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This paper presents the experimental aspects of a robotic system used for detection, sorting and grading of paper objects from visual features. The experimental system is used as an automated paper recycling system. The system uses simplified stereo vision to compute position estimate of unknown objects. A vector of geometrical and textural features is used for sorting the paper, according to its grade. The grading process uses additional contact sensors for refining grading from visual features. Supervised learning is used for training the decision system to separate paper from other objects. The experimental aspects of the training process are presented in detail. A special vacuum gripper was designed for gripping paper objects detected by the vision system. The experimental results prove the efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for motion planning in outdoor environments that takes into account specific characteristics of the terrain. Instead of decomposing the robot configuration space into "free" and "occupied", we consider the existence of several regions with different navigation costs. Costs are determined experimentally by navigating the robot through the regions and measuring the influence of the terrain on its motion. In this paper we measure the robot vertical acceleration, which reflects the terrain roughness. A path planning algorithm is used to determine a sequence of triangles that minimize the navigation cost. Robot control is accomplished by a piecewise continuous vector field that drives the robot through those regions. This vector field allows the robot velocity to change according to the characteristics of the terrain. Experimental results with a differential driven, all terrain mobile robot illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Time-to-collision can be directly measured from a spatio-temporal image sequence obtained from an uncalibrated camera. This it would appear to offer a simple, elegant measurement for use in obstacle avoidance. However, previous techniques for computing time to collision from an optical flow have proven impractical for real applications. This paper present a new approach for computing time to collision (TTC) based on the idea of measuring the rate of change of the "intrinsic scale". Intrinsic scale is a geometric invariant that is valid at most points in an image, and can be rapidly determined using a multi-resolution pyramid. In this paper we develop the approach and demonstrate its feasibility by comparing the results with range measurements obtained from a laser ranging device on a moving vehicle. Experimental results show that this is a simple method to obtain reliable TTC with a low computational cost. [ABSTRACT FROM AUTHOR]

This paper presents a contribution to programming by human demonstration, in the context of compliant motion task specification for sensor-controlled robot systems that physically interact with the environment. One wants to learn about the geometric parameters of the task and segment the total motion executed by the human into subtasks for the robot that can each be executed with simple compliant motion task specifications. The motion of the human demonstration tool is sensed with a 3D camera, and the interaction with the environment is sensed with a force sensor in the human demonstration tool. Both measurements are uncertain, and do not give direct information about the geometric parameters of the contacting surfaces, or about the contact formations encountered during the human demonstration. The paper uses a Bayesian Sequential Monte Carlo method (also known as a particle filter) to do the simultaneous estimation of the contact formation (discrete information) and the geometric parameters (continuous information). The simultaneous contact formation segmentation and the geometric parameter estimation are helped by the availability of a contact state graph of all possible contact formations. The presented approach applies to all compliant motion tasks involving polyhedral objects with a known geometry, where the uncertain geometric parameters are the poses of the objects. The approach has been verified in real world experiments, in which it is able to discriminate in realtime between 245 different contact formations of the contact state graph. [ABSTRACT FROM AUTHOR]

This paper presents a contribution to automatic model building in quadratic polynomial environments, in the context of programming by human demonstration. A human operator moves a demonstration tool equipped with a probe, in contact with an unknown environment. The motion of the demonstration tool is sensed with a 3D camera, and the interaction with the environment is sensed with a force/torque sensor. Both measurements are uncertain, and do not give direct information about the different objects in the environment (such as cylinders, spheres, planes,...) and their geometric parameters. This paper uses a Bayesian Sequential Monte Carlo method or particle filter, to recognize the different discrete objects that form the environment, and simultaneously estimate the continuous geometric parameters of these different quadratic polynomial objects. The result is a complete geometric model of an environment, with different quadratic polynomial objects at its building blocks. The approach has been verified using real world experimental data, in which it is able to recognize three different unknown quadratic polynomial objects, and estimate their geometric parameters. [ABSTRACT FROM AUTHOR]

