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Against the special requirements of realistic in the intelligent virtual environment, this paper takes full account of the impact of personality on behavior, and proposes a “plan-TO-action” converter based on Gaussian probability distribution function and the FSM (Finite State Machine). The converter's work is to evaluate the parameters such as time and place in the planning process, combined with...
This paper develops a new constructing approach of an appropriate membership function to integrate a given probability density function and fuzzy Shannon entropy extending the statistical theory into the heuristic method based on the human cognitive behavior and subjectivity. The proposed approach is formulated as a more general mathematical programming problem than previous approaches due to using...
For effective human-robot interaction, a robot should be able to make prediction about future circumstance. This enables the robot to generate preparative behaviors to reduce waiting time, thereby greatly improving the quality of the interaction. In this paper, we propose a novel probabilistic temporal prediction method for proactive interaction that is based on a Bayesian network approach. In our...
In application domains characterized by dynamic changes and non-deterministic action outcomes, it is frequently difficult for agents or robots to operate without any human supervision. Although human feedback can help an agent learn a rich representation of the task and domain, humans may not have the expertise or time to provide elaborate and accurate feedback in complex domains. Widespread deployment...
Q-Ball imaging (QBI) is a successful and widely used high angular resolution diffusion imaging (HARDI) technique which can compute orientation distribution function (ODF). This technique only needs single shell HARDI data and does not require any assumption about the diffusion signal outside the sampling sphere. However the originally proposed ODF (the radial project of the probability density function...
In this paper, a multi-agent search scheme is presented that supports the recognition of activities and, thus, learning methods for cooperative human-robot interaction. In our approach, stochastic models of human search activity are used to estimate state for HRI. The robot updates a Probabilistic Distribution Function of the target object using the observations and the estimated state of human peers...
Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with...
PC-SVM is a new developed support vector machine classifier with probabilistic constrains which presence of samples probability in each class is determined based on a distribution function. The presence of noise causes incorrect calculation of support vectors thereupon margin can not be maximized. In the Pc-SVM, constraints boundaries and constraints occurrence have probability density functions which...
Image segmenting is one of the most important steps in movie and image processing and the machine vision applications. The evaluating methods of image segmenting that recently introduced. These evaluation metrics extract some features for each region in a segmented image. In this paper using probabilistic model that utilize the information of pixels (mean and variance) in each region to balance the...
In this paper, an online estimation method of multijoint human arm viscoelasticity for the case of unknown variance is considered. For the unknown variance process of a human multijoint arm, an estimation method of the human multijoint arm viscoelasticity is proposed. The proposed method includes two estimators, the first estimator is to calculate the variance, and the human multijoint arm viscoelasticity...
The goal of Information Extraction is to automatically generate structured pieces of information from the relevant information contained in text documents. Machine Learning techniques have been applied to reduce the cost of Information Extraction system adaptation. However, elements of human supervision strongly bias the learning process. Unsupervised learning approaches can avoid these biases. In...
We present a motion descriptor for human action recognition where appearance and shape information are unreliable. Unlike other motion-based approaches, we leverage image characteristics specific to human movement to achieve better robustness and lower computational cost. Drawing on recent work on motion recognition with ballistic dynamics, an action is modeled as a series of short correlated linear...
The niche theory is derived from ecology. Introducing this theory into the area of human resource management and using it as an evaluation model for human resource, is an innovation as to enterprise management theory. Based on the previous research, we try to improve niche measurement methods of human resources and propose a more scientific and valuable evaluation model. Case study explains the process...
We discuss a conceptually new extension of our previous works in which we proposed a concept of a consensus reaching support system based on a new, gradual notion of consensus devised in the framework of fuzzy preference relations and a fuzzy majority. Here, first of all, we propose the use of action rules as a tool to generate some advice as to the further running of discussion in the group. Moreover,...
This paper proposes the concept of inverse perlin noise which is a reverse process of perlin noise. The proposed method enables users to feel the haptic sensation when he/she rubs on the surface of an object. We extract individual random signals from a target object modeled by perlin noise and apply them to haptic actuators. In order to evaluate the proposed method, we compute errors between the original...
A number of action recognition methods make use of spatio-temporal features. These features often consist of local spatio-temporal descriptors centered at locations provided by an interest point detector. The extracted descriptors will then serve as input to classification algorithms. The correct scale of these descriptors is an essential parameter to be determined. Improved information quality has...
Traditionally, automated scoring system uses semantic similarity between words and the weight of words to calculate semantic similarity between student's answer and standard answer. It doesn't consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance. This article presents a novel approach called dependency-based weighted...
In this paper, the texture property ??coarseness?? is modeled by means of type-2 fuzzy sets, relating representative coarseness measures (our reference set) with the human perception of this texture property. The type-2 approach allows to face both the imprecision in the interpretation of the measure value and the uncertainty about the coarseness degree associated to a measure value. In our study,...
From the esthetic aspects of the users,this article mainly proposes the design method of the product form esthetic feeling that is based on the users' aesthetic levels. There are three levels to the users aesthetic feeling: The aesthetic feeling level X conveys the visual order and simplification of form, the Y aesthetic feeling express the product's purpose, function,the thought communication or...
The application of motivation psychology on crowd simulation is an interesting topic. Agents in a crowd should have motivations and behaviours, and make decisions in a certain virtual environment by themselves. A formalization of motivation is presented by fuzzy mathematical methods and a computational motivation model for agents is set up. An agent's local motion within a group is based on Helbing's...
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