The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.
Recently, artificial intelligence parking control systems have increased the recognition rate of vehicle license plates using deep learning, but there is a problem that they cannot determine vehicles with fake license plates. Despite these security problems, several institutions have been using the existing system so far. For example, in an experiment using a counterfeit license plate, there are cases of successful entry into major government agencies. This paper proposes an improved system over the existing artificial intelligence parking control system to prevent vehicles with such fake license plates from entering. The proposed method is to use the degree of matching of the front feature points of the vehicle as a passing criterion using the ORB algorithm that extracts information on feature points characterized by an image, just as the existing system uses the matching of vehicle license plates as a passing criterion. In addition, a procedure for checking whether a vehicle exists inside was included in the proposed system to prevent the entry of the same type of vehicle with a fake license plate. As a result of the experiment, it showed the improved performance in identifying vehicles with fake license plates compared to the existing system. These results confirmed that the methods proposed in this paper could be applied to the existing parking control system while taking the flow of the original artificial intelligence parking control system to prevent vehicles with fake license plates from entering.
This study was conducted to examining the socio-cultural impact of the COVID-19 pandemic that swept the world around 2020, and the transformation of norms and social problems due to COVID-19. For this, the characteristics of changes in the socio-cultural norms of the 14th century European Black Death, a representative example of the pandemic, were derived, and based on this, the COVID-19 pandemic was analyzed. The Black Death served as an opportunity to change social norms based on the existing religious authority and the power of the feudal system to the Enlightenment. The population declination and labor shortage also promoted commercialization and mechanization. Printing, which spread during this period, led to the popularization of knowledge, which raised the level of thinking and led to epochal scientific development. This became the foundation of the Industrial Revolution. Like the recent Black Death, COVID-19 has triggered changes in social norms. The technological environment of metaverse, a mixture of virtual and reality, has changed the norm of a consistent identity into free and open identities exerting various potentials through alternate characters. In addition, meme, which are about people being friendly to those with the same worldview as him on the metaverse, weakened the sense of isolation in non-face-to-face situations. Artificial intelligence (AI), which developed during the COVID-19 pandemic, has entered the stage of being used for creative activities beyond the function of assisting humans. Discussions were held on what new social problems would be created by the social norms changed due to the COVID-19 pandemic.
This study is a phenomenological study on the dream insight process of intern counselors. The purpose of this study is to promote growth as a professional counselor based on the process of giving meaning to and recognizing their experiences through insight into the unconscious. Therefore, we used dream-integrated art therapy to have a process of recognizing unconscious dreams that clearly express expectations for oneself, others, and the world, and to explore this process more flexibly. The research participants selected three students enrolled in the master's and doctor's courses from among the applicants who applied through public relations and adopted the phenomenological Giorgi research method for data collection and analysis through in-depth interviews with the research participants. The main research results are as First, the intern counselors had a motive for wanting their own unconscious insight through dream-integrated art therapy and were able to gain insight into the problems revealed in their unconscious through dreams, music, and art media. Second, it was found that the intern counselors felt a deepening of concentration and comfort through recent dreams, music, and art media in common. Third, as a defense mechanism that was revealed without the combination of dream integrated art therapy, the avoidance tendency of not wanting to reveal oneself was common, but this showed a gradual decrease. Fourth, it was reported that intern counselors gained flexibility for themselves about the future growth direction had an opportunity to accept themselves, and had a plan for the future direction to become professional counselors. Therefore, it is suggested that follow-up studies using various media, studies to verify the effectiveness of the dream integration program, and various case studies are necessary.
This study was related to the cases in which pre-service elementary teachers designed math lessons tailored to math underachievers with learning trajectories and universal design for learning. Learning trajectories can be a basis to identify students' current state of understanding and development, and make a lesson plan responsively tailored to underachievers' state. And universal design for learning is a framework that removes potential barriers that may exist in math lessons from the time the lessons are planned, and guides the rich learning environment accessible to all learners. In order to provide an experience of designing math lessons considering the characteristics of math underachievers, this study required pre-service elementary teachers to create learning trajectories and make lesson plans with the principles of universal design for learning. The characteristics of the learning trajectories shown in the lesson plans and the results of applying the principles of universal design for learning were analyzed. By discussing the results, implications were derived regarding the necessity of lesson planning for math underachievers and the development of lesson planning competency of pre-service elementary mathematics teachers in teacher education.
