Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most ...Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.展开更多
Introduction:The current worldwide electric power&heat&cool production has a negative impact on the environment by emissions and enormous leaks of low-potential waste heat.Transformation of unused industrial l...Introduction:The current worldwide electric power&heat&cool production has a negative impact on the environment by emissions and enormous leaks of low-potential waste heat.Transformation of unused industrial low power heat into“renewable heat”useful to enhance the efficiency of the system is essential and actual innovation in the field of worldwide environmental protection.By introducing and defining the terminology of low-potential,“renewable”,“green heat”has created a new,parallel category of research in the energy sector.Traditional co-generation systems produce heat for space heating and hot water and generate electricity.Moving to tri-generation allows growing demand for air conditioning for homes,offices and commercial spaces such as server rooms and switchboards to be met simultaneously or on a seasonal basis.Tri-generation,or combined cooling,heat and power,is the process by which some of the heat produced by a co-generation plant is used to generate chilled water for air conditioning or refrigeration.Usually an absorption chiller is linked to the plant to provide this functionality.The technical solution is related to the new efficient manner and system of simultaneous generation of heat/cold from multiple heat sources,which has not yet been known,but in practice required.New system also enables advantageous utilization of solar power in supporting of the cooling output.The innovative system can be operated also within the existing central heating distribution systems.展开更多
This study focuses on resource block allocation issue in the downlink transmission systems of the Long Term Evolution (LTE). In existing LTE standards, all Allocation Units (AUs) allocated to any user must adopt the s...This study focuses on resource block allocation issue in the downlink transmission systems of the Long Term Evolution (LTE). In existing LTE standards, all Allocation Units (AUs) allocated to any user must adopt the same Modulation and Coding Scheme (MCS), which is determined by the AU with the worst channel condition. Despite its simplicity, this strategy incurs significant performance degradation since the achievable system throughput is limited by the AUs having the worst channel quality. To address this issue, a two-step resource block allocation algorithm is proposed in this paper. The algorithm first allocates AUs to each user according to the users' priorities and the number of their required AUs. Then, a re-allocation mechanism is introduced. Specifically, for any given user, the AUs with the worst channel condition are removed. In this manner, the users may adopt a higher MCS level, and the achievable data rate can be increased. Finally, all the unallocated AUs are assigned among users without changing the chosen MCSs, and the total throughput of the system is further enhanced. Simulation results show that thanks to the proposed algorithm, the system gains higher throughput without adding too many?complexities.展开更多
基金Supported by Shaanxi Province Key Research and Development Project (2021GY-280)the National Natural Science Foundation of China (No.61834005,61772417,61802304)。
文摘Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.
文摘Introduction:The current worldwide electric power&heat&cool production has a negative impact on the environment by emissions and enormous leaks of low-potential waste heat.Transformation of unused industrial low power heat into“renewable heat”useful to enhance the efficiency of the system is essential and actual innovation in the field of worldwide environmental protection.By introducing and defining the terminology of low-potential,“renewable”,“green heat”has created a new,parallel category of research in the energy sector.Traditional co-generation systems produce heat for space heating and hot water and generate electricity.Moving to tri-generation allows growing demand for air conditioning for homes,offices and commercial spaces such as server rooms and switchboards to be met simultaneously or on a seasonal basis.Tri-generation,or combined cooling,heat and power,is the process by which some of the heat produced by a co-generation plant is used to generate chilled water for air conditioning or refrigeration.Usually an absorption chiller is linked to the plant to provide this functionality.The technical solution is related to the new efficient manner and system of simultaneous generation of heat/cold from multiple heat sources,which has not yet been known,but in practice required.New system also enables advantageous utilization of solar power in supporting of the cooling output.The innovative system can be operated also within the existing central heating distribution systems.
文摘This study focuses on resource block allocation issue in the downlink transmission systems of the Long Term Evolution (LTE). In existing LTE standards, all Allocation Units (AUs) allocated to any user must adopt the same Modulation and Coding Scheme (MCS), which is determined by the AU with the worst channel condition. Despite its simplicity, this strategy incurs significant performance degradation since the achievable system throughput is limited by the AUs having the worst channel quality. To address this issue, a two-step resource block allocation algorithm is proposed in this paper. The algorithm first allocates AUs to each user according to the users' priorities and the number of their required AUs. Then, a re-allocation mechanism is introduced. Specifically, for any given user, the AUs with the worst channel condition are removed. In this manner, the users may adopt a higher MCS level, and the achievable data rate can be increased. Finally, all the unallocated AUs are assigned among users without changing the chosen MCSs, and the total throughput of the system is further enhanced. Simulation results show that thanks to the proposed algorithm, the system gains higher throughput without adding too many?complexities.
