In today's media environment, TV programmers and advertisers must strive ever harder to attract the attention of audiences. Yet what may be even more crucial is engaging audiences in conversations on social media and nourishing stronger relationships. To provide insights into how to improve audience experiences through social media television coviewing (STVC) behaviors, this study investigates audience motivations for using social networking sites (SNSs) while watching sports program (i.e., social media television coviewing-STVC) and examines relationships between identified motivations and key audience engagement outcomes. The results reveal four motivations for STVC behaviors: sports-related interaction seeking, information seeking, convenience seeking, and socializing. Further, results reveal that sports-related interaction seeking, information seeking, and socializing motivations are significant predictors of satisfaction, investment, and commitment to the program. Audience engagement outcomes are not predicted, however, by convenience seeking or by variables pertaining to SNS-use regarding STVC behaviors.

The purpose of this study is to investigate the effect of consumers' engagement through SNS sports advertisements on purchase intention through advertising attitudes and product trust for the disabled. In other words, it was intended to investigate how the disabled people's intention to purchase products is formed by acting on various sports product advertisements that are seen during SNS activities. Accordingly, a survey was conducted on 300 people with disabilities participating in sports for the disabled. As a result, it was found that functional engagement had a positive effect on both advertising attitude and product trust, and advertising attitude and product trust had a positive effect on purchase intention. However, emotion engineering and communal engineering were found to have a negative effect on advertising attitudes and not on product trust.

For web application vulnerability diagnosis, from the development stage to the operation stage, it is possible to stably operate the web only when there is a policy that is commonly applied to each task through diagnosis of vulnerabilities, removal of vulnerabilities, and rapid recovery from web page damage. KISA presents 28 evaluation items for technical vulnerability analysis of major information and communication infrastructure. In this paper, we diagnose the vulnerabilities in the automobile goods shopping mall website and suggest security measures according to the vulnerabilities. As a result of diagnosing 28 items, major vulnerabilities were found in three items: cross-site scripting, cross-site request tampering, and insufficient session expiration. Cookie values were exposed on the bulletin board, and personal information was exposed in the parameter values related to passwords when personal information was edited. Also, since the session end time is not set, it was confirmed that session reuse is always possible. By suggesting security measures according to these vulnerabilities, the discovered security threats were eliminated, and it was possible to prevent breaches in web applications and secure the stability of web services.

Touch sensor interface has become the most useful input device in a smartphone. Unlike keypad/keyboard interfaces used in electronic dictionaries and feature phones, smartphone's touch interfaces allow for the recognition of various gestures that represent distinct features of each application's input. In this paper, we analyze application-specific input patterns that appear in smartphone's touch interfaces. Specifically, we capture touch input patterns from various Android applications, and analyze them. Based on this analysis, we observe a certain unique characteristics of application's touch input patterns. This can be utilized in various useful areas like user authentications, prevention of executing application by illegal users, or digital forensic based on logged touch patterns.

The purpose of this study is to investigate the structural relationship among brand dependence, brand attitude, brand satisfaction and repurchase intention of online golf goods consumers. To achieve the purpose of this study, a survey was conducted on consumers who had experience in purchasing golf goods online by visiting golf driving ranges in Seoul and Kyeonggi area. A total of 200 people were surveyed and 197 data were used for the final data processing. SPSS 23 and AMOS 23 were used for data processing. We obtained the following results. First, brand dependence had a positive effect on brand attitude, but it did not have a significant effect on repurchase intention.Second, brand attitude had a positive effect on brand satisfaction and repurchase intention; third, brand satisfaction had a positive effect on repurchase intentionFirst, face has been shown to have a significant impact on symbolic consumption propensity. Second, symbolic consumption tendencies have a significant impact on product satisfaction and intention to purchase new products. Third, product satisfaction has been shown to have a negative impact on the intention of purchasing new products.

Polar transmitter can support multi-band and multi-mode operation. The efficiency of frequency usage can be increased if polar transmitters can transmit multi-carrier signals. In this paper the configuration of polar transmitters is investigated to generate multi-carrier signals. Spectrum and CCDF Simulation results of two-carrier signals generated by the polar transmitter can be used to design of PM and AM path in a polar transmitter.

Data access bias can be observed in various types of computing systems. In this paper, we characterize the data access bias in modern mobile computing platforms. In particular, we focus on the access bias of data observed at three different subsystems based on our experiences. First, we show the access bias of file data in mobile platforms. Second, we show the access bias of memory data in mobile platforms. Third, we show the access bias of web data and web servers. We expect that the characterization study in this paper will be helpful in the efficient management of mobile computing systems.

