• 한국멀티미디어학회논문지
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• 제25권3호
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2991-3007
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• 2022

Two dimensional locality preserving projections (2D-LPP) is an improved algorithm of 2D image to solve the small sample size (SSS) problems which locality preserving projections (LPP) meets. It's able to find the low dimension manifold mapping that not only preserves local information but also detects manifold embedded in original data spaces. However, 2D-LPP is simple and elegant. So, inspired by the comparison experiments between two dimensional linear discriminant analysis (2D-LDA) and linear discriminant analysis (LDA) which indicated that matrix based methods don't always perform better even when training samples are limited, we surmise 2D-LPP may meet the same limitation as 2D-LDA and propose a novel matrix exponential method to enhance the performance of 2D-LPP. 2D-MELPP is equivalent to employing distance diffusion mapping to transform original images into a new space, and margins between labels are broadened, which is beneficial for solving classification problems. Nonetheless, the computational time complexity of 2D-MELPP is extremely high. In this paper, we replace some of matrix multiplications with multiple multiplications to save the memory cost and provide an efficient way for solving 2D-MELPP. We test it on public databases: random 3D data set, ORL, AR face database and Polyu Palmprint database and compare it with other 2D methods like 2D-LDA, 2D-LPP and 1D methods like LPP and exponential locality preserving projections (ELPP), finding it outperforms than others in recognition accuracy. We also compare different dimensions of projection vector and record the cost time on the ORL, AR face database and Polyu Palmprint database. The experiment results above proves that our advanced algorithm has a better performance on 3 independent public databases.

• Smart Structures and Systems
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• 제30권6호
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• 한국군사과학기술학회지
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• 제25권6호
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• 정보보호학회논문지
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• 제33권4호
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• pp.647-659
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• 2023

범죄수사에서 디지털증거가 중요해지면서 법정에서 다툼이 많아지고 있다. 매체가 다양화되고 분석범위가 확장되면서 디지털포렌식에 대한 전문성 수준도 높아지고 있다. 그러나 아직까지 디지털증거분석관의 역량을 정의하거나 전문성을 판단하는 역량모델 개발은 이루어지지 않고 있다. 디지털증거분석관에게 필요한 역량을 도출한 일부 연구가 있었으나 여전히 미흡한 수준이다. 따라서 본 연구에서는 전문가 FGI, 델파이 조사 등의 방법론을 활용해 총 9개의 역량군 25개의 역량평가 요소를 정의하였다. 구체적으로 디지털포렌식 이론, 디지털증거 수집 및 관리, 디스크포렌식, 모바일포렌식, 영상포렌식, 침해사고포렌식, DB포렌식, 임베디드(IoT)포렌식, 클라우드포렌식으로 규정하였다. 디지털증거분석관 역량모델은 향후 선발, 교육훈련, 성과평가 등 다양한 분야에 활용할 수 있을 것으로 기대한다.

• 한국엔터테인먼트산업학회논문지
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• 제13권1호
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• pp.139-156
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• 2019

동계스포츠는 레저인구의 확대와 함께 국내 및 국제수준의 대회가 자주 개최됨에 따라 수요자 중심의 스포츠 정보가 더욱 필요해지고 있다. 특히 기상 및 환경정보는 동계스포츠대회를 조직체는 물론이고 직접 진행을 하는 경기운영요원 및 지도자와 선수 모두에게 필수적인 요소가 되고 있다. 이 연구는 동계 스포츠 설상 종목을 4개의 종목군으로 그룹화하여 각 그룹별 기상 및 환경정보에 의해 경기운영 및 경기력을 위한 제고하는 중요성 요인이 무엇인가를 구명(究明)하는데 목적이 있었다. 이 연구는 질적연구방법에 의해 이루어졌으며, 11명의 동계스포츠 관련자들이 유목적적 표집법(purposeful sampling)에 의해 정보제공자로서 선정하였다. 심층면담(in-depth interview)을 통해 얻은 자료는 내용분석(content analysis)과 함께 유형화(categorizing)과정으로 분석되었다. 이러한 과정을 통해 얻은 결론은 동계 스포츠경기를 위해 종목별 특화된 기상 및 환경정보 요소들에 의해 경기운영과 경기력에 중요하게 고려해야 하는 요인들이 구명하였다. 이는 동계스포츠 특성에 맞는 세분화된 정보를 제공하여 정보수요자의 활용과 정보재생산의 의미를 갖는다.

• Journal of Hospice and Palliative Care
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• 제25권2호
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• pp.85-97
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• 2022

Purpose: This study aimed to describe nurses' perceived needs and barriers to pediatric palliative care (PPC). Methods: Mixed methods with an embedded design were applied. An online survey was conducted for nurses who participated in the End-of-Life Nursing Education Consortium- Pediatric Palliative Care (ELNEC-PPC) train-the-trainer program, of whom 63 responded. Quantitative data were collected with a survey questionnaire developed through the Delphi method. The 47 items for needs and 15 items for barriers to PPC were analyzed with descriptive statistics. Qualitative data were collected through open-ended questions and analyzed with topic modeling techniques. Results: The mean scores of most subdomains of the PPC needs were 3.5 or higher out of 4, and those of PPC barriers ranged from 3.22 to 3.56, indicating the items in the questionnaire developed in this study properly reflect each factor. The needs for PPC were divided into 4 categories: "children and adolescents," "families," "PPC management system," and "community-based PPC." Meanwhile, PPC barriers were divided into 3 categories: "healthcare delivery system," "healthcare provider," and "client." The keywords derived from the topic modeling were perception, palliative, children, and education for necessities and lack, perception, medical care, professional care providers, service, and system for barriers to PPC. Conclusion: In this study, by using mixed-methods, items of nurses' perceived needs and barriers to PPC were identified, categorized, and weighted, and their meanings were explored. For the stable establishment of PPC, the priority should be given to improving perceptions of PPC, establishing an appropriate system, and training professional care providers.

• 산업경영시스템학회지
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• 제46권3호
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.