Predictive models Research Papers - Academia.edu (original) (raw)

Command Control, Communication Computer and Intelligence (C4I) systems enables modern military forces to achieve information superiority in the battlefield. C4I are complex System of systems (SOS) where individual systems interact locally to achieve global SOS behaviors. To build software for C4I systems conventional software engineering SwE process and practices have shortcomings and are not capable to support certain aspect of these systems. If C4I systems fail to operate as required due to the fact that SwE process was unable to fulfill its requirements, the consequences may not be tolerated because of the criticality of the mission of these systems in information warfare (IW). This paper highlights the distinguished characteristics and operational requirements of C4I systems which poses challenges to SwE process and practices. This paper also discuss the possible future research areas in order to enhance SwE process so that better software could drive these complex systems as required.

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- Computer Architecture, Software Engineering, Computational Modeling, Complexity Theory

Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.

- by José Rodriguez
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- Engineering, Power Electronics, Digital Control, Nonlinear Control
- by Bo-Wen Shen
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- Distributed Computing, Visualization, Computational Modeling, Multiscale Modeling
- by Donald Brown
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- Computer Science, Control Systems, Sensor Fusion, Predictive models
- by Juan Contreras
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- Time Series, Genetic Algorithms, Fuzzy set theory, Neural Networks

The realistic performance of a multi-input multi-output (MIMO) communication system depends strongly on the spatial correlation properties introduced by clustering in the propagation environment. Simulating realistic correlated channels is essential to predict the performance of real MIMO systems. Since the modeling method of the correlated channels suggested by the Third Generation Partnership Project (3GPP) channel model can result in considerable implementation complexity for large networks, this paper presents a computationally efficient method to approximately calculate the spatial correlation matrix for channel models such as the 3GPP channel model, which are based on clusters of scatterers. This proposed approximation method is on the basis of using the Taylor series expansion to the steering vectors for uniform linear arrays (ULAs) and a moderate angle spread of the cluster. The approximation method is evaluated in terms of the mean square error (MSE) of the approximated correlation matrix, and by the cumulative distribution function (CDF) of the mutual information of the MIMO channel. This shows that the proposed approximation method is close for angle spread of the cluster within 10 , with high efficiency and low complexity.

- by Alister Burr
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- Computational Modeling, Computer Networks, Communication System, MIMO
- by Massoud Amin
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- Engineering, Multiagent Systems, Telecommunications, Transportation

Photovoltaic (PV) system performance can be degraded by a series of factors affecting the PV generator, such as partial shadows, soiling, increased series resistance and shunting of the cells. This concern has led to greater interest in improving PV system operation and availability through automatic supervision and condition monitoring of the PV system components, especially for small PV installations, where no specialized personnel is present at the site. This work proposes a PV array condition monitoring system based on a PV array performance model. The system is parameterized online, using regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where the performance model is used to predict the power output of the PV array. Utilizing the predicted and measured PV array output power values, the condition monitoring system is able to detect power losses above 5%, occurring in the PV array.

- by Sergiu Viorel
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- Computer Science, Production, Fault Detection, Regression Analysis

In this paper, we investigate the effectiveness of a financial time-series forecasting strategy which exploits the mul- tiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). To better utilize the detailed information in the lower scales of wavelet coef- ficients (high frequencies) and general (trend) information in the higher scales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the indi- vidual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representatio...

- by Barry Flower
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- History, Computer Science, Time Series, Neural Networks
- by Mark Clement
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- Parallel Computing, Parallel Programming, Parallel Processing, Data Visualization

An emerging problem in mission planning is consideration of the communication requirements of the mission participants. Determining the appropriate resources to satisfy the communication needs of a battle group requires detailed simulation that incorporates terrain effects, mobility, realistic network traffic models, device characteristic, etc. Providing such a detailed simulation environment that also provides the performance necessary to complete a planning study in a time-critical environment is a complex problem beyond the capabilities of existing solutions. The NETS project set out with the goal of providing a high fidelity library of network components that could be simulated n parallel on a variety of parallel architectures. It was proposed as a parallel effort that can be added to the NETWARS[1] program as additional capability for high fidelity and fast simulations. The NETS library is based on the COTS QualNet Simulator, and the public domain RTI software developed at Georgia Tech, and achieves significant performance gains on the HPC resources.

