• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.687-699
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• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.637-646
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• 2022

In this paper the comparative study on the pronunciation of Vietnamese learners and Koreans for the Korean syllable final double consonants was performed. For many errors and the suggested teaching methods related to the pronunciation of the Korean syllable final double consonants that were investigated and analyzed through linguistic research the results of this study by using the analysis tools of speech signal processing were confirmed. Thus, we suggest the new educational method in this paper. Using SVM, which is widely used in machine learning of artificial intelligence the pronunciation of Vietnamese learners and that of Koreans were compared. Being able to obtain the decision hyperplane of the SVM means that Vietnamese learners' pronunciation of the Korean syllable final double consonants is quite different from that of Koreans. Otherwise their pronunciation are pretty similar each other. The new teaching method presented in this paper is not only composed of writing and listening but is included things such as the speech signal waveform in the time domain and its corresponding energy that can be visualized to the learners.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.1-13
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• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.324-327
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• 2022

Crack inspection of aged structures is mostly conducted by inspectors using surveying tools on site and visually inspecting them. This method greatly depends on professional worker, and consumes a lot of time and money. An artificial intelligence image classification algorithm is used to make reliable and objective judgments. Since 2018, image augmentation techniques have been used in the image pre-processing stage as they lead to high performance improvement. In this study, an analysis algorithm for cracks in concrete structures was developed using image augmentation techniques, in which the accuracy and speed according to the augmentation ratio were compared and measured for optimization. As a result, it was found that 8 times of image augmentation was appropriate when the accuracy was improved and economic feasibility was taken into account.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.132-133
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• 2023

The safe evacuation of people during disasters is of utmost importance. Various life safety evacuation simulation tools have been developed and implemented, with most relying on algorithms that analyze maps to extract the shortest path and guide agents along predetermined routes. While effective in predicting evacuation routes in stable disaster conditions and short timeframes, this approach falls short in dynamic situations where disaster scenarios constantly change. Existing algorithms struggle to respond to such scenarios, prompting the need for a more adaptive evacuation route algorithm that can respond to changing disasters. Artificial intelligence technology based on reinforcement learning holds the potential to develop such an algorithm. As a fundamental step in algorithm development, this study aims to evaluate whether an evacuation algorithm developed by reinforcement learning satisfies the performance conditions of the evacuation simulation tool required by IMO MSC.1/Circ1533.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.635-642
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• 2023

With the development of artificial intelligence technology, drones are evolving beyond simple remote control tools into intelligent drones that perform missions autonomously. The importance of drones is gradually gaining attention due to the use of drones in overseas military conflicts and the analysis of the future operational environment in Korea. AMAD is proposed for the rapid development of intelligent drones. In order to develop intelligent software based on AMAD, an integrated development environment (IDE) that supports users with functions such as debugging, performance evaluation, and monitoring is essential. In this paper, we define the concepts of the development environment required for intelligent software development and describe the results of reflecting them in the design of the IDE and AMAD's agents, SVI and MPD, which are interfaced with the IDE.

• Women's Health Nursing
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• v.29 no.3
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• pp.160-178
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• 2023

Purpose: Since technology-based interventions can facilitate convenient access to healthcare for women with breast cancer, it is crucial to understand innovative approaches to maintaining the effectiveness of these interventions. Therefore, we conducted a systematic review of technology-based self-management interventions for women with breast cancer in six countries. We analyzed the characteristics of these interventions and examined their diverse health outcomes. Methods: Six databases were systematically searched to extract research articles using the keywords "breast cancer," "technology," and "self-management." The search was carried out up until June 12, 2023. From the 1,288 studies retrieved from the database search, 10 eligible papers were identified based on inclusion/exclusion criteria. Two authors independently extracted and compared the data from these articles, resolving any discrepancies through discussion. Results: Most of the 10 studies utilized web- or mobile-based technology, and one used artificial intelligence-based technology. Among the 12 health-related outcome variables, quality of life and symptom distress were the most frequently mentioned, appearing in six articles. Furthermore, an analysis of the intervention programs revealed a variety of common constructs and the involvement of managers in the self-management intervention. Conclusion: Incorporating key components such as self-management planning, diary keeping, and communication support in technology-based interventions could significantly improve the self-management process for breast cancer survivors. The practical application of technology has the potential to empower women diagnosed with breast cancer and improve their overall quality of life, by providing timely and sustainable interventions, and by leveraging available resources and tools.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.