Engineering Systems

In this paper, we analyze about the relation between stock price returns and Headline News. Headline News is very important sources of information in asset management, and is sent in large quantities every day. We study the effect of more than 13,000 Headline News sent from JIJI PRESS. We classify Headline News using Text Categorization and analyze the reaction of a stock price return for every type of News. From our research, we figure out following issues; 1) we make the Text Categorization System that has about 80% of classification accuracy, 2) this system can extract effective information to stock price returns from Headline News.

An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.

In finance task domain, it is indispensable to get and analyze information as quickly as possible. Analyst's reports are one of the important information in asset management, and these include a large amount of text information. However, it is very difficult to handle text information of analyst's reports, few research and development have been conducted. In [5] and [6] we explored the feasibility to extract valuable knowledge for asset management through text mining using analyst's reports as text data. And we found the effectiveness of keyword information. In this paper we make further research of analyst's reports. From empirical study on the practical data, we have confirmed the effectiveness of using keyword information and numerical information together: (1) the effectiveness of keyword information is different by the direction of change of earning estimate; (2) the keyword of "Upward (or Downward) surprise in forecast" has strong effect to stock price return.

Fuel cell systems are enhancing due to interest to supply electricity in remote areas as well as distributed power generation especially during the peak loads. Fuel cell generation becomes popular due to cleanliness, portability and suitability for electricity and heat generation. This paper presents the modeling of a fuel cell power plant (FCPP) in terms of fuel cell, dc-dc converter, dc-ac inverter and power system parameters, to interface with loads or grid. The control strategy of this model is hysteresis current controller (HCC). HCC is used to generate inverter switch pulse for the ac grid voltage. MATLAB/SIMULINK is used to validate the modeling and simulation of fuel cell generation and power conditioning unit. The proposed control strategy makes the distribution generation system work properly when the voltage disturbance occurs in the distribution system.

The field of Engineering Systems is distinguished from traditional engineering design in part by the issues it brings to the top. Engineering Systems focuses on abstractions like architecture and complexity, and defines system boundaries very broadly. It also seeks to apply these concepts to the process of creating systems. This paper summarizes the role and influence of architecture in complex engineering systems. Using the research literature and examples, this paper defines architecture, argues for its importance as a determinant of system behavior, and reviews its ability to help us understand and manage the design, operation, and behaviors of complex engineering systems.

Abstract:-A drainage experiment with a tank model was conducted in laboratory to examine water flow to a subsurface drain and to acquire experimental data about the water flow characteristics of layered soils to serve as a base for numerical analyses. In order to ...

Ultrasound is a widely used medical imaging technique. Tissue characterization with ultrasound has become important topic since computer facilities have been available for the analysis of ultrasound signals. Automatic liver tissue characterizations from ultrasonic scans have been long the concern of many researchers. Different techniques has been used ranging from processing the RF signals received by the transducer to using neural networks to analyze images based on image texture. In this paper, an automatic liver diseases diagnostic system is implemented for early detection of liver diseases. The proposed system classification accuracy is 96.125%. The system advantage is its high accuracy and its computation simplicity. The system can be used as a second opinion system to aid the diagnosis of liver diseases

This paper presents a solution to the boundary stabilization of a symmetric composite laminated plate in free transverse vibration. The symmetric composite laminated plate dynamics is presented by a linear fourth order partial differential equation (PDE). A linear control law is constructed to stabilize the plate. The control force consists of feedback of the velocity at the boundary of the plate. The novelty of this article is that it is possible to stabilize asymptotically a free transversely vibrating symmetric composite laminated plate with simply supported boundary condition via boundary control without resorting to truncation of the model.

This paper presents a fuzzy basis function approach for adaptive decentralized control of a class of largescale nonlinear systems with MIMO subsystems. Hybrid adaptive-robust tracking control schemes which are based on a combination of the H ∞ tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and the H ∞ tracking control performance is guaranteed. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant decentralized control with multi-controller architecture guarantee stability and convergence of the output errors to zero asymptotically, by local output-feedback. Simulation results on the control of a model of a nonlinear electrical machine are presented to illustrate the effectiveness of the proposed controller.

