Learning identification control for model-based optoelectronic packaging

IEEE Journal Of Selected Topics In Quantum Electronics, 12(5): 945-951.In this paper, we present a learning control algorithm for the packaging automation of optoelectronic systems. This automation provides high performance, low-cost alignment and packaging through the use of a model-based control theory and systemlevel modeling. The approach is to build an a priori model, specific to the assembled package’s optical power propagation characteristics. From this model, an inverse model is created and used in the “feedforward” loop. In addition to this feedforward model, the controller is designed with feedback components, along with the inclusion of a built-in optical power sensor. We introduce a learning technique, which is activated at a lower sampling frequency for specific and appropriate tasks, to improve the model used in the model-based control. Initial results are presented from an experimental test bed that is used to verify the control and learning algorithms

Model-based optoelectronic packaging automation

IEEE Journal of Selected Topics in Quantum Electronics, 10(3): pp. 445-454. http:dx.doi.org/10.1109/JSTQE.2004.828476In this paper, we present an automation technique that yields high-performance, low-cost optoelectronic alignment and packaging through the use of intelligent control theory and system-level modeling. The control loop design is based on model-based control, previously popularized in process and other control industries. The approach is to build an a priori knowledge model, specific to the assembled package’s optical power propagation characteristics, and use this to set the initial “feed-forward” conditions of the automation system. In addition to this feed-forward model, the controller is designed with feedback components, along with the inclusion of a built in optical power sensor. The optical modeling is performed with the rigorous scalar Rayleigh–Sommerfeld formulation, efficiently solved online using an angular spectrum technique. One of the benefits of using a knowledge-based control technique is that the efficiency of the automation process can be increased, as the number of alignment steps can be greatly reduced. An additional benefit of this technique is that it can reduce the possibility that attachment between optical components will occur at local power maximums, instead of the global maximum of the power distribution. Therefore, the technique improves system performance, while reducing the overall cost of the automation process

Distributed cooperative control for adaptive performance management

IEEE Internet Computing, 11(1): pp. 31-39.The authors’ distributed cooperative-control framework uses concepts from optimal control theory to adaptively manage the performance of computer clusters operating in dynamic and uncertain environments. Decomposing the overall performance-management problem into smaller subproblems that individual controllers solve cooperatively allows for the scalable control of large computing systems. The control framework also adapts to controller failures and allows for the dynamic addition and removal of controllers during system operation. This article presents a case study showing how to manage the dynamic power consumed by a computer cluster processing a time-varying Web workload

Adaptive performance control of computing systems via distributed cooperative control: Application to power management in computing clusters

Proceedings of the 3rd International Conference on Autonomic Computing, ICAC 2006, pp. 165-174.Advanced control and optimization techniques offer a theoretically sound basis to enable self-managing behavior in distributed computing models such as utility computing. To tractably solve the performance management problems of interest, including resource allocation and provisioning in such distributed computing environments, we develop a fully decentralized control framework wherein the optimization problem for the system is first decomposed into sub-problems, and each sub-problem is solved separately by individual controllers to achieve the overall performance objectives. Concepts from optimal control theory are used to implement individual controllers. The proposed framework is highly scalable, naturally tolerates controller failures, and allows for the dynamic addition/removal of controllers during system operation. As a case study, we apply the control framework to minimize the power consumed by a computing cluster subject to a dynamic workload while satisfying the specified quality-of-service goals. Simulations using real-world workload traces show that the proposed technique has very low control overhead, and adapts quickly to both workload variations and controller failures

Advanced packaging automation for opto-electronic systems

Virology, 351(2), 271-279. http://dx.doi.org/10.1016/j.virol.2006.01.051In this paper, we present a learning control algorithm used in our research of advanced opto-electronic automation, which yields high performance, low cost optoelectronic alignment and packaging through the use of intelligent control theory and system-level modeling. The learning loop technique is activated at a lower sampling frequency for specific and appropriate tasks, to improve the knowledge based control model. Our automation technique is based on constructing an a priori knowledge based model, specific to the assembled package’s optical power propagation characteristics. From this model, a piece-wise linear inverse model is created and used in the “feedforward” loop. This model can be updated for increased accuracy through the learning loop

Mortality forecasting using neural networks and an application to cause-specific data for insurance purposes

Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee-Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee-Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee-Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.

On trajectory control of magnetized spherical solids driven by magnetic force through soft medium

Manipulation of untethered millimeter-sized devices (bots) inside the human body has many medical applications. Most of the prior work focused on movement of such bots inside fluids, usually at low Reynolds numbers. Yet, many medical procedures are performed within soft tissues. Bot translation in soft materials differs dramatically from their motion in fluid. This paper focuses on trajectory control of small, untethered spheres driven magnetically in soft media commonly used to simulate tissues. While spherical bot shapes offer the advantage of potentially rapid change in the direction of motion, the main challenge in controlling trajectories through soft materials is their nonlinear and history dependent response forces associated with irreversible medium modification. This paper introduces control modules for manipulating spherical bots in soft media along elementary trajectories of circular and straight segments, from which more general trajectories may be assembled. The proposed control is based on a phenomenological model of soft media response forces. Numerical implementation of the proposed trajectory control along circular trajectories is shown to compare well with the results of experimental tests indicating that the accuracy on the order of the bot radius is readily achievable for trajectories whose radii of curvature is on the order of 10 bot radii

Controllability of magnetic manipulation of a few microparticles in fluids

IEEE Transactions on Magnetics, 43(6): pp. 2427-2429.Control theory is employed to formulate the problem of controllability of two microparticles in fluids via magnetic field. We demonstrate that a uniform external magnetic field of varying direction and magnitude provides complete local state controllability of two particles over a magnetized substrate.We propose that such an approach may improve manipulation and assembly of microparticles in fluids

Possible Life Saver: A Review on Human Fall Detection Technology

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed