The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion...The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion.They only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex situation.To aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model.Therefore,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion.The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.展开更多
Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were ...Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.展开更多
According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotiona...According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition fimction was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems： the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input （also a state-emotion）. The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.展开更多
Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the fin...Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.展开更多
OBJECTIVE Cranial radiotherapy is clinically used in the treatment of brain tumors;however,the consequent cognitive and emotional dysfunctions seriously impair the life quality of patients.LW-AFC,an active fraction co...OBJECTIVE Cranial radiotherapy is clinically used in the treatment of brain tumors;however,the consequent cognitive and emotional dysfunctions seriously impair the life quality of patients.LW-AFC,an active fraction combination extracted from classical traditional Chinese medicine prescription Liuwei Dihuang decoction,can improve cognitive and emotional dysfunctions in many animal models;however,the protective effect of LW-AFC on cranial irradiation-induced cognitive and emotional dysfunctions has not been reported.Recent studies indicate that impairment of adult hippocampal neurogenesis(AHN)and alterations of the neurogenic microenvironment in the hippocampus constitute critical factors in cognitive and emotional dysfunctions following cranial irradiation.Here,our research further investigated the potential protective effects and mechanisms of LW-AFC on cranial irradiation-induced cognitive and emotional dysfunctions in mice.METHODS LW-AFC(1.6 g·kg^(-1))was intragastrically administered to mice for 14 d before cranial irradiation(7 Gyγ-ray).AHN was examined by quantifying the number of proliferative neural stem cells and immature neurons in the dorsal and ventral hippocampus.The contextual fear conditioning test,open field test,and tail suspension test were used to assess cognitive and emotional functions in mice.To detect the change of the neurogenic microenvironment,colorimetry and multiplex bead analysis were performed to measure the level of oxidative stress,neurotrophic and growth factors,and inflammation in the hippocampus.RESULTS LW-AFC exerted beneficial effects on the contextual fear memory,anxiety behavior,and depression behavior in irradiated mice.Moreover,LW-AFC increased the number of proliferative neural stem cells and immature neurons in the dorsal hippocampus,displaying a regional specificity of neurogenic response.For the neurogenic microenvironment,LW-AFC significantly increased the contents of superoxide dismutase,glutathione peroxidase,glutathione,and catalase and decreased the content of malondialdehyde in the hippocampus of irradiated mice,accompanied by the increase in brain-derived neurotrophic factor,insulin-like growth factor-1,and interleukin-4 content.Together,LW-AFC improved cognitive and emotional dysfunctions,promoted AHN preferentially in the dorsal hippocampus,and ameliorated disturbance in the neurogenic microenvironment in irradiated mice.CONCLUSION LW-AFC ameliorates cranial irradiation-induced cognitive and emotional dysfunctions,and the underlying mechanisms are mediated by promoting AHN in the dorsal hippocampus and improving the neurogenic microenvironment.LW-AFC might be a promising therapeutic agent to treat cognitive and emotional dysfunctions in patients receiving cranial radiotherapy.展开更多
The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control...The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.展开更多
Emotional education aims at leading and adjusting students’ inner motivation of English learning,which affects students’ creative intelligence,cognitive ability and learning efficiency.Emotional education is not onl...Emotional education aims at leading and adjusting students’ inner motivation of English learning,which affects students’ creative intelligence,cognitive ability and learning efficiency.Emotional education is not only a kind of teaching means,but also teaching purpose,which plays an important role in promoting teaching effect and even decides the success or failure of English teaching.展开更多
To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introdu...To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.展开更多
Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications i...Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human,driver safety during driving,pain monitoring during surgery etc.A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region,where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion.To reduce the number of generated sub-graphs,overlap ratio metric is utilized for this purpose.After encoding the final selected sub-graphs,binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification.Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level.Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion(SAVEE)database and the final system accuracy was 90.00%.The results show significant accuracy improvements(about 2%)by the proposed system in comparison to current published works in SAVEE database.展开更多
Emotional metaphors abound in the original works and translations of A Dream of Red Mansions. Based on statistical analysis,a combination of qualitative and quantitative research methods are utilized from the perspect...Emotional metaphors abound in the original works and translations of A Dream of Red Mansions. Based on statistical analysis,a combination of qualitative and quantitative research methods are utilized from the perspective of cognitive linguistics to study the emotional metaphors appearing in the first 50 chapters of the novel and their corresponding English translations by Hawkes and the Yang couple.To generalize rules in the translation of emotional metaphors,the emotional metaphor of “Qi” is taken as an example by calculating the ratios of each kind of emotional metaphors and the frequencies of translation methods adopted.The findings suggest that when translating emotional metaphors,translators have selected their strategies from cognitive perspectives to achieve equivalence between the source language and the target language despite some challenges and restrictions.It is thus concluded that the emotional metaphor in the novel cannot be translated directly but can be approached through selection of appropriate translation methods including meaning transfer,meaning deviation and metaphor omission.展开更多
