Search results for: Bee colony optimization

Authors: S. Saehana, F. Iskandar, M. Abdullah, Khairurrijal

Abstract:

A numerical simulation of optimization all of electrospinning processing parameters to obtain smallest nanofiber diameter have been performed by employing genetic algorithm (GA). Fitness function in genetic algorithm methods, which was different for each parameter, was determined by simulation approach based on the Reneker’s model. Moreover, others genetic algorithm parameter, namely length of population, crossover and mutation were applied to get the optimum electrospinning processing parameters. In addition, minimum fiber diameter, 32 nm, was achieved from a simulation by applied the optimum parameters of electrospinning. This finding may be useful for process control and prediction of electrospun fiber production. In this paper, it is also compared between predicted parameters with some experimental results.

Keywords: Diameter, Electrospinning, GA, Nanofiber.

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Authors: V.Bharathi, Jamila Patterson, R.Rajendiran

Abstract:

Optimization of extraction of phenolic compounds from Avicennia marina using response surface methodology was carried out during the present study. Five levels, three factors rotatable design (CCRD) was utilized to examine the optimum combination of extraction variables based on the TPC of Avicennia marina leaves. The best combination of response function was 78.41 °C, drying temperature; 26.18°C; extraction temperature and 36.53 minutes of extraction time. However, the procedure can be promptly extended to the study of several others pharmaceutical processes like purification of bioactive substances, drying of extracts and development of the pharmaceutical dosage forms for the benefit of consumers.

Keywords: Avicennia marina, Central Composite RotatableDesign (CCRD), Response Surface Methodology, Total Phenoliccontents (TPC)

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Authors: Tugrul Oktay, Mehmet Konar, Mustafa Soylak, Firat Sal, Murat Onay, Orhan Kizilkaya

Abstract:

In this paper, autonomous performance of a small manufactured unmanned helicopter is tried to be increased. For this purpose, a small unmanned helicopter is manufactured in Erciyes University, Faculty of Aeronautics and Astronautics. It is called as ZANKA-Heli-I. For performance maximization, autopilot parameters are determined via minimizing a cost function consisting of flight performance parameters such as settling time, rise time, overshoot during trajectory tracking. For this purpose, a stochastic optimization method named as simultaneous perturbation stochastic approximation is benefited. Using this approach, considerable autonomous performance increase (around %23) is obtained.

Keywords: Small helicopters, hierarchical control, stochastic optimization, autonomous performance maximization, autopilots.

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Authors: R. Uporn, P. Luangpaiboon

Abstract:

This paper presents a comparative study of statistical methods for the multi-response surface optimization of a cryogenic freezing process. Taguchi design and analysis and steepest ascent methods based on the desirability function were conducted to ascertain the influential factors of a cryogenic freezing process and their optimal levels. The more preferable levels of the set point, exhaust fan speed, retention time and flow direction are set at -90oC, 20 Hz, 18 minutes and Counter Current, respectively. The overall desirability level is 0.7044.

Keywords: Cryogenic Freezing Process, Taguchi Design and Analysis, Response Surface Method, Steepest Ascent Method and Desirability Function Approach.

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Authors: Toshinori Nawata

Abstract:

In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) using the gradient optimization automatic choosing functions for nonlinear systems. Constant terms which arise from sectionwise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics. Parameters included in the control are suboptimally selected by expanding a stable region in the sense of Lyapunov with the aid of the genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics.

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Authors: D. H. Park, Y. H. Tak, H. H. Kwon, G. J. Kwon, H. G. Kim

Abstract:

The purpose of this study is to develop a forging process of automotive parts that satisfies the deformation characteristics. The analyses of temperature variation and deformation behavior of the material are important to obtain the optimal forging products. The hot compression test was carried out to know formability at high temperature. In order to define the optimum forging conditions including material temperature, strain and forging load, the commercial finite element analysis code was used to simulate the forging procedure of engine mounting parts. Experimental results were compared with the simulation results by finite element analysis. Test results were in good agreement with the simulations.

Keywords: Cold forging, hot forging, engine mounting, automotive parts, optimization.