The paper presents T1R3-type fully-isotropic redundantly-actuated parallel manipulators (PMs) with four degrees of freedom. The mobile platform has one independent translation (T1) and three independent rotations (R3). A method is proposed for structural synthesis of fully-isotropic redundantly-actuated T1R3-type PMs based on the theory of linear transformations. A one-to-one correspondence exists between the actuated joint velocity space and the external velocity space of the moving platform. The Jacobian matrix mapping the two vector spaces of fully-isotropic T1R3- type PMs presented in this paper is 4×4 identity matrix throughout the entire workspace. The condition number and the determinant of the Jacobian matrix being equal to one, the manipulator performs very well with regard to force and motion transmission capabilities. Redundant actuation is used to obtain fully-isotropic solutions. As far as we are aware, this paper presents for the first time in the literature the use of redundancy to design fully-isotropic T1R3-type PMs. [ABSTRACT FROM AUTHOR]

Consider a large group of agents chasing a small group of moving targets. Assume each agent moves at constant speed toward the closest target. This paper studies the problem of controlling the agents indirectly by specifying the motion of the targets. In particular, it considers the problem of maintaining a minimum separation distance between each pair of agents, something that is impossible to do with only one target. This paper shows that only two targets are necessary to maintain separation between four agents. It also shows results in simulation to support the conjecture that only two targets are necessary to maintain separation between any number of agents, given suitable initial conditions. [ABSTRACT FROM AUTHOR]

This paper presents a robust neural network motion tracking control methodology for piezoelectric actuation systems employed in micro/nano manipulation. This control methodology is proposed for tracking desired motion trajectories in the presence of unknown system parameters, non-linearities including the hysteresis effect, and external disturbances in the control systems. In this paper, the control methodology is established including the neural networks and a sliding scheme. In particular, the radial basis function neural networks are chosen in this study for function approximations. The stability of the closed-loop systems and convergence of the position and velocity tracking errors to zero are assured by the control methodology in the presence of the aforementioned conditions. Simulation results of the control methodology for tracking of a desired motion trajectory is presented. With the capability of motion tracking, the proposed control methodology can be utilised to realise high performance piezoelectric actuated micro/nano manipulation systems. [ABSTRACT FROM AUTHOR]

Due to their compliance and high dexterity, biologically-inspired continuum robots have attracted much interest for applications such as medical surgery, urban search and rescue, de-mining etc. In this paper, we will present an application to medical surgery-colonoscopy by designing a pneumatic-driven flexible robotic manipulator, called ColoBot. The focus of this paper lies in the sensor-based guidance control of the ColoBot in a tubular, compliant and slippery environment of human colon. The kinematic model related the position and orientation of distal end of ColoBot to the actuator inputs is firstly developed and formulated for orientation control of ColoBot. For the autonomous guidance inside the colon, a method based on a circumscribed circle is utilized to calculate the real-time reference paths from the measurement of three sensors for orientation control of the Colobot. This proposed approach can be extended to the control of continuum robots in the conditions of a dynamically confined space. The experimental results on an emulation platform will be presented in order to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we study the servomechanism problem for positive LTI systems. In particular, we consider the robust servomechanism problem of nonnegative constant reference signals for stable SISO positive LTI systems with constant disturbances, for the case when the plant model of the system is not known. A positive LTI system is an LTI system with the imposed constraint that the state, output and/or input variables be nonnegative for all time. This type of constraint is quite natural to apply, since the property of nonnegativity occurs quite frequently in practical applications and in nature, e.g. in pharmacodynamic applications, infusion of vasoactive drugs, chemical systems. A special class of positive systems that is of significant importance and appears quite frequently in the literature is the class of compartmental systems; these systems are composed of a finite number of storage devices or reservoirs and model various gas, fluid, and solid exchange networks. Positive systems have been of great interest to numerous researchers over several decades. Interesting results obtained on positive systems span the topics of reachability and controllability, realization theory for positive systems, stability control, and control via pole-assignment. However, to date, there appears that no study has been carried out on the servomechanism problem for positive LTI systems; thus we focus our attention on this topic in the paper. Various applications of the results are applied to a number of positive system problems obtained from the literature. [ABSTRACT FROM AUTHOR]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially non-convex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [ABSTRACT FROM AUTHOR]

Summary. During the last decade, pseudospectral (PS) optimal control methods have emerged as demonstrable efficient methods for computational nonlinear optimal control. Some fundamental problems on the feasibility and convergence of the Legendre PS method are addressed. In the first part of this paper, we summarize the main results published separately in a series of papers on these topics. Then, a new result on the feasibility and convergence is proved. Different from existing results in the literature, in this new theorem neither the invertibility of necessary conditions nor the existence of limit points is assumed. [ABSTRACT FROM AUTHOR]