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

In this paper, we sought ways to accept and reject advertisements using the function of a smartphone gyro sensor to enable selective transmission and reception of YouTube advertisements by linking the gyro function of mobile phones. In the case of YouTube advertisements, the skip button is activated after the advertisement is played for a certain period of time (about 5 seconds) before and after the video is played. During the time the advertisement is maintained, it can be used to achieve various goals such as increased website traffic, brand awareness, and sales induction. Since this type of advertisement is not a setting in which the transition takes place immediately, it can be seen as the most suitable setting to strengthen brand awareness. However, due to the nature of the YouTube advertising system, it is often discarded through users' skipping behavior in the process of sharing and reproducing information. Therefore, in this paper, we implemented an optional advertisement transmission system that applies the principle of gyroscope to minimize wasted advertisements and selectively maintain desired advertisements in consideration of the transmission and reception aspects of advertisement broadcasts. It measures rotational repulsive force with directional stability and car wash characteristics, and uses algorithms to identify the slope and angular speed of mobile phones to ensure that advertisements are transmitted and received. Through this, it is expected that advertisement transmission can be performed on the side of an advertisement provider, and selective advertisement reception can be performed on the side of a user.

The use of the predictive analytics (PA) powered by the artificial intelligence (AI) is more important in the movie sector during the COVID-19 pandemic, because Hollywood witnessed the impact of the 'Netflix Effect' and began to invest in data and AI. Our purpose is to discover a few cases of the AI centered PA in the movie industry value chain based on five objectives of PA: Compete, grow, enforce, improve, and satisfy. Even if movie companies' interest is to predict future success for competing with over-the-tops (OTTs) at a first glance, it is observed, once they start to use the PA with the AI, they try to utilize the enhanced PA platforms for remaining four objectives. As a result, ScriptBook, Vault, Pilot, Cinelytic and Merlin Video (Merlin) are use cases for the objective 'compete.' Movio of Vista Group International and Datorama of Salesforce are use cases for the objective 'grow.' Industrial Light & Magic (ILM) and Geena Davis Institute on Gender in Media (GDI) with Disney are use cases for the objective 'enforce.' Watson, Benjamin, and Greenlight Essential are use cases for the objective 'improve.' Disney Research (DR) with Simon Fraser University and California Institute of Technology is the use case for the objective 'satisfy.'

Digital twins are one of the promising digital technologies used to facilitate digital transformation. Therefore, it needs to continually be developed remain relevant in industry and academia. Consolidation of research is required to create a common understanding of the topic and to ensure that future research is built upon a solid foundation. Based on a bibliometric review and a thematic analysis of 217 publications on digital twins from the past two decades, this paper creates and analyzes a visual knowledge map and proposes areas for further research. To comprehensively analyze the development trends and research trends of digital twins, we performed statistical analysis of the relevant literature on digital twins within the core collection database of Web of Science. Through our research, we have shown that the current situation, trends, and hotspots of digital twin research were analyzed via CiteSpace. This study demonstrates that research on digital twins is rapidly growing in popularity, that the output of the research depends largely on the core group of authors conducting it, and that digital twins warrant cross-domain and cross-disciplinary research pathways.

Recently, as the need for food resources has increased both domestically and internationally, support for the agricultural sector for stable food supply and demand is expanding in Korea. However, according to recent media articles, the biggest problem in rural communities is the unstable profit structure. In addition, in order to confirm the profit structure, profit forecast data must be clearly prepared, but there is a lack of auxiliary data for farmers or future returnees to predict farm income. Therefore, in this paper we analyzed data over the past 15 years through time series analysis and proposes an artificial intelligence farm income prediction algorithm that can predict farm household income in the future. If the proposed algorithm is used, it is expected that it can be used as auxiliary data to predict farm profits.

In the aftermath of the global pandemic that started in 2019, there have been many changes in the import/export and supply/demand process of agricultural products in each country. Amid these changes, the necessity and importance of each country's food self-sufficiency rate is increasing. There are several conditions that must accompany efficient agricultural activities, but among them, temperature is by far one of the most important conditions. For this reason, the need for high-accuracy climate data for stable agricultural activities is increasing, and various studies on climate prediction are being conducted in Korea, but data that can visually confirm climate prediction data for farmers are insufficient. Therefore, in this paper, we propose an artificial intelligence-based temperature prediction algorithm that can predict future temperature information by collecting and analyzing temperature data of farms in Gyeonggi-do in Korea for the last 10 years. If this algorithm is used, it is expected that it can be used as an auxiliary data for agricultural activities.

