Current systems of electromyographic prostheses are controlled by machine learning techniques for gesture detection. Instance-based learning showed promising results concerning classification accuracy and robustness w...
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ISBN:
(纸本)9789897584909
Current systems of electromyographic prostheses are controlled by machine learning techniques for gesture detection. Instance-based learning showed promising results concerning classification accuracy and robustness without explicit model training. However, it suffers from high computational demands in the prediction phase, which can be problematic in real-time scenarios. This paper aims at combining such learning schemes with the concept of prototype reduction to decrease the amount of data processed in each prediction step. first, a suitability assessment of state-of-research reduction algorithms is conducted. This is followed by a practical feasibility analysis of the approach. For this purpose, several datasets of signal classes from exerting specific gestures are captured with an eight-channel EMG armband. based on the recorded data, prototype reduction algorithms are comparatively applied. The dataset reduction is characterized by the time needed for reduction as well as the possible data reduction rate. The classification accuracy when using the reduced set in cross validation is analyzed with an exemplary kNN classifier. While showing promising values in reduction time as well as excellent classification accuracy, a reduction rate of over 99% can be achieved in all tested gesture configurations. The reduction algorithms LVQ3 and DSM turn out to be particularly convenient.
We have developed a neural-network approach to classifying signals by fusing information from multiple sensors. During the past three years, we have developed concepts and algorithms for an intelligent decision suppor...
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ISBN:
(纸本)0819413240
We have developed a neural-network approach to classifying signals by fusing information from multiple sensors. During the past three years, we have developed concepts and algorithms for an intelligent decision support system (IDSS) for mine managers. The goal of the IDSS is to detect the activities of machines in an underground coal mine and to produce management reports similar to traditional industrial engineering time studies. The data we operate on is power usage of the various machines taken every 50 milliseconds. Currently we are working with data from three machines which interact with each other: a continuous miner and two shuttle cars. Detection of events was first done using numerical techniques to arrive at locally best guesses and rule-based techniques to fuse the information from the different machines. Our current research involves dynamic recurrent neural networks (a variation of recurrent cascade correlation) which replace the numerical and rule-based techniques. Our current neural networks can accurately label approximately 90% of the machine events in the training set and approximately 70% in new data sets. Neural network techniques are able to adjust to the dynamic mine environment much better than the previous algorithms, consequently, the neural network approach is more acceptable in the applications environment.
This paper describes ah approach to an arti ficial music expert We have constructed a music infor mation system for the transformation of music data and generating the database. Our current interest extends to human-l...
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We propose a method for image registration which seems to be useful under the three following conditions. first, both images are globally and roughly the result of a translation and rotation. Second, some occlusions d...
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We propose a method for image registration which seems to be useful under the three following conditions. first, both images are globally and roughly the result of a translation and rotation. Second, some occlusions due to moving objects occur from image 1 to image 2. Third, because of changes of illumination, contrast may have changed globally and even locally. Under such unfavorable conditions, correlation-based global registration may become inaccurate, because of the global compromise it yields between several displacements. Our method avoids these difficulties by defining a set of local contrast invariant features in order to achieve contrast invariant matching. A voting procedure allows to eliminate `wrong' matching features due to the displacement of small objects and yields sub-pixel accuracy. This method was tested successfully for registration of watches with moving hands and for road control applications.
This paper presents a detailed performance analysis of third-order nonlinear adaptive systems based on the Wiener model. In earlier work, we proposed the discrete Wiener model for adaptive filtering applications for a...
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This paper presents a detailed performance analysis of third-order nonlinear adaptive systems based on the Wiener model. In earlier work, we proposed the discrete Wiener model for adaptive filtering applications for any order. However, we had focused mainly on first and second-order nonlinear systems in our previous analysis. Now, we present new results on the analysis of third order systems. All the results can be extended to higher-order systems. The Wiener model has many advantages over other models such as the Volterra model. These advantages include less number of coefficients and faster convergence. The Wiener model performs a complete orthogonalization procedure to the truncated Volterra series and this allows us to use linear adaptive filtering algorithms like the LMS to calculate all the coefficients efficiently. Unlike the Gram-Schmidt procedure, this orthogonalization method is based on the nonlinear discrete Wiener model. It contains three sections: a single-input multi-output linear with memory section, a multi-input, multi-output nonlinear no-memory section and a multi-input, single-output amplification and summary section. Computer simulation results are also presented to verify the theoretical performance analysis results.
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