• Title/Summary/Keyword: Software Convergence

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The Impact of Professional Self-Concept on the Organizational Socialization of Hospital Nurses (전문직 자아개념이 병원간호사의 조직사회화에 미치는 영향)

  • Su-Hee Oh;Youngshin Song
    • Journal of Industrial Convergence
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    • v.22 no.7
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    • pp.93-102
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    • 2024
  • The purpose of this study wa to analyze the impact of nurses' perceptions of care and professional self-concept on their organizational socialization. The participants were 193 nurses working in university hospitals and general hospitals in regions C and D. Data collection took place from July to August 2016, and the data were analyzed using descriptive statistics and multiple regression analysis with IBM SPSS 22 software. The results indicated that educational level and professional self-concept are determining factors influencing organizational socialization. Nurses with an associate degree showed higher levels of organizational socialization compared to those with a bachelor's degree or higher, and higher levels of professional self-concept were associated with higher organizational socialization. Therefore, it is necessary for hospitals to implement effective human resource management that enables nurses of each educational level to quickly socialize within the organization, providing differentiated support and programs based on educational level. Additionally, continuous research is required to enhance nurses' professional self-concept and establish their work identity.

Performance Improvement of Topic Modeling using BART based Document Summarization (BART 기반 문서 요약을 통한 토픽 모델링 성능 향상)

  • Eun Su Kim;Hyun Yoo;Kyungyong Chung
    • Journal of Internet Computing and Services
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    • v.25 no.3
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    • pp.27-33
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    • 2024
  • The environment of academic research is continuously changing due to the increase of information, which raises the need for an effective way to analyze and organize large amounts of documents. In this paper, we propose Performance Improvement of Topic Modeling using BART(Bidirectional and Auto-Regressive Transformers) based Document Summarization. The proposed method uses BART-based document summary model to extract the core content and improve topic modeling performance using LDA(Latent Dirichlet Allocation) algorithm. We suggest an approach to improve the performance and efficiency of LDA topic modeling through document summarization and validate it through experiments. The experimental results show that the BART-based model for summarizing article data captures the important information of the original articles with F1-Scores of 0.5819, 0.4384, and 0.5038 in Rouge-1, Rouge-2, and Rouge-L performance evaluations, respectively. In addition, topic modeling using summarized documents performs about 8.08% better than topic modeling using full text in the performance comparison using the Perplexity metric. This contributes to the reduction of data throughput and improvement of efficiency in the topic modeling process.

Building Fire Monitoring and Escape Navigation System Based on AR and IoT Technologies (AR과 IoT 기술을 기반으로 한 건물 화재 모니터링 및 탈출 내비게이션 시스템)

  • Wentao Wang;Seung-Yong Lee;Sanghun Park;Seung-Hyun Yoon
    • Journal of the Korea Computer Graphics Society
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    • v.30 no.3
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    • pp.159-169
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    • 2024
  • This paper proposes a new real-time fire monitoring and evacuation navigation system by integrating Augmented Reality (AR) technology with Internet of Things (IoT) technology. The proposed system collects temperature data through IoT temperature measurement devices installed in buildings and automatically transmits it to a MySQL cloud database via an IoT platform, enabling real-time and accurate data monitoring. Subsequently, the real-time IoT data is visualized on a 3D building model generated through Building Information Modeling (BIM), and the model is represented in the real world using AR technology, allowing intuitive identification of the fire origin. Furthermore, by utilizing Vuforia engine's Device Tracking and Area Targets features, the system tracks the user's real-time location and employs an enhanced A* algorithm to find the optimal evacuation route among multiple exits. The paper evaluates the proposed system's practicality and demonstrates its effectiveness in rapid and safe evacuation through user experiments based on various virtual fire scenarios.