This paper proposed a real-time earlobe detection system using deep learning on the web. Existing deep learning-based detection methods often find independent objects such as cars, mugs, cats, and people. We proposed a way to receive an image through the camera of the user device in a web environment and detect the earlobe on the server. First, we took a picture of the user's face with the user's device camera on the web so that the user's ears were visible. After that, we sent the photographed user's face to the server to find the earlobe. Based on the detected results, we printed an earring model on the user's earlobe on the web. We trained an existing YOLO v5 model using a dataset of about 200 that created a bounding box on the earlobe. We estimated the position of the earlobe through a trained deep learning model. Through this process, we proposed a real-time earlobe detection system on the web. The proposed method showed the performance of detecting earlobes in real-time and loading 3D models from the web in real-time.

This study aims to analyze working hours of sprinkler heads when a fire occurs at an indoor gymnasium while sprinkler heads are installed in division of standard response type, special response type, and earlier response type. The fire scenario was designed under the assumption that the fire started from overheating of a heater in the indoor gymnasium has transferred on to a couch to spread. The analysis on the operation time of the standard response type sprinkler head, the special response type sprinkler head and the early response sprinkler head was conducted. The result showed that, in case of fire in a gymnasium, the time for opening of the heat sensor due to the heat from the fire varies by the type of the sprinkler head. When a special response type sprinkler is installed, it worked below the assessment standards. When an early response sprinkler head is installed, it worked appropriately according to assessment standards. Based on the results, we found that sprinkler heads will work properly when installed according to design relevant to laws and regulations. This means that there is a limit in installation of sprinkler heads based on the existing law-based design as for indoor gymnasiums. Again, we conclude that if sprinkler heads are installed based on design made through laws and regulations, more time will be needed for operation, making it highly likely to fail to stop a fire at an earlier point of time.

The purpose of this study is to analyze the types of latent profiles of high school students' fire safety awareness and to identify the characteristics of related variables. For this purpose, a survey was conducted from March 22 to May 25, 2021 for 1054 high school students (male; 569, female; 485) in 3 cities, in Jeollabuk-do. The latent profile was analyzed using a scale consisting of 4 sub-factors: 'fire prevention', 'fire preparedness', 'indirect fire response', and 'direct fire response'. It was checked whether there were differences according to the inter-individual differences of the latent group. As a result of the analysis, fire safety awareness of high school students was classified into three latent profiles. The three groups were named 'High Perception Type', 'Moderate Perception Type', and 'Low Perception Type' according to their types. In fire safety awareness, there is a significant difference in the individual differences according to the gender and academic achievement of the latent profile. These results are meaningful as the first study to analyze the latent profile of high school students' fire safety awareness, and it is also meaningful to provide a useful basis for the contents and methods of customized fire safety education by identifying the tendencies of spontaneous groups and their fire safety awareness.

Big data has recently been used in various industries such as tourism, medical care, distribution, and marketing. And it is evolving to the stage of collecting real-time information or analyzing correlations and predicting the future. In the tourism industry, big data can be used to identify the size and shape of the tourism market, and by building and utilizing a large-capacity database, it is possible to establish an efficient marketing strategy and provide customized tourism services for tourists. This paper has begun with anticipation of the effects that would occur when big data is actively used in the tourism field. Because the method of use must have applicability and practicality, the spatial scope will be limited to Seosan, Chungcheongnam-do, and research will be conducted. In this paper, to improve the quality of tourism courses by collecting and analyzing the number of mention data and sentiment index data on social media, which reflect the tourist's interest, preference and satisfaction. Therefore, it is used as basic data necessary for the development of new local tourism courses in the future. In addition, the development of tourism courses will be able to promote tourism growth and also revitalizing the local economy.

This study was tried to explore the mediating effect of workplace learning and self-efficacy on the relationship between technostress and job satisfaction in convalescent hospital nurses. Data were collected from 149 nurses working at one of 10 convalescent hospitals located in Korea's D region and between July 20 and August 12, 2019 and analyzed using SPSS 24.0. The mediating effects of workplace learning and self-efficacy in the relationship between technostress and job satisfaction were investigated by conducting hierarchical regression analysis and testing for significance based on bootstrapping p values. We found that workplace learning had a complete mediating effect, and self-efficacy a partial mediating effect, in the relationship between technostress and job satisfaction in convalescent hospital nurses. Exploring diverse factors and environmental features affecting job satisfaction in convalescent hospital nurses is highly relevant to clinicians, especially given the gradually increasing number of convalescent hospitals, changes during the era of technology fusion, and the strategic demands arising from an aging society.

