• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.889-899
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• 2017

Various efforts and technical development for integrating the ICT technologies to the area of special needs education have been continuing. In this paper we have studied and implemented various ICT-based disability evaluation websites and mobile applications for special needs education and also verified their usefulness from the field test at disability schools. The valuer can access the websites and mobile applications for autistic behavior or learning disability evaluation at the any places and by any devices such as laptop, PC, smartphone and tablet PC. And all the evalation results are stored into and managed at the server database and shared with websites and mobile applications to integrate together easily. From the study about disability evaluation and implementation results we have a confidence that they will be useful to support the seamless evaluation and the continuous monitoring services for the disabled at the special needs education fields.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.663-668
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• 2023

This paper describes a mobile application that can recognize and identify cats residing on a university campus using the Google's machine learning platform, 'Teachable Machine'. Machine learning, one of the core technologies of the Fourth Industrial Revolution, performs an efficient task of finding optimal results through data learning. Therefore, the model is learned and generated using the platform based on machine learning, and then implemented as an application for smartphones, so that cats can be identified simply and efficiently. In this application, if you take a picture of a cat directly on the spot or call it from the gallery, the cat is identified and information about the cat is provided. Though this system was developed for a specific university campus, it is expected that it can be extended to other campuses and other species of animals.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.545-551
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• 2023

As digital information is readily available to everyone today, the adoption of digital evidence is increasing. However, it is virtually impossible to determine the authenticity of forgery in the case of a voice recording file that has gone through a sophisticated editing process along with the spread of various voice file editing tools. This study aims to prove that forgery, which is difficult to distinguish from the original file, is possible by using insertion, deletion, linking, and synthetic editing technologies in voice recording files. This study presents the difficulty of detecting forgery by encoding a forged voice file with the same extension as the original. In addition, it was shown that forgery detection is impossible if additional transition band deletion and secondary encoding are performed only for experiments in which features occurred. Through this, this study is expected to contribute to the establishment of more stringent evidence admissibility criteria for adopting voice recording files as digital evidence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.540-546
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• 2023

The scope of IoT use has expanded due to the development of related technologies, and various sensors have been developed and distributed to meet the demand for implementing various services. Measuring alcohol concentration using a sensor can be used to prevent drunk driving, and to make this possible, accurate alcohol concentration must be measured and safe transmission from the smartphone to the server must be guaranteed. Additionally, a process of converting the measured alcohol concentration value into a standard value for determining the level of drinking is necessary. In this paper, we propose and implement a system. Security with remote servers applies SSL at the network layer to ensure data integrity and confidentiality, and the server encrypts the received information and stores it in the database to provide additional security. As a result of analyzing the accuracy of alcohol concentration measurement and communication efficiency, it was confirmed that the measurement and transmission were within the error tolerance.

• Journal of Advanced Technology Convergence
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• v.3 no.2
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• pp.9-15
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• 2024

Due to the high inflation rate of dining out, along with changes in group meals or cafeteria services, office workers are increasingly using workplace cafeterias to reduce their meal expenses even slightly. With the recent development of ICT technology, various fields are realizing that not only are smartphones becoming more popular, but they are also becoming an integration of the latest technologies. In this paper, we analyze the current status of cafeterias with a large number of customers and propose ways to improve problems or difficulties. Since most people always carry their smartphones for urgent communication or work tasks, we aim to develop a cafeteria management system that utilizes the NFC function of smartphones. By presenting the process from customer entry to menu selection, it will enable more efficient use of the cafeteria.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.269-294
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• 2024

This study analyzed the news media coverage of QRcodes in Korea over a 16-year period (2008 to 2023). A total of 13,335 articles were extracted from the Korea Press Foundation's BigKinds. A quantitative and content analysis was conducted on the news frames. The results indicated that the quantity of news coverage has increased. The greatest quantity of news coverage was observed in 2020, and the most frequently discussed topic in the news was 'IT_Science'. The results of the keyword analysis indicated that the primary words were 'QRcode', 'smartphone', 'service', 'application', and 'payment'. The news media primarily focused on the QRcode's ability to provide instant access and recognition technology. This study demonstrates that advanced information and communication technologies and the increased prevalence of mobile devices have led to a rise in the utilization of QRcodes. Furthermore, QRcodes have become a significant information media in contemporary society.

• Journal of Platform Technology
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• v.11 no.5
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• pp.97-103
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• 2023

Unlike GPS, which is an outdoor positioning technology that is universally and uniformly used all over the world, various technologies are still being developed in the field of indoor positioning technology. In order to acquire accurate indoor location information, a standard of representative indoor positioning technology is required. Recently, indoor positioning technology is expanding into the Real Time Location Service (RTLS) area based on high-precision location data. Accordingly, a new type of indoor positioning technology is being proposed. Thanks to the development of artificial intelligence, artificial intelligence-based indoor positioning technology using wireless signal data of a smartphone is rapidly developing. At this time, in the process of collecting data necessary for artificial intelligence learning, data that is distorted or inappropriate for learning may be included, resulting in lower indoor positioning accuracy. In this study, we propose a data preprocessing technology for artificial intelligence learning to obtain improved indoor positioning results through the refinement process of the collected data.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.