Adaptive Systems

Existing e-learning systems are based on the quantity of information. They fail to consider an important factor for the success of the system, namely the user. An intelligent adaptive system should adjust the content in order to ensure faster and better performances in the learning process. Moreover, it should help students to develop new, desirable learning abilities. This paper presents

Experimental performance evaluation of heat pump-based steam supply system T Kaida, I Sakuraba, K Hashimoto et al. -Fluid-Structure interaction analysis and performance evaluation of a membrane blade M. Saeedi, R. Wüchner and K.-U. Bletzinger -Optimization and performance evaluation for nutrient removal from palm oil mill effluent wastewater using microalgae Raheek I Ibrahim, Z H Wong and A W Mohammad -This content was downloaded from IP address 168.151.1.23 on 24/09

Cognitive radio has the ability to sense the environment and adapt its behavior to optimize communication features, such as quality of service in the presence of interference and noise. In physical layer to achieve this goal, different phases of sensing, channel estimation, and configuration selection are necessary. The sensing part measures the interference level, recognize the spectrum holes, and send this information to channel estimator. In the next step, channel state information (CSI) is used for data detection and also sent to the transmitter through a limited feedback. CSI feedback consists of achievable rate, SNR value, modulation or coding schemes (MCS). Feedback link improves the system performance in the cost of complexity and delay. In this paper, we present and compare different feedback schemes for cognitive radio and study the channel capacity when an imperfect feedback link is corrupted by noise and delay.

Overlay management systems face the challenges of increased complexity and heterogeneity because of the many elements involved in providing overlay services. In dynamic networks, the challenge and complexity is increased. Service composition allows simple services to be dynamically combined into more complex services in order to provide new services. In this paper, we deal with the problem of composing multiple autonomic elements to achieve system wide goals. Using a selforganizing approach, autonomic entities are dynamically and seamlessly composed into service-specific overlay networks. The paper describes and discusses the details of the composition approach. Results of extensive simulation are presented.

We describe here a new mapping method able to find out connectivity traces among variables thanks to an original mathematical approach. This method is based on an artificial adaptive system able to define the strength of the associations of each variable with all the others in any dataset, the Auto Contractive Map (AutoCM). After the training phase, the weights matrix of the AutoCM represents the warped landscape of the dataset. We apply a simple filter to the weights matrix of AutoCM system to show the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re design the overall structure of factors related to a specific disease description. This new form of data mining can be expected to contribute to a better understanding of complexity of some wicked diseases.

This work concerns a new kind of neural structure that involves a multidimensional adaptive activation function. The proposed architecture, based on multi-dimensional cubic spline, allows to collect information from the previous network layer in aggregate form. In other words the number of network connections (structural complexity) can be very low respect to the problem complexity. This fact, as experimentally demonstrated in the paper, improve the network generalization capabilities and speed up the convergence of the learning process. A specific learning algorithm is derived and experimental results, demonstrate the effectiveness of the proposed architecture.

We study the complexity of finding extreme pure Nash equilibria in symmetric network congestion games and analyse how it depends on the graph topology and the number of users. In our context best and worst equilibria are those with minimum respectively maximum total latency. We establish that both problems can be solved by a Greedy algorithm with a suitable tie breaking rule on parallel links. On series-parallel graphs finding a worst Nash equilibrium is NP-hard for two or more users while finding a best one is solvable in polynomial time for two users and NP-hard for three or more. Additionally we establish NP-hardness in the strong sense for the problem of finding a worst Nash equilibrium on a general acyclic graph.

Adaptive algorithms are succeptible to noise signals due to the perturbation of the gradient direction. Depending upon the statistics of the noise, various cost functions can be minimised. The Least Mean Kurtosis (LMK) adaptive algorithm is a stochastic gradient approach that uses the kurtosis of the error signal. Depending upon the noise distribution, the forgetting factor used in the LMK algorithm must be chosen optimally. In this paper, a version of LMK is presented, in which the forgetting factor is made time-varying such that if the output-noise distribution is unknown, which is almost always the case in practice, the algorithm will tune its forgetting factor in sympathy with the noise distribution. Hence the proposed algorithm is more flexible than the Least Mean Square (LMS) and Least Mean Fourth (LMF) adaptive algorithms under noisy conditions.

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.