This study aims to understand how an influencer's social and physical attractiveness, background and value homophily influences consumer's emotional attachment, which in turn causes user stickiness with regards the influencer's live commerce. We tested all proposed hypotheses among users of the online shopping platform "TaoBao". Ultimately, 297 questionnaires were collected by means of an online survey. The results revealed that social and physical attractiveness positively influence emotional attachment. Meanwhile only value homophily significantly affected emotional attachment, whereas background homophily did not significantly affect emotional attachment. Additionally, emotional attachment was found to significantly influence live commerce stickiness. We also investigated the moderation effect of perceived beauty trends of products sold on live commerce, where the results indicated that high beauty tends to have a higher effect on live commerce stickiness behavior. Lastly, theoretical and managerial implications have been offered.

The purpose of this study is to analyze the structural relationship between customer's pleasure, customer satisfaction, switching cost, and relationship commitment using the fitness center and provide implications. Specifically, this study aims to investigate the effect of the degree of customer's awareness of pleasure on the relationship commitment through the medium of customer satisfaction and switching cost. For this purpose, the structural equation model was constructed based on the previous studies, and the exogenous variables were pleasure, and the endogenous variables were customer satisfaction, switching costs and relationship commitment. The subjects of this study were customers who used fitness centers in the metropolitan area. The sampling method for the sample survey was a total of 277 people using convenience sampling. 257 copies were used as final data except for 20 samples that were not appropriate for the study. The statistical program for data analysis was IBM SPSS Ver. 26.0 and Amos 21.0. The specific data processing method is as follows: First, frequency analysis was conducted to understand the general characteristics of the subjects. In order to verify the validity and reliability of the research tools, confirmatory factor analysis and reliability analysis were conducted. In order to understand the theoretical relationship between each variable, structural equation model was conducted. The results of data processing on the research model are as follows: First, the pleasure of the fitness center customers had a positive effect on customer satisfaction. Second, customer satisfaction had a positive effect on the switching costs. Third, customer satisfaction had a positive effect on the customer commitment. Fourth, switching costs had a positive effect on customer commitment.

The current study investigated cyber learners' use and perceptions of online machine translation (MT) tools. The results show that learners use several MT tools frequently and extensively for various second language learning (L2) purposes according to their needs. The learners' overall perceptions of using MT for English learning were generally positive. The learners reported several advantages of machine translation: ease of use, helpful feedback, effective revision, and facilitation of self-directed learning. At the same time, a considerable number of learners were aware of MT's drawbacks, such as awkward sentences, inaccurate grammar, and inappropriate words, and thus held a negative or skeptical view on the quality and accuracy of MT. These findings have important pedagogical implications for using MT in the context of a cyber university. For successful integration of MT in English classes, teachers need to provide appropriate guidelines and training that will help learners use MT effectively.

We look at the business model development of a fan community platform 'Weverse' from two-sided platform (TSP) to multi-sided platform (MSP) and investigate its platform business model. From the Rocket Model's theoretical perspective, the results reveal that Weverse firstly focuses on inviting artists as many as possible starting from BTS, then attracts new artists' fans naturally. For success of this TSP, it forms MSP, 'Weverse Shop' to meet two sides' relevant needs timely and filtered. In third stage of connection, various partnerships are attempted in terms of open platform strategies. For instance, by combining 'VLive' and Weverse, Naver's fan platform business is transferred to Weverse. For core transaction through direct and indirect monetization, several cobranding activities are tried. Lastly, regarding optimization, newly born Weverse being launched in the first half of 2022 is supposed to create further synergies with Naver's R&D capabilities in data, AI, and other technologies like metaverse platform 'ZEPETO' which already sells clothing items of Weverse artists.