Summary. This paper gives a comparison principle for first-order PDEs of the Hamilton-Jacobi-Bellman type that arise in problems of nonlinear target control synthesis and reachability analysis under hard bounds on the controls. The emphasis is is on treating backward reachability sets for a system with moving target sets which may also turn to be forward reachability tubes of another system. The target sets are to be reached within preassigned intervals. The exact solutions to this problem, given in set-theoretic terms, are expressed as level sets to the solutions of some specific types of the HJB equation. But these solutions require fairly complicated calculation. The present paper presents an alternative approach that avoids exact solutions in favor of their upper and lower bounds, which in many cases may suffice for solving the required problems. For systems with linear structure ellipsoidal estimates are given, which ensure accurate approximations of nonconvex reachability sets through unions of ellipsoids. [ABSTRACT FROM AUTHOR]

This paper introduces some intelligent control strategies for marine craft. By looking at some existing problems in this area, e.g. the uncertainties and nonlinearities in ship dynamic model and time delay in ship actuator system, three types of control strategies are studied, namely, robust PID control for ship steering, adaptive fuzzy robust tracking control (AFRTC) for ship steering, and adaptive fuzzy Hinf control for ship roll stabilization. The effectiveness of the three control strategies are demonstrated by ship simulation. This paper is dedicated to the memory of Professor YS Yang for his pioneer work on robust adaptive fuzzy control of marine craft. [ABSTRACT FROM AUTHOR]

This paper summarizes the optimization and learning methods using Estimation of Distribution Algorithm (EDA) to generate and plan biped gaits. By formulating biped robot as mass link structure, dynamically stable and energy efficient gait generation problem is extracted as a multi-constrained and multi-objective optimization problem. Instead of traditional intelligent algorithms, EDAs are proposed to solve this high dimensional optimization problem for its clear structure and simple parameter. Simulation results of different EDAs based optimization algorithms with comparison to genetic algorithm on biped gait optimization are shown in this paper to find out the advantages and disadvantages of the proposed biped gait generation algorithms. [ABSTRACT FROM AUTHOR]

This paper explains the principle and method to calibrate the image plane center of the camera by use of laser in the pre-calibrate process of the intrinsic parameters of the visual sensor. The laser facula image has been disposed properly, so the accurate position has been located by calculating the center of mass. Consequently, the image plane center can be calibrated accurately. After the calibration, the guiding experiments show that the precision of the system is quite good which proves that camera calibrated by the method presented by this paper can satisfy the requirement of the whole system. [ABSTRACT FROM AUTHOR]

Welding seam tracking is a key issue to welding automation. In this paper, a seam tracking system based on laser structured light is presented. The paper discusses the structure of this system, and also explains the system's working principle especially the image processing algorithm. The outside-welding experiments of steel pipe show that this system is very robust to various welding noises and it can improve the welding efficiency greatly. [ABSTRACT FROM AUTHOR]

To realize weld defects detection and measure automation based on machine vision, such as dimension, shape, displacement and numbers in a given length (area), it's necessary to build a mathematic model for the imaging system. Precision of model directly affects precision of measurement for defects. In this paper, a model was built including some nonlinear factors, i.e. the input screen shape (spherical surface) of the x-ray image intensifier and the distortion of the image in CCD at small distance. A kind of reason why the electromagnetic distortion generates is given in this paper, too. [ABSTRACT FROM AUTHOR]

This paper designs a way to model on the complex welding robot system. With analyzing the characteristic of welding robot system, a fuzzy Petri net model is designed and analyzed. In order to get the performance of designed model, this paper proves the performance of model in theory. Results of model simulation experiment illustrate that the model can depicts welding robot system accurately and right. [ABSTRACT FROM AUTHOR]

In vision-based robotic arc welding had some degree of autonomy functions, sensors must be used to detect the position and orientation of a weld seam and determine its path in order to carry out the autonomy of welding process. This paper represented a method to recognize whole seam in complex robotic welding environment. According to the parameter we defined to judge the degree of image noise, then different size of filter windows were selected to preprocess the image. Algorithm to eliminate effect of complex changing background is proposed. Parameters are defined to judge if it is necessary to use this algorithm. The threshold and edge detect algorithms that adapt to the recognition for robotic welding environment is also addressed in this paper. The experiments give the recognition results of two different shapes and material of seams. The results showed that the recognized method was robust to the recognition of macroscopic seam for complex robotic welding environment. [ABSTRACT FROM AUTHOR]

