Earticle

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.

We propose a method for removing interference and noise to estimate target information. In wireless channels, information signals are subject to interference and noise, making it is difficult to accurately estimate the desired signal. To estimate the desired information signal, it is essential to remove the noise and interference from the received signal, extracting only the desired signal. If the received signal noise and interference are not removed, the estimated information signal will have a large error in distance and direction, and the exact location of the target cannot be estimated. This study aims to accurately estimate the desired target in space. The objective is to achieve more presice target estimation than existing methods and enhance target resolution.An estimation method is proposed to improve the accuracy of target estimation. The proposed target estimation method obtains optimal weights using linear constraints and the minimum variance method. Through simulation, the performance of the proposed method and the existing method is analyzed. The proposed method successfully estimated all four targets, while the existing method only estimated two targets. The results show that the proposed method has better resolutiopn and superior estimation capability than the existing method.

The cooperative communication systems using MIMO(multiple input multiple-output) relay are known as one of the most promising techniques to improve the performance and coverage of wireless communication systems. In this paper, we propose the cooperative communication systems using the relay with multi-antennas and DSTC(distributed space time coding) for decode-and-forward protocol. As using DSTC for DF(decode-and-forward), we can minimize the risk of error propagation at the wireless system using relay system. Also, the MIMO channel cab be formed by multi-antenna and DSTC at the MS(mobile station)-RS(relay station) and at the RS-BS(base station).Therefore, obtaining truly constructive the MIMO diversity and cooperative diversity gain from the proposed approach, the performance of system can be more improved than one of conventional system (relay with single antenna, no relay). The improvement in bit error rate is investigated through numerical analysis of the cooperative communication system with the proposed approach.

The purpose of this paper is to divide franchise education and restaurant education programs into three factors: educational content, educational techniques, and educational environment. After completing the franchise curriculum, we will identify what educational programs affect corporate performance and CEO's capabilities. A total of 99 copies were used as statistical analysis data by conducting a survey of those who completed the training from May 01 to May 15, 2023. The survey used the Likert 5-point scale, and for data analysis, hypothesis verification was conducted using frequency analysis, demographic analysis, and reliability analysis using SPSS23. As a result, it was confirmed that all three factors of franchise education are factors that affect performance and competency. Therefore, among franchise education programs, it is necessary to be faithful to the contents of education, use appropriate educational techniques, and prepare an educational environment well.

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

Slotted ALOHA (S-ALOHA) is a classical medium access control protocol widely used in multiple access communication networks, supporting distributed random access without the need for a central controller. Although stability and delay have been extensively studied in existing works, most of these studies have assumed ideal channel conditions or independent fading, and the impact of time-correlated wireless channels has been less addressed. In this paper, we investigate the queueing delay performance in S-ALOHA networks under time-correlated channel conditions by utilizing a Gilbert-Elliott model. Through simulation studies, we demonstrate how temporal correlation in the wireless channel affects the queueing delay performance. We find that stronger temporal correlation leads to increased variability in queue length, a larger probability of having queue overflows, and higher congestion levels in the S-ALOHA network. Consequently, there is an increase in the average queueing delay, even under a light traffic load. With these findings, we provide valuable insights into the queueing delay performance of S-ALOHA networks, supplementing the existing understanding of delay in S-ALOHA networks.

In the restaurant industry, along with the fourth industry, there is a food technology craze due to IT development. In addition, many prospective restaurant founders are increasing due to restaurant start-ups with relatively low entry barriers. And ChatGPT is causing a craze for chatbots. Therefore, the purpose of this paper is to analyze factors for restaurant start-ups with big data and implement a system to make it easier for prospective restaurant start-ups to recommend restaurant start-ups that suit them and further increase the success rate for restaurant start-ups. Therefore, this paper is meaningful in analyzing the start-up factors desired by prospective restaurant founders with big data, turning them into text, and furthermore, designing and studying the start-up factors shown as big data into a restaurant start-up chatbot system.

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.

Little attention appears to have been paid to the relevance of learning a good representation function in solving long tail tasks. Therefore, we propose a new loss function to ensure a good representation is learnt while learning to classify. We call this loss function Triplet Class-Wise Difficulty-Based (TriCDB-CE) Loss. It is a combination of the Triplet Loss and Class-wise Difficulty-Based Cross-Entropy (CDB-CE) Loss. We prove its effectiveness empirically by performing experiments on three benchmark datasets. We find improvement in accuracy after comparing with some baseline methods. For instance, in the CIFAR-10-LT, 7 percentage points (pp) increase relative to the CDB-CE Loss was recorded. There is more room for improvement on Places-LT.