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

Abstract:

Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: Decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle.

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Authors: Maumita Bhattacharya

Abstract:

Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build meta models or use an approximation of the actual fitness functions to be evaluated. These meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present two evolutionary algorithm frameworks which involve use of meta models for fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The second model, DAFHEA-II, an enhanced version of the original DAFHEA framework, incorporates a multiple-model based learning approach for the support vector machine approximator to handle noisy functions [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions demonstrate their efficiency

Keywords: Meta model, Evolutionary algorithm, Stochastictechnique, Fitness function, Optimization, Support vector machine.

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Authors: A. Raminfard, S. M. Shahrtash

Abstract:

In this paper, a new efficient method for load balancing in low voltage distribution systems is presented. The proposed method introduces an improved Leap-frog method for optimization. The proposed objective function includes the difference between three phase currents, as well as two other terms to provide the integer property of the variables; where the latter are the status of the connection of loads to different phases. Afterwards, a new algorithm is supplemented to undertake the integer values for the load connection status. Finally, the method is applied to different parts of Tabriz low voltage network, where the results have shown the good performance of the proposed method.

Keywords: Load balancing, improved leap-frog method, optimization algorithm, low voltage distribution systems.

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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: Hong Dongchen, Chen Qiuxiao, Wu Shuang

Abstract:

Systematic Science reveals the complex nonlinear mechanisms of behavior in urban system. However, when confronted with such system, most city planners in China are still utilizing simple linear thinking to learn and understand this open complex giant system. In this paper, the hypercycle theory was introduced, which is one of the basis theories of systematic science. Based on the analysis of the reasons for the failure of current urban planning in China, and in consideration of the nonlinear characteristics of the urban system as well, optimization ideas for urban planning formulation were presented such as the shift from blueprint planning to progressive planning and from the rigid urban planning management control to its dynamically monitor and in time feedback.

Keywords: Systematic science, hypercycle theory, urban planning.

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Authors: Tung-Sheng Zhan, Chih-Cheng Kao, Chin-Der Yang, Jong-Ian Tsai

Abstract:

Fossil fuel-firing power plants dominate electric power generation in Taiwan, which are also the major contributor to Green House gases (GHG). CO2 is the most important greenhouse gas that cause global warming. This paper penetrates the relationship between carbon trading for GHG reduction and power generation expansion planning (GEP) problem for the electrical utility. The Particle Swarm Optimization (PSO) Algorithm is presented to deal with the generation expansion planning strategy of the utility with independent power providers (IPPs). The utility has to take both the IPPs- participation and environment impact into account when a new generation unit is considering expanded from view of supply side.

Keywords: Carbon Trading, CO2 Emission, GenerationExpansion Planning (GEP), Green House gases (GHG), ParticleSwarm Optimization (PSO).

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Authors: Yendiyarov Sergei, Zobnin Boris, Petrushenko Sergei

Abstract:

Usually, the solid-fuel flow of an iron ore sinter plant consists of different types of the solid-fuels, which differ from each other. Information about the composition of the solid-fuel flow usually comes every 8-24 hours. It can be clearly seen that this information cannot be used to control the sintering process in real time. Due to this, we propose an expert system which uses indirect measurements from the process in order to obtain the composition of the solid-fuel flow by solving an optimization task. Then this information can be used to control the sintering process. The proposed technique can be successfully used to improve sinter quality and reduce the amount of solid-fuel used by the process.

Keywords: sintering process, particle swarm optimization, optimal control, expert system, solid-fuel

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

Abstract:

Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: Beam structures, layerwise, optimization, variable angle tow, neural network

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Authors: Samee Ullah Khan, C. Ardil

Abstract:

We model the process of a data center as a multi- objective problem of mapping independent tasks onto a set of data center machines that simultaneously minimizes the energy consump¬tion and response time (makespan) subject to the constraints of deadlines and architectural requirements. A simple technique based on multi-objective goal programming is proposed that guarantees Pareto optimal solution with excellence in convergence process. The proposed technique also is compared with other traditional approach. The simulation results show that the proposed technique achieves superior performance compared to the min-min heuristics, and com¬petitive performance relative to the optimal solution implemented in UNDO for small-scale problems.

Keywords: Energy-efficient computing, distributed systems, multi-objective optimization.

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Authors: M. Farahnakian, M.R. Razfar, S. Elhami-Joosheghan

Abstract:

This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

Keywords: cutting parameters, face milling, surface roughness, artificial neural network, Electromagnetism-like algorithm,

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Authors: Clio G. Vossou, Ioannis N. Koukoulis, Christopher G. Provatidis

Abstract:

The present work encounters the solution of the defect identification problem with the use of an evolutionary algorithm combined with a simplex method. In more details, a Matlab implementation of Genetic Algorithms is combined with a Simplex method in order to lead to the successful identification of the defect. The influence of the location and the orientation of the depressed ellipsoidal flaw was investigated as well as the use of different amount of static data in the cost function. The results were evaluated according to the ability of the simplex method to locate the global optimum in each test case. In this way, a clear impression regarding the performance of the novel combination of the optimization algorithms, and the influence of the geometrical parameters of the flaw in defect identification problems was obtained.

Keywords: Defect identification, genetic algorithms, optimization.

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Authors: Leila Vafajoo, Farhad Khorasheh, Mehrnoosh Hamzezadeh Nakhjavani, Moslem Fattahi

Abstract:

In the present study, a procedure was developed to determine the optimum reaction rate constants in generalized Arrhenius form and optimized through the Nelder-Mead method. For this purpose, a comprehensive mathematical model of a fixed bed reactor for dehydrogenation of heavy paraffins over Pt–Sn/Al2O3 catalyst was developed. Utilizing appropriate kinetic rate expressions for the main dehydrogenation reaction as well as side reactions and catalyst deactivation, a detailed model for the radial flow reactor was obtained. The reactor model composed of a set of partial differential equations (PDE), ordinary differential equations (ODE) as well as algebraic equations all of which were solved numerically to determine variations in components- concentrations in term of mole percents as a function of time and reactor radius. It was demonstrated that most significant variations observed at the entrance of the bed and the initial olefin production obtained was rather high. The aforementioned method utilized a direct-search optimization algorithm along with the numerical solution of the governing differential equations. The usefulness and validity of the method was demonstrated by comparing the predicted values of the kinetic constants using the proposed method with a series of experimental values reported in the literature for different systems.

Keywords: Dehydrogenation, Pt-Sn/Al2O3 Catalyst, Modeling, Nelder-Mead, Optimization

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Authors: A. Zolfagharian, M.Z. Md. Zain, A. R. AbuBakar, M. Hussein

Abstract:

Application of flexible structures has been significantly, increased in industry and aerospace missions due to their contributions and unique advantages over the rigid counterparts. In this paper, vibration analysis of a flexible structure i.e., automobile wiper blade is investigated and controlled. The wiper generates unwanted noise and vibration during the wiping the rain and other particles on windshield which may cause annoying noise in different ranges of frequency. A two dimensional analytical modeled wiper blade whose model accuracy is verified by numerical studies in literature is considered in this study. Particle swarm optimization (PSO) is employed in alliance with input shaping (IS) technique in order to control or to attenuate the amplitude level of unwanted noise/vibration of the wiper blade.

Keywords: Input shaping, noise reduction, particle swarmoptimization, wiper blade

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Authors: R. Kanmaniraja, R. Freshipali, J. Abdullah, K. Niranjan, K. Balasubramani, V. R. Sanal Kumar

Abstract:

The present environmental issues have made aircraft jet noise reduction a crucial problem in aero-acoustics research. Acoustic studies reveal that addition of chevrons to the nozzle reduces the sound pressure level reasonably with acceptable reduction in performance. In this paper comprehensive numerical studies on acoustic characteristics of different types of chevron nozzles have been carried out with non-reacting flows for the shape optimization of chevrons in supersonic nozzles for aerospace applications. The numerical studies have been carried out using a validated steady 3D density based, k-ε turbulence model. In this paper chevron with sharp edge, flat edge, round edge and U-type edge are selected for the jet acoustic characterization of supersonic nozzles. We observed that compared to the base model a case with round-shaped chevron nozzle could reduce 4.13% acoustic level with 0.6% thrust loss. We concluded that the prudent selection of the chevron shape will enable an appreciable reduction of the aircraft jet noise without compromising its overall performance. It is evident from the present numerical simulations that k-ε model can predict reasonably well the acoustic level of chevron supersonic nozzles for its shape optimization.

