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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： M. Suter S. Graf O. Ergeneman S. Schmid A. Camenzind B. J. Nelson C. Hierold Micro and Nanosystems ETH Zurich Switzerland Institute of Robotics and Intelligent Systems ETH Zurich Switzerland Particle Technology Laboratory Department of Mechanical and Process Engineering ETH Zurich Switzerland

We present a photosensitive polymer composite with superparamagnetic characteristics for the fabrication of microactuators. A uniform distribution of particles in the photosensitive polymer matrix SU-8 is achieved by applying superparamagnetic nanoparticles with the aid of a surfactant. The composite contains Fe 3 O 4 nanoparticles up to 3 vol.% (12 wt.%) with diameters of around 13 nm. Superparamagnetic composite microcantilevers are successfully fabricated and actuated in resonance by the magnetic field of an external coil.

关键词： Polymers Microactuators Magnetic materials Saturation magnetization Magnetic resonance Magnetic anisotropy Fabrication Nanoparticles Perpendicular magnetic anisotropy Actuators

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Voluntary gait speed adaptation for robot-assisted treadmill training

Voluntary gait speed adaptation for robot-assisted treadmill...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Alexander Koenig Carmen Binder Joachim v. Zitzewitz Ximena Omlin Marc Bolliger Robert Riener Spinal Cord Injury Center Medical Faculty University Hospital Balgrist University of Zurich Zurich Switzerland Sensory Motor Systems Laboratory Institute of Robotics and Intelligent Systems Department of Mechanical Engineering and Process Engineering ETH Zurich Switzerland

Robot-assisted gait training currently lacks the possibility of the robot to automatically adapt to the patient's needs and demands (so called ldquobio-cooperative control strategiesrdquo). It is desired to give the patient voluntary control over training parameters such as gait speed or joint trajectories. We implemented a control algorithm for the driven gait orthosis Lokomat that allows severely disabled stroke patients a limited and safe allowance of influence on their gait speed. To exercise gait symmetry, our algorithm can be configured such that only activity in the paretic leg will cause changes in treadmill speed. The algorithm was successfully tested with eight healthy subjects and six stroke patients.

关键词： Rehabilitation robotics Legged locomotion Medical treatment Robotics and automation Automatic control Hospitals Force measurement Virtual environment Robot sensing systems intelligent robots

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Qualitative representation of trends (QRT) as a tool for automated data-driven monitoring of on-line sensors

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IFAC Proceedings Volumes 2009年第8期42卷 1276-1281页

作者： Kris Villez Benjamin Keser Leiv Rieger modelEAU Université Laval Pavillon Pouliot 1065 av. de la Médecine Quebec G1V 0A6 Canada Laboratory of Intelligent Process Systems (LIPS) Purdue University Forney Hall of Chemical Engineering 480 Stadium Mall Drive West Lafayette IN 47906 USA

The application of on-line sensors for monitoring or real-time control of wastewater treatment plants remains a challenging problem. The primary cause for this is the unknown quality of the data obtained by such on-line instruments. In the presented work sensor-internal meta-data is used for self-diagnosis. It is verified whether the complete measurement profiles obtained during regular measurement cycles of a phosphate analyzer can help to assess the sensor's status and even indicate potential faults causing faulty behaviour. A qualitative analysis approach is taken and the obtained results indicate that measurement profiles are indeed affected by the implied fault scenarios and may thus serve for proper fault detection and diagnosis.

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Cyberinfrastructure for pharmaceutical product development

Cyberinfrastructure for pharmaceutical product development

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2007 AIChE Annual Meeting

作者： Venkatasubramanian, Venkat Laboratory for Intelligent Process Systems School of Chemical Engineering Purdue University West Lafayette IN 47906 United States

ISBN: (纸本)9780816910229

Designing new drugs and formulations with desired properties is an important and difficult problem in the pharmaceutical industry. In going from discovery to delivery in the market via several intermediate stages, a staggering amount of information of different types, ranging from raw data to lab reports to sophisticated math models, is shared and revised by humans with the aid of computational tools in each stage. Traditional trial-and-error design approaches are laborious and expensive, that delay time-to-market as well as miss some potential solutions. A new paradigm is needed that increases the idea flow, broadens the search horizon, and archives the knowledge from today's successes to accelerate those of tomorrow. However, due to the incompatibility among such diverse types of data, information and knowledge, an appropriate automated decision support environment to address these needs is also very much lacking. Additionally, at present most of the information and knowledge are generated and processed directly by humans. With the onset of information and knowledge explosion, it is clear that we need intelligent software systems to effectively manage and access information for efficient decision making. Proper informatics support to drug discovery and process development is crucial in order to achieve speed to market, and getting the process right the first time. In addition, a systematic way to convert the raw data gathered from process analytical technologies (PAT) to information and first principles knowledge that can be used for real-time decision making is also lacking. All these present considerable challenges, as well as great opportunities, for a new model-based informatics framework to address these important problems. In this talk, I will review the state of the art and present an overview of emerging trends in developing a novel cyberinfrastructure framework that enables the management of complexity, accumulation of knowledge, systematic hypotheses tes