Analysis of Educational System and Workforce Development Needs for Urban Air Mobility in Daegu-Gyeongbuk (대구경북지역 도심항공교통의 교육 체계 및 인력 양성 수요에 대한 분석)

  • Wooram Lee
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.4
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    • pp.701-710
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    • 2024
  • This study conducted a survey of companies in the aviation, drone, and Urban Air Mobility (UAM) sectors to analyze the educational and workforce needs, identifying essential competencies and technical training required. The study also proposed potential areas for collaboration between universities and industry regarding educational methods. Key findings and implications of the survey were derived. The results indicated that the most critical consideration for hiring was job-specific skills in the respective field. The most essential quality for workforce training was identified as enhancing the ability to use various equipment and software related to the major field. In the UAM sector, there was a high demand for personnel and education related to aircraft and components, with the highest demand being for lightweight manufacturing technology for aircraft. This study can serve as foundational data for addressing the educational needs in this field.

Enhanced Machine Learning Preprocessing Techniques for Optimization of Semiconductor Process Data in Smart Factories (스마트 팩토리 반도체 공정 데이터 최적화를 위한 향상된 머신러닝 전처리 방법 연구)

  • Seung-Gyu Choi;Seung-Jae Lee;Choon-Sung Nam
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.4
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    • pp.57-64
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    • 2024
  • The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

Latent topics-based product reputation mining (잠재 토픽 기반의 제품 평판 마이닝)

  • Park, Sang-Min;On, Byung-Won
    • Journal of Intelligence and Information Systems
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    • v.23 no.2
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    • pp.39-70
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    • 2017
  • Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

Cross-cultural Adaptation and Psychometric Evaluation of the Korean Version of the A-ONE (한국판 일상생활활동중심 작업기반 신경행동평가(A-ONE)의 개발 및 평가)

  • Kang, Jaewon;Park, Hae Yean;Kim, Jung-Ran;Park, Ji-Hyuk
    • Therapeutic Science for Rehabilitation
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    • v.10 no.2
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    • pp.109-128
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    • 2021
  • Objective : The purpose of this study was to develop a Korean version of the Activities of Daily Living (ADL)-focused Occupation-Based Neurobehavioral Evaluation (A-ONE) through cross-cultural adaptation and examine its validity and reliability. Methods : This study translated the A-ONE into Korean and performed cross-cultural adaptation for the Korean population. After the development of the Korean version of the A-ONE, cross-cultural and concurrent validities were analyzed. Internal consistency, test-retest reliability, and inter-rater reliability were also evaluated. Results : We adapted three items to the Korean culture. The Korean version of the A-ONE showed high cross-cultural validity with a content validity index (I-CVI) >0.9. It correlated with the Functional Independence Measure (FIM) (r=0.52-0.77, p<0.001), except for communication. Cronbach's α was 0.58-0.93 for the functional independence scale (FI) and 0.42-0.93 for the neurobehavioral specific impairment subscale (NBSIS). Intraclass correlation coefficients (ICCs) indicated high test-retest and inter-rater reliability for FI (ICC=0.79-1.00 and 0.75-1.00, respectively) and NBSIS (ICC=0.74-1.00 and 0.72-1.00, respectively). Conclusion : The Korean version of the A-ONE is well adapted to the Korean culture and has good validity and reliability. It is recommended to evaluate ADL performance skills and neurobehavioral impairments simultaneously in Korea.

Evaluating of the Effectiveness of RTK Surveying Performance Based on Low-cost Multi-Channel GNSS Positioning Modules (다채널 저가 GNSS 측위 모듈기반 RTK 측량의 효용성 평가)