Keywords: Supersonic nozzle, Chevron, Acoustic level, Shape Optimization of Chevron Nozzles, Jet noise suppression.

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Authors: B. Marasović, S. Pivac, S. V. Vukasović

Abstract:

Constructing a portfolio of investments is one of the most significant financial decisions facing individuals and institutions. In accordance with the modern portfolio theory maximization of return at minimal risk should be the investment goal of any successful investor. In addition, the costs incurred when setting up a new portfolio or rebalancing an existing portfolio must be included in any realistic analysis. In this paper rebalancing an investment portfolio in the presence of transaction costs on the Croatian capital market is analyzed. The model applied in the paper is an extension of the standard portfolio mean-variance optimization model in which transaction costs are incurred to rebalance an investment portfolio. This model allows different costs for different securities, and different costs for buying and selling. In order to find efficient portfolio, using this model, first, the solution of quadratic programming problem of similar size to the Markowitz model, and then the solution of a linear programming problem have to be found. Furthermore, in the paper the impact of transaction costs on the efficient frontier is investigated. Moreover, it is shown that global minimum variance portfolio on the efficient frontier always has the same level of the risk regardless of the amount of transaction costs. Although efficient frontier position depends of both transaction costs amount and initial portfolio it can be concluded that extreme right portfolio on the efficient frontier always contains only one stock with the highest expected return and the highest risk.

Keywords: Croatian capital market, Fractional quadratic programming, Markowitz model, Portfolio optimization, Transaction costs.

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Authors: P. Azimi, B. Behtoiy

Abstract:

One of the most famous techniques which affect the efficiency of a production line is the assembly line balancing (ALB) technique. This paper examines the balancing effect of a whole production line of a real auto glass manufacturer in three steps. In the first step, processing time of each activity in the workstations is generated according to a practical approach. In the second step, the whole production process is simulated and the bottleneck stations have been identified, and finally in the third step, several improvement scenarios are generated to optimize the system throughput, and the best one is proposed. The main contribution of the current research is the proposed framework which combines two famous approaches including Assembly Line Balancing and Optimization via Simulation technique (OvS). The results show that the proposed framework could be applied in practical environments, easily.

Keywords: Assembly line balancing problem, optimization via simulation, production planning.

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Authors: Chun-guang Pan, Ying-wu Chen

Abstract:

Risk response planning is of importance for software project risk management (SPRM). In CMMI, risk management was in the third capability maturity level, which provides a framework for software project risk identification, assessment, risk planning, risk control. However, the CMMI-based SPRM currently lacks quantitative supporting tools, especially during the process of implementing software project risk planning. In this paper, an economic optimization model for selecting risk reduction actions in the phase of software project risk response planning is presented. Furthermore, an example taken from a Chinese software industry is illustrated to verify the application of this method. The research provides a risk decision method for project risk managers that can be used in the implementation of CMMI-based SPRM.

Keywords: Software project, risk management, CMMI, riskresponse planning.

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Authors: Shrawan Kumar, D. K. Lobiyal

Abstract:

In this paper, we propose improved versions of DVHop algorithm as QDV-Hop algorithm and UDV-Hop algorithm for better localization without the need for additional range measurement hardware. The proposed algorithm focuses on third step of DV-Hop, first error terms from estimated distances between unknown node and anchor nodes is separated and then minimized. In the QDV-Hop algorithm, quadratic programming is used to minimize the error to obtain better localization. However, quadratic programming requires a special optimization tool box that increases computational complexity. On the other hand, UDV-Hop algorithm achieves localization accuracy similar to that of QDV-Hop by solving unconstrained optimization problem that results in solving a system of linear equations without much increase in computational complexity. Simulation results show that the performance of our proposed schemes (QDV-Hop and UDV-Hop) is superior to DV-Hop and DV-Hop based algorithms in all considered scenarios.

