Off-line Applications Research Articles

Optimization of antibody immobilization for on‐line or off‐line immunoaffinity chromatography

The covalent immobilization of antibodies to solid supports for immunoaffinity (IA) purification is widely used in the biological sciences. However, relative immobilization yields, immobilization stability, and antigen-binding capacity vary significantly with the antibody and protocol used. A systematic study was conducted to determine the most versatile antibody immobilization method for use in on-line and off-line IA chromatography applications using commonly accessible immobilization methods. Four chemistries were examined using polyclonal and monoclonal antibodies and antibody fragments. We evaluated a method to survey optimal elution conditions and estimated immobilization yields, matrix stability, antigen binding capacities, and antigen recovery of different IA matrices. Some mAbs were sensitive to aminogroup-based immobilization, i.e., losing antigen binding capabilities after immobilization especially using epoxy chemistry. In general, the most optimal covalent antibody immobilization for on-line IA-LC-MS was achieved using aminogroup immobilization of intact antibodies by epoxy- or aldehyde-activated POROS R20-matrices and in some cases by chemical crosslinking to Protein G-POROS. Protein G-based matrices are very stable showing essentially no decline in performance after 50 application-wash-elution-reequilibration cycles and being easily prepared within 2-3 h of working time with a typical antibody coupling yield of above 80%. In off-line applications where constant flow conditions are not used, covalent crosslinking onto Protein G-POROS or Protein G-agarose is to be recommended.

Application of a simulation-based dynamic traffic assignment model

The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1191 nodes, 228 zones, representing and 4964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.

Automatic recognition of the common carotid artery in longitudinal ultrasound B-mode scans

Many morphological and dynamic properties of the common carotid artery (CCA), e.g. lumen diameter, distension and wall thickness, can be measured non-invasively with ultrasound (US) techniques. As common to other medical image segmentation processes, this requires as a preliminary step the manual recognition of the artery of interest within the ultrasound image. In real-time US imaging, such manual initialization procedure interferes with the difficult task of the sonographer to select and maintain a proper image scan plane. Even for off-line US segmentation the requirement for human supervision and interaction precludes full automation. To eliminate user interference and to speed up processing for both real-time and off-line applications, we developed an algorithm for the automatic artery recognition in longitudinal US scans of the CCA. It acts directly on the envelopes of received radio frequency echo signals, eventually composing the ultrasound image. In order to properly exploit the information content of the arterial structure the envelopes are decimated, according to the two-dimensional resolution characteristics of the echo system, thereby substantially decreasing computational load. Subsequently, based upon the expected diameter range and a priori knowledge of the typical pattern in the echo envelope of the arterial wall-lumen complex, parametrical template matching is performed, resulting in the location of the lumen position along each echo line considered. Finally, in order to reject incorrect estimates, a spatial and temporal clustering method is applied. Adequate values for the parameters involved in the processing are obtained via off-line testing of the proposed algorithm on 128 echo data recordings from 45 subjects. Using those robust parameter values, correct and fast recognition of the artery is achieved in more than 98% of the 6185 processed frames. Since these results are obtained via rigorous data decimation and using a cascade of rather simple steps, the proposed automatic algorithm is suitable for real-time recognition of the CCA.

Fully Automated Model-Based Performance Analysis Procedure for On-Line and Off-Line Applications

The development of modern gas turbines is accompanied by an extensive prototype testing. To evaluate performance, the engines are fitted with a substantial number of sensors measuring pressures, temperatures, shaft speeds, etc. The test data are analyzed by procedures calculating data that are not directly measured, e.g., component efficiencies and turbine temperatures. Engine manufacturers usually apply such procedures for a detailed analysis after the test (off-line) as well as to ensure a safe and efficient engine operation during the test (on-line). Especially for the latter application, a fast and reliable assessment of the performance of the engine and its components as well as a fast and reliable detection of measurement faults are required. The paper describes a fully automated performance analysis procedure for off-line as well as on-line applications. The basis of the procedure is a full thermodynamic model representing the expected behavior of the engine to be tested. By such a model-based analysis procedure, a comparison between test data and the expected engine behavior is performed. The way the comparison is done allows detecting and isolating measurement faults and component malfunctions. In case of a detected measurement fault, the measurement will be excluded automatically from the analysis. At MTU Aero Engines GmbH the procedure is regularly applied for on-line and off-line test data analysis of different engine types. Compared to the procedures used thus far, many measurement faults can be detected quickly and reliably.

Handling Critical Data and Observability

Real-time data redundancy is the main prerequisite for power system state estimation. This article develops a practical, efficient, numerical method for the on-line identification of critical data (single and groups) at the observability analysis step of the state estimation process. A reprocessing scheme is also proposed for updating critical sets whenever measurements become unavailable. In off-line applications, working independently of a previously installed state estimator, the proposed method can be a useful tool for the design of reliable and robust metering systems. Indices to reveal the criticality level of a given metering plan are proposed.