  • Kim, Chi-Hun;Oh, Seong-Jong;Lee, Yong-Chang
    • Journal of Cadastre & Land InformatiX
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    • v.52 no.2
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    • pp.53-65
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    • 2022
  • According to the advancement of the GNSS satellite positioning system, the module of hardware and operation software reflecting accuracy and economical efficiency is implemented in the user sector including the multi-channel GNSS receiver, the multi-frequency external antenna and the mobile app (App) base public positioning analysis software etc., and the multichannel GNSS RTK positioning of the active configuration method (DIY, Do it yourself) is possible according to the purpose of user. Especially, as the infrastructure of multi-GNSS satellite is expanded and the potential of expansion of utilization according to various modules is highlighted, interest in the utilization of multi-channel low-cost GNSS receiver module is gradually increasing. The purpose of this study is to review the multi-channel low-cost GNSS receivers that are appearing in the mass market in various forms and to analyze the utilization plan of the "address information facility investigation project" of the Ministry of Public Administration and Security by constructing the multi-channel low-cost GNSS positioning module based RTK survey system (hereinafter referred to as "multi-channel GNSS RTK module positioning system"). For this purpose, we constructed a low-cost "multi-channel GNSS RTK module positioning system" by combining related modules such as U-blox's F9P chipset, antenna, Ntrip transmission of GNSS observation data and RTK positioning analysis app through smartphone. Kinematic positioning was performed for circular trajectories, and static positioning was performed for address information facilities. The results of comparative analysis with the Static positioning performance of the geodetic receivers were obtained with 5 fixed points in the experimental site, and the good static surveying performance was obtained with the standard deviation of average ±1.2cm. In addition, the results of the test point for the outline of the circular structure in the orthogonal image composed of the drone image analysis and the Kinematic positioning trajectory of the low cost RTK GNSS receiver showed that the trajectory was very close to the standard deviation of average ±2.5cm. Especially, as a result of applying it to address information facilities, it was possible to verify the utility of spatial information construction at low cost compared to expensive commercial geodetic receivers, so it is expected that various utilization of "multi-channel GNSS RTK module positioning system"

A Study on the Development of High Sensitivity Collision Simulation with Digital Twin (디지털 트윈을 적용한 고감도 충돌 시뮬레이션 개발을 위한 연구)

  • Ki, Jae-Sug;Hwang, Kyo-Chan;Choi, Ju-Ho
    • Journal of the Society of Disaster Information
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    • v.16 no.4
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    • pp.813-823
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    • 2020
  • Purpose: In order to maximize the stability and productivity of the work through simulation prior to high-risk facilities and high-cost work such as dismantling the facilities inside the reactor, we intend to use digital twin technology that can be closely controlled by simulating the specifications of the actual control equipment. Motion control errors, which can be caused by the time gap between precision control equipment and simulation in applying digital twin technology, can cause hazards such as collisions between hazardous facilities and control equipment. In order to eliminate and control these situations, prior research is needed. Method: Unity 3D is currently the most popular engine used to develop simulations. However, there are control errors that can be caused by time correction within Unity 3D engines. The error is expected in many environments and may vary depending on the development environment, such as system specifications. To demonstrate this, we develop crash simulations using Unity 3D engines, which conduct collision experiments under various conditions, organize and analyze the resulting results, and derive tolerances for precision control equipment based on them. Result: In experiments with collision experiment simulation, the time correction in 1/1000 seconds of an engine internal function call results in a unit-hour distance error in the movement control of the collision objects and the distance error is proportional to the velocity of the collision. Conclusion: Remote decomposition simulators using digital twin technology are considered to require limitations of the speed of movement according to the required precision of the precision control devices in the hardware and software environment and manual control. In addition, the size of modeling data such as system development environment, hardware specifications and simulations imitated control equipment and facilities must also be taken into account, available and acceptable errors of operational control equipment and the speed required of work.

Neutral Data Generation Algorithm for EPC-based Manufacturing Process Collaboration (EPC 기반의 제조 공정 협업을 위한 중립 데이터 생성 알고리즘)

  • Kim, Dong-Gi;Kim, Seung-Hee
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.1
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    • pp.1-9
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    • 2020
  • In highly complex EPC(engineering, procurement, construction)-based manufacturing processes such as shipbuilding and marine plants, it is essential to prepare a way to enable organic working at the site referring to each other's detailed working data for collaboration between partner companies. However, companies cannot share the progress of the sites including working information in real-time due to the use of SW unique to each company and the complex site management system. As a result, the construction process is delayed, and resources are used inefficiently. This study developed a neutral data generation algorithm that can apply the working information in various file formats to a collaborative manufacturing process. In addition, this study verified the accuracy of the algorithm by applying the developed algorithm to the manufacturing process of piping in shipbuilding and marine plants, developing the SW for visualization of working information using the generated neutral data, and comparing the coordinate, shape & dimension and the kind, number, and spec. of BOM. The result confirmed that the accuracy of the coordinate of the neutral data was 99%, and that of the shape & dimension of the neutral data and BOM Spec. was both 100%. It is thought that this study can be used for overcoming the restrictions in information sharing due to the development of informatization at companies and maximizing the share of working file information in a complex manufacturing process.