KJC Korea
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In this paper, we propose a base address candidate selection method using the $gp register and page granularity as a way to build a static analysis environment for firmware based on MIPS architecture. As a way to shorten the base address search time, which is a disadvantage of the base address candidate selection method through inductive reasoning in existing studies, this study proposes a method to perform page-level search based on the $gp register in the existing base address candidate selection method as a reference point for search. Then, based on the proposed method, a base address search tool is implemented and a static analysis environment is constructed to prove the validity of the target tool. The results show that the proposed method is faster than the existing candidate selection method through inductive reasoning.

In this paper, we propose a one-class vibration anomaly detection system for bearing defect diagnosis. In order to reduce the economic and time loss caused by bearing failure, an accurate defect diagnosis system is essential, and deep learning-based defect diagnosis systems are widely studied to solve the problem. However, it is difficult to obtain abnormal data in the actual data collection environment for deep learning learning, which causes data bias. Therefore, a one-class classification method using only normal data is used. As a general method, the characteristics of vibration data are extracted by learning the compression and restoration process through AutoEncoder. Anomaly detection is performed by learning a one-class classifier with the extracted features. However, this method cannot efficiently extract the characteristics of the vibration data because it does not consider the frequency characteristics of the vibration data. To solve this problem, we propose an AutoEncoder model that considers the frequency characteristics of vibration data. As for classification performance, accuracy 0.910, precision 1.0, recall 0.820, and f1-score 0.901 were obtained. The network design considering the vibration characteristics confirmed better performance than existing methods.

In this paper, we propose a method for improving raw images captured in a low light condition based on deep learning considering the image signal processing. In the case of a smart phone camera, compared to a DSLR camera, the size of a lens or sensor is limited, so the noise increases and the reduces the quality of images in low light conditions. Existing deep learning-based low-light image processing methods create unnatural images in some cases since they do not consider the lens shading effect and white balance, which are major factors in the image signal processing. In this paper, pixel distances from the image center and channel average values are used to consider the lens shading effect and white balance with a deep learning model. Experiments with low-light images taken with a smart phone demonstrate that the proposed method achieves a higher peak signal to noise ratio and structural similarity index measure than the existing method by creating high-quality low-light images.

The purpose of this study is to shed light on the relationship between perceived value(PV), attitude toward the exhibition(ATYCSE), and behavioral intention toward the exhibition(BITYCSE) through literature review and quantitative research, focusing on <Yumi's Cells Special Exhibition (2020)> as an Instagrammable exhibition. The exhibition has strong entertainment experience quality, and taking pictures has a positive correlation with the satisfaction as well as the immersion, while sharing the viewing experience on Instagram does not influence on the ATYCSE in terms of the PV. Satisfaction also has meaningful correlations with the immersion and the detail factors of BITYCSE. In particular, it can be confirmed that the storytelling factor occupied a superiority over the exhibit factors or the exhibition environment of the Instagram-friendly exhibition, and through this, the importance of storytelling was confirmed. This research unveils implications for the influence of the interactivity and participatory features of the Instagrammable exhibition on the ATYCSE as a potential factor of PV, and the importance of storytelling in Instagrammable exhibitions.

We present an efficient packet transmission strategy for massively multiplayer online first-person shooter (MMOFPS) games using movement-adaptive packet transmission interval. The player motion in FPS games shows a wide spectrum of movement variability both in speed and orientation, where there is room for reducing the number of packets to be transmitted to the server depending on the predictability of the character's movement. In this work, the degree of variability (nonlinearity) of the player movements is measured at every packet transmission to calculate the next transmission time, which implements the adaptive transmission frequency according to the amount of movement change. Server-side prediction with a few auxiliary heuristics is performed in concert with the incoming packets to ensure reliability for synchronizing the connected clients. The comparison of our method with the previous fixed-interval transmission scheme is presented by demonstrating them using a test game environment.

In this paper, based on the generalization and popularization of NFT, the utilization idea of using NFT in e-sports was proposed. We considered ways to utilize NFTs to make access to e-sports easy for all users and to secure users from various age groups. To this end, cases of NFTs with diversity in e-sports platforms were analyzed by type, and the degree of use of NFTs in e-sports was identified through a survey. As a result of the study, it was found that the NFT experience in the e-sports game was highly satisfactory and the desire to experience it again was strong. As NFTs have ownership and scarcity as important characteristics, they can respond well to the demand for owning unique items in e-sports. In addition, in marketing, by promoting limited edition products with scarcity, it is possible to promote marketing that creates value with high profitability. When using NFT in e-sports, various NFT functions are combined regardless of the type of sport, so NFT can become an economic infrastructure.