Simultaneous dynamic optimization strategies: Recent advances and challenges

Simultaneous dynamic optimization strategies, also known as direct transcription methods, have been applied in a wide number of domains. These include off-line applications like optimal control, trajectory planning, nonlinear parameter estimation and optimization of reactors and batch processes, as well as on-line applications such as nonlinear model predictive control, nonlinear state estimation and dynamic, real-time optimization. Here we discuss recent advances for the simultaneous approach and emphasize the characteristics, benefits and challenges related to these strategies. In particular, we compare the properties of solutions generated by simultaneous approaches to those of classical variational methods, for a variety of problem classes. We next demonstrate why simultaneous strategies are especially beneficial for dynamic systems with unstable modes, with path constraints and for large-scale, structured problems. Finally, we outline a number of challenges and open research questions that will further improve the effectiveness of these methods on a wider range of applications.

Vibration based Fault Diagnosis of induction Motor

All types of rotating machines even new ones generate some level of vibrations. Small level of machine vibration is acceptable, however higher level and increasing trends are symptoms of abnormal machine performance. Therefore, machine vibration analysis becomes one of the important tools for machine fault identification. This paper presents an integrated pattern recognition method based on Euclidean distance measure for vibration diagnostic of machine health. The overall process is subdivided into stages of data acquisition, preprocessing, feature extraction and calculation of distance index. A computerized on-line monitoring system has been developed to record the frame vibrations, temperature, speed, line voltages and line currents from a three-phase induction motor for different operating conditions. The proposed method is suitable for both off-line and online applications. The method has been successfully tested for a large number of vibration data collected from the set of five identical test motors under different operating conditions. The test results establish the accuracy, effectiveness and reliability of the method.

Application of parallel computing to stochastic parameter estimation in environmental models

Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems.

Automated Monitoring and Analysis of Circuit Breaker Operation

This paper describes a solution for automated analysis of circuit breaker operation. The analysis is based on a record of waveforms taken from the circuit breaker control circuit by using a portable recorder and manually forcing an operation of the breaker. This solution was driven by a need to perform the analysis in a more timely and consistent manner than what is available with existing technology. The solution is implemented using advanced wavelet transforms for waveform feature extraction and an expert system for decision making. A web-based database solution for storing and retrieving both the field-recorded and processed data is also implemented. The software is developed in two versions: for field (substation) as well as off-line (office) applications.

The new IBA self-shielded dynamitron accelerator for industrial applications

Radiation Dynamics Inc. (RDI), currently a member of the IBA Group (Ion Beam Applications based Louvain-la-Neuve, Belgium), has been supplying accelerators since its founding in 1958. These systems supplied for both industrial processing and research application for electrons and ions have proven to be reliable and robust. Today's demands in the industrial sector have driven the design and development of a new version of our Dynamitron ®. This new system, envisioned to operate at electron energies up to 1.5 MeV, in many cases can be supplied with integral shielding providing a small footprint requirement for placement in a facility. In the majority of these lower energy applications this allows the appropriate material handling system to be installed inside the steel radiation enclosure. Designed to deliver beam power outputs as high as 100 kW, this new system is capable of servicing the high throughput demands of today's manufacturing lines. Still retaining the positive aspects of the industrially proven Dynamitron system, this compact system can be tailored to meet a variety of in-line or off-line processing applications.

Flexible Image-Based Photometric Reconstruction using Virtual Light Sources

Photometric reconstruction is the process of estimating the illumination and surface reflectance properties of an environment, given a geometric model of the scene and a set of photographs of its surfaces. For mixed-reality applications, such data is required if synthetic objects are to be correctly illuminated or if synthetic light sources are to be used to re-light the scene. Current methods of estimating such data are limited in the practical situations in which they can be applied, due to the fact that the geometric and radiometric models of the scene which are provided by the user must be complete, and that the position (and in some cases, intensity) of the light sources must also be specified a-priori. In this paper, a novel algorithm is presented which overcomes these constraints, and allows photometric data to be reconstructed in less restricted situations. This is achieved through the use of virtual light sources which mimic the effect of direct illumination from unknown luminaires, and indirect illumination reflected off unknown geometry. The intensity of these virtual light sources and the surface material properties are estimated using an iterative algorithm which attempts to match calculated radiance values to those observed in photographs. Results are presented for both synthetic and real scenes that show the quality of the reconstructed data and its use in off-line mixed-reality applications.