关键词： Decision making

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Tower of Babel: intelligent data and knowledge management for decision support in pharmaceutical engineering

Tower of Babel: Intelligent data and knowledge management fo...

引用

2007 AIChE Annual Meeting

作者： Venkatasubramanian, Venkat Laboratory for Intelligent Process Systems School of Chemical Engineering Purdue University West Lafayette IN 47906 United States

ISBN: (纸本)9780816910229

The compelling drivers for the pharmaceutical industry are speed to market and getting the manufacturing process right the first time. However, getting there is a complex challenge, particularly with respect to information management. Getting there involves going through the following stages after the viability of a newly discovered molecule is established: laboratory scale, pilot plant scale and commercial scale manufacturing. During this a staggering amount of information of different types, ranging from raw data to lab reports to sophisticated math models, is shared and revised by computational tools in each stage. Subsequent to process development, technical specifications and reports must be developed to satisfy regulatory requirements. However, due to the Tower of Babel like incompatibility among such diverse types of data, information, and knowledge, an appropriate automated decision support environment to address these needs is very much lacking. Additionally, at present most of the information and knowledge are generated and processed directly by humans. With the onset of information and knowledge explosion, it is clear that we need intelligent software systems to effectively manage and access information for efficient decision making. Proper informatics support to process development is crucial in order to achieve speed to market, and getting the process right the first time. To date the industrial response to all these challenges, however, has been sub-optimal. Even with rapid progress on information integration and sharing in business functions (such as ERP systems) and on plant floor (such as MES systems), the area of process/product development has been largely neglected. Several individual islands of automation exist, but a comprehensive, integrated decision support environment that integrates these islands does not exist. Therefore, practitioners must make do with a limited computer-based assistance to acquire, manage, analyze and interpret complex p

关键词： Decision making

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Soft sensor for predicting crude oil distillation side streams using evolving takagi-sugeno fuzzy models

Soft sensor for predicting crude oil distillation side strea...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jose J. Macias-Hernandez Plamen Angelov Xiaowei Zhou Process Engineering Department CEPSA (Tenerife Refinery) and Chem. Engineering Department University of La Laguna Tenerife Spain Intelligent Systems Research Laboratory Department of Communication Systems Lancaster University Lancaster UK

Prediction of the properties of the crude oil distillation side streams based on statistical methods and laboratory-based analysis has been around for decades. However, there are still many problems with the existing estimators that require a development of new techniques especially for an on-line analysis of the quality of the distillation process. The nature of non-linear characteristics of the refinery process, the variety of properties to measure and control and the narrow window that normally refinery processes operates in are only some of the problems that a prediction technique should deal with in order to be useful for a practical application. There are many successful application cases that refinery units use real plant data to calibrate models. They can be used to predict quality properties of the gas oil, naphtha, kerosene and other products of a crude oil distillation tower. Some of these are distillation end points and cold properties (freeze, cloud). However, it is difficult to identify, control or compensate the dynamic process behaviour and the errors from instrumentation for an online model prediction.

关键词： Petroleum Predictive models Poles and towers Laboratories Takagi-Sugeno model Economic forecasting Monitoring Refining Temperature measurement Statistical analysis

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An Agent Matching Architecture for Bilateral Contacts in Multi Agent Markets

An Agent Matching Architecture for Bilateral Contacts in Mul...

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IEEE International Conference on intelligent Computer Communication and processing (ICCP)

作者： Hamed Kebriaei Vahid Johari Majd Ashkan Rahimi-Kian Intelligent Control Systems Lab. Electrical Engineering Dept. Tarbiat Modares University Tehran Iran Intelligent Control Systems Laboratory Electrical Engineering Department Tarbiat Moalem University Tehran Iran Control and Intelligent Process Center of Excellence Electrical Engineering Department University of Tehran Tehran Tehran Iran

In this paper, an agent matching architecture for bilateral contracts of a multi agent market is proposed. The bilateral market contains a number of sellers and buyers who face opponents for negotiation and signing bilateral contracts. Each agent has a hierarchical representation of trading commodity attributes, where based on that it will create a tree structure of attributes. Using this structure, we apply a modified version of fuzzy similarity algorithm to compute similarity between each pair of buyer and seller agents' trees. Then, we use game theory and the concept of Stackelberg equilibrium to assess the matchmaking among the seller and buyer agents. Agents who lose contract or want to sign a new contract have a chance to restart the procedure. Through a case study for bilateral contracts in energy market, the applicability of our agent matching architecture is illustrated.