The paper suggests a new approach to navigation of mobile robots, based on nonlinear model predictive control and using a navigation function as a control Lyapunov function. In this approach, the nonlinear optimal control problem is treated using randomized algorithms. The advantage of the proposed combination of navigation functions for robot motion planning with randomized algorithms within an MPC framework, is that the control design offers stability by design, is platform independent, and allows the designer to tradeoff performance for (computation) speed, according to the application requirements.

- by Herbert Tanner
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- Optimal Control, Model Predictive Control, Motion Planning, Stability

Abstruct-Carrier transport plays an important role and can significantly affect the ultra-fast properties of quantum-well (QW) lasers. We present a detailed multi-mode time-domain large-signal dynamic model including the effects of carrier transport, suitable for the high-speed QW lasers. It is based on the well-proven transmission-line laser modelling technique with the addition of a multilevel system of coupled rateequations. Simulated results from studies of both the static and small-signal properties are compared with measurements from another laboratory. Our model can accurately predict the modulation-bandwidth discontinuity in QW laser structures with large separate-confinement-heterostructure (SCH) regions. We use large-signal simulations to predict increased damping of transient responses and larger turn-on delays caused by the effects of carrier transport. Our large-signal simulations also show that an increase in the turn-on delay times is expected in QW structures with large carrier transport times across the SCH region, whereas the inter-well transport times do not affect the turn-on delay times significantly. 1077-26013/95$04.00 0 1995 IEEE

- by Dalma Novak
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- Quantum Physics, Time-Delay Systems, Damping, Predictive models

A configurable integrated test (CIT) model has been developed for GaAs monolithic microwave integrated circuit (MMIC) production control. The optimal process/test strategy for an MMIC in production phase can easily be predicted from this model. The adaptive nature of the CIT model also suggests suitability of the screen criteria and quantifies the necessity of each test step. Application of this CIT method to MMIC manufacturing will result in significant cost reductions. The CIT theory and application examples are presented in this paper.

- by Gary NG
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- Manufacturing Engineering, Production Control, Mmic, Predictive models
- by Hamed Shakouri
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- Fuzzy set theory, Linear Programming, Forecasting, Energy Consumption

We incorporate a higher order measurement-based model for printer dot interactions within the iterative direct binary search (DBS) halftoning algorithm. We also present an efficient strategy for evaluating the change in computational cost... more

- by Jan Allebach
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- Iterative Methods, Predictive models, Spatial resolution, Higher order

We propose a unification framework for three-dimensional shape reconstruction using physically based models. A variety of 3D shape reconstruction techniques have been developed in the past two decades, such as shape from stereopsis, from shading, from texture gradient, and from structured lighting. However, the lack of a general theory that unifies these shape reconstruction techniques into one framework hinders the effort of a synergistical image interpretation scheme using multiple sensors/information sources. Most shape-from-X techniques use an “observable” (e.g., the stereo disparity, intensity, or texture gradient) and a model, which is based on specific domain knowledge (e.g., the triangulation principle, reflectance function, or texture distortion equation) to predict the observable, in 3D shape reconstruction. We show that all these “observable–prediction-model” types of techniques can be incorporated into our framework of energy constraint on a flexible, deformable image frame. In our algorithm, if the observable does not confirm to the predictions obtained using the corresponding model, a large “error” potential results. The error potential gradient forces the flexible image frame to deform in space. The deformation brings the flexible image frame to “wrap” onto the surface of the imaged 3D object. Surface reconstruction is thus achieved through a “package wrapping” or a “shape deformation” process by minimizing the discrepancy in the observable and the model prediction. The dynamics of such a wrapping process are governed by the least action principle which is physically correct. A physically based model is essential in this general shape reconstruction framework because of its capability to recover the desired 3D shape, to provide an animation sequence of the reconstruction, and to include the regularization principle into the theory of surface reconstruction.

- by Jih-Fang Wang
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- Physics, Packaging, Pattern Recognition, Image Reconstruction

The ability to predict episodes of acute hypotension (abnormal drop in arterial blood pressure) would be of immense benefit to the healthcare community, and is therefore a focus of research in both medical and engineering domains. This paper presents the use of Hidden Markov Models to predict the onset of acute hypotension, using blood pressure measurements over time. Our use of HMMs has been motivated by their ability to characterize sequential/temporal trends in a given time signal. This lends the ability to infer the health status based on blood pressure information collected over an interval of time, rather than just instantaneous measurements. We have tested the proposed technique on standard physiological signal datasets available online and have obtained promising results. As part of a bigger project, we see potential in the proposed technique being used in real time health monitoring systems.

