• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.1
• /
• pp.67-74
• /
• 2022

In recent, the number of real-time traffic information sources and providers has increased as increasing smartphone users and intelligent transportation system facilities installed at roadways including vehicle detection system (VDS), dedicated short-ranged communications (DSRC), and global positioning system (GPS) probe vehicle. The accuracy of such traffic information would vary with these heterogeneous information sources or spatiotemporal traffic conditions. Therefore, the purpose of this study is to propose an empirical strategy of heterogeneous information fusion to improve the accuracy of real-time traffic information. To carry out this purpose, travel speed data collection based on the floating car technique was conducted on 227 freeway links (or 892.2 km long) and 2,074 national highway links (or 937.0 km long). The average travel speed for 5 probe vehicles on a specific time period and a link was used as a ground truth measure to evaluate the accuracy of real-time heterogeneous traffic information for that time period and that link. From the statistical tests, it was found that the proposed fusion strategy improves the accuracy of real-time traffic information.

• Journal of Bio-Environment Control
• /
• v.31 no.4
• /
• pp.452-459
• /
• 2022

One of the important factors among the smart farm factors is environmental measurement. This study tried to design an environmental measurement monitoring system through Bluetooth wireless communication with LoRa using the open source programs Arduino, App Inventor, and Node Red. This system consists of Arduino, LoRa shield, temperature and humidity sensor (SHT10), and carbon dioxide sensor (K30). The environmental measurement system is configured as a system that allows the sensor to collect environmental data and transmit it to the user through wireless communication to conveniently monitor the farm environment. As libraries used in the Arduino program, LoRa.h, Sensirion.h, LiquidCrystal_I2C.h and K30_I2C.h were used. When receiving environmental data from the sensor at regular intervals, coding using average value was used for data stabilization. An Android-based app was developed using Node Red and App Inventor program as the user interface. It can be seen that the environmental data for the sensor is well collected with the screen output to the serial screen of Arduino, the screen of the smartphone, and the user interface of Node Red. Through these open source-based platforms and programs will be applied to various agricultural applications.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.77-84
• /
• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.

• Therapeutic Science for Rehabilitation
• /
• v.11 no.3
• /
• pp.93-106
• /
• 2022

Objective : The COVID-19 pandemic has brought non-face-to-face healthcare service delivery system. Research into telehealth system and its efficacy remains unclear. Methods : Seven healthy adults participated in this study to investigate effects of tele-monitoring with the speech-to-text (STT) application to induce changes in occupational activities on occupational balance in healthy adults. Subjects were requested to choose occupational activities they wanted to have researched and then register them to the STT application. The STT application provided an alarm to check whether the pre-registered activities were performed on time, and whether the subjects performed it by their voice. The subjects were followed for 1 week, with assessments at baseline, and after 1-week's tele-monitoring. Results : Our findings showed that the subjects were willing to participate in tele-monitoring with the STT application with high adherence and satisfaction. In addition, there was a significant improvement in occupational activities related to health (p<.05). Specifically, adherence, satisfaction, and efficacy of the tele-monitoring with the STT application could successfully bring occupational balance in short-term periods. Conclusion : These findings highlight that tele-monitoring with a smartphone could be considered as one promising way to restore occupational balance in lockdown after the COVID-19 outbreak.

• Therapeutic Science for Rehabilitation
• /
• v.10 no.4
• /
• pp.39-52
• /
• 2021

Objective : This study aimed to identify the characteristics and analyze the quality of studies on memory improvement using a single-subject research design. Methods : Six studies were selected through the Research Information Sharing Service (RISS), Korean Studies Information Service System (KISS), and National Digital Science Library (NDSL). Keywords were memory training, stroke, early dementia, mild cognitive impairment, and single-subject research design. The characteristics and quality levels were analyzed between January 2011 and October 2020. Results : Regarding the quality level, the middle level (7-10 points) was 66.7% of the four articles, and the high level (11-14 points) was 33.3% of the two articles. Reversal designs were used in all studies. Independent variables were errorless learning, smartphone application, cognitive training system (CoTras), spaced retrieval training (SRT) with errorless learning, spaced retrieval training, and iPad applications. The dependent variables included memory, attention, electroencephalogram, instrumental activities of daily living, depression etc., which increased after the intervention. The total session, study period, and intervention time were varied. Conclusion : In single-subject research design related to memory training, occupational therapy intervention was confirmed as an effective method for improving memory and attention. The quality level of single-subject research design was moderate or higher, and high-quality level of studies should be conducted by additional design to increase the validity in the future.