Multiview extensive partition operators for semantic video object extraction

Occlusion/disocclusion is one of the fundamental problems for semantic video object (SVO) extraction, where pixel-wise accuracy is required. This issue is critical because the degradation in tracking due to object occlusion/disocclusion significantly increases the amount of user interaction required in off-line video editing applications. We present an approach based on the application of an extensive operator on a lattice of partitions, which exploits information from various views of the scene, based on a probabilistic formulation. Our multiview operator builds on the regional application of the maximum a posteriori principle, by integrating a single-view region classification stage with a multiview stage that improves classification for those disoccluded regions labeled as uncertain. Results on several real sequences show that our approach improves the SVO tracking compared to the single-view case and that, as a result, increases the quality of the extracted SVOs and reduces the total amount of user interaction.

Beyond backyard astronomy: professional telescopes onlinefor schools

Since the Telescopes in Education (TIE) project began in 1993 schools have had access, via telephone lines, to a high quality telescope on Mt Wilson in California. Highgate School in London has been an enthusiastic participant and this has benefited physics students in many ways. Now two more robotically controlled telescopes are becoming available to UK schools. Off-line applications to the Liverpool Telescope (La Palma, Canary Islands) via the National Schools Observatory and real-time astronomical observing for education, through access to the Faulkes Telescopes (Hawaii and Australia), give schools unprecedented access to research quality astronomical data. We should be careful to consider what can be gained from such projects to maximize their potential.

Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm

The Levenberg-Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss-Newton algorithms. The advantages and disadvantages of the different algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally the recursive Levenberg-Marquardt algorithm has better convergence properties than the other algorithms.

Robust Agent Teams via Socially-Attentive Monitoring

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.

On-line particulate detection in molten polymer streams

Polymer-based businesses face productivity losses due to unwanted particles, gels, agglomerates and other contaminates. The ability to detect, reduce and eliminate them directly impacts product properties, product quality, recycle, waste generation, manufacturing equipment downtime and human resources. A commercial instrument which has the capability to monitor (detect, size and count) these contaminates is the Kayeness/Flow Vision Polymer Event Detector and its replacement the Continuous On-Line Analyzer (COLA 2000). Both instruments use visible light in a forward light blocking technique to obtain images of events that are analyzed for size and number count. Probes that withstand high temperature and pressure may be installed in a number of process locations. Examples of off-line and in-line applications will be discussed.

Automatic detection of sluggish control loops

This paper describes a procedure for the automatic detection of sluggish control loops obtained from conservatively tuned controllers. A measure, the Idle index, of the sluggishness of the control loop is defined. The Idle index describes the relation between times of positive and negative correlation between the control and measurement signal increments. It can be determined with a very small amount of process knowledge, and is suitable for both on-line and off-line applications.

High-power-factor single-stage converter with robust controller for universal off-line applications

This paper presents a robust-controlled single-stage converter (SSC) operating either in discontinuous conduction mode (DCM) or in continuous conduction mode (CCM) to achieve high power factor for universal line voltage range from 90 to 264 V/sub rms/. At light load, the proposed system is operated in the DCM, while at heavy load the system is operated in the CCM, thus, the DC-link voltage can be properly controlled within a certain voltage range and the efficiency can be improved as well. The overall system is with a robust controller and is operated in a fixed frequency. In the design, the load variation and line voltage variation are treated as plant uncertainty and control disturbance, respectively. The H/sup /spl infin// robust control method is adopted to design a proper robust controller to achieve robust stability and robust performance. The measured results from a prototype have confirmed the effectiveness and feasibility of the proposed system.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

Estimation of Latency Changes and Relative Amplitudes in Somatosensory Evoked Potentials Using Wavelets and Regression

Changes in onset latency and relative amplitudes of somatosensory evoked potentials (SEP) may be a convenient and reliable neurophysiological indicator of depth of anesthesia. However, to derive the components is very difficult mathematically and visual inspection or alternatively the peak-latency estimation is usually employed. A methodology for estimating the components was developed for both real-time and off-line applications based on the combination of the wavelet transforms (WT), geometric analysis, artificial intelligence (AI), and mathematical analysis of the first positive wave of SEPs. The WT together with AI constitutes a feature extraction engine for localizing the first positive peak and negative valley and hence relative amplitudes. The latency change between two averages is obtained by shifting one average toward another to achieve a best match along the positive inflections. The inflection, based on the peak, is modeled as a regression line and is refined using a steepness inference algorithm. Results from simulation and anesthetized rats show that it is reliable in comparison with visual inspection, robust to amplitude variation and signal distortion, and efficient in computation, and hence it is suitable for automation. Comparisons of interobserver variability and analysis of method agreement suggest that the method can be used as a substitute for estimations by visual inspection.