Artificial hand - from a robotic perspective

The human hand provides proof that the anthropomorphic configuration, properly controlled, is successful and gives a target to aim at for artificial hand/robot hand researchers. In this paper we discuss the human hand physiology and grasp capabilities. We then provide design on a double thumb, two finger robotic hand. Architecture of the hand, fingers and their dynamic modelling is discussed. Finally, results are reported on the performance of a finger in the hand.

Partial state estimation for linear systems with output and input time delays.

This paper deals with the problem of partial state observer design for linear systems that are subject to time delays in the measured output as well as the control input. By choosing a set of appropriate augmented Lyapunov-Krasovskii functionals with a triple-integral term and using the information of both the delayed output and input, a novel approach to design a minimal-order observer is proposed to guarantee that the observer error is ε-convergent with an exponential rate. Existence conditions of such an observer are derived in terms of matrix inequalities for the cases with time delays in both the output and input and with output delay only. Constructive design algorithms are introduced. Numerical examples are provided to illustrate the design procedure, practicality and effectiveness of the proposed observer.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.

Predicting the pilling propensity of fabrics through artificial neural network modeling

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.

Knowledge extraction from a mixed transfer function artificial neural network

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Knowledge extraction from a mixed transfer function artificial neural network

One of the big problems with Artificial Neural Networks (ANN) is that their results are not intuitively clear. For example, if we use the traditional neurons, with a sigmoid activation function, we can approximate any function, including linear functions, but the coefficients (weights) in this approximation will be rather meaningless. To resolve this problem, this paper presents a novel kind of ANN with different transfer functions mixed together. The aim of such a network is to i) obtain a better generalization than current networks ii) to obtain knowledge from the networks without a sophisticated knowledge extraction algorithm iii) to increase the understanding and acceptance of ANNs. Transfer Complexity Ratio is defined to make a sense of the weights associated with the network. The paper begins with a review of the knowledge extraction from ANNs and then presents a Mixed Transfer Function Artificial Neural Network (MTFANN). A MTFANN contains different transfer functions mixed together rather than mono-transfer functions. This mixed presence has helped to obtain high level knowledge and similar generalization comparatively to monotransfer function nets in a global optimization context.

Artificial formation forces for stable aggregation of multi-agent system

This paper introduces, a robust and stable algorithm based on artificial formation forces, for multi-agent system (MAS) aggregation in 2D space. The MAS model with artificial forces; consists of inter-member collision avoidance element, formation generation element and a velocity based damping element; is analysed for stability and convergence. Computer simulations are used to illustrate stability and convergence, and to demonstrate effectiveness of the algorithm.

Geometric formations in swarm aggregation: an artificial formation force based approach

Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc.

Learning to detect texture objects by artificial immune approaches

This paper introduces a novel method to detect texture objects from satellite images. First, a hierarchical strategy is developed to extract texture objects according to their roughness. Then, an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy. In this approach, texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection algorithm inspired by human immune system is employed to evolve antibodies. The population of antibodies is iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into the optimal antibody using the evolution principles of the clonal selection. Experimental results of texture object detection on satellite images are presented to illustrate the merit and feasibility of the proposed method.

The effect of artificial defects on the compressive properties of hollow sphere cellular aluminium (HSCA)

Hollow sphere cellular aluminium (HSCA) samples were fabricated by bonding together two kinds of single aluminium hollow spheres with the same outside diameter of 4 mm but different wall thicknesses of 0.1 mm and 0.3 mm, in which the hollow spheres with the thinner sphere wall thickness were used as artificial defects. Four types of HSCA samples with the same relative density but various distributions of artificial defects were prepared by simple cubic packing. For comparing, HSCA sample without defective hollow spheres inside was also prepared. The effects of the distribution of the artificial defects on the deformation behaviours and mechanical properties were investigated by compressive tests. Results indicated that the nominal stress - nominal strain curve and the deformation behavior of the HSCA samples varied with the distribution of the artificial defects in spite of the same relative density. It is therefore suggested that the deformation behavior and mechanical property of cellular materials were also significantly affected by the distribution of defects. In particular, the plateau stress of the HSCA samples increased with the decrease in number of contact points between the normal hollow spheres and the defective hollow spheres in the loading direction during deformation.

Control of lead and trail limbs during obstacle crossing following stroke

Background and Purpose. Obstacle crossing is compromised following stroke. The purpose of this study was to quantify modifications during obstacle clearance following stroke.

Subjects. Twelve subjects with stroke and 12 subjects without stroke participated in the study.

Methods. Kinematic variables were measured while participants crossed a 4-cm-high obstacle. Subjects with stroke walked at a self-selected speed; subjects without stroke walked at a comparable speed and at a self-selected speed.

Results. Several modifications were observed following stroke with both groups walking at self-selected speeds. The affected lead limb was positioned closer to the obstacle before crossing. Affected trail-limb clearance over the obstacle was reduced. Both affected and unaffected lead and trail limbs landed closer to the obstacle after clearance. Swing time was increased in the affected lead limb after obstacle clearance. Fewer modifications were detected at matched walking speed; the trail limb still landed closer to the obstacle.

Discussion and Conclusion. Modifications during obstacle crossing following stroke may be partly related to walking speed. The findings raise issues of safety because people with stroke demonstrated reduced clearance of a 4-cm obstacle and limb placement closer to the obstacle after clearance.

Does balance or motor impairment of limbs discriminate the ambulatory satus of stroke survivors?

Objective: This study was performed to determine if ambulatory function is governed by motor impairment of limbs or balance ability in subjects with hemiplegia caused by stroke.
Design: Seven patients who walked with physical assistance (FIM(TM) 4) after stroke and 13 who walked independently with assistive devices (FIM 6) were compared with 13 healthy subjects. Motor impairment of limbs was evaluated with the Fugl-Meyer Assessment. The Berg Balance Scale and limit of stability test of the Smart Balance Master were used to evaluate balance ability.
Results: The FIM 6 group and the controls were best differentiated by motor impairment of the paretic limbs and limit of stability in the backward direction. Motor impairment of the upper limb and limit of stability in direction toward the paretic side separated the FIM 4 from the FIM 6 group. Upper limb motor impairment and the Berg Balance Scale consistently separated the three subject groups.
Conclusions: Motor impairment in the paretic upper limb and balance dysfunction should be addressed in treatments working toward independent ambulation.