This paper gives a brief review on intelligent robotic technologies in industrial applications. The goal is not to conduct a thorough analysis of the state-of-art technologies, but rather to point out the directions for future technology development. Based on the market success of force control and machine vision in industry automation, this paper contends the arrival of the golden period of intelligent robotic technologies. The potential growth markets at present and in the near future are primarily assembly, and machining, where vision and force sensing continue to be the enabling technologies. As robot manufacturers start to embrace this technology wave, there are many technical challenges to be solved. The major problem for industrial R&D is how to safely and cost-effectively integrate sensor-driven functionalities with existing pure planning based position control system. For academic research, robust, efficient and effective sensor fusion and data mining methods should be the focus. [ABSTRACT FROM AUTHOR]

In this paper, we develop a direct adaptive control framework for uncertain linear nonnegative and compartmental dynamical systems with unknown time delay. The specific focus of the paper is on compartmental pharmacokinetic models and their applications to drug delivery systems. In particular, we develop a Lyapunov-Krasovskiibased direct adaptive control framework for guaranteeing set-point regulation of the closed-loop system in the nonnegative orthant in the presence of unknown system time delay. The framework additionally guarantees nonnegativity of the control signal. Finally, we demonstrate the framework on a drug delivery model for general anesthesia involving system time delays. [ABSTRACT FROM AUTHOR]

This paper assumes familiarity with the basic Control and Dynamics, as covered in undergraduate courses. It introduces the different alternative system representations for linear systems and provides a quick review of the fundamental mathematical tools, which are essential for the treatment of the more advanced notions in Linear Systems. The paper focuses on some fundamental concepts underpinning the study of linear systems and dynamics and which play a crucial role in the analysis and design of control systems; thus, the paper deals with notions such as those of Controllability, Observability, Stability, poles and zeros and re lated dynamics and their properties under different compensation schemes. There is a concrete flavour in the current approach which runs through this presentation and this is that of the underlying algebraic structure. The term "structure" refers to aspects of the state space/transfer function description, which remain invariant under a variety of transformations. The set of transformations considered here are of the compensation type and include state feedback and output injection, dynamic compensation, as well as of the representation type transformations that include state, input, and output co-ordinate transformations. This structure stems from the system description and defines the nature of the dynamics and the related geometric proper ties and these in turn define what it is possible to achieve under feedback; such an approach is known as a structural approach. Central to our analysis are the notions of poles and zeros. The poles of a system are crucial characteristics of the internal system dynamics, characterise system free response, stability and general aspects of the performance of a system. The poles of a system are affected by the different compensation schemes and their assignment is the subject of many design methodologies aiming at shaping the internal system dynamics under different compensation schemes. The notion of zeros is more complex, since they express the interaction between internal dynamics and the effort to control and observe the system and they are thus products of overall system design, that apart from process synthesis involves selection of actuation and measurement schemes for the system. The significance of zeros is mainly due to that they remain invariant under a large set of compensation schemes, as well as that they define limits of what can be achieved under compensation. This makes zeros crucial for design, since they are part of those factors characterising the potential of a given system to achieve certain design objectives under compensation. The invariance of zeros implies that their design is an issue that has to be addressed outside the traditional control design; this requires understanding of the zero formation process and involves early design stages mechanisms such as process instrumentation. Poles and zeros are conceptually inverse concepts (resonances, antiresonances) and such mechanisms are highlighted throughout the paper. The role of system structure in characterising different system properties is central to this paper and it is defined by a set of discrete and continuous invariants; these invariants characterise a variety of key system properties and their type/values define the structure of canonical forms and determine somehow the potential of a given system for compensation. Invariants and canonical forms under the general transformation group are linked to compensation theory, whereas those associated with representation transformations play a key role in system identification. The emphasis in this article is to provide an overview of the fundamentals concepts, the back ground mathematical tools, explain their dynamic significance and link them to problems of control and systems design. [ABSTRACT FROM AUTHOR]

In this paper we analyze the equations of motion of a submerged rigid body. Our motivation is based on recent developments done in trajectory design for this problem. Our goal is to relate some properties of singular extremals to the existence of decoupling vector fields. The ideas displayed in this paper can be viewed as a starting point to a geometric formulation of the trajectory design problem for mechanical systems with potential and external forces. [ABSTRACT FROM AUTHOR]

