Search results for: computational duration

Authors: Christopher Paterson, Richard Curry, Alan Purvis, Simon Johnson

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Emerging Bio-engineering fields such as Brain Computer Interfaces, neuroprothesis devices and modeling and simulation of neural networks have led to increased research activity in algorithms for the detection, isolation and classification of Action Potentials (AP) from noisy data trains. Current techniques in the field of 'unsupervised no-prior knowledge' biosignal processing include energy operators, wavelet detection and adaptive thresholding. These tend to bias towards larger AP waveforms, AP may be missed due to deviations in spike shape and frequency and correlated noise spectrums can cause false detection. Also, such algorithms tend to suffer from large computational expense. A new signal detection technique based upon the ideas of phasespace diagrams and trajectories is proposed based upon the use of a delayed copy of the AP to highlight discontinuities relative to background noise. This idea has been used to create algorithms that are computationally inexpensive and address the above problems. Distinct AP have been picked out and manually classified from real physiological data recorded from a cockroach. To facilitate testing of the new technique, an Auto Regressive Moving Average (ARMA) noise model has been constructed bases upon background noise of the recordings. Along with the AP classification means this model enables generation of realistic neuronal data sets at arbitrary signal to noise ratio (SNR).

Keywords: Action potential detection, Low SNR, Phase spacediagrams/trajectories, Unsupervised/no-prior knowledge.

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Authors: O. Kratochvíl, J. Križan

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The paper deals with the analysis of the dynamic response of footbridges under human - induced dynamic loads. This is a frequently occurring and often dominant load for footbridges as it stems from the very purpose of a footbridge - to convey pedestrian. Due to the emergence of new materials and advanced engineering technology, slender footbridges are increasingly becoming popular to satisfy the modern transportation needs and the aesthetical requirements of the society. These structures however are always lively with low stiffness, low mass, low damping and low natural frequencies. As a consequence, they are prone to vibration induced by human activities and can suffer severe vibration serviceability problems, particularly in the lateral direction. Pedestrian bridges are designed according to first and second limit states, these are the criteria involved in response to static design load. However, it is necessary to assess the dynamic response of bridge design load on pedestrians and assess it impact on the comfort of the user movement. Usually the load is considered a person or a small group which can be assumed in perfect motion synchronization. Already one person or small group can excite significant vibration of the deck. In order to calculate the dynamic response to the movement of people, designer needs available and suitable computational model and criteria. For the calculation program ANSYS based on finite element method was used.

Keywords: Footbridge, dynamic analysis, vibration serviceability of footbridges, lateral vibration, stiffness, dynamic force, walking force, slender suspension footbridges, natural frequencies and vibration modes, rhythm jumping, normal walking.

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: Agent communication, introspective agent, isolation of agent, policy enforcement system.

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Authors: Yusri Yusof, Stephen Newman, Aydin Nassehi, Keith Case

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The changing economic climate has made global manufacturing a growing reality over the last decade, forcing companies from east and west and all over the world to collaborate beyond geographic boundaries in the design, manufacture and assemble of products. The ISO10303 and ISO14649 Standards (STEP and STEP-NC) have been developed to introduce interoperability into manufacturing enterprises so as to meet the challenge of responding to production on demand. This paper describes and illustrates a STEP compliant CAD/CAPP/CAM System for the manufacture of rotational parts on CNC turning centers. The information models to support the proposed system together with the data models defined in the ISO14649 standard used to create the NC programs are also described. A structured view of a STEP compliant CAD/CAPP/CAM system framework supporting the next generation of intelligent CNC controllers for turn/mill component manufacture is provided. Finally a proposed computational environment for a STEP-NC compliant system for turning operations (SCSTO) is described. SCSTO is the experimental part of the research supported by the specification of information models and constructed using a structured methodology and object-oriented methods. SCSTO was developed to generate a Part 21 file based on machining features to support the interactive generation of process plans utilizing feature extraction. A case study component has been developed to prove the concept for using the milling and turning parts of ISO14649 to provide a turn-mill CAD/CAPP/CAM environment.

