Search results for: Field data based model

Authors: Do Hoai Nam, Keiko Udo, Akira Mano

Abstract:

This paper presents the combination of different precipitation data sets and the distributed hydrological model, in order to examine the flood runoff reproductivity of scattered observation catchments. The precipitation data sets were obtained from observation using rain-gages, satellite based estimate (TRMM), and numerical weather prediction model (NWP), then were coupled with the super tank model. The case study was conducted in three basins (small, medium, and large size) located in Central Vietnam. Calculated hydrographs based on ground observation rainfall showed best fit to measured stream flow, while those obtained from TRMM and NWP showed high uncertainty of peak discharges. However, calculated hydrographs using the adjusted rainfield depicted a promising alternative for the application of TRMM and NWP in flood modeling for scattered observation catchments, especially for the extension of forecast lead time.

Keywords: Flood forecast, rainfall-runoff model, satellite rainfall estimate, numerical weather prediction, quantitative precipitation forecasting.

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Authors: M. S. Osman, A. A. Tharwat, I. A. El-Khodary, A. G. Chalabi

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In this paper, we propose a multiple objective optimization model with respect to portfolio selection problem for investors looking forward to diversify their equity investments in a number of equity markets. Based on Markowitz-s M-V model we developed a Fuzzy Mixed Integer Multi-Objective Nonlinear Programming Problem (FMIMONLP) to maximize the investors- future gains on equity markets, reach the optimal proportion of the budget to be invested in different equities. A numerical example with a comprehensive analysis on artificial data from several equity markets is presented in order to illustrate the proposed model and its solution method. The model performed well compared with the deterministic version of the model.

Keywords: Equity Markets, Future Scenarios, PortfolioSelection, Multiple Criteria Fuzzy Optimization

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Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: Data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks, WSN.

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Authors: Haydar Kepekçi, Baha Zafer, Hasan Rıza Güven

Abstract:

Noise disturbance is one of the major factors considered in the fast development of aircraft technology. This paper reviews the flow field, which is examined on the 2D NACA0015 and 3D NACA0012 blade profile using SST k-ω turbulence model to compute the unsteady flow field. We inserted the time-dependent flow area variables in Ffowcs-Williams and Hawkings (FW-H) equations as an input and Sound Pressure Level (SPL) values will be computed for different angles of attack (AoA) from the microphone which is positioned in the computational domain to investigate effect of augmentation of unsteady 2D and 3D airfoil region noise level. The computed results will be compared with experimental data which are available in the open literature. As results; one of the calculated Cp is slightly lower than the experimental value. This difference could be due to the higher Reynolds number of the experimental data. The ANSYS Fluent software was used in this study. Fluent includes well-validated physical modeling capabilities to deliver fast, accurate results across the widest range of CFD and multiphysics applications. This paper includes a study which is on external flow over an airfoil. The case of 2D NACA0015 has approximately 7 million elements and solves compressible fluid flow with heat transfer using the SST turbulence model. The other case of 3D NACA0012 has approximately 3 million elements.

Keywords: Aeroacoustics, Ffowcs-Williams and Hawkings equations, SST k-ω turbulence model, Noise Disturbance, 3D Blade Profile, 2D Blade Profile.

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Authors: Pardeep Singh, Sanjay Sharma

Abstract:

Content-Based Image Retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of difering sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. In several articles, content based access to medical images for supporting clinical decision making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into Picture Archiving and Communication Systems (PACS) have been created. This paper gives an overview of soft computing techniques. New research directions are being defined that can prove to be useful. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text based retrieval methods as they exist at the moment.

