Search results for: Cost optimization modelling

Authors: Hansoo Kim, Seunghak Shin

Abstract:

The differential column shortening in tall buildings can be reduced by improving material and structural characteristics of the structural systems. This paper proposes structural methods to reduce differential column shortening in reinforced concrete tall buildings; connecting columns with rigidly jointed horizontal members, using outriggers, and placing additional reinforcement at the columns. The rigidly connected horizontal members including outriggers reduce the differential shortening between adjacent vertical members. The axial stiffness of columns with greater shortening can be effectively increased by placing additional reinforcement at the columns, thus the differential column shortening can be reduced in the design stage. The optimum distribution of additional reinforcement can be determined by applying a gradient based optimization technique.

Keywords: Column shortening, long-term behavior, optimization, tall building.

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free overfall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, Support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, Support vector machine (Polynomial and rbf) models and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free overfall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: Air entrainment rate, dissolved oxygen, regression, SVM, weir.

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: Magnetic Resonance Image, C-means model, image segmentation, information entropy.

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: Acute hepatitis, Medical resource cost, Artificial neural network, Support vector regression.

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Authors: Ayed Salman, Fawaz Al-Anzi, Aseel Al-Minayes

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Heuristics-based search methodologies normally work on searching a problem space of possible solutions toward finding a “satisfactory" solution based on “hints" estimated from the problem-specific knowledge. Research communities use different types of methodologies. Unfortunately, most of the times, these hints are immature and can lead toward hindering these methodologies by a premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the population. In this paper, a novel Decision Maturity framework (DMF) is introduced as a solution to this problem. The framework simply improves the decision on the direction of the search by materializing hints enough before using them. Ideas from this framework are injected into the particle swarm optimization methodology. Results were obtained under both static and dynamic environment. The results show that decision maturity prevents premature converges to a high degree.

Keywords: Heuristic Search, hints, Particle Swarm Optimization, Decision Maturity Framework.

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Authors: Jieh-Haur Chen, Pei-Fen Huang

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This paper presents an integrated model that automatically measures the change of rivers, damage area of bridge surroundings, and change of vegetation. The proposed model is on the basis of a neurofuzzy mechanism enhanced by SOM optimization algorithm, and also includes three functions to deal with river imagery. High resolution imagery from FORMOSAT-2 satellite taken before and after the invasion period is adopted. By randomly selecting a bridge out of 129 destroyed bridges, the recognition results show that the average width has increased 66%. The ruined segment of the bridge is located exactly at the most scour region. The vegetation coverage has also reduced to nearly 90% of the original. The results yielded from the proposed model demonstrate a pinpoint accuracy rate at 99.94%. This study brings up a successful tool not only for large-scale damage assessment but for precise measurement to disasters.

Keywords: remote sensing image, damage assessment, typhoon disaster, bridge, ANN, fuzzy, SOM, optimization.

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Authors: Malay K. Pakhira, Rajat K. De

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In this paper, a pipelined version of genetic algorithm, called PLGA, and a corresponding hardware platform are described. The basic operations of conventional GA (CGA) are made pipelined using an appropriate selection scheme. The selection operator, used here, is stochastic in nature and is called SA-selection. This helps maintaining the basic generational nature of the proposed pipelined GA (PLGA). A number of benchmark problems are used to compare the performances of conventional roulette-wheel selection and the SA-selection. These include unimodal and multimodal functions with dimensionality varying from very small to very large. It is seen that the SA-selection scheme is giving comparable performances with respect to the classical roulette-wheel selection scheme, for all the instances, when quality of solutions and rate of convergence are considered. The speedups obtained by PLGA for different benchmarks are found to be significant. It is shown that a complete hardware pipeline can be developed using the proposed scheme, if parallel evaluation of the fitness expression is possible. In this connection a low-cost but very fast hardware evaluation unit is described. Results of simulation experiments show that in a pipelined hardware environment, PLGA will be much faster than CGA. In terms of efficiency, PLGA is found to outperform parallel GA (PGA) also.

Keywords: Hardware evaluation, Hardware pipeline, Optimization, Pipelined genetic algorithm, SA-selection.