- by Abhishek Singh
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- Physiology, Biomedical Engineering, Speech Acoustics, Signal Analysis

Micromanipulation tools are not yet commonly used in the industry or in the research due to the lack of natural and intuitive human-computer interfaces. This work proposes a vision based approach using a Kinect RGB-Depth sensor to provide a "metaphor-free" interface. An intention prediction approach is proposed, based on a cognitive science computational model, to allow a more natural interaction without any prior instructions. This model is compared to a vision based gesture recognition approach in terms of naturalness and intuitiveness. It shows an improvement in user performance in terms of duration and success of the task, and a qualitative preference for the proposed approach evaluated by a user survey.

- by Sinan D Haliyo and +1
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- Computational Modeling, Gesture Recognition, Virtual Reality, Predictive models

In multi-tenant cloud systems today, provisioning of resources for new tenancy is based on selection from a catalogue published by the cloud provider. The published images are generally a stack of appliances with Infrastructure (IaaS) and Platform (PaaS) layers and optionally Application layers (SaaS). Such a ready-made model enables quicker and streamlined resource provisioning to clients. However, this approach poses certain challenges to clients in the short run and providers in the long run. Unique tenancy requirements from each client are forcibly generalized by selecting one of the available images from the catalogue as the tenancy requirements are not modeled or validated to start with. Moreover, resource provisioning is mostly done towards addressing the peak load expectations in the tenancy. Such a static approach does not help in adapting to dynamically changing tenancy requirements, most often leading to the tenants owning and subsequently paying for more than what they n...

- by Nanjangud Narendra
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- Memory Management, QoS, Quality of Service, Predictive models

... and small scale fading in indoor wireless communication channels. Reinaldo A. Valenzuela, Dmitry Chizhik and Jonathan Ling ... References [I] R. A. Valenzuela, 0. Landron, DL Jacobs, "Estimating Local Mean Signal Strength of... more

- by Reinaldo Valenzuela
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- Mathematics, Computational Modeling, Statistical Analysis, Wireless Systems

In this paper, we deal with the subject of cross-gain modulation (XGM) and cross-phase modulation (XPM) semiconductor optical amplifier-based wavelength conversion of optical channels carrying subcarrier multiplexing signals in a comprehensive way. The equations and models that describe the conversion process and the resulting harmonic and intermodulation distortions are obtained showing the superior performance of XPM over XGM in terms of second-and third-order distortion and contrast ratio. Experimental results for XGM-based wavelength conversion that confirm the results predicted by our theoretical models are presented, and finally, we consider the specific application of wavelength conversion of optical channels carrying full frequency plans such as that of cable television applications.

- by Bruce Leonardo Oliva Ortega
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- Mathematics, Optics, Medical Imaging, Fiber Optics

The growing demand for link bandwidth and node capacity is a frequent phenomenon in IP network backbones. Within this context, traffic prediction is essential for the network operator. Traffic prediction can be undertaken based on link traffic or on origin-destination (OD) traffic which presents better results. This work investigates a methodology for traffic prediction based on multidimensional OD traffic, focusing on the stage of short-term traffic prediction using Principal Components Analysis as a technique for dimensionality reduction and a Local Linear Model based on K-means as a technique for prediction and trend analysis. The results validated with data on a real network present a satisfactory margin of error for use in practical situations.

- by José Everardo Bessa Maia
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- Spine, Principal Component Analysis, Linear Model, Predictive models
- by Azlan Mohd Zain
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- Artificial Intelligence, Fuzzy Logic, Genetic Algorithms, Modeling

A new empirical large-signal model for highpower GaN HEMTs utilizing an improved drain current (Ids) model is presented. The new Ids formulation accurately predicts the asymmetric bell-shaped transconductance (gm) over a large drain-source bias range which is crucial in modeling high-power GaN HEMTs. A method of utilizing a combination of pulsed-gate (PGIV) and pulsed-gate-and-drain (PIV) IV measurements to characterize the dispersive behavior of GaN HEMT nonlinear Ids characteristics is developed. Dispersion due to self heating is modeled by modifying Ids parameters as a function of the temperature change and drain-source bias. Dispersion due to trapping is modeled using an effective gate-source voltage model. Accurate predictions of the RF small-signal and large-signal performance are demonstrated for two quiescent biases.

