An intelligent walking-aid robot is presented for walking assistance, training and rehabilitation of the elderly. The robot is intended to provide physical support and mobility aid for the old people during their walk...
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ISBN:
(纸本)9781479951055
An intelligent walking-aid robot is presented for walking assistance, training and rehabilitation of the elderly. The robot is intended to provide physical support and mobility aid for the old people during their walking based on recognizing their motion intentions online. A force measuring system comprised of force sensing resisters (FSRs) is designed to obtain the interaction forces between the user and the robot. The user's motion intention is then estimated by analyzing the relationship between the measured interaction forces and human intention force/torque. Further the estimated intention is used to guide the admittance based motion control of robot. To ensure a safe walking, a laser range finder (LRF) is used to detect the distance between the robot and the user's legs. A distance restraint control is also designed and taken into action when the distance is found beyond a safe threshold. Experiments were conducted and the results verified the effectiveness of the developed robot system and control methods.
A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a...
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A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges associated with simultaneous power and thermal management under current-based control.
作者:
Guang-Song HanZhi-Hong GuanJie ChenDing-Xin HeMing ChiCollege of Automation
Huazhong University of Science and Technology Wuhan 430074 China and the Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 China
A multi-tracking problem of multi-agent networks is investigated in this paper where multi-tracking refers to that the states of multiple agents in each subnetwork asymptotically converge to the same desired trajector...
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A multi-tracking problem of multi-agent networks is investigated in this paper where multi-tracking refers to that the states of multiple agents in each subnetwork asymptotically converge to the same desired trajectory in the presence of information exchanges among *** multi-tracking of first order multi-agent networks with directed topologies was ***-triggered protocols were proposed along with triggering functions to solve the stationary multi-tracking and bounded dynamic *** self-triggered scheduling is obtained, and the system does not exhibit Zeno *** examples are provided to illustrate the effectiveness of the obtained criteria.
In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the Resource-Constrained Project Scheduling Problem(RCPSP). This algorithm is based on traditional Serial Scheduling scheme (SSS...
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In this paper, we propose an Activity-List based Nested Partitions algorithm for solving the Resource-Constrained Project Scheduling Problem(RCPSP). This algorithm is based on traditional Serial Scheduling scheme (SSS) and partitions the feasible solution space which is formulated by activity-lists into subregions by the nested partitions approach. We also utilize Double Justification as local search to improve the solutions. The algorithm is tested on J120 in PSPLIB with the result that the algorithm is relatively effective for solving large-scale, complex RCPSPs.
Column Generation (CG) technique is popularly applied in solving the crew scheduling problem of large size, which is generally modeled as an Integer Linear Programming (ILP) problem. The traditional CG algorithms for ...
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This paper investigates the application of radio frequency identification (RFID) technology to eliminate the misplacement problems in the supply chain, which consists of a risk-neutral manufacturer and a risk-averse r...
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This paper investigates the application of radio frequency identification (RFID) technology to eliminate the misplacement problems in the supply chain, which consists of a risk-neutral manufacturer and a risk-averse retailer. By considering both fixed cost and tag cost of RFID implementation, we study the agents' incentives to adopt RFID in both uncoordinated and coordinated cases. We focus on analyzing the impact of risk attitudes on the agents’ incentives and on the supply chain coordination. The central semi-deviation is adopted to measure the retailer's risk attitude. In the uncoordinated case, we find that, in order to induce the retailer to adopt RFID, the manufacturer must assume more fixed cost if the retailer is more risk-averse. In the coordinated case, we first show that the standard revenue sharing contract does not always coordinate the channel. If the channel is coordinated, we observe that the agents’ incentives will be perfectly aligned and independent of the risk attitudes, if the revenue sharing ratio equals the fixed cost sharing ratio. Then we propose a risk-sharing contract that offers the risk protection to the retailer, to achieve the channel coordination. An interesting finding is that the manufacturer's incentive will not decrease with the tag cost, if she takes much risk from the retailer. The corresponding impacts of RFID adoption on the two contracts are also analyzed in this paper. Finally, a case study in a tobacco industry is presented to show the real RFID cost in practice.
Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is design...
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Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide...
Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.
This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with...
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This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.
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