This paper begins by recalling how a constraint distribution on a configuration manifold induces a Dirac structure together with an implicit Lagrangian system, a construction that is valid even for degenerate Lagrangians. In such degenerate cases, it is shown in this paper that an implicit Hamiltonian system can be constructed by using a generalized Legendre transformation, where the primary constraints are incorporated into a generalized Hamiltonian on the Pontryagin bundle. Some examples of degenerate Lagrangians for L-C circuits, nonholonomic systems, and point vortices illustrate the theory. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a novel way to formulate control problems with partial observations of stochastic systems. Themethod is based on stochastic realization theory. The contribution of the paper is the stochastic realization approach to stochastic control with partial observations. The motivation for the paper are the weaknesses of control with partial observations based on the separation principle. The separation property holds for the stochastic control problem with a Gaussian system and a quadratic cost function (LQG), but does not hold for at least one optimal stochastic control problem, LEQG, and may not hold for most other stochastic control problems. Moreover, control of decentralized stochastic systems or control of stochastic dynamic games becomes unsolvable or unnatural in the existing approaches in the literature. Therefore a novel approach may be explored. [ABSTRACT FROM AUTHOR]

This paper is dedicated to Giorgio Picci on the occasion of his 65th birthday. I have come to appreciate Giorgio not only as a great friend but also as a great scholar. When we first met at Brown University in 1973, he introduced me to his seminal paper [29] on splitting subspaces, which became the impetus for our joint work on the geometric theory of linear stochastic systems [23,24,25,26]. This led to a life-long friendship and a book project that never seemed to converge, but now is close to being finished [27]. I have learned a lot from Giorgio. The present paper grew out of a discussion in our book project, when Giorgio taught me about the connections between prediction-error identification and the Kullback-Leibler criterion. These concepts led directly into the recent theory of analytic interpolation with complexity constraint, with which I have been deeply involved in recent times. I shall try to explain these connections in the following paper [ABSTRACT FROM AUTHOR]

Why are probabilistic roadmap (PRM) planners "probabilistic"? This paper tries to establish the probabilistic foundations of PRM planning and reexamines previous work in this context. It shows that the success of PRM planning depends mainly and critically on the assumption that the configuration space C of a robot often verifies favorable "visibility" properties that are not directly dependent on the dimensionality of C. A promising way of speeding up PRM planners is to extract partial knowledge on such properties during roadmap construction and use this knowledge to adjust the sampling measure continuously. This paper also shows that the choice of the sampling source—pseudo-random or deterministic—has small impact on a PRM planner's performance, compared to that of the sampling measure. These conclusions are supported by both theoretical arguments and empirical results. [ABSTRACT FROM AUTHOR]

The theory of behavioral communication for humanoid robots that interact with humans is discussed in this paper. For behavioral communication, it is fundamental for a humanoid robot to recognize the meaning of the whole body motion of a human. According to the previous works, it can be done in the symbolic level by adopting the proto-symbol space defined by the Hidden Markov Models based on the mimesis theory. The generation of robot motions from the proto-symbols is also to be done in the same framework. In this paper, we first introduce the meta proto-symbols that stochastically represent and become signifiants of the interaction of a robot and a human. The meta proto-symbols are a little more abstract analogy of the proto-symbols and recognize/generate the relationship of the two. A hypothesis is then proposed as the principle of fundamental communication. The experimental result follows. [ABSTRACT FROM AUTHOR]

This paper describes one aspect of our approach in developing an intelligent off-road autonomous vehicle, the Intelligent Off-road Navigator (ION), as team Desert Buckeyes from the Ohio State University for the DARPA Grand Challenge 2005. The real-time navigation is one of the critical components in an autonomous ground vehicle system. In this paper, we focus on the navigation module, whose main responsibility is to generate smooth and obstacle-free local paths with appropriate speed setpoints. For the smooth path generation, we introduce a polynomial interpolation method. To generate obstacle-free paths, a steering controller utilizing a fuzzy obstacle avoidance algorithm is presented. A speed fuzzy controller is introduced to generate the speed setpoints. These two fuzzy controllers collaborate with each other to guide our vehicle ION to the goal safely. The obstacle avoidance algorithm proposed in this paper was also tested in simulations and on small robots successfully. Other issues related to the navigation module are discussed in the paper as well, such as the vehicle's system structure and its finite state machine. As a result, ION achieved great performance in the National Qualification Event (NQE), covered about 30miles in the Nevada Desert with complete autonomous operations, and finished 10th in the Grand Challenge 2005. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate the application of a recently developed theory for distributed nonlinear model predictive control (NMPC) to a promising domain for NMPC: dynamic management of supply chain networks. Recent work by the first author provides a distributed implementation of NMPC for application in large scale systems comprised of cooperative dynamic subsystems. By the implementation, each subsystem optimizes locally for its own policy, and communicates the most recent policy to those subsystems to which it is coupled. Stabilization and feasibility are guaranteed for arbitrary interconnection topologies, provided each subsystem not deviate too far from the previous policy, consistent with traditional MPC move suppression penalties. In this paper, we demonstrate the scalability and performance of the distributed implementation in a supply chain simulation example, where stages in the chain update in parallel and in the presence of cycles in the interconnection network topology. Using anticipative action, the implementation shows improved performance when compared to a nominal management policy that is derived in the supply chain literature and verified by real supply chain data. [ABSTRACT FROM AUTHOR]