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Authors: Raja Singh Khela, R. K. Bansal, K. S. Sandhu, A. K. Goel

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Self-Excited Induction Generator (SEIG) builds up voltage while it enters in its magnetic saturation region. Due to non-linear magnetic characteristics, the performance analysis of SEIG involves cumbersome mathematical computations. The dependence of air-gap voltage on saturated magnetizing reactance can only be established at rated frequency by conducting a laboratory test commonly known as synchronous run test. But, there is no laboratory method to determine saturated magnetizing reactance and air-gap voltage of SEIG at varying speed, terminal capacitance and other loading conditions. For overall analysis of SEIG, prior information of magnetizing reactance, generated frequency and air-gap voltage is essentially required. Thus, analytical methods are the only alternative to determine these variables. Non-existence of direct mathematical relationship of these variables for different terminal conditions has forced the researchers to evolve new computational techniques. Artificial Neural Networks (ANNs) are very useful for solution of such complex problems, as they do not require any a priori information about the system. In this paper, an attempt is made to use cascaded neural networks to first determine the generated frequency and magnetizing reactance with varying terminal conditions and then air-gap voltage of SEIG. The results obtained from the ANN model are used to evaluate the overall performance of SEIG and are found to be in good agreement with experimental results. Hence, it is concluded that analysis of SEIG can be carried out effectively using ANNs.

Keywords: Self-Excited Induction Generator, Artificial NeuralNetworks, Exciting Capacitance and Saturated magnetizingreactance.

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Authors: Ana Paula P. dos Santos, Claudia R. Andrade, Edson L. Zaparoli

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Gas turbine air inlet cooling is a useful method for increasing output for regions where significant power demand and highest electricity prices occur during the warm months. Inlet air cooling increases the power output by taking advantage of the gas turbine-s feature of higher mass flow rate when the compressor inlet temperature decreases. Different methods are available for reducing gas turbine inlet temperature. There are two basic systems currently available for inlet cooling. The first and most cost-effective system is evaporative cooling. Evaporative coolers make use of the evaporation of water to reduce the gas turbine-s inlet air temperature. The second system employs various ways to chill the inlet air. In this method, the cooling medium flows through a heat exchanger located in the inlet duct to remove heat from the inlet air. However, the evaporative cooling is limited by wet-bulb temperature while the chilling can cool the inlet air to temperatures that are lower than the wet bulb temperature. In the present work, a thermodynamic model of a gas turbine is built to calculate heat rate, power output and thermal efficiency at different inlet air temperature conditions. Computational results are compared with ISO conditions herein called "base-case". Therefore, the two cooling methods are implemented and solved for different inlet conditions (inlet temperature and relative humidity). Evaporative cooler and absorption chiller systems results show that when the ambient temperature is extremely high with low relative humidity (requiring a large temperature reduction) the chiller is the more suitable cooling solution. The net increment in the power output as a function of the temperature decrease for each cooling method is also obtained.

Keywords: Absorption chiller, evaporative cooling, gas turbine, turbine inlet cooling.

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Authors: J. P. Panda, K. Sasmal, H. V. Warrior

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Eddy viscosity models in turbulence modeling can be mainly classified as linear and nonlinear models. Linear formulations are simple and require less computational resources but have the disadvantage that they cannot predict actual flow pattern in complex geophysical flows where streamline curvature and swirling motion are predominant. A constitutive equation of Reynolds stress anisotropy is adopted for the formulation of eddy viscosity including all the possible higher order terms quadratic in the mean velocity gradients, and a simplified model is developed for actual oceanic flows where only the vertical velocity gradients are important. The new model is incorporated into the one dimensional General Ocean Turbulence Model (GOTM). Two realistic oceanic test cases (OWS Papa and FLEX' 76) have been investigated. The new model predictions match well with the observational data and are better in comparison to the predictions of the two equation k-epsilon model. The proposed model can be easily incorporated in the three dimensional Princeton Ocean Model (POM) to simulate a wide range of oceanic processes. Practically, this model can be implemented in the coastal regions where trasverse shear induces higher vorticity, and for prediction of flow in estuaries and lakes, where depth is comparatively less. The model predictions of marine turbulence and other related data (e.g. Sea surface temperature, Surface heat flux and vertical temperature profile) can be utilized in short term ocean and climate forecasting and warning systems.