- by Kelvin Yuk
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- Dispersion, Model, Intrusion Detection, Gallium nitride

A quad-beam polymer optical accelerometer, based in the modulation of the total losses as a function of the acceleration, is presented in this letter. Three waveguides are defined on the structure: two at the edges and the third in the middle of a movable seismic mass suspended by polymer springs. A displacement of the mass, caused by acceleration, increases the losses due to the misalignment between the waveguides. Optical simulations predict an extremely high optical sensitivity of 11 dB/g. The novelty of the proposed device is the use of polymer as a mechanical/optical material. The sensitivity of the accelerometer has been measured to be at least 6 dB/g, much higher than any previously reported optical accelerometer based on the same operation principle. These results confirm the validity of the proposed high-sensitivity low-cost polymer accelerometer.

- by Andreu Llobera
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- Optical Waveguides, Accelerometers, Acceleration, Predictive models

Software quality models are a well-accepted means to support quality management of software systems. Over the last 30 years, a multitude of quality models have been proposed and applied with varying degrees of success. Despite successes and standardisation efforts, quality models are still being criticised, as their application in practice exhibits various problems. To some extent, this criticism is caused by an unclear definition of what quality models are and which purposes they serve. Beyond this, there is a lack of explicitly stated requirements for quality models with respect to their intended mode of application. To remedy this, this paper describes purposes and usage scenarios of quality models and, based on the literature and experiences from the authors, collects critique of existing models. From this, general requirements for quality models are derived. The requirements can be used to support the evaluation of existing quality models for a given context or to guide further quality model development.

- by Elmar Juergens and +1
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- Quality Management, Stochastic processes, Software Quality, Standardisation

Performance is a key feature of large-scale computing systems. However, the achieved performance when a certain program is executed is significantly lower than the maximal theoretical performance of the large-scale computing system. The model-based performance evaluation may be used to support the performance-oriented program development for large-scale computing systems. In this paper we present a hybrid approach for performance modeling and prediction of parallel and distributed computing systems, which combines mathematical modeling and discrete-event simulation. We use mathematical modeling to develop parameterized performance models for components of the system. Thereafter, we use discreteevent simulation to describe the structure of system and the interaction among its components. As a result, we obtain a highlevel performance model, which combines the evaluation speed of mathematical models with the structure awareness and fidelity of the simulation model. We evaluate empirically our approach with a real-world material science program that comprises more than 15,000 lines of code.

- by Sabri Pllana
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- Distributed Computing, Parallel Computing, Materials Science, Computational Modeling
- by Richard Magdalena Stephan and +1
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- Engineering, Magnetic Levitation, Physical sciences, Predictive models
- by Sangram Ganguly
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- Remote Sensing, Carbon Cycle, Multidisciplinary, Global change

Disturbances of the cerebrospinal fluid (CSF) flow in the brain can lead to hydrocephalus, a condition affecting thousands of people annually in the US. Considerable controversy exists about fluid and pressure dynamics, and about how the brain responds to changes in flow patterns and compression in hydrocephalus. This paper presents a new model based on the first principles of fluid mechanics. This model of fluid-structure interactions predicts flows and pressures throughout the brain's ventricular pathways consistent with both animal intracranial pressure (ICP) measurements and human CINE phase-contrast magnetic resonance imaging data. The computations provide approximations of the tissue deformations of the brain parenchyma. The model also quantifies the pulsatile CSF motion including flow reversal in the aqueduct as well as the changes in ICPs due to brain tissue compression. It does not require the existence of large transmural pressure differences as the force for ventricular expansion. Finally, the new model gives an explanation of communicating hydrocephalus and the phenomenon of asymmetric hydrocephalus.

Quantitative modeling, of large arteries, plays an important role in predicting and describing functional hemodynamic components. Here we present a descending thoracic aortic model based upon the nonlinear Van der Pol equation. The model is created by modification of the solution to this second order differential equation. The model displays a stroke volume of 97.82 ml and an average velocity of 22 cm/s for a heart rate of 70 bpm. An aortic radius of 1.16 cm is assumed.