Plant-wide control is attracting considerable interest, both as a challenging research field and because of its practical importance. It is a topic [1] characterized by complexity in terms of the number and type of equipments involved, diversity of aims, and lack of adequate models and control policies. In this paper, the MPC control of the final part of a beet sugar factory, the so-called sugar house or sugar end, where sugar crystals are made, is presented. Perhaps the most characteristic aspect of its operation is that batch and continuous units operate jointly, which introduce the need for combining on-line scheduling with continuous control. As such, it is a hybrid process that requires non-conventional control techniques. The paper presents a methodology and a predictive controller that takes into account both, the continuous objectives and manipulated variables, as well as the ones related to the discrete operation and logic of the batch units, and, at the end, simulation results of the controller operation are provided. [ABSTRACT FROM AUTHOR]

Advanced model based control is a promising technology that can improve the productivity of industrial processes. In order to find its way into regular applications, advanced control must be integrated with the industrial control systems. Modern control systems, on the other hand, need to extend the reach of traditional automation systems - beyond control of the process - to also cover the increasing amount of information technology (IT) required to successfully operate industrial processes in today's business markets. The Industrial IT System 800xA from ABB provides a scalable solution that spans and integrates loop, unit, area, plant, and interplant controls. This paper introduces the 800xA and the underlying Aspect Object technology. It is shown how model knowledge and optimization solver technology are integrated into the 800xA framework. This way, advanced model based control solutions can be set up in an open and modularly structured way. New model and solver aspects can be combined with available aspects covering standard functionality like process connectivity, management of process data, trend&history data and application data, as well as operator graphics. A Nonlinear Model-based Predictive Controller (NMPC) for power plant start-up is treated as example. This paper discusses how NMPC can be integrated with a modern control system so that standard concepts are re-used for this advanced model based control concept. [ABSTRACT FROM AUTHOR]

Nonlinear model predictive controllers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a state space formulation, a finite prediction horizon, a performance specification in terms of desired closed loop response characteristics for the outputs, and costs on incremental manipulated variable action. The controller can utilize fundamental or empirical models. The simulation and optimization problems are solved simultaneously using sequential quadratic programming (SQP). In the paper, we present results illustrating regulatory and grade transition (servo) control by NLMPC on several industrial polymerization processes. The paper outlines the NLMPC technology employed, describes the current status in industry for extending linear model predictive control to nonlinear processes or applying NLMPC directly, and identifies several needs for improvements to components of NLMPC. [ABSTRACT FROM AUTHOR]

This paper considers discrete-time nonlinear, possibly discontinuous, systems in closed-loop with model predictive controllers (MPC). The aim of the paper is to provide a priori sufficient conditions for asymptotic stability in the Lyapunov sense and input-to-state stability (ISS), while allowing for both the system dynamics and the value function of the MPC cost to be discontinuous functions of the state. The motivation for this work lies in the recent development of MPC for hybrid systems, which are inherently discontinuous and nonlinear. For a particular class of discontinuous piecewise affine systems, a new MPC set-up based on infinity norms is proposed, which is proven to be ISS to bounded additive disturbances. This ISS result does not require continuity of the system dynamics nor of the MPC value function. [ABSTRACT FROM AUTHOR]