Radio waves must pass through the unstable atmosphere for successful wireless data transmission from space to ground stations. Data link algorithms required by the International Space Data Systems Advisory Committee (CCSDS) must be capable of detecting and resynchronizing cryptographic and receiver-side errors. However, error recovery is not part of the CCSDS requirements. This paper proposes an algorithm that enables robustness and error recovery against various noises. We experimented with environments such as Gaussian, Salt, Pepper, and S&P noise through noise reduction filters, filters that improve sharpness, and EDSR. In addition, we compare similar algorithms SES Alarmed and DSES Alarmed.

In this paper, we introduce a system that tracks and shares the position of objects based on tags. After receiving the location information of objects through the tag location tracking app, the location of the tag is shared as a group, and the shared users also check the location of objects in real time. Our system offer a differentiated function that allows multiple users to manage and supervise the location of objects, compared to legacy systems. The GPS module and Bluetooth are connected to the Arduino board to obtain the location information of the tag and check it through the Android app. We used Android Studio to create app, and the tag brings up the location of the object. The location of the tag is stored in the phpMyadmin DB and the latitude/longitude is received to the Android app and displayed on the map of the app. The proposed system will be useful for loss prevention and managing public goods.

Finite field arithmetic operations play an important role in a variety of applications, including modern cryptography and error correction codes. In this paper, we propose an efficient multiplication algorithm over finite fields using the Montgomery multiplication algorithm. Existing multipliers can be implemented using AND and XOR gates, but in order to reduce time and space complexity, we propose an algorithm using NAND and NOR gates. Also, based on the proposed algorithm, an efficient semi-systolic finite field multiplier with low space and low latency is proposed. The proposed multiplier has a lower area-time complexity than the existing multipliers. Compared to existing structures, the proposed multiplier over finite fields reduces space-time complexity by about 71%, 66%, and 33% compared to the multipliers of Chiou et al., Huang et al., and Kim-Jeon. As a result, our multiplier is proper for VLSI and can be successfully implemented as an essential module for various applications.

Currently, due to Covid-19, non-face-to-face activities are underway in various fields, and non-face-to-face education is also necessary in the education field. In this paper, we develop and utilize a metaverse-based realistic education platform that combines the latest realistic 3D technology and XR interactive technology to enhance students' understanding of the latest technology and strengthen their educational capabilities. To this end, we understand the main technologies of metaverse in terms of education, investigate contents and application cases of education using metaverse, and compare them with the proposed realistic educational platform. In the future, non-face-to-face education is expected to account for an important portion, and more effective learning is expected through the metaverse-based realistic educational platform developed in this paper when instructor lectures the MZ generation in a virtual world called metaverse.

In this paper, we propose factors that make log analysis difficult and design technique for detecting various objects embedded in the logs which helps in the subsequent analysis. In today’s IT systems, logs have become a critical source data for many advanced AI analysis techniques. Although logs contain wealth of useful information, it is difficult to directly apply techniques since logs are semi-structured by nature. The factors that interfere with log analysis are various objects such as file path, identifiers, JSON documents, etc. We have designed a BERT-based object pattern recognition algorithm for these objects and performed object identification. Object pattern recognition algorithms are based on object definition, GROK pattern, and regular expression. We find that simple pattern matchings based on known patterns and regular expressions are ineffective. The results show significantly better accuracy than using only the patterns and regular expressions. In addition, in the case of the BERT model, the accuracy of classifying objects reached as high as 99%.

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

In this paper, we investigate the feasibility of functional programming in the Scala language. The main issue is to what extent Scala is able to handle major properties of functional programming such as lambda expression, high-order functions, generic types, algebraic data types, and monads. For this purpose, we implement an interpreter of an imperative language. In this implementation, the same functional programming techniques are applied to both Haskell and Scala languages, and then these two versions of implementations are compared and analyzed. The abstract syntax tree of an imperative language is expressed as algebraic data types with generics and enum classes in Scala, and the state transition of imperative languages is implemented by using state monad. Extension and given, new features of Scala, are used as well.

In this paper, we developed a traffic signal control system for emergency situations that can minimize loss of property and life by actively controlling traffic signals in a certain section in response to emergency situations. When the emergency vehicle terminal transmits an emergency signal including identification information and GPS information, the surrounding image is obtained from the camera, and the object is analyzed based on deep learning to output object information having information such as the location, type, and size of the object. After generating information tracking this object and detecting the signal system, the signal system is switched to emergency mode to identify and track the emergency vehicle based on the received GPS information, and to transmit emergency control signals based on the emergency vehicle's traveling route. It is a system that can be transmitted to a signal controller. This system prevents the emergency vehicle from being blocked by an emergency control signal that is applied first according to an emergency signal, thereby minimizing loss of life and property due to traffic obstacles.