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Authors: Krzysztof Walczak

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Fractional delay FIR filters design method based on the differential evolution algorithm is presented. Differential evolution is an evolutionary algorithm for solving a global optimization problems in the continuous search space. In the proposed approach, an evolutionary algorithm is used to determine the coefficients of a fractional delay FIR filter based on the Farrow structure. Basic differential evolution is enhanced with a restricted mating technique, which improves the algorithm performance in terms of convergence speed and obtained solution. Evolutionary optimization is carried out by minimizing an objective function which is based on the amplitude response and phase delay errors. Experimental results show that the proposed algorithm leads to a reduction in the amplitude response and phase delay errors relative to those achieved with the Least-Squares method.

Keywords: Fractional Delay Filters, Farrow Structure, Evolutionary Computation, Differential Evolution

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Authors: P. Subbaraj, V. Rajasekaran

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This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.

Keywords: Combined ANN, Evolutionary Programming, Particle Swarm Optimization, Genetic Algorithm and Peak load forecasting.

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Authors: Omaima N. Ahmad AL-Allaf

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Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.

Keywords: Image watermarking, genetic algorithm, particle swarm optimization, discrete wavelet transform.

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Authors: N. Gigauri, V. Kukhalashvili, A. Surmava, L. Intskirveli, L. Gverdtsiteli

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Tbilisi, a large city of the South Caucasus, is a junction point connecting Asia and Europe, Russia and republics of the Asia Minor. Over the last years, its atmosphere has been experienced an increasing anthropogenic load. Numerical modeling method is used for study of Tbilisi atmospheric air pollution. By means of 3D non-linear non-steady numerical model a peculiarity of city atmosphere pollution is investigated during background western light air. Dust concentration spatial and time changes are determined. There are identified the zones of high, average and less pollution, dust accumulation areas, transfer directions etc. By numerical modeling, there is shown that the process of air pollution by the dust proceeds in four stages, and they depend on the intensity of motor traffic, the micro-relief of the city, and the location of city mains. In the interval of time 06:00-09:00 the intensive growth, 09:00-15:00 a constancy or weak decrease, 18:00-21:00 an increase, and from 21:00 to 06:00 a reduction of the dust concentrations take place. The highly polluted areas are located in the vicinity of the city center and at some peripherical territories of the city, where the maximum dust concentration at 9PM is equal to 2 maximum allowable concentrations. The similar investigations conducted in case of various meteorological situations will enable us to compile the map of background urban pollution and to elaborate practical measures for ambient air protection.

Keywords: Numerical modelling, source of pollution, dust propagation, western light air.

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Authors: Aïssa Rezzoug

Abstract:

This paper is aimed to bring new elements that demonstrate the tide caused the groundwater to rise in the shoreline band, on which the urban areas occurs, especially in the western coastal cities of the Kingdom of Saudi Arabia like Jeddah. The reason for the last events of Jeddah inundation was the groundwater rise in the city coupled at the same time to a strong precipitation event. This paper will illustrate the tide participation in increasing the groundwater level significantly. It shows that the reason for internal groundwater recharge within the urban area is not only the excess of the water supply coming from surrounding areas, due to the human activity, with lack of sufficient and efficient sewage system, but also due to tide effect. The research study follows a quantitative method to assess groundwater level rise risks through many in-situ measurements and mathematical modelling. The proposed approach highlights groundwater level, in the urban areas of the city on the shoreline band, reaching the high tide level without considering any input from precipitation. Despite the small tide in the Red Sea compared to other oceanic coasts, the groundwater level is considerably enhanced by the tide from the seaside and by the freshwater table from the landside of the city. In these conditions, the groundwater level becomes high in the city and prevents the soil to evacuate quickly enough the surface flow caused by the storm event, as it was observed in the last historical flood catastrophe of Jeddah in 2009.

Keywords: Flood, groundwater rise, Jeddah, tide.

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Authors: E. Giovanis

Abstract:

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

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Authors: Young-Seok Choi

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This work presents a new type of the affine projection (AP) algorithms which incorporate the sparsity condition of a system. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weighting for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results exhibit that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.

Keywords: System identification, adaptive filter, affine projection, sparsity, sparse system.

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Authors: Jarrod Trevathan, Trina Myers

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Proprietary sensor network systems are typically expensive, rigid and difficult to incorporate technologies from other vendors. When using competing and incompatible technologies, a non-proprietary system is complex to create because it requires significant technical expertise and effort, which can be more expensive than a proprietary product. This paper presents the Sensor Abstraction Layer (SAL) that provides middleware architectures with a consistent and uniform view of heterogeneous sensor networks, regardless of the technologies involved. SAL abstracts and hides the hardware disparities and specificities related to accessing, controlling, probing and piloting heterogeneous sensors. SAL is a single software library containing a stable hardware-independent interface with consistent access and control functions to remotely manage the network. The end-user has near-real-time access to the collected data via the network, which results in a cost-effective, flexible and simplified system suitable for novice users. SAL has been used for successfully implementing several low-cost sensor network systems.