Keywords: CBIR, GA, Rough sets, CBMIR

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Authors: Nuo Duan, Yi Pik Cheng

Abstract:

This paper presents the data of a series of two-dimensional Discrete Element Method (DEM) simulations of a large-diameter rigid monopile subjected to cyclic loading under a high gravitational force. At present, monopile foundations are widely used to support the tall and heavy wind turbines, which are also subjected to significant from wind and wave actions. A safe design must address issues such as rotations and changes in soil stiffness subject to these loadings conditions. Design guidance on the issue is limited, so are the availability of laboratory and field test data. The interpretation of these results in sand, such as the relation between loading and displacement, relies mainly on empirical correlations to pile properties. Regarding numerical models, most data from Finite Element Method (FEM) can be found. They are not comprehensive, and most of the FEM results are sensitive to input parameters. The micro scale behaviour could change the mechanism of the soil-structure interaction. A DEM model was used in this paper to study the cyclic lateral loads behaviour. A non-dimensional framework is presented and applied to interpret the simulation results. The DEM data compares well with various set of published experimental centrifuge model test data in terms of lateral deflection. The accumulated permanent pile lateral displacements induced by the cyclic lateral loads were found to be dependent on the characteristics of the applied cyclic load, such as the extent of the loading magnitudes and directions.

Keywords: Cyclic loading, DEM, numerical modelling, sands.

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Authors: Sirapat Lookrak, Anol Paisal

Abstract:

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

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

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Authors: Anis Gharbi

Abstract:

This paper considers the problem of scheduling maintenance actions for identical aircraft gas turbine engines. Each one of the turbines consists of parts which frequently require replacement. A finite inventory of spare parts is available and all parts are ready for replacement at any time. The inventory consists of both new and refurbished parts. Hence, these parts have different field lives. The goal is to find a replacement part sequencing that maximizes the time that the aircraft will keep functioning before the inventory is replenished. The problem is formulated as an identical parallel machine scheduling problem where the minimum completion time has to be maximized. Two models have been developed. The first one is an optimization model which is based on a 0-1 linear programming formulation, while the second one is an approximate procedure which consists in decomposing the problem into several two-machine subproblems. Each subproblem is optimally solved using the first model. Both models have been implemented using Lingo and have been tested on two sets of randomly generated data with up to 150 parts and 10 turbines. Experimental results show that the optimization model is able to solve only instances with no more than 4 turbines, while the decomposition procedure often provides near-optimal solutions within a maximum CPU time of 3 seconds.

Keywords: Aircraft turbines, Scheduling, Identical parallel machines, 0-1 linear programming, Heuristic.

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Authors: Fred Lacy

Abstract:

A superconductor has the ability to conduct electricity perfectly and exclude magnetic fields from its interior. In order to understand electromagnetic characteristics of superconductors, their material properties need to be examined. To facilitate this understanding, a theoretical model based on concepts of electromagnetics is presented to explain the electrical and magnetic properties of superconductors. The permittivity response is the key aspect of the model and it describes the electrical resistance response and why it vanishes at the material’s critical temperature. The model also explains the behavior of magnetic fields and why they cannot exist inside superconducting materials. The theoretical concepts and equations associated with this model are used to demonstrate that they are sufficient in describing the behavior of both type I and type II (or high temperature) superconductors. This model is also able to explain why superconductors behave differently than perfect conductors. As a result, examining the permittivity response and understanding electromagnetic field theory provides insight into the major aspects associated with superconducting materials.

Keywords: Ampere’s law, permittivity, permeability, resistivity, Schrödinger wave equation.

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Authors: R. Behmanesh, I. Rahimi

Abstract:

recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Keywords: RNN, DOE, regression, control chart.

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Authors: Jin-Wei Liang, Hung-Yi Chen, Lung Lin

Abstract:

In this paper feedforward controller is designed to eliminate nonlinear hysteresis behaviors of a piezoelectric stack actuator (PSA) driven system. The control design is based on inverse Prandtl-Ishlinskii (P-I) hysteresis model identified using particle swarm optimization (PSO) technique. Based on the identified P-I model, both the inverse P-I hysteresis model and feedforward controller can be determined. Experimental results obtained using the inverse P-I feedforward control are compared with their counterparts using hysteresis estimates obtained from the identified Bouc-Wen model. Effectiveness of the proposed feedforward control scheme is demonstrated. To improve control performance feedback compensation using traditional PID scheme is adopted to integrate with the feedforward controller. 

Keywords: The Bouc-Wen hysteresis model, Particle swarm optimization, Prandtl-Ishlinskii model.

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Authors: C. Ardil

Abstract:

A new mathematical model for calculating the temperature field of the profile part of the cooled blades of gas turbines is developed. The theoretical substantiation of the method is based on the application of the method of potential theory (the method of boundary integral equations). The effectiveness of the implementation of the developed mathematical model is confirmed on the basis of a computational experiment.