Abstracr-Predictive coding is a promising approach for speech coding. In this paper, we review the recent work on adaptive predictive coding of speech signals, with particular emphasis on achieving high speech quality at low bit rates (less than 10 kbits/s). Efficient prediction of the redundant structure in speech signals is obviously important for proper functioning of a predictive coder. It is equally important to ensure that the distortion in the coded speech signal be perceptually small. The subjective loudness of quantization noise depends both on the short-time spectrum of the noise and its relation to the short-time spectrum of the speech signal. The noise in the formant regions is partially masked by the speech signal itself. This masking of quantization noise by speech signal allows one to use low bit rates while maintaining high speech quality. This paper will present generalizations of predictive coding for minimizing subjective distortion in the reconstructed speech signal at the receiver. The quantizer in predictive coders quantizes its input on a sample-by-sample basis. Such sample-by-sample (instantaneous) quantization creates difficulty in realizing an arbitrary noise spectrum, particularly at low bit rates. We will describe a new class of speech coders in this paper which could be considered to be a generalization of the predictive coder. These new coders not only allow one to realize the precise optimum noise spectrum which is crucial to achieving very low bit rates, but also represent the important first step in bridging the gap between waveform coders and vocoders without suffering from their limitations.

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. In this paper, we develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

Signs, symbols, and signals are essential to the survival and evolution of all complex functional organizations that utilize "information." We discuss basic semiotic relations inherent in signalling systems (communication), scientific models (epistemology), adaptive devices (control), and biological organisms (construction). For each of these different functional realms, basic syntactic, semantic, and pragmatic relations are outlined. An evolutionary semiotics seeks to explain how new semiotic relationships can evolve over time. New signalling channels appear in communications systems by the construction of new ways of sending and detecting signals; new observables appear in scientific models through the physical construction of new measuring devices; new "feature primitives" emerge in devices through adaptive construction of sensors. These physical construction and selection processes have analogues in biological evolution. We discuss the crucial role that symbols play in living organizations (the central role of DNA in the self-production of the organism) and in the evolutionary process (inheritability of plans). "Semiotic evolution" and the "semiotics of evolution" thus point us in a common direction, towards a unified, "evolutionary semiotics."

In petabyte-scale distributed file systems that decouple read and write from metadata operations, behavior of the metadata server cluster will be critical to overall system performance and scalability. We present a dynamic subtree partitioning and adaptive metadata management system designed to efficiently manage hierarchical metadata workloads that evolve over time. We examine the relative merits of our approach in the context of traditional workload partitioning strategies, and demonstrate the performance, scalability and adaptability advantages in a simulation environment.

ailures of large power transdeveloped an adaptive, intelliformers are problematic for ur reasons. Generally, large transformers are situated so that gent, monitoring system for large power transformers. Four large transformers on the Boston Edison system are under continuous surveillance by this system, which can summon attention to anomalous operation through paging failures present operational problems to the system. In addition, large power transformers are encased in tanks of flammable t t e n t j~n to anomalous oneration and environmentally hazardous fluid. Failures are often accompa-devices. The monitoring system offers two advantages over more

The authors propose a general fuzzy classification scheme with learning ability using an adaptive network. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions, are calibrated with backpropagation. To explain this approach, the concept of adaptive networks is introduced and a supervised learning procedure based on a gradient descent algorithm is derived to update the parameters in an adaptive network. The proposed architecture is applied to two problems: two-spiral classification and Iris categorization. From the experimental results, it is concluded that the adaptively adjusted classifier performs well on an Iris classification problem. The results are discussed from the viewpoint of feature selection

In many applications of speech processing the noise reveals some specific features. Although the noise could be quite broadband, there are a limited number of dominant frequencies, which carry the most of its energy. This fact implies the usage of narrow-band notch filters that must be adaptive in order to track the changes in noise characteristics. In present contribution, a method and a system for noise suppression are developed. The method uses adaptive notch filters based on second-order Gray-Markel lattice structure. The main advantages of the proposed system are that it has very low computational complexity, is stable in the process of adaptation, and has a short time of adaptation. Under comparable SNR improvement, the proposed method adjusts only 3 coefficients against 250-450 for the conventional adaptive noise cancellation systems. A framework for a speech recognition system that uses the proposed method is suggested.

The breakdown of a medical equipment in service is of particular concern because of its possible use in critical conditions. This may be a direct life support situation as therapy or an indirect effect such as providing information on the patient's health status. The signs of equipment failure may not always be apparent to clinical staff. Therefore they should be inspected periodically. Scheduled inspections help ensure the safety and efficacy of the medical equipment. In this study, a software tool is developed to implement a risk oriented prioritization of devices for preventive maintenance inspections. The database contains the equipment inventory, and related data files. The reports based on the inspection results, help determining a better scheduling than the constant sweeping of all devices.

"John Paul Getty is rumored to have said that, “In times of rapid change, experience is our worst enemy.” The problem of leadership “baggage” is acute because what was functional in one setting is dysfunctional in a new context. Complexity and networked reality conspire against hierarchical models where decisions flow down and information flows up.