Keywords: Eddy viscosity, turbulence modeling, GOTM, CFD.

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Authors: Zahra Ghadampour, Gholamreza Rakhshandehroo

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A concern that researchers usually face in different applications of Artificial Neural Network (ANN) is determination of the size of effective domain in time series. In this paper, trial and error method was used on groundwater depth time series to determine the size of effective domain in the series in an observation well in Union County, New Jersey, U.S. different domains of 20, 40, 60, 80, 100, and 120 preceding day were examined and the 80 days was considered as effective length of the domain. Data sets in different domains were fed to a Feed Forward Back Propagation ANN with one hidden layer and the groundwater depths were forecasted. Root Mean Square Error (RMSE) and the correlation factor (R2) of estimated and observed groundwater depths for all domains were determined. In general, groundwater depth forecast improved, as evidenced by lower RMSEs and higher R2s, when the domain length increased from 20 to 120. However, 80 days was selected as the effective domain because the improvement was less than 1% beyond that. Forecasted ground water depths utilizing measured daily data (set #1) and data averaged over the effective domain (set #2) were compared. It was postulated that more accurate nature of measured daily data was the reason for a better forecast with lower RMSE (0.1027 m compared to 0.255 m) in set #1. However, the size of input data in this set was 80 times the size of input data in set #2; a factor that may increase the computational effort unpredictably. It was concluded that 80 daily data may be successfully utilized to lower the size of input data sets considerably, while maintaining the effective information in the data set.

Keywords: Neural networks, groundwater depth, forecast.

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Authors: M. A. Askari, M. A. Nazari, P. Perrier, Y. Payan

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Growth and remodeling of biological structures have gained lots of attention over the past decades. Determining the response of living tissues to mechanical loads is necessary for a wide range of developing fields such as prosthetics design or computerassisted surgical interventions. It is a well-known fact that biological structures are never stress-free, even when externally unloaded. The exact origin of these residual stresses is not clear, but theoretically, growth is one of the main sources. Extracting body organ’s shapes from medical imaging does not produce any information regarding the existing residual stresses in that organ. The simplest cause of such stresses is gravity since an organ grows under its influence from birth. Ignoring such residual stresses might cause erroneous results in numerical simulations. Accounting for residual stresses due to tissue growth can improve the accuracy of mechanical analysis results. This paper presents an original computational framework based on gradual growth to determine the residual stresses due to growth. To illustrate the method, we apply it to a finite element model of a healthy human face reconstructed from medical images. The distribution of residual stress in facial tissues is computed, which can overcome the effect of gravity and maintain tissues firmness. Our assumption is that tissue wrinkles caused by aging could be a consequence of decreasing residual stress and thus not counteracting gravity. Taking into account these stresses seems therefore extremely important in maxillofacial surgery. It would indeed help surgeons to estimate tissues changes after surgery.

Keywords: Finite element method, growth, residual stress, soft tissue.

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: Quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction.

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Authors: Jungho Choi, Youngwan Cho

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This paper presents an algorithm for the recognition and tracking of moving objects, 1/10 scale model car is used to verify performance of the algorithm. Presented algorithm for the recognition and tracking of moving objects in the paper is as follows. SURF algorithm is merged with Lucas-Kanade algorithm. SURF algorithm has strong performance on contrast, size, rotation changes and it recognizes objects but it is slow due to many computational complexities. Processing speed of Lucas-Kanade algorithm is fast but the recognition of objects is impossible. Its optical flow compares the previous and current frames so that can track the movement of a pixel. The fusion algorithm is created in order to solve problems which occurred using the Kalman Filter to estimate the position and the accumulated error compensation algorithm was implemented. Kalman filter is used to create presented algorithm to complement problems that is occurred when fusion two algorithms. Kalman filter is used to estimate next location, compensate for the accumulated error. The resolution of the camera (Vision Sensor) is fixed to be 640x480. To verify the performance of the fusion algorithm, test is compared to SURF algorithm under three situations, driving straight, curve, and recognizing cars behind the obstacles. Situation similar to the actual is possible using a model vehicle. Proposed fusion algorithm showed superior performance and accuracy than the existing object recognition and tracking algorithms. We will improve the performance of the algorithm, so that you can experiment with the images of the actual road environment.

