Fred M. Discenzo

Distributed multi-agent architecture for automation systems

Abstract In the 21st century, industrial automation will be greatly benefited by the advances in electronics, information systems, and process technology. However, these technological advances are still separate islands of automation. We believe that multi-agent systems will help the future of automation by providing flexible and scalable ways to integrate the different parts. This paper reports preliminary results of an ongoing research project that demonstrates advanced automation in a highly distributed architecture that is made of a synergy of intelligent agents, control, and physical devices. This was built to achieve the goals of reduced manning and improved readiness and survivability in US Navy shipboard systems.

Dynamic High-Cadence Cycling Improves Motor Symptoms in Parkinson's Disease.

Rationale Individuals with Parkinson’s disease (PD) often have deficits in kinesthesia. There is a need for rehabilitation interventions that improve these kinesthetic deficits. Forced (tandem) cycling at a high cadence improves motor function. However, tandem cycling is difficult to implement in a rehabilitation setting. Objective To construct an instrumented, motored cycle and to examine if high cadence dynamic cycling promotes improvements in motor function. Method This motored cycle had two different modes: dynamic and static cycling. In dynamic mode, the motor maintained 75–85u2009rpm. In static mode, the rider determined the pedaling cadence. UPDRS Motor III and Timed Up and Go (TUG) were used to assess changes in motor function after three cycling sessions. Results Individuals in the static group showed a lower cadence but a higher power, torque and heart rate than the dynamic group. UPDRS score showed a significant 13.9% improvement in the dynamic group and only a 0.9% improvement in the static group. There was also a 16.5% improvement in TUG time in the dynamic group but only an 8% improvement in the static group. Conclusion These findings show that dynamic cycling can improve PD motor function and that activation of proprioceptors with a high cadence but variable pattern may be important for motor improvements in PD.

Neural Net Based Torque Sensor Using Birefringent Materials

Abstract Photoelasticity can be used to accurately measure surface strains or stresses in a part or structure. In this paper we describe the use of a photoelastic transducer and neural net image processing to estimate the torque of stationary and rotating shafts. A strain sensitive (photoelastic) plastic cylinder is attached to the shaft and illuminated by polarized light. As the shaft torque varies the photoelastic plastic displays the corresponding shaft strain as a two-dimensional fringe pattern when viewed through an optical polarizer. The strain that causes this observed optical pattern is a complex function of the torque applied to the shaft. In this paper, we describe the use of neural net image processing to estimate this function to realize an optical torque sensor. A CCD camera/image processing system was used to acquire and process the optical patterns. A neural net torque estimator was trained with these fringe patterns and tested against a laboratory strain gauge torque sensor. Our experiments show that the neural net torque estimator can accurately estimate (to within a few percent) the applied torque for both static and slowly rotating (

Cost-Based Dynamic Reconfiguration System for Evolving Holarchies

Autonomous, highly distributed control architectures are composed of a significant number of holons/agents that can reason and act on behalf of represented processes or artifacts in a coordinated manner. Depending on the social organization capabilities of the agents, the autonomous system could evolve into complex agent organizations called temporal holarchies. Cost-based negotiation supports the holarchy formation. Dynamic hierarchical teamworks architecture of middle-agents is described to increase robustness of the architecture.

Next Generation Pump Systems Enable New Opportunities For Asset Management And Economic Optimization

There is growing interest in cost-effective techniques that can detect the earliest stage of degradation or malfunction and predict machinery failure. New diagnostic and prognostic techniques may be effectively coupled with novel control techniques in the context of an intelligent motor-pump-control system. An integrated intelligent system is described for pumping applications that can sense the operating condition and health of the components of a hydraulic system and automatically change the operation of the motor-pump system. The change in control is goal-directed whereby the prescribed operating change is intended to achieve previously defined operating goals or performance objectives. 9 NEXT GENERATION PUMP SYSTEMS ENABLE NEW OPPORTUNITIES FOR ASSET MANAGEMENT AND ECONOMIC OPTIMIZATION

Multi-agent technology for robust control of shipboard chilled water system

Abstract This paper reports preliminary results of an ongoing research project that demonstrates distributed architecture based on agents applied in the area of industrial automation. This architecture has been built to achieve the goals of improved survivability and readiness of US Navy shipboard systems. We show benefits of multiagent systems in the area where flexibility, survivability, and scalability are required. We present the architecture of multi-agent system, internal structure of agent, planning technique based on plan templates, fault-tolerant structure of middle-agents, and development environment for agents.