Globalized cities are currently showing changes due to autonomous driving (AD). It is also maximizing globalization connections in cities where autonomous mobility is as complex as AD. The purpose of this study is to reveal that cities that realize AD and mobility will grow into globalized cities. Several cities, including New York and Shanghai, have attempted and are in progress, but failed cities are increasing. Although the technology of AD and the trust of citizens are prioritized, the city that has built the city's infrastructure is expected to be a city that has succeeded in AD. This is because commercialized cities or AVs will become hubs for mobility globalization, excluding rapid climate change or AV companies, and empirical analysis has been conducted that if AVs fail in metropolitan New York due to urban complexity (population density), urban economy size (GRDP), patents, number of consumers, infrastructure public EV chargers, and road quality. It examines whether the realization of AD by region and country affects overall national innovation. As a result, even if AV succeeds in large cities such as New York, Seoul, which has a higher population density (complexity), has a negative meaning, and a more similar Tokyo has a positive meaning. It can be seen that regional research on AV should also be prioritized in large cities such as Shanghai. This means that in order for AV to be realized in each city, the construction of AI infrastructure data must be actively changed to establish globalization of cities for economic growth as autonomous mobility.

<Deep Sea> is an 3D animated film that stands out for its exceptional special effects and distinctive artistic style. The film employs a multitude of dazzling and vibrant ink particles, creating a strong sense of threedimensionality and weightlessness, while simultaneously portraying a dreamlike and elegant representation of a deep sea ink painting. Furthermore, through the utilization of fragmented stream of consciousness narrative technique, the film establishes a unique artistic effect infused with a Chinese atmosphere. This paper by analyzing the unique particle ink art style and color and stream of consciousness narrative methods in film, this paper discusses the innovative art style generated by traditional ink art style combined with threedimensional technology, and the integration of traditional ink art ideas and artistic conception in animated films.The objective is to cultivate a new ink art style and prove the importance of traditional cultural expression in animated films, while providing new perspectives for the future application of traditional art in animation.

In the era of continuous progress, concerts have emerged as a significant medium for music performance, providing audiences with both musical enjoyment and a means of relaxation. The study examines pivotal moments and milestones in concert history, highlighting the emergence of novel elements such as visual presentations, integration of multimedia, virtual reality experiences, and metaverse concerts. By scrutinizing the repercussions of these changes on the concert experience, the study sheds light on the transformative influence of technology on concert formats, audience engagement, and artistic expression. Moreover, it delves into the challenges and opportunities arising from technological advancements in the contemporary concert landscape. The insights gained from this research contribute to a comprehensive comprehension of the dynamic interplay between technology and concert forms, thereby laying the foundation for future scholarly discourse and advancements within the field.

Anomaly detection in human movements can improve safety in indoor workplaces. In this paper, we design a framework for detecting anomalous trajectories of humans in indoor spaces based on a variational autoencoder (VAE) with Bi-LSTM layers. First, the VAE is trained to capture the latent representation of normal trajectories. Then the abnormality of a new trajectory is checked using the trained VAE. In this step, the anomaly score of the trajectory is determined using the trajectory reconstruction error through the VAE. If the anomaly score exceeds a threshold, the trajectory is detected as an anomaly. To select the anomaly threshold, a new metric called D-score is proposed, which measures the difference between recall and precision. The anomaly threshold is selected according to the minimum value of the D-score on the validation set. The MIT Badge dataset, which is a real trajectory dataset of workers in indoor space, is used to evaluate the proposed framework. The experiment results show that our framework effectively identifies abnormal trajectories with 81.22% in terms of the F1-score.

This study focuses on the behavioural lexical classification for extracting animation character actions and the analysis of the character’s upper and lower body movements. The behaviour and state of characters in the animation industry are crucial, and digital technology is enhancing the industry’s value. However, research on animation motion application technology and behavioural lexical classification is still lacking. Therefore, this study aims to classify the predicates enabling animation motion, differentiate the upper and lower body movements of characters, and apply the behavioural lexicon's motion data. The necessity of this research lies in the potential contributions of advanced character motion technology to various industrial fields, and the use of the behavioural lexicon to elucidate and repurpose character motion. The research method applies a grammatical, behavioural, and semantic predicate classification and behavioural motion analysis based on the character’s upper and lower body movements.