Keywords: SURF, Optical Flow Lucas-Kanade, Kalman Filter, object recognition, object tracking.

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Authors: Yuri Boiko, Ci Lin, Iluju Kiringa, Tet Yeap

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The performance of a permanent magnet brushless direct current (BLDC) motor controlled by the Kalman filter based position-sensorless drive is studied in terms of its dependence from the system’s parameters variations. The effects of the system’s parameters changes on the dynamic behavior of state variables are verified. Simulated is the closed loop control scheme with Kalman filter in the feedback line. Distinguished are two separate data sampling modes in analyzing feedback output from the BLDC motor: (1) equal angular separation and (2) equal time intervals. In case (1), the data are collected via equal intervals  of rotor’s angular position i, i.e. keeping  = const. In case (2), the data collection time points ti are separated by equal sampling time intervals t = const. Demonstrated are the effects of the parameters changes on the sensorless control flow, in particular, reduction of the instability torque ripples, switching spikes, and torque load balancing. It is specifically shown that an efficient suppression of commutation induced instability torque ripples is an achievable selection of the sampling rate in the Kalman filter settings above a certain critical value. The computational cost of such suppression is shown to be higher for the motors with lower induction values of the windings.

Keywords: BLDC motor, Kalman filter, sensorless drive, state variables, instability torque ripples reduction, sampling rate.

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Authors: Cristina M. R. Caridade, Ana Rita F. Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with health care facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool, developed in MATLAB using Graphical User Interface, for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing to view original images and fusion images, compare processed and original images, adjust parameters and save images. The tool proposed in an innovative teaching and learning environment, consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques, necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides a real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: Image fusion, image processing, teaching-learning simulation tool, biomedical engineering education.

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Authors: O. Adjoul, A. Feugier, K. Benfriha, A. Aoussat

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In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.

Keywords: Availability, design for maintenance, DFM, dynamic maintenance, life cycle cost, LCC, maintenance free operating period, MFOP, simultaneous optimization.

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: Artificial neural networks, breast cancer, cancer dataset, classifiers, cervical cancer, F-score, logistic regression, machine learning, precision, recall, support vector machine.

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: Analog circuits, digital circuits, memristors, neuromorphic computing systems.

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Authors: Yash Pal, A.Swarup, Bhim Singh

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This paper presents a novel control strategy of a threephase four-wire Unified Power Quality (UPQC) for an improvement in power quality. The UPQC is realized by integration of series and shunt active power filters (APFs) sharing a common dc bus capacitor. The shunt APF is realized using a thee-phase, four leg voltage source inverter (VSI) and the series APF is realized using a three-phase, three leg VSI. A control technique based on unit vector template technique (UTT) is used to get the reference signals for series APF, while instantaneous sequence component theory (ISCT) is used for the control of Shunt APF. The performance of the implemented control algorithm is evaluated in terms of power-factor correction, load balancing, neutral source current mitigation and mitigation of voltage and current harmonics, voltage sag and swell in a three-phase four-wire distribution system for different combination of linear and non-linear loads. In this proposed control scheme of UPQC, the current/voltage control is applied over the fundamental supply currents/voltages instead of fast changing APFs currents/voltages, there by reducing the computational delay and the required sensors. MATLAB/Simulink based simulations are obtained, which support the functionality of the UPQC. MATLAB/Simulink based simulations are obtained, which support the functionality of the UPQC.

