In this paper we propose the depth-poselets using body-part-poses and also propose the method to recognize the gesture. Since the gestures are composed of sequential poses, in order to recognize a gesture, it should emphasize to obtain the time series pose. Because of distortion and high degree of freedom, it is difficult to recognize pose correctly. So, in this paper we used partial pose for obtaining a feature of the pose correctly without full-body-pose. In this paper, we define the 16 gestures, a depth image using a learning image was generated based on the defined gestures. The depth poselets that were proposed in this paper consists of principal three-dimensional coordinates of the depth image and its depth image of the body part. In the training process after receiving the input defined gesture by using a depth camera in order to train the gesture, the depth poselets were generated by obtaining 3D joint coordinates. And part-gesture HMM were constructed using the depth poselets. In the testing process after receiving the input test image by using a depth camera in order to test, it extracts foreground and extracts the body part of the input image by comparing depth poselets. And we check part gestures for recognizing gesture by using result of applying HMM. We can recognize the gestures efficiently by using HMM, and the recognition rates could be confirmed about 89%.
Daily science classes, which have been continued as part of the spread of participatory science culture, has taken the lead in popularizing science as an effective out-of-school experiential and research activity. However, due to the recent COVID-19 situation, daily science classes have also become an environment in which there is no choice but to switch to non-face-to-face or to combine face-to-face and non-face-to-face education. Therefore, in this study, we examine how elementary school students participating in the non-face-to-face daily science class program change their usual fields of interest, perception of science and technology, interest about science, and scientific competency. In addition, the educational effectiveness of the non-face-to-face daily science class improved by comparing the differences in perceptions of students and parents, and future operation plans were sought. As a result of the study, after participating in the non-face-to-face daily science class program, students' interest in science and technology development, future technology, environmental pollution, and social media increased, and their interest in games decreased. Also, students' interest in science and technology activities, interest in science, and scientific competency also increased. This shows that non-face-to-face daily science class education is effective. Therefore, it was suggested that it is necessary to diversify the learning topics and content levels of the daily science class program, to expand the opportunities of non-face-to-face science education for underprivileged learners, and to develop and share science content using the latest media.
Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.
Journal of Korea Entertainment Industry Association
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v.15
no.4
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pp.217-227
/
2021
The study aims to explore the rapidly changing university sports-related lecture environment from the perspective of professors and seek comprehensive and practical improvements in the online lecture environment for both professors and learners. In order to achieve the purpose of this study, six professors related to physical education at the university were selected as research participants, and in-depth interviews were used for about four months from September 2020 to December 2020. Examples of the problems, counterplans, and the improvement plans of professors who related to sports were shown as the results. First, the difficulties faced by professors have been divided into online and offline lecture problems, which are Internet and media possession, online lecture place amulet, professor-learner communication disorder, attendance verification and evaluation, COVID-19 infection, and face-to-face lecture place restriction since the outbreak of COVID-19. Second, professors' response to online and offline lecture problems was diversification of communication media, telecommuting, providing online learning videos, replacing and reinforcing practical classes, which were found to be somewhat lacking in government and school support systems. Finally, since the COVID-19 outbreak, Sports-related lecture's continuous problems and the professor's responses require the improvements such as government-level guidelines, university-level expansion of the venues for on- and off-line lecture, devising online lecture programs that enhance professor's convenience, and adjusting the number of participants for on- and off-line lecture.
Journal of the Korean Society of Floral Art and Design
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no.42
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pp.97-116
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2020
In this paper, the need for lifelong education and distance education is increasing due to the decrease of population and the increase of life expectancy. In addition, the popularization and everydayization of education, which combines daily life and learning, is an educational feature. Individuals can more easily access knowledge, and video plays an important role. Video content is the most basic medium that leads to the popularization and daily life of education. With the development of information and communication technology, popularization of media content production and editing technology, anyone can easily create and share. The video education contents business is expected to increase globally through SNS. Especially, the video contents education industry related to flower design is regarded as a suitable content field in an era where environment is essential. In the modern era, characterized by the "one-person household, one-person media" era, the environment of plants protects people from stress by restoring human emotional efficiency, environmental comfort, and stability. In other words, because humans have a preference for nature, plants play an important role for humanity recovery. Against this backdrop, flower design is expected to be a promising industrial sector with high growth, high value added and high job creation effects. In the era of the fourth revolution of the human race, competitive video contents are expected to influence the growth of the future country. will be.
An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.
Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.
If we may practice the nutrition education planned on the basis which carefully grasped the inappropriate behavioral determinants of middle-school students, it might be an effective method achieving the change in perception and behavior improving the distorted perception about the ideal body shape, so we are to suggest the 8 week program of body shape perception improvement for successful nutrition education as follows. The body shape perception improvement program is a step-by-step group consulting program. At the introduction stage, we let them understand the meaning of true beauty and body change of teenage period and forming of sexual identity. At the stage of perception conversion, we let them have the opportunity to observe the status of body perception of the teenager and self-observation. At the stage of correction, we let them criticize the distorted body image in the society with mass media at the same time with the self-reflection. At the stage of maintenance and evaluation, we suggested the behavior guidance while preparing it. Setting this as the basis, we applied the contents such as the evaluations through cultural sharing events making somethings while directly participating. As the target groups to practice education were middle school students, we considered the learning level and behavioral features of the middle school students, and composed the programs including the methods such as role play, watching real things, media production, discussions and experiences. If the program of body shape perception improvement developed at this study could be utilized at the field of schools, the teenagers can change their ways of thought naturally avoiding the view about unified appearance rightly perceiving negative self-image that the teenagers can have and if the group consulting can be practiced regularly at each school, many students may experience the change in perception, so it might solicit the improvement of health of the families and local societies as well as that of the individual student.
Interaction methods and technologies for mutual exploration based on user behavior are evolving variously. Especially, in recent years, with the development of a wide range of sensors, they have developed from contact to non-contact methods. However, developers' senseless definitions of the interaction methods have made the exploration process quite complicated, which rather creates the hassle of users needing to learn the interaction guide defined by the developers before experiencing the exhibition contents. In this context, in order to make visitors smoothly communicate with exhibition contents, a preliminary study on easy interaction for users of various ages is needed, and in particular, research on improving the usability of user interaction is also essential when developing non-contact exhibition contents. So, in this study, a method to reduce the confusion between developers and users was sought by researching non-contact interaction that could be universally interacted with in the field of exhibition contents and proposing behavior designs. First, based on the narrative structure of cultural resources, existing studies were reviewed and the points of interactions as cultural contents were derived. Then the most efficient search process was selected among non-contact behaviors based on hand gestures that allow users to naturally guess and learn interaction methods. Furthermore, on the basis of the meaning of non-linear narrative-based interaction and the analysis results of spatial behavior elements, affordance behavior with high learning effect and efficiency was derived. Through this research process, an action that helps users to understand non-contact interaction naturally in the process of exploring exhibition-type cultural contents and to utilize non-contact interaction in the process of immersion in exhibition contents is proposed as a final model.
Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.
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