Xiaohongshu is called China's Instagram and is leading overseas product purchases and culture sharing. The purpose of this study is to investigate the structural relationship between Xiaohongshu service quality, emotional response perceived by users, and adhesion to confirm the impact on Xiaohongshu's adhesion at a time when non-face-to-face activities due to COVID-19 have increased. This study distributed and collected questionnaires from October 1st to October 7th, 2021, targeting 210 online shopping mall users. The research results were derived from a total of 206 questionnaires, excluding 4 questionnaires such as omission of record contents, and the causal relationship of the existing PAD model was attempted to be reported by revising and supplementing the existing PAD model. As a result of the study, first, it was confirmed that design among service quality had a positive effect only on ventilation during the user's emotional response. Second, it was confirmed that information among service quality had a positive effect on pleasure and ventilation among users' emotional responses. Third, it was found that security among service quality had a positive effect on pleasure among users' emotional responses. Finally, it was found that pleasure and ventilation had a positive effect on adhesion in the user's emotional response. Based on this result, it is expected that it will be used for operation on other online platforms than the plan for the development of Xiaohongshu.

With the development of blockchain technology, the scope of blockchain applications has expanded rapidly. Blockchain decentralization allows transaction participants to make transparent and safe transactions without a third trust agency. A distributed ledger-based system enables transparent and trusted business for anonymous users. For this reason, many companies apply blockchain to various fields such as logistics, electronic voting, and real estate. Despite this interest, there are still not enough case studies confirming the potential of blockchain as a concrete business model. Therefore, it is necessary to study how blockchain technology can change the existing business model and connect it to a new business model. In this paper, we propose blockchain-based business models and workflow types in various fields such as healthcare, logistics, and energy. We also present application cases. We expect to help companies apply blockchain to their business.

The purpose of this study is to identify the level of polypharmacy use, drug knowledge, and drug misuse behavior in the elderly, and to understand the correlation between them and their effect on drug misuse behavior. The study design was a descriptive survey study, and the participants of the study were 215 elderly people from the local community center. The research tool used drug knowledge, drug misuse behavior, and the data collection period was from February 8 to 19, 2021. The data analysis were descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient, and regression analysis. As a result of the study, a significant correlation variable for the drug knowledge of the elderly showed a significant correlation with prescription and non-prescription, r=.145 (p<0.05), and r=.-. 136, which showed a negative significant correlation (p<0.05). As for the significant correlation variable in the drug misuse behavior of the elderly, when prescription and non-prescription were combined, there was a significant correlation with r=.256 (p<0.01), and when not using drugs, r=.-.225 was negative. showed a significant correlation (p<0.01). In terms of the effect on drug misuse behavior, chronic disease =.145, prescription and non-prescription use = .233, which had a positive effect, and non-prescription = -.328, indicating a negative and significant effect. The provision of education on the safe use of drugs by the elderly should first be provided in the community. In addition, we need systematic education and social support for the transmission of correct knowledge on multi-drug use by the elderly and for health management.

The purpose of this study is to estimate the factors that affect college students' drinking needs and spending. An analysis model to estimate the determinants affecting drinking needs was applied with a truncated Poisson model and a truncated negative binomial model. Tests to select more appropriate models of the two types were made using the comparison of log-likelihood function and the over-dispersion test. The analysis result was interpreted by applying the truncated negative binomial model as the truncated Poisson model showed over-dispersion. We also applied the Tobit model to analyze the determinantsthat affect college students' expenditure on drinking. According to the analysis, gender, grade, allowance and parental occupation were the factors influencing statistics, and gender, type of household income, and student religion were the factors influencing expenditure.

Obesity is caused by the accumulation of triglycerides in adipocytes by the differentiation and lipid synthesis process of pre-adipocytes, and excessive accumulation of adipocytes by the activated Adipogenesis process within the differentiated cells. Therefore, inhibiting the differentiation of adipocyte cells or controlling the adipogenesis process is known as an effective treatment method for obesity. This study evaluates the inhibition of Red beet root extract on pancreatic lipase and pre-adipocyte cell differentiation. Also it evaluates the Red beet root extract activities on C/EBP-𝛼,𝛽, and PPAR-𝛄. The experiments proved that the Red beet root extract inhibits pancreatic lipase by concentration dependency. Further, in 3T3-L1 inhabitation experiment, it was found Red beet root extract inhibited adipocyte formation. Red beet root extract also inhibits the expression of C/EBP-𝛼, C/EBP-𝛽, and PPAR-𝛾 which effect the process of adipocytic differentiation. We therefore concluded that RBE has a high potential to further studies on anti-obesity effect.