This paper surveys recent work of the author with several collaborators, principally Feng Lin, Weilin Wang, and Tae-Sic Yoo; they are kindly acknowledged. Decentralized control of discrete event systems, where local controllers cannot explicitly communicate in real-time, is considered in the first part of the paper. Then the problem of real-time communication among a set of local discrete-event controllers (or diagnosers) is discussed. The writing is descriptive and is meant to inform the reader about important conceptual issues and some recently-completed or on-going research efforts. [ABSTRACT FROM AUTHOR]

This paper describes a new intrusion detection system in the indoor environment, which is based on the airborne sonar technology and pattern classification. It utilizes an ultrasonic transmitter and a receiver to receive the pulse-echo. The simplest feature of echoes, envelope is extracted to train and test the pattern classifiers. The correct classification rates of K-nearest-neighbor (KNN) and Back-Propagation (BP) network classifiers are compared in this paper. Experiment results show that this system has a good performance on the intrusion detection. [ABSTRACT FROM AUTHOR]

This paper propose the development of a security requirement items and its process of the profile and security target(ISO/IEC 15408). This paper estimate the security requirement items and progress management that manage the security requirements and the progress and its problems. For projects in similar domains, it is possible to remove security risk items and to manage progress by using security lifecycle and progress milestone, which can greatly improve the software process. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative version of SVM using string vectors as its input data. A string vector is defined as a finite ordered set of words. Almost all of machine learning algorithms including traditional versions of SVM use numerical vectors as their input data. In order to apply such machine learning algorithms to classification or clustering, any type of raw data should be encoded into numerical vectors. In text categorization or text clustering, representing documents into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. Although traditional versions of SVM are tolerable to huge dimensionality, they are not robust to sparse distribution in their training and classification. Therefore, in order to avoid this problem, this research proposes another version of SVM, which uses string vectors given as an alternative type of structured data to numerical vectors. For applying the proposed version of SVM to text categorization, documents are encoded into string vectors by defining conditions of words as their features and positioning words corresponding to the conditions into each string vector. A similarity matrix, word by word, which defines semantic similarities of words, should be built from a given training corpus, before using the proposed version of SVM for text categorization. Each semantic similarity given as an element of the matrix is computed based on collocations of two words within their same documents over a given corpus. In this paper, inner product of two string vectors as the proposed kernel function indicates an average semantic similarity over their elements. In order to validate the proposed version of SVM, it will be compared with a traditional version of SVM using linear kernel functions in text categorization on two test beds. [ABSTRACT FROM AUTHOR]

This paper proposes a new model of dynamic window binding by analyzing the window and attributes of the object, and based on this, a mechanism that offers a seamless multimedia view without interfering with concurrency control is also suggested. This paper presents the design of the intelligence QOS model for adaptive concurrency control, which is running on RCSM(Reconfigurable Context Sensitive Middleware) for ubiquitous networks. RCSM provides standardized communication protocols to interoperate an application with others under dynamically changing situations. As the result of this system's experimental implementation and analysis of the increase of delay factors accordant with the size of collaboration increase, the simulation results clearly show that the seam increases seriously in that case, and that the dynamic window binding mechanism proposed in this paper is worth implementation and effective when a large scale of collaboration is required. [ABSTRACT FROM AUTHOR]

This paper is devoted to discussion case-deletion methodologies of influence assessments in linear mixed models (LMM) for longitudinal data. The paper presents two case-deletion influence measures to detect the influential individuals in the context of LMM with unknown covariance structures. These two measures can be easily used to real data analysis. [ABSTRACT FROM AUTHOR]

Data association, one of the key and difficult problems for multi-target tracking, is the decision process of linking the measurements or the tracks deemed to be of common origin under certain criteria. All typical data association algorithms can be deducted into special Multidimensional Assignment Problem. However, present S-D assignment algorithm only associates the synchronous measurements from different sensors, which only generate the static result. In this paper, the static assignment algorithm(S-D) has been generalized to the dynamic Multidimensional Assignment Problem by means of combining the measurements set and the tracks set. The main challenge in the assignment problem is that it is NP-hard. The solution using Hopfield neural network (HNN) is presented in this paper. The simulation results illustrate that this method can decrease the computing burden greatly. [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm of mining multidimensional association rules in relational database is proposed. First, design a particular structure including many indexes to save multidimensional itemsets, which makes time of finding itemsets decrease. Basing on statistic idea, the algorithm saves all frequent 1-itemsets and their support members at scanning the database for the first time. Frequent k-itemsets will be generated with no necessary to scan the database more times because of using these support members. Compared with some traditional algorithms of mining association rules, the algorithm presented in this paper has better executive efficiency and expansibility, which is proved in our experiments. [ABSTRACT FROM AUTHOR]