In this paper, we propose a new station selection algorithm to solve the illegal parking problem of shared electric scooters and improve the service quality. Recently, as a solution to the urban transportation problem, shared electric scooters are attracting attention as the first and last mile means between public transportation and final destinations. As a result, the shared electric scooter market grew rapidly, problems caused by electric scooters are becoming serious. Therefore, in this study, text data are collected to understand the nature of the problem, and the problems related to shared scooters are viewed from the perspective of pedestrians and users in ‘LDA Topic Modeling’, and a station extension algorithm is based on this. Some parking lots have already been installed, but the existing parking lot location is different from the actual area of tow. Therefore, in this study, we propose an algorithm that can install stations at high actual tow density using mixed clustering technology using K-means after primary clustering by DBSCAN, reflecting the ‘current state of electric scooter tow in Seoul’.

This study examined the changes in lower body muscle activity and foot pressure during progressive squat exercise in male college students. It was conducted to help efficient exercise guidance by identifying and recognizing muscle imbalance using EMG and smart shoes and providing immediate feedback. The subjects of the study were 20 students from D University. As a result of this study, as the squat load increased, the activity of all muscles except for the left semitendinosus muscle and the anterior tibialis muscle significantly increased among. Foot pressure, when the squat load was increased, the pressure of the forefoot(FF) increased significantly and the pressure of the rear foot(RF) decreased significantly. Therefore, providing immediate feedback using a wearable device will prevent muscle imbalance and provide effective exercise guidance.

Recently, interest in the environment and well-being is increasing due to changes in people's level of consciousness. Accordingly, it is trying to reduce disposable products, and interest in "water diet" is increasing as a way to maintain a healthy body. Many people use portable tumblers to drink water or drinks, and in particular, recently, they have evolved into a smarter form that provides various functions to increase user convenience. Existing studies have limited drinks to "water," so there is a limit to monitoring various drinks. To solve this problem, this paper produced a smart tumbler that can judge drinks using IoT technology and monitor the intake of drinks. In addition, it became possible to measure the intake more accurately by judging the case of throwing away the beverage without drinking it. Based on the data of various IoT-based sensors, the user can identify the intake amount of each drink in a dedicated application and receive various conveniences.

This study aims to verify the relationship between employer's perception of employment for the disabled and employment satisfaction, and the mediating effect of perception of employment assistance for the disabled and the moderating effect of education and training support. For the purpose of this study, 4,332 companies were reorganized among the data of the 2021 Survey on Employment of Persons with Disabilities conducted by the Korea Employment Agency for the Disabled. Data analysis used SPSS 23.0 and AMOS 23.0, and structural equation model analysis was performed to understand the relationship between variables. As a result of the study, there was a significant influence between employer's perception of employment for the disabled and employment satisfaction, and the partial mediating effect of employment company contribution was confirmed. In addition, it was found that there was a moderating effect according to the presence or absence of education and training support. Through the results of this study, it is significant that factors that can affect environmental composition were considered in various ways to increase the employment rate of the disabled, and based on these results, implications for increasing the employment rate of the disabled were presented.

This study was conducted to evaluate the association between knowledge of Coronavirus disease 2019 (COVID-19), perception of infection control and practice of infection control among dental hygienists. The questionnaires consisted of 9 demographic questions, 10 questions about COVID-19 knowledge, and 36 questions about perception and practice of infection control. The study analyzed 120 participants‘ data gathered from May 1 to May 31, 2021. For data analysis, T-test, ANOVA, and Pearson correlation were used. As a result, COVID-19 knowledge was 6.59 out of 10, the perception of infection control was 3.57 out of 4 and the practice of infection control was 3.55 out of 4. The COVID-19 knowledge(r=0.485) and perception of infection control(r=0.614) were significantly positively related to practice of infection control. To improve the practice of infection control in the dental field, education of infection control should be mandatory for dental hygienists. Also, the practice of infection control following “Dental Infection Control Standard Policy & Procedure” must be mandatory.