The paper proposes a methodology especially focused on the generation of strategic plans of action, emphasizing the relevance of having a structured timeframe classification for the actions. The methodology explicitly recognizes the relevance of long-term goals as strategic drivers, which must insure that the complex system is capable to effectively respond to changes in the environment. In addition, the methodology

More than a decade ago it was discovered that some it with other estimators, two different types of simulation LAN traffic exhibits self-similar rather than Poisson behaviour studies were performed. The first one is using fractional and there is ongoing research towards finding and improving Gaussian noise (fGn) sequences generated by Dietrichsuitable estimators which may help to characterize various Newsam algorithm [8, 9], which generates exact self-similar types of network traffic. Such characterization can be potensequences. The second one is using a fractional tially applied for control purposes such as traffic shaping, load auegressivem ong one 1S usmg a II]. balancing, etc. The Hurst exponent is used to measure the in-autoregressive moving average (FARIMA) process [10, 11]. tensity of long-range dependence (LRD) in the network traffic. After that the same set of estimators is tested on the trace Despite having several existing estimators, LRD analysis is still files for MPEG-encoded musical videos and movies. impeded by the difficulty of actual identification of its intensity. The rest of the paper is organised as follows. Section II This paper continues work on estimating the Hurst exponent shows the relationship between the autocorrelation function from the autocorrelation function, a so-called HEAF estimator (ACF) and LRD. Section III introduces the HEAF estimator. introduced earlier by the authors. It also compares HEAF with a few existing estimators such as Wavelet, Higuchi, aggregated The results of simulation experiments are presented in secvariance time (V/T) and Rescaled-range (R/S). The simulation tion IV and experiments on MPEG-encoded movie traces in studies show that HEAF can be used to capture the LRD in the Section V. The properties of the HEAF estimator are shown network traffic if true process is fGn and FARIMA.

This paper introduces a new approach targeting the RWA problem for static traffic and under the general assumptions of multiple requests between the same SD pairs and multifiber links. The case of infeasibility of the problem is also discussed and integrated into our frame work. We first present a categorization of the RWA problems according to the different assumptions involved. In addition, a classification of the available approaches is presented along with a brief comparison and overview of each. The proposed approach is tested by applying it to different network models; results for NSFNET and cost239 networks are presented. Furthermore, the results are also compared with a recent heuristic approach

Conventional patient monitoring is performed by generating alarms when vital signs exceed pre-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a patient's vital signs are deemed to have become sufficiently "extreme". Our proposed formulation operates many orders of magnitude faster than existing methods, allowing on-line learning of models, leading ultimately to patientspecific monitoring.

This paper presents a fuzzy basis function approach for adaptive decentralized control of a class of largescale nonlinear systems with MIMO subsystems. Hybrid adaptive-robust tracking control schemes which are based on a combination of the H ∞ tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and the H ∞ tracking control performance is guaranteed. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant decentralized control with multi-controller architecture guarantee stability and convergence of the output errors to zero asymptotically, by local output-feedback. Simulation results on the control of a model of a nonlinear electrical machine are presented to illustrate the effectiveness of the proposed controller.

Abstract In this paper, we propose an incremental software development process that addresses reliability concerns, from early to late stages of software development. Contrary to existing techniques, in our proposal we merge two dependability means: fault prevention and fault forecasting techniques in order to build reliable distributed software systems. The design stage is focused on obtaining coherent specification of each individual component using an incremental refinement technique. After obtaining a consistent specification of ...

Knowledge discovery from databases, in the descriptive approach, includes clustering analysis (CA) as an alternative to estimate how a set of objects is organized in the space of their dimensions. The main objective in this task is to find "natural" groups that could exhibit some meaning. Considering the strong subjectivity that underlies this process, an important issue refers to the relationships among the CA players when looking for a model that could adjust the data. In this work, a model for actions coordination that provides an order to drive the relationships among CA players is presented. This model is presented as a conceptual contribution towards the construction of a computational environment to support effective conversations in a subjective context.

This paper presents a control synthesis software tool for manufacturing systems with a driven dynamic event. This tool is based on an original control synthesis algorithm which solves the forbidden state problem (FSP) under the following hypothesis: 1) each event may occur at time instants specified by an interval or a union of intervals; 2) two types of events are considered: controllable and incontrollable events and 3) the evolution of time is discrete. This tool generates a live and maximally permissive controller which can 1) act on the arriving date of controllable events or 2) completely forbid some controllable events. The aim is to guaranty the respect of the given specifications.