Keywords: Power Quality, UPQC, Harmonics, Load Balancing, Power Factor Correction, voltage harmonic mitigation, currentharmonic mitigation, voltage sag, swell

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Authors: Alia Alghosoun, Ashraf Osman, Mohammed Seaid

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A coupled two-layer finite volume/finite element method was proposed for solving dam-break flow problem over deformable beds. The governing equations consist of the well-balanced two-layer shallow water equations for the water flow and a linear elastic model for the bed deformations. Deformations in the topography can be caused by a brutal localized force or simply by a class of sliding displacements on the bathymetry. This deformation in the bed is a source of perturbations, on the water surface generating water waves which propagate with different amplitudes and frequencies. Coupling conditions at the interface are also investigated in the current study and two mesh procedure is proposed for the transfer of information through the interface. In the present work a new procedure is implemented at the soil-water interface using the finite element and two-layer finite volume meshes with a conservative distribution of the forces at their intersections. The finite element method employs quadratic elements in an unstructured triangular mesh and the finite volume method uses the Rusanove to reconstruct the numerical fluxes. The numerical coupled method is highly efficient, accurate, well balanced, and it can handle complex geometries as well as rapidly varying flows. Numerical results are presented for several test examples of dam-break flows over deformable beds. Mesh convergence study is performed for both methods, the overall model provides new insight into the problems at minimal computational cost.

Keywords: Dam-break flows, deformable beds, finite element method, finite volume method, linear elasticity, Shallow water equations.

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Authors: Lina Wu, Ye Li, Jia Liu

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We are interested in solving Liouville-type problems to explore constancy properties for maps or differential forms on Riemannian manifolds. Geometric structures on manifolds, the existence of constancy properties for maps or differential forms, and energy growth for maps or differential forms are intertwined. In this article, we concentrate on discovery of solutions to Liouville-type problems where manifolds are Euclidean spaces (i.e. flat Riemannian manifolds) and maps become real-valued functions. Liouville-type results of vanishing properties for functions are obtained. The original work in our research findings is to extend the q-energy for a function from finite in L^q space to infinite in non-L^q space by applying p-balanced technique where q = p = 2. Calculation skills such as Hölder's Inequality and Tests for Series have been used to evaluate limits and integrations for function energy. Calculation ideas and computational techniques for solving Liouville-type problems shown in this article, which are utilized in Euclidean spaces, can be universalized as a successful algorithm, which works for both maps and differential forms on Riemannian manifolds. This innovative algorithm has a far-reaching impact on research work of solving Liouville-type problems in the general settings involved with infinite energy. The p-balanced technique in this algorithm provides a clue to success on the road of q-energy extension from finite to infinite.

Keywords: Differential Forms, Hölder Inequality, Liouville-type problems, p-balanced growth, p-harmonic maps, q-energy growth, tests for series.

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Authors: Amira Abbes Kheriji, Faouzi Bouani, Mekki Ksouri, Mohamed Ben Ahmed

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Model Predictive Control (MPC) is increasingly being proposed for real time applications and embedded systems. However comparing to PID controller, the implementation of the MPC in miniaturized devices like Field Programmable Gate Arrays (FPGA) and microcontrollers has historically been very small scale due to its complexity in implementation and its computation time requirement. At the same time, such embedded technologies have become an enabler for future manufacturing enterprises as well as a transformer of organizations and markets. Recently, advances in microelectronics and software allow such technique to be implemented in embedded systems. In this work, we take advantage of these recent advances in this area in the deployment of one of the most studied and applied control technique in the industrial engineering. In fact in this paper, we propose an efficient framework for implementation of Generalized Predictive Control (GPC) in the performed STM32 microcontroller. The STM32 keil starter kit based on a JTAG interface and the STM32 board was used to implement the proposed GPC firmware. Besides the GPC, the PID anti windup algorithm was also implemented using Keil development tools designed for ARM processor-based microcontroller devices and working with C/Cµ langage. A performances comparison study was done between both firmwares. This performances study show good execution speed and low computational burden. These results encourage to develop simple predictive algorithms to be programmed in industrial standard hardware. The main features of the proposed framework are illustrated through two examples and compared with the anti windup PID controller.

Keywords: Embedded systems, Model Predictive Control, microcontroller, Keil tool.