In our time sky-rocketing complexity overwhelms us. Cause-effect relationships have become mysterious: the flap of butterfly wings in Hong-Kong may affect the path of a jetliner in New York. We feel insecure and alone in the face of the unknown and can no longer predict events. A common response is to turn to authority. The alternative is richer collaboration, sharing strength and perspective. “Life emerges where there are rich connections.” Chaordic leaders resist taking control because they know that focus is more important than individual behaviors. The future belongs to the leader as host.
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As modern software-based systems and applications gain in versatility and functionality, the ability to manage inconsistent resources and service disparate user requirements becomes increasingly imperative. Furthermore, as systems increase in complexity, rectification of system faults and recovery from malicious attacks become more difficult, labor-intensive, expensive, and error-prone. These factors have actuated research dealing with the concept of self-healing systems. Self-healing systems attempt to “heal” themselves in the sense of ...

The performance of a tracking filter can be evaluated in terms of the filterpsilas optimality conditions. Testing for optimality is necessary because the estimation error covariance as provided by the filter is not a reliable indicator of performance, which is known to be ldquooptimisticrdquo (inconsistent) particularly when there are model mismatches and target maneuvers. The conventional root-mean square (RMS) error value and its variants are widely used for performance evaluation in simulation and testing but it is not feasible for real-time operations where the ground truth is hardly available. One approach for real-time reliability assessment is optimality self online monitoring (OSOM) investigated in this paper. Statistical tests for optimality conditions are formulated. Simulation examples are presented to illustrate their possible use in evaluation and adaptation.

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Diverse image-based tracking schemes of robot moving in free motion have been proposed, and experimentally validated, whose position and velocity image tracking errors converge to zero. However, visual servoing for constrained motion robot tasks has not been addressed so as to provide control schemes that guarantee simultaneous tracking of position, velocity and contact force trajectories for dynamic robot models. The main difficulty may steam from the fact that camera information cannot be used to drive force trajectories.Recognizing this fact, in this paper a unique error manifold that includes position-velocity errors and force errors in orthogonal complements is proposed under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. This seemingly fusion of all tracking errors into a unique error variable allows to propose a new control system which guarantees local exponential tracking of all error trajectories. A neural network, driven by an orthogonalized second order sliding mode surface is derived to compensate approximately for nonlinear robot dynamics. Residual errors that arises because of the finite size o the neural network are finally eliminated via two sliding modes. Simulations results are presented and discussed.

In recent years there has been an increased interest in modelling systems whose structure changes dynamically, for example to study mobility. At the same time, there is a plethora of simulation languages for discrete-event systems that include an explicit notion of time, but few, if any, support explicitly the notion of structural change. In this paper we introduce a process description language which addresses these issues explicitly. We discuss an application to modelling networks of servers.

É com muito gosto que aproveito esta oportunidade para agradecer às pessoas que me apoiaram, tornando possível o desenvolvimento deste trabalho. Ao professor Óscar Mortágua Pereira, meu orientador, pelo seu incansável apoio e pelo esclarecimento das dúvidas que me foram surgindo ao longo do projeto. Ao professor Rui Luís Aguiar, meu co-orientador, pela forma como me apoiou nalgumas fases do projeto. Agradeço também aos meus pais e avós que nunca deixaram de acreditar em mim e cujo incentivo me permitiu a realização deste curso. A todos um "muito obrigado". keywords relational databases, policies, access control, adaptive systems, business tiers.

Abstract| Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (Adaptive-Network-based Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed.

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neural networks. M-ANFIS model adopts Mamdani fuzzy inference system which has advantages in consequent part. Experiment results of applying M-ANFIS to evaluate traffic Level of service show that M-ANFIS, as a new hybrid algorithm in computational intelligence, has great advantages in non-linear modeling, membership functions in consequent parts, scale of training data and amount of adjusted parameters.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Math2Mat aims at automatically generating a VHDL description of a mathematical description written in Octave/Matlab. The generation creates a synthesizable RTL description using floating point operators (32 or 64 bits) combined in a fully pipelined way. Emphasis is put on the throughput attainable by the design, especially in the "for loop" implementation. The software also offers a graphical user interface, letting the developer manage the different parameters before generation. Verification can also be launched from the software, a SystemVerilog testbench being automatically generated.

Efficient and precise sensorless speed control of a permanent-magnet synchronous motor (PMSM) requires accurate knowledge of rotor flux, position, and speed. In the literature, many sensorless schemes have been presented, in which the accurate estimation of rotor flux magnitude, position, and speed is guaranteed by detecting the back electromotive force (EMF). However, these schemes show great sensitivity to stator resistance mismatch and system noise, particularly, during low-speed operation. In this paper, an indirect-rotor-field-oriented-control scheme for sensorless speed control of a PMSM is proposed. The rotor-flux position is estimated by direct integration of the estimated rotor speed to reduce the effect of the system noise. The stator resistance and the rotor-flux speed and magnitude are estimated adaptively using stable model reference adaptive system estimators.