Abstract In this paper, we propose an incremental software development process that addresses reliability concerns, from early to late stages of software development. Contrary to existing techniques, in our proposal we merge two dependability means: fault prevention and fault forecasting techniques in order to build reliable distributed software systems. The design stage is focused on obtaining coherent specification of each individual component using an incremental refinement technique. After obtaining a consistent specification of ...

Conventional patient monitoring is performed by generating alarms when vital signs exceed predetermined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a patient's vital signs are deemed to have become sufficiently "extreme". Our proposed formulation operates many orders of magnitude faster than existing methods, allowing on-line learning of models, leading ultimately to patientspecific monitoring.

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a neural network, e.g., in the Viisage-FaceFINDER video surveillance system. We describe the optimization of such a network by a hybrid algorithm combining evolutionary computation and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy.

Stable direct and indirect adaptive fuzzy controllers are presented for a class of interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections, which are represented not only in the canonical forms [1] but also in the general forms. Hybrid adaptive robust tracking control schemes which are based upon a combination of the H∞ tracking theory, and fuzzy control design are developed such that all the states and signals are bounded and an H∞ tracking control performance is guaranteed without imposing any constraints or assumptions about the interconnections. Extensive simulation example of two-link rigid robotics manipulator is provided to verify the effectiveness of the proposed algorithm.

Abstract. This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic resonance spectroscopic signals. The expert diagnosis of human brain tu-mours can benefit from computer-aided assistance, which has to be read-ily ...

This paper presents a solution to the boundary stabilization of a symmetric composite laminated plate in free transverse vibration. The symmetric composite laminated plate dynamics is presented by a linear fourth order partial differential equation (PDE). A linear control law is constructed to stabilize the plate. The control force consists of feedback of the velocity at the boundary of the plate. The novelty of this article is that it is possible to stabilize asymptotically a free transversely vibrating symmetric composite laminated plate with simply supported boundary condition via boundary control without resorting to truncation of the model.

Finding information from manufacturing quality information to support product design decisions is necessary for improving design quality. Based on the review of group technology and quality bill of material, the classification and coding of manufacturing quality information is proposed in which combines the manufacturing quality information families with part families. Then the application of classified and coded manufacturing quality information

More than a decade ago it was discovered that some it with other estimators, two different types of simulation LAN traffic exhibits self-similar rather than Poisson behaviour studies were performed. The first one is using fractional and there is ongoing research towards finding and improving Gaussian noise (fGn) sequences generated by Dietrichsuitable estimators which may help to characterize various Newsam algorithm [8, 9], which generates exact self-similar types of network traffic. Such characterization can be potensequences. The second one is using a fractional tially applied for control purposes such as traffic shaping, load auegressivem ong one 1S usmg a II]. balancing, etc. The Hurst exponent is used to measure the in-autoregressive moving average (FARIMA) process [10, 11]. tensity of long-range dependence (LRD) in the network traffic. After that the same set of estimators is tested on the trace Despite having several existing estimators, LRD analysis is still files for MPEG-encoded musical videos and movies. impeded by the difficulty of actual identification of its intensity. The rest of the paper is organised as follows. Section II This paper continues work on estimating the Hurst exponent shows the relationship between the autocorrelation function from the autocorrelation function, a so-called HEAF estimator (ACF) and LRD. Section III introduces the HEAF estimator. introduced earlier by the authors. It also compares HEAF with a few existing estimators such as Wavelet, Higuchi, aggregated The results of simulation experiments are presented in secvariance time (V/T) and Rescaled-range (R/S). The simulation tion IV and experiments on MPEG-encoded movie traces in studies show that HEAF can be used to capture the LRD in the Section V. The properties of the HEAF estimator are shown network traffic if true process is fGn and FARIMA.

This paper deals with a segmentation (classification) problem which arises in the diagnostic and treatment of shoulder disorders. Classical techniques can be applied successfully to solve the binary problem but they do not provide a suitable method for the multiphase ...

Abstract—In this paper, a wavelet-based decomposition level with scale invariant feature transform (SIFT) is proposed to solve the problem of image registration. At the highest wavelet-based pyramid level, mutual-information (MI) based registration is applied and rough similarity (linear ...

