37865441
Adapting robot kinematics for human-arm motion recognition
This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. First of all, the conventional robotic model has been employed to build a generic vision model for a human-arm, that is we utilise the robot kinematics to construct a stick model. Secondly, the qualitative robotic model is adopted to learn and construct the QNTs where human-arm motion is termed as, whose execution is consistent and could be easily characterised by a definite space-time trajectory in configuration space. Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. Experimental evaluation has demonstrated the effectiveness of the proposed method in human-arm motion classification, and our future work is focused on extending the proposed method to recognise complex human motion, e.g. walking and running.
11680467
Predictive unsupervised organisation in marine engine fault detection
This paper utilises topological learners, the self organising map in combination with the k means algorithm to organise potential engine faults and the respective location of faults, focussing on a 12 cylinder 2 stroke marine diesel engine. This method is applied to reduce the numerosity of the data presented to a user by selecting representative samples from a number of clusters to enable efficient diagnosis. The novelty of the approach centres around the sparsity of the dataset compared to the majority of fault diagnosis techniques, and the potential for improved safety and efficiency within the marine industry compared to existing diagnosis systems. The accuracy of the SOM and k means, as well as the neural gas algorithm is compared to the standard accuracy of the k means algorithm to validate the algorithmpsilas performance and application to this domain, where it can be seen that topological learners have much potential to be applied to the field of fault diagnosis.
18886117
An Effective Human Motion Classification Approach using Knowledge Representation in Qualitative Normalised Templates
Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion.
37115701
Qualitative classification of descent phases in commercial flight data
Flight data from commercial aircraft in the descent is analysed using one-class classification techniques to identify possible unstable approaches. The method considers snapshots of flight parameters at certain heights in the descent and identifies any abnormalities. Combination rules are used to analyse the flight as a whole and it is found that it is possible to detect abnormal flights with a good degree of accuracy.
42776203
Fixed-Point Attractor Analysis for a Class of Neurodynamics
Nearly all models in neural networks start from the assumption that the input-output characteristic is a sigmoidal function. On parameter space, we present a systematic and feasible method for analyzing the whole spectrum of attractorsall-saturated, all-but-one-saturated, all-but-twosaturated, and so onof a neurodynamical system with a saturated sigmoidal function as its input-output characteristic. We present an argument that claims, under a mild condition, that only all-saturated or all but-one-saturated attractors are observable for the neurodynamics. For any given all-saturated configuration (all-but-one-saturated configuration ) the article shows how to construct an exact parameter region R()(()) such that if and only if the parameters fall within R()(()), then () is an attractor (a fixed point) of the dynamics. The parameter region for an all-saturated fixed-point attractor is independent of the specific choice of a saturated sigmoidal function, whereas for an all-but-one-saturated fixed point, it is sensitive to the input-output characteristic. Based on a similar idea, the role of weight normalization realized by a saturated sigmoidal function in competitive learning is discussed. A necessary and sufficient condition is provided to distinguish two kinds of competitive learning: stable competitive learning with the weight vectors representing extremes of input space and being fixed-point attractors, and unstable competitive learning. We apply our results to Linsker's model and (using extreme value theory in statistics) the Hopfield model and obtain some novel results on these two models.
9882695
Rigorously Defining and Analyzing Medical Processes: An Experience Report
This paper describes our experiences in defining the processes associated with preparing and administrating chemotherapy and then using those process definitions as the basis for analyses aimed at finding and correcting defects. The work is a collaboration between medical professionals from a major regional cancer center and computer science researchers. The work uses the Little-JIL language to create precise process definitions, the Propel system to specify precise process requirements, and the FLAVERS system to verify that the process definitions adhere to the requirement specifications. The paper describes how these technologies were applied to successfully identify defects in the chemotherapy process. Although this work is still ongoing, early experiences suggest that this approach can help reduce medical errors and improve patient safety. The work has also helped us to learn about the desiderata for process definition and analysis technologies, both of which are expected to be broadly applicable to other domains.
60537960
An orthodontic expert system
Abstract A Fuzzy Relational Inference Language, FRIL has been used to generate an expert system to help non-specialist dentists with orthodontic problems. The use of fuzzy relations, descriptors and numbers has been found useful in modelling the thought processes of orthodontic specialists.
14034669
Google TV search: dual-wielding search and discovery in a large-scale product
In 2006 Google designed and implemented a TV Show search system which featured a dual-navigation control combining a powerful search system with an interactive, personalize-able TV listings grid. The dual-control system allowed users to move fluidly between explicit search and general discovery, obtain detailed information without leaving the global context and construct a personal channel for easy access and recommendations. As a complete system, TV Search integrated with Google's general search page through a mini-listings onebox and also exported information out to Google calendar, Gmail and TiVo. Building this TV Search system as a large-scale product meant addressing many technological, business and fine-grained user experience details. This paper describes the design process including the early designs, the user studies and the details of the final system including the mini-guide, main page dual-control and the integrated services on the detail pages.
10827335
A novel approach for analyzing dynamics in neural networks with saturated characteristics
Current theories about the dynamics of neural networks with nonlinear characteristics and parameterized by set of parameters are mostly based on approximations in one way or another. In this paper we first introduce a rigorous approach which allows us to check in which parameter region a given saturated state is an attractor of the dynamics: a saturated state w=(wi, i=1,...,N)∈{-1,1}N is an attractor of the dynamics if and only if there is a local field gap between neurons in J+(w)={i, wi=1} and J-(w)={i, wi=−1}. Then we apply the result to analyze several models in neural networks. In particular in the Hopfield model we calculate the capacity and give an exact relation between the capacity and the threshold.