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: Filippo Ranalli, Forest Flager, Martin Fischer

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This paper presents a ground structure method to optimize the topology and discrete member sizing of steel frame structures in order to minimize total installed cost, including material, fabrication and erection components. The proposed method improves upon existing cost-based ground structure methods by incorporating constructability considerations well as satisfying both strength and serviceability constraints. The architecture for the method is a bi-level Multidisciplinary Feasible (MDF) architecture in which the discrete member sizing optimization is nested within the topology optimization process. For each structural topology generated, the sizing optimization process seek to find a set of discrete member sizes that result in the lowest total installed cost while satisfying strength (member utilization) and serviceability (node deflection and story drift) criteria. To accurately assess cost, the connection details for the structure are generated automatically using accurate site-specific cost information obtained directly from fabricators and erectors. Member continuity rules are also applied to each node in the structure to improve constructability. The proposed optimization method is benchmarked against conventional weight-based ground structure optimization methods resulting in an average cost savings of up to 30% with comparable computational efficiency.

Keywords: Cost-based structural optimization, cost-based topology and sizing optimization, steel frame ground structure optimization, multidisciplinary optimization of steel structures.

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Authors: Ying-Ming Su

Abstract:

Fujian earthen building which was known as a classic for ecological buildings was listed on the world heritage in 2008 (UNESCO) in China. Its design strategy can be applied to modern architecture planning and design. This study chose two different cases (Round Atrium: Er-Yi Building, Double Round Atrium: Zhen-Chen Building) of earthen building in Fu-Jian to compare the ventilation effects of different atrium forms. We adopt field measurements and computational fluid dynamics (CFD) simulation of temperature, humidity, and wind environment to identify the relationship between external environment and atrium about comfort and to confirm the relationship about atrium H/W (height/width). Results indicate that, through the atrium convection effect, it makes the natural wind guides to each space surrounded and keeps indoor comfort. It illustrates that the smaller the ratio of the H/W which is the relationship between the height and the width of an atrium is, the greater the wind speed generated within the street valley. Moreover, the wind speed is very close to the reference wind speed. This field measurement verifies that the value of H/W has great influence of solar radiation heat and sunshine shadows. The ventilation efficiency is: Er-Yi Building (H/W =0.2778) > Zhen-Chen Building (H/W=0.3670). Comparing the cases with the same shape but with different H/W, through the different size patios, airflow revolves in the atriums and can be brought into each interior space. The atrium settings meet the need of building ventilation, and can adjust the humidity and temperature within the buildings. It also creates good ventilation effect.

Keywords: Traditional folk houses, Atrium, Earthen building, Ventilation, Building microclimate, PET.

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Authors: Wichai Pattanapol, Sarah J. Wakes, Michael J. Hilton, Katharine J.M. Dickinson

Abstract:

Turbulence modeling of large-scale flow over a vegetated surface is complex. Such problems involve large scale computational domains, while the characteristics of flow near the surface are also involved. In modeling large scale flow, surface roughness including vegetation is generally taken into account by mean of roughness parameters in the modified law of the wall. However, the turbulence structure within the canopy region cannot be captured with this method, another method which applies source/sink terms to model plant drag can be used. These models have been developed and tested intensively but with a simple surface geometry. This paper aims to compare the use of roughness parameter, and additional source/sink terms in modeling the effect of plant drag on wind flow over a complex vegetated surface. The RNG k-ε turbulence model with the non-equilibrium wall function was tested with both cases. In addition, the k-ω turbulence model, which is claimed to be computationally stable, was also investigated with the source/sink terms. All numerical results were compared to the experimental results obtained at the study site Mason Bay, Stewart Island, New Zealand. In the near-surface region, it is found that the results obtained by using the source/sink term are more accurate than those using roughness parameters. The k-ω turbulence model with source/sink term is more appropriate as it is more accurate and more computationally stable than the RNG k-ε turbulence model. At higher region, there is no significant difference amongst the results obtained from all simulations.

Keywords: CFD, canopy flow, surface roughness, turbulence models.

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Authors: Jean Berger, Nassirou Lo, Martin Noel

Abstract:

Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: Search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization.