• The Journal of the Korea Contents Association
• /
• v.22 no.2
• /
• pp.125-134
• /
• 2022

Due to the development of smartphone technology, as of 2020, 91.9% of people use the Internet[1] to frequently acquire information through websites and mobile apps. As the number of homepages in charge of providing information is increasing every year, the number of applications for web vulnerability diagnosis, which diagnoses the safety of homepages, is also increasing. In the existing web vulnerability check, the number of diagnostic personnel should increase in proportion to the number of homepages that need diagnosis because the diagnosticians manually test the homepages for vulnerabilities. In reality, however, there is a limit to securing a web vulnerability diagnosis manpower, and if the number of diagnosis manpower is increased, a lot of costs are incurred. To solve these problems, an automatic diagnosis tool is used to replace a part of the manual diagnosis. This paper explores a new method to expand the current automatic diagnosis range. In other words, automatic diagnosis possible items were derived by analyzing the impact of web vulnerability diagnosis items. Furthermore, automatic diagnosis identified possible items through comparative analysis of diagnosis results by performing manual and automatic diagnosis on the website in operation. In addition, it is possible to replace manual diagnosis for possible items, but not all vulnerability items, through the improvement of automatic diagnosis tools. This paper will explore some suggestions that can help improve plans to support and implement automatic diagnosis. Through this, it will be possible to contribute to the creation of a safe website operating environment by focusing on the parts that require precise diagnosis.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.807-812
• /
• 2022

Recently, as the number of voice recording files submitted as court evidence increases, the number of cases claiming forgery is also increasing. If the audio recording file structure and metadata, which are objective grounds, are completely forged, it is actually impossible to detect forgery of the sophisticated audio recording file. It is extremely rare for the court to reject the file structure and metadata analysis performed with the forged audio recording file. The purpose of this study is to prove that forgery of voice recording file structure and metadata is easily possible. To this end, in this study, it was introduced that forgery detection is impossible when the 'mixed paste' function, which enables sophisticated editing based on the typification of the editing method of voice recording files, is applied. Moreover, it has been proven through experiments that forgery of file structure and metadata is possible. Therefore, a stricter standard for judging the admissibility of evidence is required when the audio recording file is adopted as digital evidence. This study will not only contribute to the standard of integrity in the adoption of digital evidence by judges, but will also contribute to the method of constructing a dataset for artificial intelligence in detecting forgery of recorded files that is expected to be developed in the future.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.3
• /
• pp.79-89
• /
• 2022

With the popularity and the advanced graphics hardware technology of mobile devices, various mobile applications that help children with physical activities have been studied. This paper presents SandUp, a mobile application that guides the play of building sand castles using artificial intelligence and augmented reality(AR) technology. In the process of building the sandcastle, children can interactively explore the target virtual sandcastle through the smartphone display using AR technology. In addition, to help children complete the sandcastle, SandUp informs the sand shape and task required step by step and provides visual and auditory feedback while recognizing progress in real-time using the phone's camera and deep learning classification. We prototyped our SandUp app using Flutter and TensorFlow Lite. To evaluate the usability and effectiveness of the proposed SandUp, we conducted a questionnaire survey on 50 adults and a user study on 20 children aged 4~7 years. The survey results showed that SandUp effectively helps build the sandcastle with proper interactive guidance. Based on the results from the user study on children and feedback from their parents, we also derived usability issues that can be further improved and suggested future research directions.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.175-185
• /
• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.1
• /
• pp.41-49
• /
• 2023

A convolutional neural network (CNN) is a representative algorithm for implementing artificial neural networks. CNNs have improved on the issues of rapid increase in calculation amount and low object classification rates, which are associated with a conventional multi-layered fully-connected neural network (FNN). However, because of the rapid development of IT devices, the maximum resolution of images captured by current smartphone and tablet cameras has reached 108 million pixels (MP). Specifically, a traditional CNN algorithm requires a significant cost and time to learn and process simple, high-resolution images. Therefore, this study proposes an improved CNN algorithm for implementing an object classification learning model for simple, high-resolution images. The proposed method alters the adjacency matrix value of the pooling layer's max pooling operation for the CNN algorithm to reduce the high-resolution image learning model's creation time. This study implemented a learning model capable of processing 4, 8, and 12 MP high-resolution images for each altered matrix value. The performance evaluation result showed that the creation time of the learning model implemented with the proposed algorithm decreased by 36.26% for 12 MP images. Compared to the conventional model, the proposed learning model's object recognition accuracy and loss rate were less than 1%, which is within the acceptable error range. Practical verification is necessary through future studies by implementing a learning model with more varied image types and a larger amount of image data than those used in this study.