The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of con...The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.展开更多
Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research field.To this end,we conducted a systematic literature review on serious games for science educatio...Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research field.To this end,we conducted a systematic literature review on serious games for science education to reveal research trends and patterns.We discussed the role of virtual reality(VR),AI,and augmented reality(AR)games in teaching science subjects like physics.Specifically,we covered the research spanning between 2011 and 2021,investigated country-wise concentration and most common evaluation methods,and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.展开更多
Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics simila...Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.展开更多
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1I1A1A01055652).
文摘The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.
文摘Teaching science through computer games,simulations,and artificial intelligence(AI)is an increasingly active research field.To this end,we conducted a systematic literature review on serious games for science education to reveal research trends and patterns.We discussed the role of virtual reality(VR),AI,and augmented reality(AR)games in teaching science subjects like physics.Specifically,we covered the research spanning between 2011 and 2021,investigated country-wise concentration and most common evaluation methods,and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.
基金supported by the National Natural Science Foundation of China(Grant No.42271078)the Key Research and Development Program of Shaanxi(Grant No.2024SF-YBXM669)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0902)。
文摘Co-seismic landslide detection is essential for post-disaster rescue and risk assessment after an earthquake event.However,a variety of ground objects,including roads and bare land,have spectral characteristics similar to those of co-seismic landslides,making it difficult to gather information and assess their impact rapidly and accurately.Therefore,an automatic detection method based on a deep learning model,named ENVINet5,with multiple features(ENVINet5_MF)was proposed to solve this problem and improve the detection accuracy of co-seismic landslides.The ENVINet5_MF method is advantageous for co-seismic landslide detection because it features a landslide gain index(LGI)that effectively eliminates the spectral interference of bare land and roads.We conducted two experiments using multi-temporal PlanetScope images acquired in Hokkaido,Japan,and Mainling,China.The accuracy evaluation and rationality analysis show that ENVINet5_MF performed better than comparative methods and that the co-seismic landslide areas detected by ENVINet5_MF were the most consistent with ground reference data.The findings of this study suggest that ENVINet5_MF can provide an efficient and accurate method for coseismic landslide detection to ensure a rapid response to co-seismic landslide disasters.