Keywords: Wireless sensor networks, Error term, DV-Hop algorithm, Localization.

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Authors: Arash Hassanpour Isfahani, Siavash Sadeghi

Abstract:

Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles due to their unique merits. However they have two major drawbacks i.e. high cost and small speed range. In this paper an optimal design of a permanent magnet machine is presented. A reduction of permanent magnet material for a constant torque and an extension in speed and torque ranges are chosen as the optimization aims. For this purpose the analytical model of the permanent magnet synchronous machine is derived and the appropriate design algorithm is devised. The genetic algorithm is then employed to optimize some machine specifications. Finally the finite element method is used to validate the designed machine.

Keywords: Design, Finite Element, Hybrid electric vehicle, Optimization, Permanent magnet synchronous machine.

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Authors: Jasmin Velagic, Nedim Osmic, Bakir Lacevic

Abstract:

In this paper the neural network-based controller is designed for motion control of a mobile robot. This paper treats the problems of trajectory following and posture stabilization of the mobile robot with nonholonomic constraints. For this purpose the recurrent neural network with one hidden layer is used. It learns relationship between linear velocities and error positions of the mobile robot. This neural network is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control inputs. The performance of the proposed system is investigated using a kinematic model of the mobile robot.

Keywords: Mobile robot, kinematic model, neural network, motion control, adaptive learning rate.

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Authors: M. Bassoui, M. Ferfra, M. Chraygane, M. Ould Ahmedou, N. Elghazal, A. Belhaiba

Abstract:

Currently, the High voltage power supply for microwave generators with one magnetron uses a single-phase transformer with magnetic shunt. To contribute in the development of technological innovation in industry of manufacturing of power supplies of magnetrons for microwaves, ovens for domestic or industrial use, this original work treats the optimization of a new three-phase high voltage power supply for industrial microwaves generators with N magnetrons by phase (Treated case N=1), from its modeling with Matlab-Simulink. The design of this power supply uses three π quadruple models equivalents of new three-phase transformer with magnetic shunt of each phase. Every one supplies at its output a voltage doubler cell composed of a capacitor and a diode that in its output supplies only one magnetron.  In this work we will define a strategy that aims to reduce the volume of the transformer and the weight and cost of the entire system of the high voltage power supply, while respecting the conditions recommended by the manufacturer, concerning the current flowing in each magnetron: (Imax <1.2 A, IAv ≈ 300 mA).

 

Keywords: Optimization, Three-phase transformer, Modeling, power supply, magnetrons, Matlab Simulink, High Voltage

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Authors: Toshinori Nawata

Abstract:

In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) for nonlinear systems with constrained input using weighted gradient optimization automatic choosing functions. Constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: Augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics.

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Authors: Andrew Randles, Ilker Ocak, Cheam Daw Don, Navab Singh, Alex Gu

Abstract:

Nine Degrees of Freedom (9 DOF) systems are already in development in many areas. In this paper, an integrated pressure sensor is proposed that will make use of an already existing monolithic 9 DOF inertial MEMS platform. Capacitive pressure sensors can suffer from limited sensitivity for a given size of membrane. This novel pressure sensor design increases the sensitivity by over 5 times compared to a traditional array of square diaphragms while still fitting within a 2 mm x 2 mm chip and maintaining a fixed static capacitance. The improved design uses one large diaphragm supported by pillars with fixed electrodes placed above the areas of maximum deflection. The design optimization increases the sensitivity from 0.22 fF/kPa to 1.16 fF/kPa. Temperature sensitivity was also examined through simulation.

Keywords: Capacitive pressure sensor, 9 DOF, 10 DOF, sensor, capacitive, inertial measurement unit, IMU, inertial navigation system, INS.

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Authors: Laurent Thiry, Michel Hassenforder

Abstract:

This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries.

Keywords: Data transformation, functional programming, information server, optimization.

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