Search results

This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem.

At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers of neural networks to accomplish the path planning task.

The NAO biomimetic robot respond quickly and correctly to the dynamic environment. The simulation experiments show that the deep neural network outperforms in dynamic and static environment than the conventional method.

A new method of deep learning based biomimetic robot path planning is proposed. The authors designed a multi-layer CNN which includes max-pooling layer and convolutional kernel. Then, the first and second layers features can be extracted by these kernels. Finally, the authors use the sparse auto encoder training algorithm to train the CNN so as to accomplish the path planning task of NAO robot.

This paper aims to explore language strategies and techniques in Chinese mediation that are adopted by mediators to motivate and facilitate compromise among disputants.

Ten cases were audio-recorded on the spot, transcribed for analysis in their Chinese form, and then translated into English for English readers. The translation of excerpts used in this paper to demonstrate points was double checked to ensure accuracy. Discourse analysis was adopted to explore the meanings and functions of the utterances in these excerpts.

It is found that power is embedded in the mediator's position and in his/her role in the mediation. Furthermore, neutrality is less of a concern as compared to justice in the mediator's terms. Finally, socio-cultural indications of the language strategies and techniques were drawn about contemporary Chinese society.

When dealing with Chinese people in conflict, one may emphasize common goals and bring in external reasons such as seniority, face, and status to motivate and facilitate compromise.

Studying transcripts of cases that were recorded in real time and recently is rare in studies of Chinese mediation. Studying what people actually say provides us data in reality, in contrast to the ideals as well as what they say they do in mediation.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

Ranking groups (schools, regions, counties) according to the average score of their constituent parts – say, ranking schools by the academic achievements of students – is a common yardstick in evaluation and a cornerstone of any planning process. In this paper we show that under certain circumstances the ranking of groups, unlike the ranking of individuals, can be affected by the examiner. The fact that a ranking reversal is possible does not necessarily imply that the test itself is deficient, it merely reveals that the groups are non-homogeneous and therefore, the ranking of groups is meaningless. An investigation of the conditions under which such manipulation can occur leads us to suggest new statistical indicators as warning signals that can help one recognize such situations if and when they arise. It turns out that these indicators are related to the indicators for finding whether one distribution dominates another.

This study aims to look into consumers' green consumption intention by incorporating social media exposure, environmental concern and habit into theory of planned behavior (TPB) in the context of China advocating green consumption.

Based on the data collected from online survey, this study use partial least squares structural equation modeling (PLS-SEM) to analysis the measurement model and structure model.

The results show attitude, social media exposure, environmental concern and subjective norms are important determinants of consumers' intention to purchase green products.

This study explores the new associations between the major constructs of the TPB model in green consumption. This study also expanded the TPB model by adding three extra factors, namely social media exposure, habit and environmental concern. In addition, this study attempts to bridge the gap between attitude and intention from a new perspective by using social media exposure as a moderating variable.

We reformulate regression modeling so that ideas often associated with field theory and social network analysis can be brought to bear at every stage in the computation and interpretation of regression coefficients in studies of organizations. Rather than “transcending” general linear reality, we seek to get more out of it. We formulate a dual to regression modeling based on using the variables to learn about the cases. We illustrate our ideas by applying the new approach to a database of hundreds of violent extremist organizations, focusing on understanding which groups use or pursue unconventional weapons (chemical, biological, radiological, nuclear).

The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.

An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.

Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.

Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.