• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Spatial Information Research
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• v.1 no.1
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• pp.73-87
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• 1993

This paper reviews the topic of GIS, the academic setting of GIS, GIS model curricula and the possibility GIS education in Korea. The topics which might be included in a science of geographic information consist of data collection and measurement, data capture, spatial statistics, data modeling and theories of spatial data, data structures, algorithms and processes, display, analytical tools, institutional, managerial and ethical issues. The problems in teaching a course on GIS in higher education are reviewed. Because of their technological, integrative, and rapidly changing nature, GIS pose major challenges to their education system which it is ill equipped to meet. In higher education a number of initiatives have been taken to provide education about and training with, GIS. The possible GIS curricula are suggested. These curricula are divided into 3 major sections, relating GIS context, technical issues and application issues. The prospects of GIS appears lo depend largely upon the future cooperation of academia, government, and industry

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.576-584
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• 2022

As an important branch of cognitive psychology, "constructivism" is called a "revolution" in contemporary educational psychology, which has a profound influence on the field of pedagogy and psychology. Based on "WOS" database, this study selects "WOS Core database" and "KCI database", uses CiteSpace visualization software as analysis tool, and makes knowledge map analysis on the research literature of "constructivism" in the field of education in recent 35 years. Analysis directions include annual analysis, network connection analysis by country(region) branch, author, institution or University, and keyword analysis. The purpose of the analysis is to grasp the subject areas, research hotspots and future trends of the research on constructivism, and to provide theoretical reference for the research on constructivism. There are three conclusions from the study. 1. Studies on the subject of constructivism have continued from the 1980s to the present. It is now in a period of steady development. 2. Countries concerned with the subject of constructivism mainly include the United States, Canada, Britain, Australia and the Netherlands. The main research institutions and authors are mainly located in these countries. 3. Currently, the keywords constructivism research focus on the clusters of "instructional strategies", and the development of science and technology is affecting individual learning. In the future, instructional strategies will become the focus of structural constructivism research. With the development of instructional technology, it is necessary to conduct research related to the development of new teaching models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).