Keywords: Sensor networks, hardware abstraction, middleware integration platform, sensor web enablement.

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Authors: Hiba Basim Alwan, Ku Ruhana Ku-Mahamud

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A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. The main problems of SVM are selecting feature subset and tuning the parameters. Discretizing the continuous value of the parameters is the most common approach in tuning SVM parameters. This process will result in loss of information which affects the classification accuracy. This paper presents two algorithms that can simultaneously tune SVM parameters and select the feature subset. The first algorithm, ACO_R-SVM, will tune SVM parameters, while the second IACO_MV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. Three benchmark UCI datasets were used in the experiments to validate the performance of the proposed algorithms. The results show that the proposed algorithms have good performances as compared to other approaches.

Keywords: Continuous ant colony optimization, incremental continuous ant colony, simultaneous optimization, support vector machine.

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Authors: Mehdi Seifbarghy, Sajjad Jalali, Seyed Habib A. Rahmati

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Recently studies in area of supply chain network (SCN) have focused on the disruption issues in distribution systems. Also this paper extends the previous literature by providing a new biobjective model for cost minimization of designing a three echelon SCN across normal and failure scenarios with considering multi capacity option for manufacturers and distribution centers. Moreover, in order to solve the problem by means of LINGO software, novel model will be reformulated through a branch of LP-Metric method called Min-Max approach.

Keywords: Scenario programming, Distribution, Multi-echelon supply chain design, Reliable facility

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

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Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (Bi)digraphs, rough set theory, systems of interacting agents, complex systems.

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Authors: N. Arulanand, K. Premalatha

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Bloom filter is a probabilistic and memory efficient data structure designed to answer rapidly whether an element is present in a set. It tells that the element is definitely not in the set but its presence is with certain probability. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the number of hash function is sufficiently large. For spam detection, weight is attached to each set of elements. The spam weight for a word is a measure used to rate the e-mail. Each word is assigned to a Bloom filter based on its weight. The proposed work introduces an enhanced concept in Bloom filter called Bin Bloom Filter (BBF). The performance of BBF over conventional Bloom filter is evaluated under various optimization techniques. Real time data set and synthetic data sets are used for experimental analysis and the results are demonstrated for bin sizes 4, 5, 6 and 7. Finally analyzing the results, it is found that the BBF which uses heuristic techniques performs better than the traditional Bloom filter in spam detection.

Keywords: Cuckoo search algorithm, levy’s flight, metaheuristic, optimal weight.

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Authors: R. Hocine, K. Belkacemi, A. Boukortt, A. Boudjemai

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When designing satellites, one of the major issues aside for designing its primary subsystems is to devise its thermal. The thermal management of satellites requires solving different sets of issues with regards to modelling. If the satellite is well conditioned all other parts of the satellite will have higher temperature no matter what. The main issue of thermal modelling for satellite design is really making sure that all the other points of the satellite will be within the temperature limits they are designed. The insertion of power electronics in aerospace technologies is becoming widespread and the modern electronic systems used in space must be reliable and efficient with thermal management unaffected by outer space constraints. Many advanced thermal management techniques have been developed in recent years that have application in high power electronic systems. This paper presents a Three-Dimensional Modal Transmission Line Matrix (3D-TLM) implementation of transient heat flow in space power electronics. In such kind of components heat dissipation and good thermal management are essential. Simulation provides the cheapest tool to investigate all aspects of power handling. The 3DTLM has been successful in modeling heat diffusion problems and has proven to be efficient in terms of stability and complex geometry. The results show a three-dimensional visualisation of self-heating phenomena in the device affected by outer space constraints, and will presents possible approaches for increasing the heat dissipation capability of the power modules.

Keywords: Thermal management, conduction, heat dissipation, CTE, ceramic, heat spreader, nodes, 3D-TLM.

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Authors: Kaibalya Prasad Panda, Sreyasee Rout

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LLC (Inductor-inductor-capacitor) resonant converter has lots of advantages over other type of resonant converters which include high efficiency, more reliable and have high power density. This paper presents the design and analysis of a full bridge LLC resonant converter. In addition to the operational principle, the ZVS and ZCS conditions are also explained with the DC characteristics. Simulation of the LLC resonant converter is performed in MATLAB/ Simulink and the practical prototype setup is analyzed in Proteus software. The result is verified through analysis and design of a low cost, 200 watt prototype converter.

