted the attention of shipping industry. Ice crystals existing in the seawater of Arctic channel which enter the seawater system of the ship with the seawater were found blocking the seawater pipe. The appearance of cooler paralysis, auxiliary machine error and even ship power system paralysis may be happened if seriously. In order to reduce the effect of high temperature in auxiliary equipment, seawater system will use external ice-water to participate in the cooling cycle and achieve the state of its flow. The distribution of ice crystals in seawater pipe can be achieved. As the ice slurry system is solid liquid two-phase system, the flow process of ice-water mixture is very complex and diverse. In this paper, the flow process in seawater pipe of ice slurry is simulated with fluid dynamics simulation software based on k-ε turbulence model. As the ice packing fraction is a key factor effecting the distribution of ice crystals, the influence of ice packing fraction on the flowing process of ice slurry is analyzed. In this work, the simulation results show that as the ice packing fraction is relatively large, the distribution of ice crystals is uneven in the flowing process of the seawater which has such disadvantage as increase the possibility of blocking, that will provide scientific forecasting methods for the forming of ice block in seawater piping system. It has important significance for the reliability of the operating of polar ships in the future.

Keywords: Ice slurry, seawater pipe, ice packing fraction, numerical simulation.

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Authors: Sima Ziaee, Khosro Jafarpur

Abstract:

Among all mechanical joining processes, welding has been employed for its advantage in design flexibility, cost saving, reduced overall weight and enhanced structural performance. However, for structures made of relatively thin components, welding can introduce significant buckling distortion which causes loss of dimensional control, structural integrity and increased fabrication costs. Different parameters can affect buckling behavior of welded thin structures such as, heat input, welding sequence, dimension of structure. In this work, a 3-D thermo elastic-viscoplastic finite element analysis technique is applied to evaluate the effect of shell dimensions on buckling behavior and entropy generation of welded thin shells. Also, in the present work, the approximated longitudinal transient stresses which produced in each time step, is applied to the 3D-eigenvalue analysis to ratify predicted buckling time and corresponding eigenmode. Besides, the possibility of buckling prediction by entropy generation at each time is investigated and it is found that one can predict time of buckling with drawing entropy generation versus out of plane deformation. The results of finite element analysis show that the length, span and thickness of welded thin shells affect the number of local buckling, mode shape of global buckling and post-buckling behavior of welded thin shells.

Keywords: Buckling behavior, Elastic viscoplastic model, Entropy generation, Finite element method, Shell dimensions.

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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