Artificial emotion model based on reinforcement learning mechanism of neural network

A hierarchical-processed frame construction of artificial emotion model for intelligent system is proposed in the paper according to the basic conclusion of emotional psychology. The general method of emotion processing, which considers only one single layer, has been changed in the presented construction. An artificial emotional development model is put forward based on reinforcement learning mechanism of neural network. The new model takes the emotion itself as reinforcement signal and describes its different influences on action learning efficiency corresponding to different individualities. In the end, simulation result based on child playmate robot is discussed and the effectiveness of the model is verified.

Emotion model of interactive virtual humans on the basis of MDP

Emotion plays an essential role in the adaptation and social communication of organisms.Similarly,an appro-priately timed and clearly expressed emotion is a central requirement for believable interactive virtual humans.Presently,incorporating emotion into virtual humans has gained increasing attention in the academia and industry.This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality,e-learning,entertainment,etc.This paper introduces an emotion model of artificial psychology,in which the transition of emotion can be viewed as a Markov process and the relation of emotion,external incentive and personality can be described by a Markov decision process(MDP).In order to demonstrate the approach,this paper integrates the emotion model into a system composed of voice recognition and a realistic facial model.Thus,the model could be used for generating a variety of emotional expressions of autonomous,interactive virtual human beings.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Emotional Gait Generation for a Humanoid Robot

In this paper, an emotional mathematical model and affective state probability description space of a humanoid robot are set up on the basis of psycho-dynamics＇ psychological energy and affective energy conservation law. The emotional state transferring process and hidden Markov chain algorithm of stimulating transition process are then studied. The simulation results show that the mathematical model is applicable to the authentic affective state change rule of human beings. Finally, the gait generation experiment results of control signal and electric current tracking wave-form are presented to demonstrate the validity of the proposed mathematical model.

禅宗文化在茶具伴手礼造型设计中的运用

目的探索禅宗文化在伴手礼造型设计中的应用策略,并设计出具有禅宗文化内涵的茶具伴手礼。方法禅宗文化意境的营造从造型、质感、色彩三个方面展开分析,确定禅宗文化伴手礼的形式特征,对禅宗文化伴手礼的造型设计从本能、行为、反思三个情感层次进行分析,使产品设计中“禅”与“艺”相结合。通过用户调研,对禅宗文化提取文化因子作为造型设计的要素,运用AHP层次分析法计算出禅宗文化因子用户需求权重,将较高权重值的文化因子作为最终伴手礼造型创新设计的重要依据。结论禅宗文化伴手礼设计将优秀传统元素和现代美学融为一体,将传统艺术合理地渗透到现代产品设计语言之中,推动人文与艺术的融合,提高文化创意品牌价值,弘扬和传承中华文化。

父母心理控制和儿童消极情绪性对学前儿童问题行为的共同作用:“素质-压力”还是“差别易感性”

本研究以226名学前儿童和其父母为研究对象,从儿童气质-家庭环境交互作用的视角,采用父母报告的方法收集数据,考察了父母心理控制及儿童消极情绪性对儿童问题行为的共同作用。结果发现:在预测外化问题时,父亲和母亲心理控制的主效应均显著,同时父亲心理控制与儿童消极情绪性的交互作用也显著。显著性区域分析发现,高消极情绪性的学前儿童更易受高父亲心理控制的不利影响,也更易受低父亲心理控制的有利影响,该结果支持差别易感性模型;在预测内化问题时,父亲和母亲心理控制的主效应及其与儿童消极情绪性的交互作用均不显著,说明无论是对高消极情绪性还是低消极情绪性儿童,父母心理控制对其内化问题的影响相对较弱。

Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

确定选修课程开设量上限值的情感性概率模型与演示

本文为探讨实行学分制高校所关心的“关于选修课程的开设量问题”,在充分考虑情感因素的重要作用基点上 ,建立了一个方便操作、符合实际的情感性概率模型 .

基于萨提亚和情绪取向的综合家庭治疗在甲基苯丙胺依赖患者中的应用效果评价

目的:探讨基于萨提亚和情绪取向的综合家庭治疗在甲基苯丙胺依赖患者心理及社会功能康复方面的应用效果。方法:采用随机对照试验设计,将纳入研究的80名戒毒康复人员随机分配入干预组(n=40)和对照组(n=40)。干预组接受萨提亚与情绪取向家庭治疗(共8次,每次2.5小时),对照组接受认知行为干预。干预前后采用激惹及抑郁和焦虑自评量表、正性负性情绪量表、戒毒信心量表和新型毒品依赖患者社会适应评估量表进行测量。采用协方差分析对干预效果进行评价。结果:接受干预后,干预组的抑郁分量表得分低于对照组(P=0.013),正性情绪分量表(P=0.007)、戒毒信心量表(P=0.001)和新型毒品依赖患者社会适应评估量表(P=0.004)得分均高于对照组。结论:基于萨提亚和情绪取向的综合家庭治疗可有效改善甲基苯丙胺依赖患者的抑郁症状,强化其正性情绪,提高其戒毒信心和社会适应水平。