Keywords: Modeling of temperature fields, gas turbine blades, integral methods, cooled blades, gas turbines.

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Authors: Maumita Bhattacharya

Abstract:

Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Keywords: Evolutionary algorithm, Fitness function, Optimization, Meta-model, Stochastic method.

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Authors: M. Alrasheedi

Abstract:

The double exponential model (DEM), or Laplace distribution, is used in various disciplines. However, there are issues related to the construction of confidence intervals (CI), when using the distribution.In this paper, the properties of DEM are considered with intention of constructing CI based on simulated data. The analysis of pivotal equations for the models here in comparisons with pivotal equations for normal distribution are performed, and the results obtained from simulation data are presented.

Keywords: Confidence intervals, double exponential model, pivotal equations, simulation

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Authors: S. Xiao, M. B. Shuai, M. F. Chu

Abstract:

The hydrolysis kinetics of polycrystalline lithium hydride (LiH) in argon at various low humidities was measured by gravimetry and Raman spectroscopy with ambient water concentration ranging from 200 to 1200 ppm. The results showed that LiH hydrolysis curve revealed a paralinear shape, which was attributed to two different reaction stages that forming different products as explained by the 'Layer Diffusion Control' model. Based on the model, a novel two-stage rate equation for LiH hydrolysis reactions was developed and used to fit the experimental data for determination of Li2O steady thickness Hs and the ultimate hydrolysis rate vs. The fitted data presented a rise of Hs as ambient water concentration cw increased. However, in spite of the negative effect imposed by Hs increasing, the upward trend of vs remained, which implied that water concentration, rather than Li2O thickness, played a predominant role in LiH hydrolysis kinetics. In addition, the proportional relationship between vsHs and cw predicted by rate equation and confirmed by gravimetric data validated the model in such conditions.

Keywords: Hydrolysis kinetics, ‘Layer Diffusion Control’ model, Lithium hydride

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Authors: Changwoo Byun, Kyounghan Lee, Seog Park

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XML is becoming a de facto standard for online data exchange. Existing XML filtering techniques based on a publish/subscribe model are focused on the highly structured data marked up with XML tags. These techniques are efficient in filtering the documents of data-centric XML but are not effective in filtering the element contents of the document-centric XML. In this paper, we propose an extended XPath specification which includes a special matching character '%' used in the LIKE operation of SQL in order to solve the difficulty of writing some queries to adequately filter element contents using the previous XPath specification. We also present a novel technique for filtering a collection of document-centric XMLs, called Pfilter, which is able to exploit the extended XPath specification. We show several performance studies, efficiency and scalability using the multi-query processing time (MQPT).

Keywords: XML Data Stream, Document-centric XML, Filtering Technique, Value-based Predicates.

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

Abstract:

Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words, classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar.

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Authors: Iqbal Hossain, Monzur Alam Imteaz, Mohammed Iqbal Hossain

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Estimation of stormwater pollutants is a pre-requisite for the protection and improvement of the aquatic environment and for appropriate management options. The usual practice for the stormwater quality prediction is performed through water quality modeling. However, the accuracy of the prediction by the models depends on the proper estimation of model parameters. This paper presents the estimation of model parameters for a catchment water quality model developed for the continuous simulation of stormwater pollutants from a catchment to the catchment outlet. The model is capable of simulating the accumulation and transportation of the stormwater pollutants; suspended solids (SS), total nitrogen (TN) and total phosphorus (TP) from a particular catchment. Rainfall and water quality data were collected for the Hotham Creek Catchment (HTCC), Gold Coast, Australia. Runoff calculations from the developed model were compared with the calculated discharges from the widely used hydrological models, WBNM and DRAINS. Based on the measured water quality data, model water quality parameters were calibrated for the above-mentioned catchment. The calibrated parameters are expected to be helpful for the best management practices (BMPs) of the region. Sensitivity analyses of the estimated parameters were performed to assess the impacts of the model parameters on overall model estimations of runoff water quality.

Keywords: Calibration, Model Parameters, Suspended Solids, TotalNitrogen, Total Phosphorus.