Design and Development of a Smart Exercise Bike for Motor Rehabilitation in Individuals With Parkinson's Disease

Recent studies in rehabilitation of Parkinsons disease (PD) have shown that cycling on a tandem bike at a high pedaling rate can reduce the symptoms of the disease. In this paper, a smart motorized bicycle has been designed and built for assisting Parkinsons patients with exercise to improve motor function. The exercise bike can accurately control the riders experience at an accelerated pedaling rate, while capturing real-time test data. Here, the design and development of the electronics and hardware as well as the software and control algorithms are presented. Two control algorithms have been developed for the bike: one that implements an inertia load (static mode) and one that implements a speed reference (dynamic mode). In static mode, the bike operates as a regular exercise bike with programmable resistance (load) that captures and records the required signals, such as heart rate, cadence, and power. In dynamic mode, the bike operates at a user-selected speed (cadence) with programmable variability in speed that has been shown to be essential to achieve the desired motor performance benefits for PD patients. In addition, the flexible and extensible design of the bike permits readily changing the control algorithm and incorporating additional I/O as needed to provide a wide range of riding experiences. Furthermore, the network-enabled controller provides remote access to bike dand one that implements a speed reference (dynamic mode). In static mode, the bike operates as a regular exercise bike with programmable resistance (load) that captures and records the required signals, such as heart rate, cadence, and power. In dynamic mode, the bike operates at a user-selected speed (cadence) with programmable variability in speed that has been shown to be essential to achieve the desired motor performance benefits for PD patients. In addition, the flexible and extensible design of the bike permits readily changing the control algorithm and incorporating additional I/O as needed to provide a wide range of riding experiences. Furthermore, the network-enabled controller provides remote access to bike data during a ridingata during a riding session.

Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson's Disease

Variability in severity and progression of Parkinsons disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinsons Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinsons disease.

Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals with Parkinson's Disease.

To assess and validate the Smart Exercise Bike designed for Parkinsons Disease (PD) rehabilitation, 47 individuals with PD were randomly assigned to either the static or dynamic cycling group, and completed three sessions of exercise. Heart rate, cadence and power data were captured and recorded for each patient during exercise. Motor function for each subject was assessed with the UPDRS Motor III test before and after the three exercise sessions to evaluate the effect of exercise on functional abilities. Individuals who completed three sessions of dynamic cycling showed an average of 13.8% improvement in the UPDRS, while individuals in the static cycling group worsened by 1.6% in UPDRS. To distinguish the static and dynamic cycling groups by biomechanical and physiological features, the complexity of the recorded signals (cadence, power, and heart rate) was examined using approximate entropy (ApEn), sample entropy (SaEn) and spectral entropy (SpEn) as measures of variability. A multiple linear regression (MLR) model was used to relate these features to changes in motor function as measured by the UPDRS Motor III scale. Pattern variability in cadence was greater in the dynamic group when compared to the static group. In contrast, variability in power was greater for the static group. UPDRS Motor III scores predicted from the pattern variability data were correlated to measured scores in both groups. These results support our previous study which explained how variability analysis results for biomechanical and physiological parameters of exercise can be used to predict improvements in motor function.

Managed Complexity in An Agent-based Vent Fan Control System Based on Dynamic Re-configuration

Developments in advanced control techniques are occurring in parallel with advances in sensors, algorithms, and architectures that support next-generation condition-based maintenance systems. The emergence of Multi-agent Systems in the Distributed Artificial Intelligence arena is providing important new capabilities that can significantly improve automation system performance, survivability, adaptability, and scalability. The new capabilities provided by multi-agent systems has shifted control system research into a very challenging and complex domain. A multi-agent system approach enables us to encapsulate the fundamental behavior of intelligent devices as autonomous components. These components exhibit primitive attitudes to act on behalf of equipment or complex processes. Using this approach, we have implemented a set of cooperating systems that manage the operation of an axial vent fan.