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and as the market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

This paper proposes two kinds of single-balanced direct conversion quadrature receivers using selfoscillating LMVs in which the voltage-controlled oscillator (VCO) itself operates as a mixer while generating an oscillation. The two LMVs are complementary coupled and series coupled to generate the quadrature oscillating signals, respectively. Using a 65 nm CMOS technology, the proposed quadrature receivers are designed and simulated. Oscillating at around 2.4 GHz frequency, the complementary coupled quadrature receiver achieves the phase noise of ‒28 dBc/Hz at 1KHz offset and ‒109 dBc/Hz at 1 MHz offset frequency. The other series coupled receiver achieves the phase noise of ‒31 dBc/Hz at 1KHz offset and ‒109 dBc/Hz at 1 MHz offset frequency. The simulated voltage conversion gain of the two single-balanced receivers is 37 dB and 45 dB, respectively. The double-sideband noise figure of the two receivers is 5.3 dB at 1 MHz offset. The quadrature receivers consume about 440 μW dc power from a 1.0-V supply.

Although the use of batteries is rapidly increasing worldwide to improve carbon neutrality and energy efficiency, performance degradation due to the increase in the number of uses is inevitable as it is a finite resource that can be applied according to capacity and specifications. Deterioration and failure of batteries are recognized as important problems in various applications using batteries, including electric vehicles. In order to solve these problems, a diagnostic technology capable of accurately predicting battery life and grasping state information is required, but it is difficult in a non-linear form due to internal structure or chemical change. In this paper, the factors that generally cause battery performance change are directly applied to check whether there are external changes and impedance changes in the battery, and to analyze whether they affect battery life. Impedance change trends and result values were confirmed using a universal impedance spectroscopy method and a self-developed internal impedance measurement method. The results did not significantly affect the impedance change trend. It was confirmed that the increase in the number of times of battery use was prominent in the impedance change trend.

The Code of Ethics for Newspaper Advertising in Korea, first implemented in 1976 and subsequently revised in 1976, 1996, and 2021, is a critical regulatory instrument for the country's advertising sector. However, the specialized domain of "advertising ethics," particularly the "code of advertising ethics," remains under-explored. This research addresses this scholarly gap, providing an empirical analysis of the 2021 amendment's revision trajectory. This study employs a robust methodological approach, integrating expert interviews and small-group AHP-based surveys. This approach allows for a comprehensive understanding of the revision needs, referencing existing ethical codes studies, and comparing similar ethics codes nationally and internationally. The research further investigates key challenges such as personal data protection and copyright issues in the rapidly evolving digital media landscape, while preserving the existing code's inherent value. The findings are expected to significantly contribute to the emerging field of advertising ethics in Korea, offering practical implications for future code revisions.

Web application security education that can provide practical experience is needed to reduce damage caused by the recent increase in web application vulnerabilities and to strengthen security. In this paper, we proposed a scenario-based web application education method, applied the proposed method to classes, and analyzed the results. In order to increase the effectiveness of scenario-based education, a real-life practice environment to perform scenarios and instructions to be performed by learners are needed. As an example of the proposed method, instructions to be performed by learners from the viewpoint of the attacker and the victim were shown in a practice environment to teach XSS and SQL injection vulnerabilities. After applying the proposed method to the class for students majoring in cyber security, when the lecture evaluation results were analyzed, it was shown that the learner's interest, understanding, and major ability all improved.

Physical education bodybuilders compete by means of external appearance, and more and more people are starting bodybuilding with an interest in improving their individual constitution and diet. However, some of the bodybuilders in sports for life started using banned substances to show off their appearance or to expect good results in bodybuilding competitions. Prohibited drugs only have a short-term positive effect on the subject, and the seriousness of side effects was greater when taking the drug for a long time. An education program that can provide professional education and information on drugs to bodybuilding athletes for life sports should be preceded, and a system that can check regular health should be introduced if necessary. A periodic doping education program for bodybuilders for sport for life is needed to focus on the positive changes in bodybuilding through banned substances and to educate and understand the side effects and damage to life that occur later. Therefore, in order to prevent doping, it is essential to educate various aspects of doping, and it is required to expand the scope not only to elite athletes but also to participants as sports for all

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Qlearning.

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.

Due to the growing importance of ESG management, various studies have been conducted to explore the relationship between ESG performance and corporate value. The purpose of this study is to investigate how a company's ESG performance impacts its corporate value. The research findings indicate that there is difficulty in explaining the relationship between ESG performance of Korean IT companies and firm value in a straightforward manner. However, the results demonstrate that companies with higher profitability, higher foreign ownership, and higher R&D expenditure tend to have a positive impact of ESG ratings on corporate value. Based on these results, we can infer that Korean IT companies can enhance their corporate value by increasing R&D investments to develop innovative products that improve profitability. Additionally, attracting higher foreign investments can also positively influence ESG performance and subsequently increase corporate value. Acknowledging these factors can help companies realize the significance of ESG performance in elevating their overall corporate value.