In the optimization of Artificial Neural Networks (ANNs) via Evolutionary Algorithms (EAs) and the implementation of the necessary training for the objective function, there is often a trade-off between efficiency and flexibility. Pure software solutions on general-purpose processors tend to be slow because they do not take advantage of the inherent parallelism, whereas hardware realizations usually rely on optimizations that reduce the range of applicable network topologies, or they attempt to increase processing efficiency by means of low-precision data representation. This paper presents, first of all, a study that shows the need of heterogeneous platform (CPU-GPU-FPGA) to accelerate the optimization of ANNs using genetic algorithms and, secondly, an implementation of a platform based on embedded systems with hardware accelerators implemented in FPGA (Field Pro-grammable Gate Array). The implementation of the individuals on a remote low-cost Altera FPGA allowed us to obtain a 3x-4x acceleration compared with a 2.83 GHz Intel Xeon Quad-Core and 6x-7x compared with a 2.2GHz AMD Opteron Quad-Core 2354.

Conventional patient monitoring is performed by generating alarms when vital signs exceed predetermined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a patient's vital signs are deemed to have become sufficiently "extreme". Our proposed formulation operates many orders of magnitude faster than existing methods, allowing on-line learning of models, leading ultimately to patient-specific monitoring.

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a neural network, e.g., in the Viisage-FaceFINDER video surveillance system. We describe the optimization of such a network by a hybrid algorithm combining evolutionary computation and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy.

Abstract This paper is concerned with the concept of a Mixed Reality (MR) stage environment as networked layering of physical space and virtual environments. The MR stage enables up to 5 performers to interact through intuitive free body interfaces. The goal is the creation of ...

In this paper we present the design of an agent system for medicine usage management. Part of it is an intelligent ambient agent which incorporates an explicit representation of a dynamical system model to estimate the medicine level in the patient's body. By simulation the ambient agent is able to analyse whether the patient intends to take the medicine too early or too late, and can take measures to prevent this.

Due to limitations of classical weighted average aggregation operators, there is an increase usage of fuzzy integrals, like the Sugeno and Choquet integrals, as alternative aggregation operators. However, their applicability has been threatened by the crux of determining the fuzzy measures in real problems. One way to determine these measures is by using learning data and optimizing the parameters. In this paper we made a comparative study of two well known optimization algorithms, Genetic Algorithm and Gradient Descent to determine fuzzy measures. Two illustrative cases are used to compare the algorithms and assess their performance.

More than a decade ago it was discovered that some it with other estimators, two different types of simulation LAN traffic exhibits self-similar rather than Poisson behaviour studies were performed. The first one is using fractional and there is ongoing research towards finding and improving Gaussian noise (fGn) sequences generated by Dietrichsuitable estimators which may help to characterize various Newsam algorithm , which generates exact self-similar types of network traffic. Such characterization can be potensequences. The second one is using a fractional tially applied for control purposes such as traffic shaping, load auegressivem ong one 1S usmg a II].

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a neural network, e.g., in the Viisage-FaceFINDER video surveillance system. We describe the optimization of such a network by a hybrid algorithm combining evolutionary computation and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy.

A realization of obtaining energy concentration along instantaneous frequency (IF) for blurry Time Frequency Images (TFIs) is proposed using Neural Networks (NNs). NN captures the fundamental principle at work once trained with various spectrogram TFIs by defining various edges present in input data. Unknown spectrogram TFIs of multi component signals, are then given as test images to the trained NN. Measuring the information content in each input & resultant TFI thus manifest the effectiveness of NNs in TFI analysis.

There is growing evidence of the relevance of human behavioral factors in the success of development of new products, processes and services. The evidence is even clearer when the forces affecting the development and evolution of long-lived, large, and open complex socio-technical systems are researched.  Methods that study the architecture of such types of systems can help scholars and practitioners to better understand, manage, and develop socio-technical systems. We propose an approach and a method to address these needs that is grounded in the theory of systems architecture and builds on the strengths of Object Process Methodology (OPM) and the process for representing Complex Large-scale Interconnected Open Socio-technical (CLIOS) systems. We do so by integrating these methods into the CLIOS-OPM Integrated Method (COIM). COIM is conducive to studying a system’s architecture and its evolution, as it is enhanced by a set of qualitative methods for answering questions about the reasons (why) and process (how) of change in human-made systems over time.