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Authors: C. Li, G. Coates, N. Johnson, M. McGuinness

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In the UK, flooding is responsible for significant losses to the economy due to the impact on businesses, the vast majority of which are Small and Medium Enterprises (SMEs). Businesses of this nature tend to lack formal plans to aid their response to and recovery from disruptive events such as flooding. This paper reports on work on how an agent-based model (ABM) is being developed based on interview data gathered from SMEs at-risk of flooding and/or have direct experience of flooding. The ABM will enable simulations to be performed allowing investigations of different response strategies which SMEs may employ to lessen the impact of flooding, thus strengthening their resilience.

Keywords: ABM, Flood response, SMEs, Business continuity.

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Authors: Juan Li, Jiawen Qiu, Yuchuan Chu, Tianping Zhang, Wei Meng, Yanhui Jia, Xiaohui Liu

Abstract:

This article developed an ion thruster optic system sputter erosion depth numerical 3D model by IFE-PIC (Immersed Finite Element-Particle-in-Cell) and Mont Carlo method, and calculated the downstream surface sputter erosion rate of accelerator grid; compared with LIPS-200 life test data. The results of the numerical model are in reasonable agreement with the measured data. Finally, we predicted the lifetime of the 20cm diameter ion thruster via the erosion data obtained with the model. The ultimate result demonstrated that under normal operating condition, the erosion rate of the grooves wears on the downstream surface of the accelerator grid is 34.6μm⁄1000h, which means the conservative lifetime until structural failure occurring on the accelerator grid is 11500 hours.

Keywords: Ion thruster, accelerator gird, sputter erosion, lifetime assessment.

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Authors: Ivan Yatchev, Ewen Ritchie

Abstract:

Comparison of two approaches for the simulation of the dynamic behaviour of a permanent magnet linear actuator is presented. These are full coupled model, where the electromagnetic field, electric circuit and mechanical motion problems are solved simultaneously, and decoupled model, where first a set of static magnetic filed analysis is carried out and then the electric circuit and mechanical motion equations are solved employing bi-cubic spline approximations of the field analysis results. The results show that the proposed decoupled model is of satisfactory accuracy and gives more flexibility when the actuator response is required to be estimated for different external conditions, e.g. external circuit parameters or mechanical loads.

Keywords: Coupled problems, dynamic models, finite elementanalysis, linear actuators, permanent magnets.

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: Anomaly detection, autoencoder, data centers, deep learning.

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Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Toshifumi Hiramatsu, Satoshi Morita, Manuel Pencelli, Marta Niccolini, Matteo Ragaglia, Alfredo Argiolas

Abstract:

Automation technologies for agriculture field are needed to promote labor-saving. One of the most relevant problems in automated agriculture is represented by controlling the robot along a predetermined path in presence of rough terrain or incline ground. Unfortunately, disturbances originating from interaction with the ground, such as slipping, make it quite difficult to achieve the required accuracy. In general, it is required to move within 5-10 cm accuracy with respect to the predetermined path. Moreover, lateral velocity caused by gravity on the incline field also affects slipping. In this paper, a path-tracking controller for tracked mobile robots moving on rough terrains of incline field such as vineyard is presented. The controller is composed of a disturbance observer and an adaptive controller based on the kinematic model of the robot. The disturbance observer measures the difference between the measured and the reference yaw rate and linear velocity in order to estimate slip. Then, the adaptive controller adapts “virtual” parameter of the kinematics model: Instantaneous Centers of Rotation (ICRs). Finally, target angular velocity reference is computed according to the adapted parameter. This solution allows estimating the effects of slip without making the model too complex. Finally, the effectiveness of the proposed solution is tested in a simulation environment.

Keywords: Agricultural robot, autonomous control, path-tracking control, tracked mobile robot.