- by Glen Atlas
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- Predictive models, Oscillators, Circuits, Immune system

Both the M.Sc. and Ph.D. degrees are in the area of computer science. Until February 1983, he was DP Manager of a bank in Greece. During the Spring term of 1983 he was a Visiting Associate Professor at the Michigan Technological University, Houghton. He is now Professor at the National Techni-Michael Hatzopoulos was born in Athens, Greece. He received the cal University of Athens. His research interests include physical data-B.S. degree inn mathematics from the University of Athens, Athens, in base design, distributed databases, and spatial processing. 1971, and the M.Sc. and Ph.D. degrees in computer science from the Dr. Kollias is a member of the Association for Computing Machinery

- by Mohamed Kaâniche
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- Software Testing, Software Reliability, Failure Analysis, Hardware

The four-stage evaporator is the core of the process in the manufacture of concentrated grape juice. The dynamic features of this process are very complex due to inputs and outputs constraints, time delays, loop interactions and the persistent unmeasured disturbances that affect it. Therefore, this kind of process requires a robust control in order to assure a stable operation taking into account the changes in the organoleptic properties of the raw material and, to guarantee the quality of the concentrated product. This work proposes an adaptive neural model to control of a fourstage evaporator in a grape juice concentration plant. In order to obtain a more accurate process description the neural model is trained with data from simulation of a phenomenological model and afterwards, is validated with actual plant data. This strategy allows to carry out the training without to introduce disturbance in the real plant. Neural networks of different size are trained and the performance of one of the neural models is compared with the first principles model. In a last step, the performance of a model predictive control based on the neural model is evaluated for disturbance rejection and compared with a MPC controller based on the phenomenological model and with a PI controller. The achieved results allow us to conclude that the developed neural model predictive control is adequate to control effectively the four-stage evaporator.

- by Graciela Suárez and +1
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- Chemical Engineering, Model Predictive Control, Adaptive Control, Neural Networks
- by Nazim Madhavji
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- Information Systems, Project Management, Productivity, Databases

Process-induced variations are an important consideration in the design of integrated circuits. Until recently, it was sufficient to model die-to-die shifts in device performance, leading to the well known worst-case modeling and design methodology . However, current and near-future integrated circuits are large enough that device and interconnect parameter variations within the chip are as important as those same variations from chip to chip. This presents a new set of challenges for process modeling and characterization and for the associated design tools and methodologies.

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.

- by Moataz Ahmed
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- Computer Science, Software Engineering, Machine Learning, Fuzzy Logic
- by Soumitro Banerjee
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- Power Electronics, Bifurcation, Predictive models, Subharmonics

Partial Recurrent Neural Networks (PRNN) belong to the family of Artificial Neural Networks. Due to their specific architecture, PRNN are wellsuited to forecast time series data. Their ability to outperform well-known statistical forecasting models has been demonstrated in some application domains. However, the potential of PRNN in business decision support and sales forecasting in particular has received relatively little attention. The paper strives to close this research gap. In particular, the paper provides a managerial introduction to PRNN and assesses their forecasting performance vis-à-vis challenging statistical benchmarks using real-world sales data. The sales time series are selected such that they encompass several characteristic patterns (e.g., seasonality, trend, etc.) and differ in shape and length. Such heterogeneity is commonly encountered in sales forecasting and facilitates a holistic assessment of PRNN, and their potential to generate operationally accurate forecasts.

- by Stefan Lessmann
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- Forecasting, Time series analysis, Recurrent Neural Networks, Predictive models

ABSTRACT This paper presents a short comparison of different existing and proposed wave prediction models, which in turn are used to control wave energy converters (WEC) in irregular waves. The objective of the control action is to increase the energy conversion. The prediction models are compared based on the contribution to the prediction of the individual filter weights, in an effort to reduce the filter order. The controller is based on adjusting the short term stiffness and damping of the power take off (PTO) of the WEC. The control action, which is a fuzzy logic (FL) based design, is supported by utilizing nominal damping and stiffness values determined for the given sea state and instantaneous wave using a simple genetic algorithm (GA). The WEC chosen is comprised of a spherical buoy and is modeled using the bond-graph technique. The simulation carried out indicates the proposed algorithm increases the energy conversion of WEC in irregular waves compared to the no control case.

- by Marco Schoen
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- Graph Theory, Fuzzy Logic, Genetic Algorithms, Fuzzy Logic Control

Abstmt-A new algorithm is presented for adaptive notch filtering and parametric spectral estimation of multiple narrow-band or sine wave signals in an additive broad-band process. The algorithm is of recursive prediction error (RPE) form and uses a special constrained model of infinite impulse response (IIR) with a minimal number of parameters. The convergent fdter is characterized by highly narrow bandwidth and uniform notches of desired shape. For sufficiently large data sets, the variances of the sine wave frequency estimates are of the same order of magnitude as the Cramer-Rao bound. Results from simulations illustrate the performance of the algorithm under a wide range of conditions.