As a key link in human-computer interaction,emotion recognition can enable robots to correctly perceive user emotions and provide dynamic and adjustable services according to the emotional needs of different users,whi...As a key link in human-computer interaction,emotion recognition can enable robots to correctly perceive user emotions and provide dynamic and adjustable services according to the emotional needs of different users,which is the key to improve the cognitive level of robot service.Emotion recognition based on facial expression and electrocardiogram has numerous industrial applications.First,three-dimensional convolutional neural network deep learning architecture is utilized to extract the spatial and temporal features from facial expression video data and electrocardiogram(ECG)data,and emotion classification is carried out.Then two modalities are fused in the data level and the decision level,respectively,and the emotion recognition results are then given.Finally,the emotion recognition results of single-modality and multi-modality are compared and analyzed.Through the comparative analysis of the experimental results of single-modality and multi-modality under the two fusion methods,it is concluded that the accuracy rate of multi-modal emotion recognition is greatly improved compared with that of single-modal emotion recognition,and decision-level fusion is easier to operate and more effective than data-level fusion.展开更多
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.展开更多
基金the National Natural Science Foundation of China(No.61873196 and No.61501336)the Natural Science Foundation of Hubei Province(2019CFB778)+1 种基金the National Defense Pre-research Foundation of Wuhan University of Science and Technology(GF202007)the Postgraduate Innovation and Entrepreneurship Foundation of Wuhan University of Science and Technology(JCX2020095).
文摘The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion.They only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex situation.To aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model.Therefore,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion.The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.
基金Project(61072087)supported by the National Natural Science Foundation of ChinaProject(2010011020-1)supported by the Natural Scientific Foundation of Shanxi Province,ChinaProject(20093010)supported by Graduate Innovation Fundation of Shanxi Province,China
文摘Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.
基金Project(2006AA04Z201) supported by the National High-Tech Research and Development Program of China
文摘According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition fimction was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems： the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input （also a state-emotion）. The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.
基金Project(2012R1A1A2042625) supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education
文摘Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.
文摘OBJECTIVE Cranial radiotherapy is clinically used in the treatment of brain tumors;however,the consequent cognitive and emotional dysfunctions seriously impair the life quality of patients.LW-AFC,an active fraction combination extracted from classical traditional Chinese medicine prescription Liuwei Dihuang decoction,can improve cognitive and emotional dysfunctions in many animal models;however,the protective effect of LW-AFC on cranial irradiation-induced cognitive and emotional dysfunctions has not been reported.Recent studies indicate that impairment of adult hippocampal neurogenesis(AHN)and alterations of the neurogenic microenvironment in the hippocampus constitute critical factors in cognitive and emotional dysfunctions following cranial irradiation.Here,our research further investigated the potential protective effects and mechanisms of LW-AFC on cranial irradiation-induced cognitive and emotional dysfunctions in mice.METHODS LW-AFC(1.6 g·kg^(-1))was intragastrically administered to mice for 14 d before cranial irradiation(7 Gyγ-ray).AHN was examined by quantifying the number of proliferative neural stem cells and immature neurons in the dorsal and ventral hippocampus.The contextual fear conditioning test,open field test,and tail suspension test were used to assess cognitive and emotional functions in mice.To detect the change of the neurogenic microenvironment,colorimetry and multiplex bead analysis were performed to measure the level of oxidative stress,neurotrophic and growth factors,and inflammation in the hippocampus.RESULTS LW-AFC exerted beneficial effects on the contextual fear memory,anxiety behavior,and depression behavior in irradiated mice.Moreover,LW-AFC increased the number of proliferative neural stem cells and immature neurons in the dorsal hippocampus,displaying a regional specificity of neurogenic response.For the neurogenic microenvironment,LW-AFC significantly increased the contents of superoxide dismutase,glutathione peroxidase,glutathione,and catalase and decreased the content of malondialdehyde in the hippocampus of irradiated mice,accompanied by the increase in brain-derived neurotrophic factor,insulin-like growth factor-1,and interleukin-4 content.Together,LW-AFC improved cognitive and emotional dysfunctions,promoted AHN preferentially in the dorsal hippocampus,and ameliorated disturbance in the neurogenic microenvironment in irradiated mice.CONCLUSION LW-AFC ameliorates cranial irradiation-induced cognitive and emotional dysfunctions,and the underlying mechanisms are mediated by promoting AHN in the dorsal hippocampus and improving the neurogenic microenvironment.LW-AFC might be a promising therapeutic agent to treat cognitive and emotional dysfunctions in patients receiving cranial radiotherapy.
