• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.56-67
• /
• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.813-814
• /
• 2023

매년 졸음 및 부주의 운전으로 인한 교통사고로 인명 및 재산 피해가 끊이지 않고 있다. 즉각적인 졸음 감지만을 위한 기존 시스템의 단점과 한계를 보완하고자, 본 논문에서는 위험 행동을 감지한 후 당시 사진과 데이터를 저장하고 이를 점수로 환산하여 장기적인 운전 습관 개선을 목표로 하는 운전자 관리 감독 시스템을 구현하였다. 이 시스템은 화물차 운전자와 같이 장시간 운전을 하는 대상에게 안전 주행을 장려하고 올바른 운전문화를 확립하게 하여 교통안전에 긍정적 역할을 담당할 수 있다.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.6_2
• /
• pp.729-735
• /
• 2021

Recently Artificial Intelligence(AI) has been developed and used in various fields. Especially AI recognition technology can perceive and distinguish images so it should plays a significant role in quality inspection process. For stability of autonomous driving technology, semiconductors inside automobiles must be protected from external electromagnetic wave(EM wave). As a shield film, a thin polymeric material with hole shaped micro-patterns created by a laser processing could be used for the protection. The shielding efficiency of the film can be increased by the hole structure with appropriate pitch and size. However, since the sensitivity of micro-machining for some parameters, the shape of every single hole can not be same, even it is possible to make defective patterns during process. And it is absolutely time consuming way to inspect all patterns by just using optical microscope. In this paper, we introduce a AI inspection system which is based on web site AI tool. And we evaluate the usefulness of AI model by calculate Area Under ROC curve(Receiver Operating Characteristics). The AI system can classify the micro-patterns into normal or abnormal ones displaying the text of the result on real-time images and save them as image files respectively. Furthermore, pressing the running button, the Hardware of robot arm with two Arduino motors move the film on the optical microscopy stage in order for raster scanning. So this AI system can inspect the entire micro-patterns of a film automatically. If our system could collect much more identified data, it is believed that this system should be a more precise and accurate process for the efficiency of the AI inspection. Also this one could be applied to image-based inspection process of other products.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.2
• /
• pp.385-391
• /
• 2022

Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.51-58
• /
• 2020

The blind people have difficulty living an independent clothing life. The furniture and home appliance are adding AI or IoT with the recent growth of the smart appliance market. To support the independent clothing life of the blind, this paper suggests a smart wardrobe with closet control function, voice recognition function and clothes information recognition using CNN algorithm. The number of layers of the model was changed and Maxpooling was adjusted to create the model to increase accuracy in the process of recognizing clothes. Early Stopping Callback option is applied to ensure learning accuracy when creating a model. We added Dropout to prevent overfitting. The final model created by this process can be found to have 80 percent accuracy in clothing recognition.

• Smart Media Journal
• /
• v.9 no.3
• /
• pp.41-45
• /
• 2020

Recently, artificial intelligence (AI) and the Internet of things (IoT), which are represented by machine learning and deep learning among IT technologies related to the Fourth Industrial Revolution, are applied to our real life in various fields through various researches. In this paper, IoT and AI using object recognition technology are applied to classify clothing. For this purpose, the image dataset was taken using webcam and raspberry pi, and GoogLeNet, a convolutional neural network artificial intelligence network, was applied to transfer the photographed image data. The clothing image dataset was classified into two categories (shirtwaist, trousers): 900 clean images, 900 loss images, and total 1800 images. The classification measurement results showed that the accuracy of the clean clothing image was about 97.78%. In conclusion, the study confirmed the applicability of other objects using artificial intelligence networks on the Internet of Things based platform through the measurement results and the supplementation of more image data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.9
• /
• pp.1176-1182
• /
• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.2
• /
• pp.207-213
• /
• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of the Korean Society for information Management
• /
• v.39 no.3
• /
• pp.165-193
• /
• 2022

It can be said that digital technology is at the center of the change in the modern work environment. In particular, in general public institutions that prove their work with records produced by business management systems and document production systems, the record management system is also the work environment itself. Gimpo City applied for the 2021 public cloud leading project of the National Information Society Agency (NIA) to proactively respond to the 4th industrial revolution technology era and implemented a public cloud-based AI-OCR technology enhancement project with 330 million won in support of 330 million won. Through this, it was converted into data beyond the limitations of non-electronic records limited to search and image viewing that depend on standardized index values. In addition, a 98% recognition rate was realized by applying a new technology called AI-OCR. Since digital technology has been used to improve work efficiency, productivity, development cost, and record management service levels of internal and external users, we would like to share the direction of enhancing expertise in the record management and implementation of work environment innovation.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.6
• /
• pp.799-805
• /
• 2024

Recently, generative artificial intelligence (AI) technology has been rapidly developing and its application fields are expanding beyond text, voice, image, object recognition, time-series forecasting, and natural language processing to the creative design field that AI was thought to be incapable of. We aim for an exploratory approach to study the cognitive model of pattern designers using generative AI. To this end, we used GPT 4o, which is the most well-known generative AI, and applied the protocol analysis method, a cognitive science research method, to the pattern design process. Four design graduate students were selected as subjects and pilot and main experiment were conducted. Voice recording and video capture were performed to collect data. The protocol method applied the concurrent protocol method, which simultaneously expresses what comes to mind while performing the task. The collected verbal data was used to classify the design process by segmenting words and developing a coding scheme to establish a framework for analysis. As a result, analysis, selection, visualization, evaluation, and optimization were discovered. We expect to present design guidelines for pattern design practice.