The purpose of this study was to provide a 360-degree video on intensive care unit admission guidance to family members admitted to the intensive care unit, and then to identify anxiety, safety perception, and satisfaction. This study was a single-group pre-post design, and the data collection period was from October 1, 2020 to August 30, 2021. The subjects of this study were 19 people who applied 360 degree hospitalization guide video. For data analysis, SPSS WIN 24.0 program was used, and real number, percentage, mean, standard deviation, minimum value, maximum value, and Wilcoxon signed rank test were used. The subjects' anxiety before intervention was an average score of 6.21±2.30 and the anxiety after intervention was an average score of 3.95±2.46, which was statistically significant (z=4.13, p<.001). The safety consciousness of the subjects before the intervention was an average of 4.08±0.39 and the safety consciousness after the intervention was an average of 4.54±0.48, which was statistically significant (z=5.00, p=.001). The highest level of satisfaction with the 360-degree hospitalization guidance image of the subjects was 4.58±0.51 and the lowest was 4.16±0.96. In this study, when 360-degree hospitalization guide video was applied, there was a difference in anxiety and safety perception, and satisfaction was high. Based on the research results, various programs for guardian education can be developed and utilized in the future.

The human auricle is the first part to receive sound from the outside. In this part, the frequency range of human recognizable form is divided and organized. In this study, we propose modeling by applying a single sound source to the surface of the human auricle. This means that when the sound pressure of a low frequency (low frequency) sound enters the pinna, the impedance felt at the tip of a part of the non-linear surface of the pinna is mainly due to the tensile force at the end of the part of the non-linear surface of the pinna. By expressing the situation of moving at a very small speed, the characteristic impedance of the pinna was confirmed to be negative infinity, and it was also confirmed that the speed at the tip of a part of the non-linear surface of the pinna was 0 in the anti-resonance state. It was found that the wave propagation phenomenon that determines the characteristics of the filter is determined by how large the wavelength, kL, is compared to the length of the tip of a part of the non-straight surface of the pinna. Humans first receive sounds from outside through their ears. The auricle is non-linear and has a curved shape, and it is known that it analyzes frequencies while receiving external sounds. The human ear has an audible frequency range of 20Hz - 20,000Hz. Through the study, we applied the characteristics of the notch filter to hypothesize that the human audible frequency range is separated from the auricle, and applied filter theory to analyze it, and as a result, meaningful results were obtained. The curved part and the inner part of the auricle function as a trumpet, collecting sounds, and at the same time amplifying the weak sound of a specific band. The point was found and the shape of the envelope detected in the auricle was found. Selectivity for selecting sounds coming from the outside is the formula of the pinna that implements the function of Q. The function of distinguishing human-recognizable sound from the pinna from low to high through frequency analysis is performed in the pinna, and the 2-3kHz area, where human hearing threshold is the most sensitive, is also the acoustic impedance of the most recessed area of the pinna. It can be seen that starting from.

In order for small electric vehicles to drive on hilly roads in Korea, methods to improve the climbing ability and power performance of vehicles should be taken. In order to improve the power performance of small electric vehicles, the performance of motors mounted on electric vehicles should be improved. However, if the performance of the motor is improved to improve the power performance of the electric vehicle, it is possible to lower the price competitiveness accordingly. In addition, the power consumption of the battery is rapidly increased to drive the high-performance motor, so in order to introduce the small electric vehicle into the domestic market, various problems must be overcome. In order to commercialize small electric vehicles that do not emit harmful exhaust gases to the human body in the hilly domestic terrain, it is effective to introduce a separate continuously variable transmission system that can improve the climbing ability and power transmission ability. In this study, we propose a proprietary model of continuously variable transmissions that can be applied to small electric vehicles. The proposed continuously variable transmission is equipped with a spring in the driving pulley and the driven pulley, and has the advantage of performing a shift that increases torque in a situation where the vehicle needs to increase torque when driving on a hill. In addition, the basic design for commercialization of the proposed continuously variable transmission was carried out, and the prototype manufactured and attached to the body of a small electric vehicle.

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

As society gradually enters a virtual, non-face-to-face society, the use of online content is increasing as well. In particular, as smartphones are thoroughly established in our daily life, the platforms of webtoons, mobile broadcasting, and education are shifting from personal computers to smartphones. Recently, the development of the Over-The-Top media service (OTT service) enabled streaming services of various media contents through the internet and activation of IPTV. Therefore, the rapid increase of popularity of short-form content is a natural phenomenon with smartphone platforms with fast, improvised, and endless communication. Lately, TikTok became the favored platform with prosumers, defined as people who are both producers and consumers. In this study, I studied the experiential response of YouTube and TikTok users as representative examples of a short-form content platform developed after the 2000s, the flourishing years of digital content with a length of 30 seconds.

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..