基金supported by the Defense Science and Technology Key Laboratory Fund of Luoyang Electro-optical Equipment Institute,Aviation Industry Corporation of China(6142504200108).
文摘The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.
文摘Emotional education aims at leading and adjusting students’ inner motivation of English learning,which affects students’ creative intelligence,cognitive ability and learning efficiency.Emotional education is not only a kind of teaching means,but also teaching purpose,which plays an important role in promoting teaching effect and even decides the success or failure of English teaching.
基金Project(61403422)supported by the National Natural Science Foundation of ChinaProject(17C1084)supported by Hunan Education Department Science Foundation of ChinaProject(17ZD02)supported by Hunan University of Arts and Science,China
文摘To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.
文摘Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human,driver safety during driving,pain monitoring during surgery etc.A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region,where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion.To reduce the number of generated sub-graphs,overlap ratio metric is utilized for this purpose.After encoding the final selected sub-graphs,binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification.Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level.Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion(SAVEE)database and the final system accuracy was 90.00%.The results show significant accuracy improvements(about 2%)by the proposed system in comparison to current published works in SAVEE database.
基金Research on Emotional Metaphor and Its Translation Process—A Case Study on English Translation of A Dream of Red Mansions(N O.SK2017A0912)Logistics English Teaching Team(NO.2015jxtd106)Funding Project for Cultivating Top Talents in Universities and Colleges of Anhui Provincial Education Department in 2019(NO.gxyq2019248)
文摘Emotional metaphors abound in the original works and translations of A Dream of Red Mansions. Based on statistical analysis,a combination of qualitative and quantitative research methods are utilized from the perspective of cognitive linguistics to study the emotional metaphors appearing in the first 50 chapters of the novel and their corresponding English translations by Hawkes and the Yang couple.To generalize rules in the translation of emotional metaphors,the emotional metaphor of “Qi” is taken as an example by calculating the ratios of each kind of emotional metaphors and the frequencies of translation methods adopted.The findings suggest that when translating emotional metaphors,translators have selected their strategies from cognitive perspectives to achieve equivalence between the source language and the target language despite some challenges and restrictions.It is thus concluded that the emotional metaphor in the novel cannot be translated directly but can be approached through selection of appropriate translation methods including meaning transfer,meaning deviation and metaphor omission.
基金supported by the Open Funding Project of National Key Laboratory of Human Factors Engineering(Grant NO.6142222190309)。
文摘As a key link in human-computer interaction,emotion recognition can enable robots to correctly perceive user emotions and provide dynamic and adjustable services according to the emotional needs of different users,which is the key to improve the cognitive level of robot service.Emotion recognition based on facial expression and electrocardiogram has numerous industrial applications.First,three-dimensional convolutional neural network deep learning architecture is utilized to extract the spatial and temporal features from facial expression video data and electrocardiogram(ECG)data,and emotion classification is carried out.Then two modalities are fused in the data level and the decision level,respectively,and the emotion recognition results are then given.Finally,the emotion recognition results of single-modality and multi-modality are compared and analyzed.Through the comparative analysis of the experimental results of single-modality and multi-modality under the two fusion methods,it is concluded that the accuracy rate of multi-modal emotion recognition is greatly improved compared with that of single-modal emotion recognition,and decision-level fusion is easier to operate and more effective than data-level fusion.
文摘Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