Distributed Shared Memory (DSM) systems provide a logically shared memory over physically distributed memory to enable parallel computation on Networks of Workstations (NOWs). In this paper, we propose an infrastructure for DSM systems to utilize idle cycles in the network by thread migration.

Recent natural or man-made disasters around the world have provided compelling evidence that transportation system plays a crucial role in the emergency evacuation and have stressed the need for effective evacuation traffic management to maximize the utilization of the transportation system and to minimize fatalities and losses. This paper presents a model reference adaptive control ͑MRAC͒ framework for real-time traffic management under emergency evacuation. Distinct from the well-studied evacuation planning, real-time traffic management for evacuation aims to dynamically guide ͑control͒ traffic flow under evacuation in such a way that certain system objective ͑e.g., minimization of fatalities or property losses͒ could be achieved. The proposed framework is based on both dynamic network modeling techniques and adaptive control theory, by considering the traffic network under evacuation as a dynamic system. First, a prescriptive dynamic traffic assignment model is applied to predict, in a short-term and rolling-horizon manner, the desired traffic states based on certain system optimal objectives. This model will serve as a reference point for the adaptive control. Then, the adaptive control system integrates these desired states and the current prevailing traffic conditions collected via the sensing system to produce real-time traffic control schemes. Finally, these traffic control schemes are implemented in the field to guide the real-world traffic flow to evolve towards the desired states. Simulation studies provided in this paper show that the proposed framework based on MRAC can significantly improve the performance of real-time evacuation traffic management.

This paper studies the difference between Persistent Random Constants (PRC) and Digit Concatenation as methods for generating constants. It has been shown that certain problems have different fitness landscapes depending on how they are represented, independent of changes to the combinatorial search space, thus changing problem difficulty. In this case we show that the method for generating the constants can also influence how hard the problem is for Genetic Programming.

a b d This paper focuses on the issue of developing self-adapting automatic object detection systems for improving their performance. ' b o general methodologies for performance improvement are first introduced. They are based on parameter optimizing and input adapting. Different modified Hebbian learning rules are developed to build adaptive feature extractors which transform the input data into a desired form for a given algorithm. To show its feasibility, an input adaptor for object detection is designed as an example and tested using multisensor data (optical, SAR, and FLIR). Test results are presented and discussed in the paper.

This paper presents a nonlinearly parameterized controller for the adaptive control of base-isolated buildings subjected to a set of near-fault earthquakes. The control scheme is based on discrete direct adaptive control, wherein the system response is minimized under parameter uncertainties. Stable tuning laws for the controller parameters are derived using the Lyapunov approach. The controller utilizes a linear combination of nonlinear basis functions, and estimates the desired control force online. The measurements that are necessary to generate the control force to reduce the system responses under earthquake excitations are developed based on the adaptive systems theory. The main novelty in this paper is to approximate the nonlinear control law using a nonlinearly parameterized neural network, without an explicit training phase. A perturbed model is used to initialize the controller parameters in order to simulate the uncertainty in the mathematical modeling that typically exists in representing civil structures. Performance of the proposed control scheme is evaluated on a full-scale nonlinear three-dimensional (3-D) base-isolated benchmark structure. The lateral-torsion superstructure behavior and the bi-axial interaction of the nonlinear bearings are incorporated. The results show that the proposed controller scheme can achieve good response reductions for a wide range of near-fault earthquakes, without a corresponding increase in the superstructure response.

The purpose of this exploratory case study is to investigate the possibilities and connections among complex adaptive systems and the writing processes of three Colorado State University students in an upper-division composition course taught in a computer-networked classroom as it relates to their physical-material environment. I attempt to fill in the gap of knowledge concerning how the physical-material environment interacts with the complex social and psychological dimensions to affect college students' composition processes. Three primary research questions drive this case study: how does the class setting shape activity within the classroom? What are student attitudes towards reading, writing, and computers? What material resources do students use when they approach a composition assignment? Utilizing classroom observations, writing samples, interviews, surveys, screencapturing software, and audio and video recordings of the writing processes of students, I analyze three students' writing processes from multiple perspectives to capture an indepth understanding of how some individual students write. Overall, my findings suggest that the writing process is a complex adaptive process. The most significant areas that this case study illuminates for further research is the impact of economic and social initial conditions that students grow up in developing their writing skills in terms of access to information technologies, interactions with writing, and attitudes towards writing.