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Authors: Wei-Yi Yang, Li-Hui Lee, Hsiao-Li Gien, Hsing-Yi Chu, Yi-Ting Chou, Der-Ming Liou

Abstract:

The American Health Level Seven (HL7) Reference Information Model (RIM) consists of six back-bone classes that have different specialized attributes. Furthermore, for the purpose of enforcing the semantic expression, there are some specific mandatory vocabulary domains have been defined for representing the content values of some attributes. In the light of the fact that it is a duplicated effort on spending a lot of time and human cost to develop and modify Clinical Information Systems (CIS) for most hospitals due to the variety of workflows. This study attempts to design and develop sharing RIM-based components of the CIS for the different business processes. Therefore, the CIS contains data of a consistent format and type. The programmers can do transactions with the RIM-based clinical repository by the sharing RIM-based components. And when developing functions of the CIS, the sharing components also can be adopted in the system. These components not only satisfy physicians- needs in using a CIS but also reduce the time of developing new components of a system. All in all, this study provides a new viewpoint that integrating the data and functions with the business processes, it is an easy and flexible approach to build a new CIS.

Keywords: HL7, Reference Information Model (RIM), web service, process management.

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Authors: Gamze Uslu, Sebnem Baydere, Alper K. Demir

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In this study, data loss tolerance of Support Vector Machines (SVM) based activity recognition model and multi activity classification performance when data are received over a lossy wireless sensor network is examined. Initially, the classification algorithm we use is evaluated in terms of resilience to random data loss with 3D acceleration sensor data for sitting, lying, walking and standing actions. The results show that the proposed classification method can recognize these activities successfully despite high data loss. Secondly, the effect of differentiated quality of service performance on activity recognition success is measured with activity data acquired from a multi hop wireless sensor network, which introduces  high data loss. The effect of number of nodes on the reliability and multi activity classification success is demonstrated in simulation environment. To the best of our knowledge, the effect of data loss in a wireless sensor network on activity detection success rate of an SVM based classification algorithm has not been studied before.

Keywords: Activity recognition, support vector machines, acceleration sensor, wireless sensor networks, packet loss.

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Authors: Jerzy Nogiec, Kelley Trombly-Freytag, Shangping Ren

Abstract:

The promises of component-based technology can only be fully realized when the system contains in its design a necessary level of separation of concerns. The authors propose to focus on the concerns that emerge throughout the life cycle of the system and use them as an architectural foundation for the design of a component-based framework. The proposed model comprises a set of superimposed views of the system describing its functional and non-functional concerns. This approach is illustrated by the design of a specific framework for data analysis and data acquisition and supplemented with experiences from using the systems developed with this framework at the Fermi National Accelerator Laboratory.

Keywords: Distributed system, component-based technology, separation of concerns, software development, supervisory and control, QoS

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Authors: W. K. Wong, Z. X. Guo, P.Y. Mok

Abstract:

This paper presents a multi-objective order allocation planning problem with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization process, a Monte Carlo simulation technique and a heuristic pruning technique, is proposed to handle this problem. Experiments based on industrial data are conducted to validate the proposed model. Results show that (1) the proposed model can effectively solve the investigated problem by providing effective production decision-making solutions, which outperformsan NSGA-II-based optimization process and an industrial method.

Keywords: Multi-objective order allocation planning, Pareto optimization, Memetic algorithm, Mento Carlo simulation

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Authors: N. Borjalilu, P. Rabiei, A. Enjoo

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Flight Data Monitoring (FDM) program assists an operator in aviation industries to identify, quantify, assess and address operational safety risks, in order to improve safety of flight operations. FDM is a powerful tool for an aircraft operator integrated into the operator’s Safety Management System (SMS), allowing to detect, confirm, and assess safety issues and to check the effectiveness of corrective actions, associated with human errors. This article proposes a model for safety risk assessment level of flight data in a different aspect of event focus based on fuzzy set values. It permits to evaluate the operational safety level from the point of view of flight activities. The main advantages of this method are proposed qualitative safety analysis of flight data. This research applies the opinions of the aviation experts through a number of questionnaires Related to flight data in four categories of occurrence that can take place during an accident or an incident such as: Runway Excursions (RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision (MAC), Loss of Control in Flight (LOC-I). By weighting each one (by F-TOPSIS) and applying it to the number of risks of the event, the safety risk of each related events can be obtained.

Keywords: F-TOPSIS, fuzzy set, FDM, flight safety.

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Authors: Ryad Zemouri, Paul Ciprian Patic

Abstract:

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.

Keywords: Neural network, Prediction error, Recurrent RadialBasis Function Network, Reliability prediction.

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