This study aims to propose a framework for evaluating the impact of artificial intelligence (AI) services, based on the concept of AI service impact. It also suggests a model for evaluating this impact and identifies relevant factors and measurement approaches for each item of the model. The study classifies the impact of AI services into five categories: ethics, safety and reliability, compliance, user rights, and environmental friendliness. It discusses these five categories from a broad perspective and provides 21 detailed factors for evaluating each category.In terms of ethics, the study introduces three additional factors—accessibility, openness, and fairness— to the ten items initially developed by KISDI. In the safety and reliability category, the study excludes factors such as dependability, policy, compliance, and awareness improvement as they can be better addressed from a technical perspective. The compliance category includes factors such as human rights protection, privacy protection, non-infringement, publicness, accountability, safety, transparency, policy compliance, and explainability.For the user rights category, the study excludes factors such as publicness, data management, policy compliance, awareness improvement, recoverability, openness, and accuracy. The environmental friendliness category encompasses diversity, publicness, dependability, transparency, awareness improvement, recoverability, and openness.This study lays the foundation for further related research and contributes to the establishment of relevant policies by establishing a model for evaluating the impact of AI services. Future research is required to assess the validity of the developed indicators and provide specific evaluation items for practical use, based on expert evaluations.

In this paper, we develope a stock trading system that automatically buy and sell stocks in Kiwoom Securities’ HTS system. The system is made by using Kiwoom Open API+ with the Python programming language. A trading strategy is based on an enhanced system query method called a Condition-Search. The Condition- Search script is edited in Kiwoom Hero 4 HTS and the script is stored in the Kiwoom server. The Condition- Search script has the advantage of being easy to change the trading strategy because it can be modified and changed as needed. In the HTS system, up to ten Condition-Search scripts are supported, so it is possible to apply various trading methods. But there are some restrictions on transactions and Condition-Search in Kiwoom Open API+. To avoid one problem that has transaction number and frequency are restricted, a method of adjusting the time interval between transactions is applied and the other problem that do not support a threading technique is solved by an IPC(Inter-Process Communication) with multiple login IDs.

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and ‘diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into ‘purchase cluster’, ‘equipment cluster’, ‘diet cluster’, and ‘execute method cluster’. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

With the development of autonomous driving technology, as the use of software in vehicles increases, the complexity of the system increases and the difficulty of development increases. Developments that meet ISO 26262 must be carried out to reduce the malfunctions that may occur in vehicles where the system is becoming more complex. ISO 26262 for the functional safety of the vehicle industry proposes to consider functional safety from the design stage to all stages of development. Specifically at the software level, the requirements to be complied with during development and the requirements to be complied with during verification are defined. However, it is not clearly expressed about specific design methods or development methods, and it is necessary to supplement development guidelines. The importance of analysis and verification of requirements is increasing due to the development of technology and the increase of system complexity. The vehicle industry must carry out developments that meet functional safety requirements while carrying out various development activities. We propose a process that reflects the perspective of system engineering to meet the smooth application and development requirements of ISO 26262. In addition, the safety analysis/verification FMEA process for the safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to autonomous vehicles and the results were confirmed. In addition, the safety analysis/verification FMEA process for the safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to the advanced driver assistance system and the results were confirmed.

As the demand for distance education increases, interest in the management of learners' rights is increasing. Blockchain technology is a technology that guarantees the integrity of the learner's learning history, and enables learner-led learning control, data security, and sharing of learning resources. In this paper, we proposed a blockchain technology-based learning management system based on Hyperledger Fabric that can be verified through permission between nodes among blockchain platforms. Learning resources can be shared differentially according to the learning progress. Also the percentage of individual learners that can be managed. As a result of the study, the superiority of the platform in terms of convenience compared to the existing platform was demonstrated. As a result of the performance evaluation for the research in this paper, it was confirmed that the convenience was improved by more than 5%, and the performance was 4-5% superior to the existing platform in terms of learner satisfaction.

In this research, our objective was to investigate the efficacy of electrostatic precipitation in capturing mist particles. We assumed that it could be helpful in multi-functional facilities and similar environments where both humidification and dehumidification are required. We derived the air density of the humidified air based on its properties using Dalton’s law. The analysis was performed to evaluate the collection efficiency of capturing mist aerosols of various sizes. As a result, we revealed that under the conditions of a dry-bulb temperature of 26.0°C and relative humidity of 8%, the system achieved a collection efficiency of 99.999% or more for aerosols larger than 2.5μm. These results indicate that electrostatic precipitation technology shows great promise as an effective method for capturing mist particles.