Well-conditioned configurations of fault-tolerant manipulators

Fault-tolerant motion of redundant manipulators can be obtained by joint velocity reconfiguration. For fault-tolerant manipulators, it is beneficial to determine the configurations that can tolerate the locked-joint failures with a minimum relative joint velocity jump, because the manipulator can rapidly reconfigure itself to tolerate the fault. This paper uses the properties of the condition numbers to introduce those optimal configurations for serial manipulators. The relationship between the manipulator's locked-joint failures and the condition number of the Jacobian matrix is indicated by using a matrix perturbation methodology. Then, it is observed that the condition number provides an upper bound of the required relative joint velocity change for recovering the faults which leads to define the optimal fault-tolerant configuration from the minimization of the condition number. The optimization problem to obtain the minimum condition number is converted to three standard Eigen value optimization problems. A solution is for selected optimization problem is presented. Finally, in order to obtain the optimal fault-tolerant configuration, the proposed method is applied to a 4-DoF planar manipulator.

Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.

Optimization of FRT Active Power Performance of a DFIG during Transient Grid Faults

Optimal fault ride-through (FRT) conditions for a doubly-fed induction generator (DFIG) during a transient grid fault are analyzed with special emphasis on improving the active power generation profile. The transition states due to crowbar activation during transient faults are investigated to exploit the maximum power during the fault and post-fault period. It has been identified that operating slip, severity of fault and crowbar resistance have a direct impact on the power capability of a DFIG, and crowbar resistance can be chosen to optimize the power capability. It has been further shown that an extended crowbar period can deliver enhanced inertial response following the transient fault. The converter protection and drive train dynamics have also been analyzed while choosing the optimum crowbar resistance and delivering enhanced inertial support for an extended crowbar period.

Minimal force jump within human and assistive robot cooperation

When an assistant robotic manipulator cooperatively performs a task with a human and the task is required to be highly reliable, then fault tolerance is essential. To achieve the fault tolerance force within the human robot cooperation, it is required to map the effects of the faulty joint of the robot into the manipulator’s healthy joints’ torque space and the human force. The objective is to optimally maintain the cooperative force within the human robot cooperation. This paper aims to analyze the fault tolerant force within the cooperation and two frameworks are proposed. Then they have been validated through a fault scenario. Finally, the minimum force jump which is the optimal fault tolerance has been achieved.

Task completion with partially-failed manipulators

An effort to maintain the availability of partially failed manipulator is addressed based on redundant trajectories obtained by primitive constraints. The objective is to facilitate the existing manipulators to continue their point to point motion tasks when a non catastrophic fault occurs into a joint. The fault is assumed as a joint locked failure. This is achieved through fault to primitive constraints mapping which gives the primitive constraints due to the faults. Then they are applied to update the manipulator constraints for the trajectory planning. Then it purposes a new trajectory in the case of availability. Finally the method is applied for a 6DOF manipulator and validated under a fault scenario within a simulation study and the results are presented.

Detecção e recuperação de falhas para a máquina de redução de grafos PEWS-AM

Web services are software units that allow access to one or more resources, supporting the deployment of business processes in the Web. They use well-defined interfaces, using web standard protocols, making possible the communication between entities implemented on different platforms. Due to these features, Web services can be integrated as services compositions to form more robust loose coupling applications. Web services are subject to failures, unwanted situations that may compromise the business process partially or completely. Failures can occur both in the design of compositions as in the execution of compositions. As a result, it is essential to create mechanisms to make the implementation of service compositions more robust and to treat failures. Specifically, we propose the support for fault recovery in service compositions described in PEWS language and executed on PEWS-AM, an graph reduction machine. To support recovery failure on PEWS-AM, we extend the PEWS language specification and adapted the rules of translation and reduction of graphs for this machine. These contributions were made both in the model of abstract machine as at the implementation level

Simultaneous synthesis of work exchange networks with heat integration

The optimal integration of work and its interaction with heat can represent large energy savings in industrial plants. This paper introduces a new optimization model for the simultaneous synthesis of work exchange networks (WENs), with heat integration for the optimal pressure recovery of process gaseous streams. The proposed approach for the WEN synthesis is analogous to the well-known problem of synthesis of heat exchanger networks (HENs). Thus, there is work exchange between high-pressure (HP) and low-pressure (LP) streams, achieved by pressure manipulation equipment running on common axes. The model allows the use of several units of single-shaft-turbine-compressor (SSTC), as well as stand-alone compressors, turbines and valves. Helper motors and generators are used to respond to any demand and excess of energy. Moreover, between the WEN stages the streams are sent to the HEN to promote thermal recovery, aiming to enhance the work integration. A multi-stage superstructure is proposed to represent the process. The WEN superstructure is optimized in a mixed-integer nonlinear programming (MINLP) formulation and solved with the GAMS software, with the goal of minimizing the total annualized cost. Three examples are conducted to verify the accuracy of the proposed method. In all case studies, the heat integration between WEN stages is essential to improve the pressure recovery, and to reduce the total costs involved in the process.

Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

A Fast and Fault-Tolerant Distributed Algorithm for Near-Optimal TDMA Scheduling in WSNs

The time division multiple access (TDMA) based channel access mechanisms perform better than the contention based channel access mechanisms, in terms of channel utilization, reliability and power consumption, specially for high data rate applications in wireless sensor networks (WSNs). Most of the existing distributed TDMA scheduling techniques can be classified as either static or dynamic. The primary purpose of static TDMA scheduling algorithms is to improve the channel utilization by generating a schedule of smaller length. But, they usually take longer time to schedule, and hence, are not suitable for WSNs, in which the network topology changes dynamically. On the other hand, dynamic TDMA scheduling algorithms generate a schedule quickly, but they are not efficient in terms of generated schedule length. In this paper, we propose a novel scheme for TDMA scheduling in WSNs, which can generate a compact schedule similar to static scheduling algorithms, while its runtime performance can be matched with those of dynamic scheduling algorithms. Furthermore, the proposed distributed TDMA scheduling algorithm has the capability to trade-off schedule length with the time required to generate the schedule. This would allow the developers of WSNs, to tune the performance, as per the requirement of prevalent WSN applications, and the requirement to perform re-scheduling. Finally, the proposed TDMA scheduling is fault-tolerant to packet loss due to erroneous wireless channel. The algorithm has been simulated using the Castalia simulator to compare its performance with those of others in terms of generated schedule length and the time required to generate the TDMA schedule. Simulation results show that the proposed algorithm generates a compact schedule in a very less time.

Comparison of media for optimal recovery of Candida albicans and Candida glabrata from blood culture

Candida spp., mainly Candida albicans, are frequently responsible for complications in immunocompromised patients. There are limited data comparing recovery efficiency using simple non-selective basal broth media.

Identifying optimal sites for natural recovery and restoration of impacted biogenic habitats in a special area of conservation using hydrodynamic and habitat suitability modelling

Selection of sites for successful restoration of impacted shellfish populations depends on understanding the dispersion capability and habitat requirements of the species involved. In Strangford Lough, Northern Ireland, the horse mussel (Modiolus modiolus) biogenic reefs cover only a fraction of their historical range with the remaining reefs badly damaged and requiring restoration. Previous experimental trials suggest that translocation of horse mussels accelerates reef recovery and has therefore been proposed as a suitable restoration technique. We used a series of coupled hydrodynamic and particle dispersal models to assess larval dispersion from remnant and translocated populations to identify suitable areas for adult live M. modiolus translocation in Strangford Lough, Northern Ireland. A maximum entropy model (MAXENT) was used to identify if dispersing larvae could reach habitat suitable for adult M. modiolus. From these we predicted if translocated mussels will reseed themselves or be able to act as larval sources for nearby reefs. The dispersal models showed that the remnant M. modiolus populations are largely self-recruiting with little connectivity between them. The majority of larvae settled near the sources and movement was largely dependent on the tides and not influenced by wind or waves. Higher reef elevation resulted in larvae being able to disperse further away from the release point. However, larval numbers away from the source population are likely to be too low for successful recruitment. There was also little connectivity between the Irish Sea and Strangford Lough as any larvae entering the Lough remained predominantly in the Strangford Narrows. The areas covered by these self-seeding populations are suitable for M. modiolus translocation according to the MAXENT model. As a result of this work and in conjunction with other field work we propose a combination of total protection of all remaining larval sources and small scale translocations onto suitable substrata in each of the identified self-recruiting areas.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

Enhancing fault management performance of two-step QoS routing algorithms in GMPLS networks

In this paper a novel methodology aimed at minimizing the probability of network failure and the failure impact (in terms of QoS degradation) while optimizing the resource consumption is introduced. A detailed study of MPLS recovery techniques and their GMPLS extensions are also presented. In this scenario, some features for reducing the failure impact and offering minimum failure probabilities at the same time are also analyzed. Novel two-step routing algorithms using this methodology are proposed. Results show that these methods offer high protection levels with optimal resource consumption

The study into fault tolerance based on rollback-recovery for clusters

The provision of fault tolerance is an important aspect to the success of distributed and cluster computing. Through this research , a transparent, autonomic and efficient fault tolerant facility was designed and implemented; thereby relieving the burden of a user having to handle and react to the failure of an application.