This study examined that have excellent antioxidant effect and expand the base of consumption of lotus leaves. We added 1%, 3%, and 5% of lotus leaf powder to traditional gochujang. pH was the highest in the gochujang with lotus leaf powder 1%(p<0.001). The moisture was significantly lower as the addition of lotus leaf powder increased(p<0.01). The viscosity was the lowest with lotus leaf powder 5% was found to be 15.61 dPa·s(p<0.001). In chromaticity, the L value was the highest in the control, the viscosity of gochujang became darker and lessened(p<0.001). The a value and b value showed the highest in the control(p<0.001). The salinity was the lowest in the gochujang with lotus leaf powder 3%(p<0.001). The sugar content decreased as the more amount of lotus leaf. Total phenol and total flavonoid contents was the highest in the gochujang with lotus leaf powder 5%. The DPPH radical scavenging ability was higher as the more amount of lotus leaf. Reducing power and α-glucosidase inhibitory activity was the highest in the gochujang with lotus leaf powder 3%. The gochujang with lotus leaf powder can be expected to have higher antioxidant activities and to be a health functional food.

The U.S. education policy making and execution process, in which residents can directly participate as members of state or local boards of education, without entrusting a small number of experts to decide on issues of sharply intertwined political interests, can be presumed that it played a role in preventing conflicts and disputes that may arise due to differences of opinion or differences in the interpretation of laws and regulations between subjects. Such a consensus system in the United States suggests a supplementary point to the local education administration system in Korea, where conflicts between various educational entities are occurring because of the current excessive dependence on one superintendent of education.

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

This paper aims to develop a model to estimate the paint thickness in a shipyard spray painting according to changes of spraying distance and speed. We acquired the experimental datasets of five different conditions with respect to the spraying distance and speed using a painting robot. In addition, we applied a preprocessing step to handle noises which might be caused by various reasons such as a nozzle damage. Our method is to transform a thickness function of a specified spraying distance and speed into another function of an unknown spraying and speed. We observed that the proposed method shows more stable and more accurate predictions compared with an artificial neural network-based approach.

The willingness of online music users to pay is the key to the protection of music copyright and the sustainable development of the industry. This paper aims to study the influencing factors of online music users' willingness to pay based on exploratory analysis. Based on the theory of customer perceived value, the unified theory of technology acceptance and use, and the theory of fan enthusiasm, the research model is constructed. Validate the obtained 583 valid data. Through analysis, I got that perceived value, interpersonal influence, fan enthusiasm, and personal payment awareness directly affect online music users' willingness to pay; practical value and hedonic value have a positive impact on perceived value, and the impact of economic cost and compilation cost has not reached a significant level; Online word-of-mouth negatively moderates the impact of perceived value on users' willingness to pay for music. Music platforms can formulate operating policies based on this.

In this paper, we developed a design thinking-based artificial intelligence education program for middle school students and applied it to verify the impact on creative problem-solving skills. The inspection tool used the Creative Problem Solving Profile Inventory (CPSPI), an inspection tool for measuring creative thinking type ability based on the CPS theory of Hwasun Lee, Jungmin Pyo, Insoo Choe(2014). CPSPI included the steps of evaluating cognitive preferences and cognitive abilities by supplementing the limitations of existing tests, and sharing and persuading one's ideas with others. Before and after applying the design thinking-based artificial intelligence education program, as a result of analyzing the creative problem-solving ability, it increased significantly in all areas. As a result of analyzing the creative problem-solving ability of middle school students, significant results were found in the areas of Problem Detection and Analysis, Idea Generation, Action plan, Execution, Persuasion and Communication. The effect of design thinking was confirmed as a teaching and learning method to improve creative problem-solving ability in artificial intelligence education.

In this paper, we propose suggestions for developing a Metaverse platform for educational purpose utilizing a Delphi study method with experts on Metaverse and digital education. 17 experts participated in the 1st study and 16 took part in the 2nd study, and data was collected via emails from January 5th to 10th for the 1st study and from January 12th to 17th for the 2nd study in 2022. Collected data in the 1st study was analyzed by applying content analysis. The results for the 1st study indicated that there were 120 sub-factors were derived from 7 main questions(the necessity of a Metaverse platform for future education, how to use the Metaverse platform for education to improve the capacities needed for future human resources, problems that may arise during education using the Metaverse platform, the functions that the Metaverse platform for education should have, the infrastructure and environment required when using the Metaverse platform for education, how to use the Metaverse effectively as a learning space, subjects and educational contents that will be effective if conducted on the Metaverse platform for education). The results for the 2nd study were presented by being ranked with calculated means of sub-factors for each question. Finally, based on the results, suggestions for building a Metaverse platform for educational purpose are stated and limitations of the study and possible future study are discussed.