The Journal of Korea Institute of Information, Electronics, and Communication Technology
/
v.15
no.2
/
pp.178-188
/
2022
The introduction of virtual power plants is actively being discussed to solve the problem of grid acceptability caused by the spread of distributed renewable energy, which is the key to achieving carbon neutrality. However, a new business such as virtual power plants is difficult to secure economic feasibility at the initial stage of introduction because it is common that there is no compensation mechanism. Therefore, appropriate support including subsidy is required at the early stage. But, it is generally difficult to obtain the cost model to determine the subsidy level because of the lack of enough data for the new business model. In this study, a survey of domestic experts on the requirements, appropriate scale, and cost required for the introduction of virtual power plants is conducted. First, resource composition scenarios are designed from the survey results to consider the impact of the resource composition on the cost. Then, the cost estimation model is obtained using the individual cost estimation data for their resource compositions using logistic regression analysis. In the case study, appropriate initial subsidy levels are analyzed and compared for the virtual power plants on the scale of 20-500MW. The results show that mid-to-large resource composition cases show 29-51% lower cost than small-to-large resource composition cases.
Journal of Korea Entertainment Industry Association
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v.15
no.8
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pp.193-202
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2021
HYBE Insight, opened in May 2021, is an exhibition space that visually displays and re-interprets the BTS Universe and its content. The space echoes the identity of BTS as an artist and the specificity of the cultural phenomenon they have generated. This study analyzes HYBE Insight's BTS exhibition by applying new museological theories and discourses. Based on a qualitative research methodology including literature review and observational method, the primary themes of the exhibition are follows: 1) a contextualized exhibition content presenting the identity of BTS and their music, 2) the role of the fandom Army's participation in (re) producing BTS content related to the characteristic of popular music museums, 3) the elements of convergence art and its contemporary implication, 4) and the illumination of cultural value in popular music museum management. As the pivotal theory of new museology suggests, investigating a specific exhibition based on each context it has created is significant. However, those studies, including exhibitions related to popular music/artists, were mostly developed in Western nations. In this respect, HYBE Insight's BTS exhibition provides considerable room to explore. In addition to the recent research on BTS, such as their convergent artistry, socio-cultural influence, the role of Army, and the characteristics of HYBE's content management, this article aims to contribute to the multifaceted research by scrutinizing the BTS exhibition content from the new museology perspective.
This study was motivated by the awareness that little attention has been paid to this issue both theoretically and empirically, despite the fact that financial abuse causes serious problems which are difficult for the elderly to recover from. This study intends to explore what the patterns of financial abuse targeting the elderly are, what causes and sustains these abuses, and what makes it difficult to counter such financial abuses. Data analysis was based on individual and group interviews of ten professionals expected to encounter the most financially abused elderly in social welfare institutions. The thematic analysis shows that financial abuse is often caused and maintained in a trusting relationship through care and protection. Because financial abuse was inflicted on the vulnerable elderly in a state of reinforced psychological dependence based on a long and trusting relationship, it appears that it has been made with tacit acknowledgement and consent. Despite those complex dynamics, it is noted that financial abuse can be judged as such only when the elderly claim to suffer from harm. Rather, intervention without victims' acknowledgement tends to be perceived as violating their right to self-determination. This reality naturally leads to the termination of the necessary interventions with the victims in an abusive situation. Based on these results, discussion focused on more realistic and diverse approaches to the issue of financial abuse of older adults.
Kim, Hyung-Jin;Kim, Kwang-Sik;Hwang, Se-Yun;Lee, Jang Hyun
Journal of Navigation and Port Research
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v.46
no.4
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pp.367-374
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2022
The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.
In these days, as cyberspace has been recognized as the fifth battlefield area following the land, sea, air, and space, attention has been focused on activities that view cyberspace as an operational and mission domain in earnest. Also, in the 21st century, cyber operations based on cyberspace are being developed as a 4th generation warfare method. In such an environment, the success of the operation is determined by the commander's decision. Therefore, in order to increase the rationality and objectivity of such decision-making, it is necessary to systematically establish and select a course of action (COA). In this study, COA is established by using the method of classifying operational elements necessary for cyber operation, and it is intended to suggest a direction for quantitative evaluation of COA. To this end, we propose a method of composing the COES (Cyber Operational Elements Set), which becomes the COA of operation, and classifying the cyber operational elements identified in the target development process based on the 5W1H Method. In addition, by applying the proposed classification method to the cyber operation elements used in the STUXNET attack case, the COES is formed to establish the attack COAs. Finally, after prioritizing the established COA, quantitative evaluation of the policy was performed to select the optimal COA.
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