Keywords: LLC, Proteus, Resonant converter ZCS, ZVS.

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Authors: Didik Nurhadiyanto , Moch Agus Choiron , Ken Kaminishi , Shigeyuki Haruyama

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At the previous study of new metal gasket, contact width and contact stress were important design parameter for optimizing metal gasket performance. However, the range of contact stress had not been investigated thoroughly. In this study, we conducted a gasket design optimization based on an elastic and plastic contact stress analysis considering forming effect using FEM. The gasket model was simulated by using two simulation stages which is forming and tightening simulation. The optimum design based on an elastic and plastic contact stress was founded. Final evaluation was determined by helium leak quantity to check leakage performance of both type of gaskets. The helium leak test shows that a gasket based on the plastic contact stress design better than based on elastic stress design.

Keywords: Contact stress, metal gasket, plastic, elastic

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Authors: K. Kalaikumar, E. Baburaj

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One of the problems to be addressed in wireless sensor networks is the issues related to cross layer communication. Cross layer architecture shares the information across the layer, ensuring Quality of Services (QoS). With this shared information, MAC protocol adapts effective functionality maintenance such as route selection on changeable sensor network environment. However, time slot assignment and neighbour route selection time duration for cross layer have not been carried out. The time varying physical layer communication over cross layer causes high traffic load in the sensor network. Though, the traffic load was reduced using cross layer optimization procedure, the computational cost is high. To improve communication efficacy in the sensor network, a self-determined time slot based Cross-Layered Neighbour Route Discovery (C-LNRD) method is presented in this paper. In the presented work, the initial process is to discover the route in the sensor network using Dynamic Source Routing based Medium Access Control (MAC) sub layers. This process considers MAC layer operation with dynamic route neighbour table discovery. Then, the discovered route path for packet communication employs Broad Route Distributed Time Slot Assignment method on Cross-Layered Sensor Network system. Broad Route means time slotting on varying length of the route paths. During packet communication in this sensor network, transmission of packets is adjusted over the different time with varying ranges for controlling the traffic rate. Finally, Rayleigh fading model is developed in C-LNRD to identify the performance of the sensor network communication structure. The main task of Rayleigh Fading is to measure the power level of each communication under MAC sub layer. The minimized power level helps to easily reduce the computational cost of packet communication in the sensor network. Experiments are conducted on factors such as power factor, on packet communication, neighbour route discovery time, and information (i.e., packet) propagation speed.

Keywords: Medium access control, neighbour route discovery, wireless sensor network, Rayleigh fading, distributed time slot assignment

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Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

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This study optimized the design parameters of a cone loudspeaker as an example of high flexibility of the product design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to optimize each parameter of the loudspeaker design. To overcome the limitation of the design problem in practice, this study presents an acoustic analysis algorithm to optimize the design parameters of the loudspeaker. The material character of cone paper and the loudspeaker edge were the design parameters, and the vibration displacement of the cone paper was the objective function. The results of the analysis showed that the design had high accuracy as compared to the predicted value. These results suggested that although the parameter design is difficult, with experience and intuition, the design can be performed easily using the optimized design found with the acoustic analysis software.

Keywords: Air viscosity, design parameters, loudspeaker, optimization.

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Authors: Ngoc Trung Ngo, Buddhima Indraratna, Cholachat Rujikiathmakjornr

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For several hundred years, the design of railway tracks has practically remained unchanged. Traditionally, rail tracks are placed on a ballast layer due to several reasons, including economy, rapid drainage, and high load bearing capacity. The primary function of ballast is to distributing dynamic track loads to sub-ballast and subgrade layers, while also providing lateral resistance and allowing for rapid drainage. Upon repeated trainloads, the ballast becomes fouled due to ballast degradation and the intrusion of fines which adversely affects the strength and deformation behaviour of ballast. This paper presents the use of three-dimensional discrete element method (DEM) in studying the shear behaviour of the fouled ballast subjected to direct shear loading. Irregularly shaped particles of ballast were modelled by grouping many spherical balls together in appropriate sizes to simulate representative ballast aggregates. Fouled ballast was modelled by injecting a specified number of miniature spherical particles into the void spaces. The DEM simulation highlights that the peak shear stress of the ballast assembly decreases and the dilation of fouled ballast increases with an increase level of fouling. Additionally, the distributions of contact force chain and particle displacement vectors were captured during shearing progress, explaining the formation of shear band and the evolutions of volumetric change of fouled ballast.