- by Arye Nehorai
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- Algorithms, Signal Processing, Cramer Rao Lower Bound, Adaptive Filter

This paper investigates the effectiveness of Model Predictive Control (MPC) in dealing with torsional vibrations and torque constraints in a two-mass elastic drive system under plant uncertainty and parameter variation. The methodology for designing low complexity MPC controllers for speed regulation and vibration suppression is discussed and validated on a real two-mass drive system. Selected experimental results confirm that the proposed MPC speed controller is very effective in dealing with tight torque constraints requirements under significant plant parameter uncertainty.

- by T. Orlowska-Kowalska and +1
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- Vibrations, Model Predictive Control, Vibration Control, Robust control

In this paper we propose a new technique that uses the bootstrap to estimate con dence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging 1] and balancing 5]. Con dence and prediction intervals are estimated that include a signi cantly improved estimate of underlying model uncertainty (i.e.) the uncertainty of our estimate of the \true" regression. Unlike existing techniques, this estimate of uncertainty will vary according to which ensemble technique is used { if the e ect of using a speci c ensemble technique is to produce less model uncertainty than using another ensemble technique, then this will be re ected in the con dence and prediction intervals. Preliminary results illustrate how our technique can provide more accurate con dence and prediction intervals (intervals that better re ect the desired level of con dence (e.g.) 90%, 95%, etc.) for neural network ensembles than previous attempts.

- by Padraig Cunningham
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- Statistical Analysis, Neural Networks, Estimation Theory, Economic Forecasting

Load forecasting is important in the operation of power systems. The characteristics of the electrical energy consumption are analyzed and its variation as an effect of several weather parameters is studied. Based on historical weather and consumption data received from a distribution system operator (DSO), numerical models of load forecasting are suggested according to electrical power consumption and on daily peak power respectively. Two linear regression models are presented: simple linear regression (SLR) with one input variable (temperature) and multiple linear regressions (MLR) with several input variables. The models are validated with historical data from other years. For daily peak power demand a MLR model has the lowest error, but for prediction of energy demand a SLR model is more accurate.

- by Carl Wallnerström
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- Power System, Risk Management, Climate, Regression Analysis

Abstract-this paper presents a study of the approaches taken to model and simulate power systems of an industrial plant. Even the best-designed electric systems occasionally experience short-circuits resulting in abnormally high currents.... more

- by Idris Ismail
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- Power System, Modeling and Simulation, Mathematical Modeling, Development

Instruction set simulators are critical tools for the exploration and validation of new programmable architectures. Due to increasing complexity of the architectures and timeto-market pressure, performance is the most important feature of an instruction-set simulator. Interpretive simulators are flexible but slow, whereas compiled simulators deliver speed at the cost of flexibility. This paper presents a novel technique for generation of fast instruction-set simulators that combines the benefit of both compiled and interpretive simulation. We achieve fast instruction accurate simulation through two mechanisms. First, we move the timeconsuming decoding process from run-time to compile time while maintaining the flexibility of the interpretive simulation. Second, we use a novel instruction abstraction technique to generate aggressively optimized decoded instructions that further improves simulation performance. Our instruction set compiled simulation (IS-CS) technique delivers upto 40% performance improvement over the best known published result that has the flexibility of interpretive simulation. We illustrate the applicability of the IS-CS technique using the ARM7 embedded processor.

In this paper, we explore the proliferation, assumptions, motivation, and behavior of various substitution models of the technological diffusion process. The underlying notion is that such an understanding helps the model user to choose the most appropriate model for the situation. This paper discusses the development, motivation, and assumptions of various deterministic and binary substitution models and compares them on the basis of their three mathematical characteristics. Further, it is shown that the study of the interrelationships between the models is useful in narrowing the choice. The behavior of the models is studied through an illustration of diffusion of innovative oxygen-steel technology in Spain and in Japan.

- by vinod kumar
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- Business, Engineering, Technological Innovation, Space Technology

is one of six departments that make up the School of Business at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported. The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. Copyright Copyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors.

- by Geoffrey Kennedy
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- Information Systems, Computer Science, Information Science, Relational Database