A second generation foot prosthesis capable of generating propulsion has been developed. This prosthesis has wide application, particularly for sports. The propulsion achieved using the device can be represented by th...
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A second generation foot prosthesis capable of generating propulsion has been developed. This prosthesis has wide application, particularly for sports. The propulsion achieved using the device can be represented by the ratio of energy released to the energy stored in the foot during the standing phase. This research presents an evaluation of a new energy-storing foot prosthesis. The results, obtained by a finite element method and computer simulation, are in good agreement with clinical data. The effects of material elasticity and the forcing function are among those factors investigated.
In control engineering models of the controlled systems are the basis for controller synthesis as well as for analytical or simulated examination of open or closed-loop behaviour. This model-based methodology is being...
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In control engineering models of the controlled systems are the basis for controller synthesis as well as for analytical or simulated examination of open or closed-loop behaviour. This model-based methodology is being transferred into automation engineering by means of a development environment for the programming of logical controllers. Petri net models of the controlled system allow an automatic computation of the control algorithm being specified by desired or forbidden states or state sequences. The control algorithms which are equally represented as Petri nets are then automatically translated into a code for programmable logical control. The models of the controlled system and the synthesized control algorithm are used for automatically generating diagnosis data for a model-based PLC diagnosis system.
A data-driven method for combining evidence from multiple sensors is presented. Empirical functions are used to compute a set of belief values for each sensor. These functions contain information about the degree of b...
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A data-driven method for combining evidence from multiple sensors is presented. Empirical functions are used to compute a set of belief values for each sensor. These functions contain information about the degree of belief in the presence of an object as well as the uncertainty about the belief. The belief values are then combined using Dempster's rule of combination. The empirical belief functions can be designed to take into account signal-to-noise characteristics and detection limits. Hard sensors that produce a yes/no output can also be modeled. Some advantages of this approach over sequential logic or pattern recognition are greater robustness with respect to faulty or inoperative sensors and more modularity.
This paper presents results on the harmonic control, in contrast to the fixed-point control as in the cases of inverted pendulum balance and truck backer-upper. In general, the harmonic control for driving a plant tra...
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ISBN:
(纸本)0780325591
This paper presents results on the harmonic control, in contrast to the fixed-point control as in the cases of inverted pendulum balance and truck backer-upper. In general, the harmonic control for driving a plant trajectory to a periodic orbit or a limit cycle is more difficult than the fixed point control. We use four a priori trajectories as well as a neural-based system identifier to train the controller. During the performance test on a planar complex, the controller is shown capable of driving the test plant to follow the predefined periodic orbit (i.e., limit cycle stability), given an arbitrary initial state and external disturbance added incidentally. The resulting planar model can be applied to real-life examples, such as the stability control of shipping-building, and smoothness control of rocking chair design.
The application of a fuzzy logic-based expert system to assess mercury bioaccumulation risk in gold mining regions around the World is described. HgEx is a heuristic system which accommodates imprecise data for pH, Eh...
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The application of a fuzzy logic-based expert system to assess mercury bioaccumulation risk in gold mining regions around the World is described. HgEx is a heuristic system which accommodates imprecise data for pH, Eh, water conductivity, biomass productivity, water transparency and contamination factor. Inaccurate data input can be handled for Hg background and sediment assays, or alternatively, measurements are transformed into linguistic expressions with respective degrees of belief to feed a heuristic model using neural equations. The paper presents a fuzzy adaptive method and show how it can be applied to model situations such as AIDS research, technological innovation and other risk-assessment problems.
Reports on the development of an expert system to support the forecasting of snow avalanches. First the authors give a general introduction to the area of snow avalanche forecasting. Subsequently, both the knowledge a...
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Reports on the development of an expert system to support the forecasting of snow avalanches. First the authors give a general introduction to the area of snow avalanche forecasting. Subsequently, both the knowledge acquisition, and the implementation/integration on IBM PC type platforms are described in some detail. Initial test results based on the current prototype implementation are reported. Finally, some lessons learned and conclusions for future development efforts are presented.
Traditionally, in large scale engineering plants, fault detection is performed through the use of fixed threshold bounds. In this detection scheme, upper and lower thresholds are placed on the plant's status data....
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Traditionally, in large scale engineering plants, fault detection is performed through the use of fixed threshold bounds. In this detection scheme, upper and lower thresholds are placed on the plant's status data. Error flags are then produced whenever the data exceeds either of its associated bounds. The major problems with this technique are that these error flags do not produce confident indications of "true" faults, and they do not reliably temporally locate the start of the faulty behaviour. In this paper, two novel model-based techniques are presented which address these problems. The first technique is an ad hoc method directed specifically at current faults within the domain of cable amplifier networks. The second technique is a more general method based on behavioural modeling through the use of a class of asymptotically stable recurrent neural networks.
This paper presents the fuzzy control of a multifingered robot hand by using the digital signal processor (DSP). Since there are five fingers and seventeen joints to be controlled simultaneously, a special designed co...
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ISBN:
(纸本)0780325591
This paper presents the fuzzy control of a multifingered robot hand by using the digital signal processor (DSP). Since there are five fingers and seventeen joints to be controlled simultaneously, a special designed control system with fuzzy controller is developed to deal with the nonlinear behavior and large computation load of the multifingered robot hand. The fuzzy controller dealing with the level of finger control, is a multiple-input-multiple-output (MIMO) fuzzy learning controller and is implemented in DSP chip. Furthermore, the communication function of the control system is designed so that the knowledge bases can be loaded for high level computation and modified during run time.
In this paper, a neural network method is applied to extract one cycle of golf swing from a continuous weight-shift signal. Weight-shift in golf swing means the continuous change of weights loaded on the left and righ...
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ISBN:
(纸本)0780325591
In this paper, a neural network method is applied to extract one cycle of golf swing from a continuous weight-shift signal. Weight-shift in golf swing means the continuous change of weights loaded on the left and right foot of the golfer. We defined eight input features which are stable to classify various shapes of swing patterns. The adopted network is a three-layered error backpropagation model. According to experimental results, identifying success rate is 97.75% using 8 input, 10 hidden and 2 output nodes. We performed experiment by changing the initial connection strengths according to a importance scale. Under ten random seeds, the learning speed and recognition rate is shown to improve when the initial connection strengths are changed by the importance scale.
Large-scale complicated systems are required to be controlled timely and appropriately. A human brain has similar functions to those of a controller of the large-scale complicated systems; it scans and recognizes sens...
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Large-scale complicated systems are required to be controlled timely and appropriately. A human brain has similar functions to those of a controller of the large-scale complicated systems; it scans and recognizes sensory inputs and outputs responses to the environments. Why does a human brain work skillfully? The key is the capability of functions distribution and learning. Functions distribution means that a specific part exists in the brain, in order to realize a specific function. For example, a live neural network has different acting parts corresponding to different network inputs or stimuli. In this paper, we have proposed a new brain-like model that we call learning Petri network (LPN). The fundamental idea is to revise Petri net. Petri net is composed of state and transition and can control firing by tokens, so it is possible for this net to realize functions distribution. The revising point is to give Petri net the ability of learning as neural network (NN). And, it is the fundamental difference from NN, that learning of the proposed method is carried out on the only network pass of the token transfer.
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