The paper will be presented the way of application of linguistic mechanisms of pattern recognition in development of intelligent information and recognition systems. Such methods are aimed to facilitate an in-depth analysis of the meaning for some selected medical patterns, especially in the form of spinal cord and coronary arteries images. The procedures proposed for semantic reasoning are based on the model of cognitive resonance analysis. Cognitive procedures have been applied to the task of semantic interpretation diagnostic images both from the central nervous system as well as from heart helical CT examinations. The application presented in this paper will show the great possibilities of lesion detection in the analysed structures used the grammar approach to the interpretation and classification tasks. [ABSTRACT FROM AUTHOR]

This paper described the mobile intelligence controller proving an automatic home service based on a user's preference in a smart home. The mobile intelligence that is presented in this paper optimizes context-aware middleware on a wireless device (a PDA) for a smart home. We presented a system architecture and class diagram of the mobile intelligence controller for automatic service of consumer electronics. We implemented an ECG sensor, environment sensor and a consumer device driver for our experiment. We applied a SVM (Support Vector Machine) to decide on home service through recognition of the user and environment contexts. To increase the pattern algorithm's precision, we applied different importance of contexts according to the characteristics of consumer electronics. Keywords: Smart Home, Home Automation Controller, Context-Aware Middleware, Intelligent Home Agent. [ABSTRACT FROM AUTHOR]

Traditional classification methods are often designed for certain types of data. They cannot be directly applied to dataset with mixed numeric and nominal data. Only after numeric data was discretized or nominal data was encoded, could algorithms work. As data should accommodate to algorithm, such learning scheme is approach oriented to some extend. This paper presents a new data mining scheme called lattice-based learning (LBL), whose central idea is formulating algorithms using basic operations on lattice structure. Since both numeric and nominal data can be easily embedded into lattices, LBL algorithms are applicable to any dataset with mixed data. We detail lattice-based classification (LBC) algorithm in this paper. The performance of LBC has been studied on different datasets. Results show that LBC is an effective method for classification with mixed data and LBL learning is a promising scheme for data mining. [ABSTRACT FROM AUTHOR]

Classification rule mining is an important function of data mining, and is applied in many data analysis tasks. The classification rule mining algorithm based-on ant colony optimization (ACO) is researched in this paper. Some improvements are implemented based on existing research to enhance classification predictive accuracy and simplicity of rules. Multi-population parallel strategy is proposed, the cost-based discretization method is adopted, and parameters in the algorithm are adjusted step by step. With these improvements, performance of the algorithm is advanced, and classification predictive accuracy is enhanced. Finally, SIMiner, a self-development data mining software system based on swarm intelligence, is applied to experiment on six data sets taken from UCI Repository on Machine Learning. The results illuminate the algorithm proposed in this paper has better performance in predictive accuracy and simplicity of rules. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy sliding mode controller with radial basis function neural network (RBFNN) for trajectory tracking of robot manipulator. The main problem of sliding mode controllers is that a whole knowledge of the system dynamics and system parameters is required to compute the equivalent control. In this paper, a RBFNN is proposed to compute the equivalent control. Computer simulations of three link robot manipulator for trajectory tracking indicate that the proposed method is a good candidate for trajectory control applications. [ABSTRACT FROM AUTHOR]

An adaptive discrete-time global sliding mode control scheme based on neural network reaching law for a class of discrete linear uncertain systems is proposed in this paper. Parameters ε and δ were determined previously in the conventional reaching law method, but they are regulated adaptively by a feedforward neural network(FNN) in this paper. Simulation results shown that all advantages of the reaching law are retained, meanwhile the dynamic features and robustness of the control system are improved effectively through shortening the reaching phase and chattering phenomenon is eliminated. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for distance measurement and control between two moving vehicles using video input from a camera mounted on a follower car behind a preceding car. The idea is to keep the computational complexity bounded by switching between different levels of image quantization, using a pyramid decomposition of the image. The distance is related to the car size in the image, hence the number of pixels representing the car. In order to achieve global bounds on the error, the controller works coarsely when the vehicles are close and at finer range for more distant vehicles. Hence this paper presents a method for trading off accuracy vs. speed of computations in a specific vehicle following application. [ABSTRACT FROM AUTHOR]