关键词： Contracts Game theory Tree data structures Decision making Contacts intelligent control intelligent agent Control systems Supply and demand Fuzzy sets

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A novel approach to proactive human-robot cooperation

A novel approach to proactive human-robot cooperation

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： O.C. Schrempf U.D. Hanebeck A.J. Schmid H. Worn Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe Karlsruhe Germany Institute for Process Control and Robotics Universität Karlsruhe Karlsruhe Germany

This paper introduces the concept of proactive execution of robot tasks in the context of human-robot cooperation with uncertain knowledge of the human's intentions. We present a system architecture that defines the necessary modules of the robot and their interactions with each other. The two key modules are the intention recognition that determines the human user's intentions and the planner that executes the appropriate tasks based on those intentions. We show how planning conflicts due to the uncertainty of the intention information are resolved by proactive execution of the corresponding task that optimally reduces the system's uncertainly. Finally, we present an algorithm for selecting this task and suggest a benchmark scenario.

关键词： Robot sensing systems intelligent robots Uncertainty Application software Humanoid robots Human robot interaction Bayesian methods intelligent sensors Laboratories Computer science

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Model-reduction by simultaneous determination of network topology and parameters: Application to modules in biochemical networks

Model-reduction by simultaneous determination of network top...

引用

05AIChE: 2005 AIChE Annual Meeting and Fall Showcase

作者： Maurya, Mano Ram Bornheimer, Scott J. Venkatasubramanian, Venkat Subramaniam, Shankar Laboratory for Bioinformatics and Computational Biology San Diego Supercomputer Center 9500 Oilman Dr San Diego CA 92093 United States Departments of Chemistry and Biochemistry and Cellular and Molecular Medicine 9500 Oilman Dr San Diego CA 92093 United States Department of Bioengineering University of California San Diego 9500 Oilman Dr San Diego CA 92093 United States Laboratory for Intelligent Process Systems School of Chemical Engineering Purdue University West Lafayette IN 47907 United States

Development and analysis of detailed quantitative models of biological systems is computationally prohibitive due to inherent complexity and nonlinearities. Even if a mathematical model can be developed, lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, reduced-order models (ROMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. In this paper, a mixed-integer nonlinear-optimization-based approach to model-reduction is presented in which, starting with a detailed biochemical model with concomitant computational details (reaction network and mathematical description), the structure and the parameters of a reduced-order model are determined simultaneously. A genetic-algorithm is used to solve the optimization problem. The methodology is demonstrated by developing a ROM for the GTPase cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signaling 4 (RGS4, a GTPase activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 14 reactions, and the ROM fits experimental data well and predicts 4 limiting signaling regimes corresponding to the extremes of receptor and GAP concentration. The method is about an order of magnitude faster than a previous method for reduced-order modeling that used a multiparametric variability analysis approach.

关键词： Living systems studies

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Fault diagnosis by qualitative trend analysis of the principal components: Prospects and some new results 5

Fault diagnosis by qualitative trend analysis of the princip...

引用

5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical processes, Safeprocess 2003

作者： Ram Maurya, Mano Rengaswamy, Raghunathan Venkatasubramanian, Venkat Laboratory for Intelligent Process Systems School of Chemical Engineering Purdue University West LafayetteIN47907 United States Department of Chemical Engineering Clarkson University PotsdamNY13699-5705 United States

Qualitative trend analysis (QTA) is a data-driven semi-quantitative technique that has been used for process monitoring and fault detection and diagnosis (FDD). Though QTA provides quick and accurate diagnosis - the increase in computational complexity of QTA with the increase in the number of sensors used for diagnosis - may prohibit its real-time application for very large-scale plants. In most of the chemical plants, the measurements are highly redundant and this redundancy can be exploited by performing principal component analysis (PCA) on the measured data. In this paper, we present a PCA-QTA technique for fault diagnosis (FD) in large-scale plants. Essentially, QTA is applied on the principal components rather than on the sensor data. The proposed approach is tested on the Tennessee Eastman (TE) process. The reduction in computational complexity in trend-extraction is about 40%. This reduction in computational complexity is expected to increase considerably for larger processes. © 2003 International Federation of Automatic Control.

关键词： Principal component analysis

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