Keywords: Railway ballast, coal fouling, discrete element modelling, discrete element method.

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Authors: Z. M. Shareefdeen, A. Aidan, W.Ahmed, M. B. Khatri, M. Islam, R. Lecheheb, F. Shams

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Air emissions from waste treatment plants often consist of a combination of Volatile Organic Compounds (VOCs) and odors. Hydrogen sulfide is one of the major odorous gases present in the waste emissions coming from municipal wastewater treatment facilities. Hydrogen sulfide (H2S) is odorous, highly toxic and flammable. Exposure to lower concentrations can result in eye irritation, a sore throat and cough, shortness of breath, and fluid in the lungs. Biofiltration has become a widely accepted technology for treating air streams containing H2S. When compared with other nonbiological technologies, biofilter is more cost-effective for treating large volumes of air containing low concentrations of biodegradable compounds. Optimization of biofilter media is essential for many reasons such as: providing a higher surface area for biofilm growth, low pressure drop, physical stability, and good moisture retention. In this work, a novel biofilter media is developed and tested at a pumping station of a municipality located in the United Arab Emirates (UAE). The media is found to be very effective (>99%) in removing H2S concentrations that are expected in pumping stations under steady state and shock loading conditions.

Keywords: biofilter media, hydrogen sulphide, pumping station, biofiltration

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Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

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This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matérn, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matérn, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian Process Regression, Ensemble Kernels, Bayesian Optimization, Pharmaceutical Sales Analysis, Time Series Forecasting, Data Analysis.

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Authors: W. Wen, G. Jackson, S. Maskill, D. G. McCartney, W. Sun

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CoNiCrAlY alloys have been widely used as bond coats for thermal barrier coating (TBC) systems because of low cost, improved control of composition, and the feasibility to tailor the coatings microstructures. Coatings are in general very thin structures, and therefore it is impossible to characterize the mechanical responses of the materials via conventional mechanical testing methods. Due to this reason, miniature specimen testing methods, such as the small punch test technique, have been developed. This paper presents some of the recent research in evaluating the mechanical properties of the CoNiCrAlY coatings at room and high temperatures, through the use of small punch testing and the developed miniature specimen tensile testing, applicable to a range of temperature, to investigate the elastic-plastic and creep behavior as well as ductile-brittle transition temperature (DBTT) behavior. An inverse procedure was developed to derive the mechanical properties from such tests for the coating materials. A two-layer specimen test method is also described. The key findings include: 1) the temperature-dependent coating properties can be accurately determined by the miniature tensile testing within a wide range of temperature; 2) consistent DBTTs can be identified by both the SPT and miniature tensile tests (~ 650 °C); and 3) the FE SPT modelling has shown good capability of simulating the early local cracking. In general, the temperature-dependent material behaviors of the CoNiCrAlY coating has been effectively characterized using miniature specimen testing and inverse method.

Keywords: CoNiCrAlY coatings, mechanical properties, DBTT, miniature specimen testing.

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Authors: R.Mohamad Idris, A.Khairuddin, M.W.Mustafa

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In this paper, a novel method using Bees Algorithm is proposed to determine the optimal allocation of FACTS devices for maximizing the Available Transfer Capability (ATC) of power transactions between source and sink areas in the deregulated power system. The algorithm simultaneously searches the FACTS location, FACTS parameters and FACTS types. Two types of FACTS are simulated in this study namely Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC). A Repeated Power Flow with FACTS devices including ATC is used to evaluate the feasible ATC value within real and reactive power generation limits, line thermal limits, voltage limits and FACTS operation limits. An IEEE30 bus system is used to demonstrate the effectiveness of the algorithm as an optimization tool to enhance ATC. A Genetic Algorithm technique is used for validation purposes. The results clearly indicate that the introduction of FACTS devices in a right combination of location and parameters could enhance ATC and Bees Algorithm can be efficiently used for this kind of nonlinear integer optimization.

Keywords: ATC, Bees Algorithm, TCSC, SVC

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Authors: Khaled S. Abdallah, Abdel-Aziz M. Mohamed

Abstract:

Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.

Keywords: Modeling, truck rental, supply chains management, simulation.

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