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In any activity connected with monitoring, diagnostics and prognostics, the accuracy and believability of results is of paramount importance. Two main kinds of diagnostic errors, called Type I and Type II or False Positives and False Negatives are encountered whenever classification, such as “good” vs “bad” is considered. This has been thoroughly discussed, especially in the field of medicine, where...
An algorithm for volumetric calibration is developed and verified practically by measuring of all geometric errors after numerical compensation. In this paper, analysis of the contribution of each of 9 translational and 9 rotational position dependent errors and each of 3 position independent errors in total displacement error vector is presented. Changing only one of the errors, and keeping all the...
This paper presents the basic principle and application of ARIMA model, and this model is used to analyze crude oil price time series and obtain the second order linear regression model. The model is used to predict the price of crude oil. On this basis, according to the residual error after fitting the existing nonlinear ARIMA model, the fitting residual error is analyzed by using the artificial...
It is well known that image representations learned through ad-hoc dictionaries improve the overall results in object categorization problems. Following the widely accepted coding-pooling visual recognition pipeline, these representations are often tightly coupled with a coding stage. In this paper we show how to exploit ad-hoc representations both within the coding and the pooling phases. We learn...
In packaging industry, the duration of the carton folding plays a fundamental role in the production process; in particular in the erection process when each panel rotates around the die-pressed lines called creases. Their bending response can be very complex, depending on forming and environment conditions. The crease mechanical properties, such as geometrical parameters, temperature, moisture and...
In an object search scenario with several small objects spread over a large indoor environment, the robot cannot infer about all of them at once. Pruning the search space is highly desirable in such a case. It has to actively select a course of actions to closely examine a selected set of objects. Here, we model the inferences about far away objects and their viewpoint priors into a decision analytic...
Time optimal trajectory planning under various hard physical constraints plays a significant role in simultaneously meeting the requirements on high productivity and high accuracy in the fields of both machining tools and robotics. In this paper, the problem of time optimal trajectory planning is first formulated, and then a novel back and forward check algorithm is proposed to solve the minimum time...
Terrain classification in field environment for mobile robot is affected by weather conditions among which illumination diversification plays a major role. With the changes in feature extraction method, classification result will vary significantly in the changing environment. This paper introduces a method of illumination recognition by analyzing the illumination distribution in visual space and...
Individuals with severe neurologic injuries often cannot participate in robotic rehabilitation because they do not retain sufficient residual motor control to initiate the robotic assistance. In these situations, brain- and body-computer interfaces have emerged as promising solutions to control robotic devices. In a previous experiment conducted with healthy subjects, we showed that detecting motor...
This paper aims at building a portable robotic hand for physically disabled people to perform basic hand movements. Surface Electromyography(EMG) signal is collected from muscles of human forearm to extract the subject's intentions of action, where six kinds of gestures are selected for discussion. An Artificial Neural Network(ANN) is trained and utilized to distinguish the desired movement according...
This paper presents a framework for classifying sit-to-stand and stand-to-sit from just two channel EMG signals taken from the left leg. Our proposed framework uses linear discriminant analysis (LDA) as the classifier and a multi-window feature extraction approach termed Consecutive Time-Windowed Feature Extraction (CTFE). We present the prelimnary results from 2 healthy subjects as a proof of concept...
A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to trasfer architectural inspiration from biology...
The automatic design of control systems for multi-robot teams that operate in real time is not affordable with traditional evolutionary algorithms mainly due to the huge computational requirements they imply. Embodied Evolution (EE) is an evolutionary paradigm that aims to address this problem through the embodiment of the individuals that make up the population in the physical robots. The interest...
This work investigates the geometric object-shape classification using the echoes generated by various kinds of obstacles in a cellular automata based virtual environment for ultra-sound propagation. The virtual environment is implemented as a JAVA platform [1] capable of emulate sound propagation in a controlled 2D environment. The echoes are preprocessed by a Feature Processor Vector Unit (FVPU)...
The performance of a tracker can be measured by two often conflicting criteria - robustness and accuracy. Recently researchers have focused on improving robustness, using adaptive appearance models. However updating the appearance model can cause drift and lower the accuracy of motion (state) estimation. These trackers generally compute 2 degree of freedom(DOF) image translation of the object, and...
In the current practice, the performance evaluation of RF-based indoor localization solutions is typically realized in non-standardized environments and following ad-hoc procedures, which hampers objective comparison and does not provide clear insight into their intrinsic properties. Many evaluation procedures also neglect important environmental factors like RF interference, diminishing the real-world...
In the future robotic applications, robot requires the ability not only to recognize human actions but also to learn incrementally and quickly. Therefore, we proposed an incremental action learning system for this future requirement. The proposed system can continuously learn new actions quickly with robust performance and less effort.
To perform many common industrial robotic tasks, e.g. deburring a work-piece, in small and medium size companies where a model of the work-piece may not be available, building a geometrical model of how to perform the task from a data set of human demonstrations is highly demanded. In many cases, however, the human demonstrations may be sub-optimal and noisy solutions to the problem of performing...
Needle EMG (Electromyogram) Exam (NEE) is an important neurological exam, and neurology interns and novice medical need repetitive training to gain the necessary skill to perform the exam. However, until now it has been difficult to reproduce multiple pathological conditions for their training, since in most cases, trainees serve as human subjects for each other. A robotic simulator that could reproduce...
Sparse Gaussian process (GP) models provide an efficient way to perform regression on large data sets. The key idea is to select a representative subset of the available training data, which induces the sparse GP model approximation. In the past, a variety of selection criteria for GP approximation have been proposed, but they either lack accuracy or suffer from high computational costs. In this paper,...
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