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Authors: Eleftheria Tzamali, Panayiota Poirazi, Ioannis G. Tollis, Martin Reczko

Abstract:

Stable bacterial polymorphism on a single limiting resource may appear if between the evolved strains metabolic interactions take place that allow the exchange of essential nutrients [8]. Towards an attempt to predict the possible outcome of longrunning evolution experiments, a network based on the metabolic capabilities of homogeneous populations of every single gene knockout strain (nodes) of the bacterium E. coli is reconstructed. Potential metabolic interactions (edges) are allowed only between strains of different metabolic capabilities. Bacterial communities are determined by finding cliques in this network. Growth of the emerged hypothetical bacterial communities is simulated by extending the metabolic flux balance analysis model of Varma et al [2] to embody heterogeneous cell population growth in a mutual environment. Results from aerobic growth on 10 different carbon sources are presented. The upper bounds of the diversity that can emerge from single-cloned populations of E. coli such as the number of strains that appears to metabolically differ from most strains (highly connected nodes), the maximum clique size as well as the number of all the possible communities are determined. Certain single gene deletions are identified to consistently participate in our hypothetical bacterial communities under most environmental conditions implying a pattern of growth-condition- invariant strains with similar metabolic effects. Moreover, evaluation of all the hypothetical bacterial communities under growth on pyruvate reveals heterogeneous populations that can exhibit superior growth performance when compared to the performance of the homogeneous wild-type population.

Keywords: Bacterial polymorphism, clique identification, dynamic FBA, evolution, metabolic interactions.

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Authors: S. Deswal, M. Pal

Abstract:

Presently various computational techniques are used in modeling and analyzing environmental engineering data. In the present study, an intra-comparison of polynomial and radial basis kernel functions based on Support Vector Regression and, in turn, an inter-comparison with Multi Linear Regression has been attempted in modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ multiple plunging jets (varying from 1 to 16 numbers). The data set used in this study consists of four input parameters with a total of eighty eight cases, forty four each for vertical and inclined multiple plunging jets. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 along with corresponding root mean square error values of 0.0025 and 0.0020 were achieved by using polynomial and radial basis kernel functions based Support Vector Regression respectively. An intra-comparison suggests improved performance by radial basis function in comparison to polynomial kernel based Support Vector Regression. Further, an inter-comparison with Multi Linear Regression (correlation coefficient = 0.973 and root mean square error = 0.0024) reveals that radial basis kernel functions based Support Vector Regression performs better in modeling and estimating mass transfer by multiple plunging jets.

Keywords: Mass transfer, multiple plunging jets, polynomial and radial basis kernel functions, Support Vector Regression.

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Authors: S. Rao Rayapudi

Abstract:

Economic Load Dispatch (ELD) is a method of determining the most efficient, low-cost and reliable operation of a power system by dispatching available electricity generation resources to supply load on the system. The primary objective of economic dispatch is to minimize total cost of generation while honoring operational constraints of available generation resources. In this paper an intelligent water drop (IWD) algorithm has been proposed to solve ELD problem with an objective of minimizing the total cost of generation. Intelligent water drop algorithm is a swarm-based natureinspired optimization algorithm, which has been inspired from natural rivers. A natural river often finds good paths among lots of possible paths in its ways from source to destination and finally find almost optimal path to their destination. These ideas are embedded into the proposed algorithm for solving economic load dispatch problem. The main advantage of the proposed technique is easy is implement and capable of finding feasible near global optimal solution with less computational effort. In order to illustrate the effectiveness of the proposed method, it has been tested on 6-unit and 20-unit test systems with incremental fuel cost functions taking into account the valve point-point loading effects. Numerical results shows that the proposed method has good convergence property and better in quality of solution than other algorithms reported in recent literature.

Keywords: Economic load dispatch, Transmission loss, Optimization, Valve point loading, Intelligent Water Drop Algorithm.

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Authors: Pedro Pablo González Pérez, Hiram I. Beltrán, Arturo Rojo-Domínguez, Máximo EduardoSánchez Gutiérrez

Abstract:

Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.

Keywords: multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

Abstract:

Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety.

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