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A Study on Performance Improvement of Recurrent Neural Networks Algorithm using Word Group Expansion Technique (단어그룹 확장 기법을 활용한 순환신경망 알고리즘 성능개선 연구)

  • Park, Dae Seung;Sung, Yeol Woo;Kim, Cheong Ghil
    • Journal of Industrial Convergence
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    • v.20 no.4
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    • pp.23-30
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    • 2022
  • Recently, with the development of artificial intelligence (AI) and deep learning, the importance of conversational artificial intelligence chatbots is being highlighted. In addition, chatbot research is being conducted in various fields. To build a chatbot, it is developed using an open source platform or a commercial platform for ease of development. These chatbot platforms mainly use RNN and application algorithms. The RNN algorithm has the advantages of fast learning speed, ease of monitoring and verification, and good inference performance. In this paper, a method for improving the inference performance of RNNs and applied algorithms was studied. The proposed method used the word group expansion learning technique of key words for each sentence when RNN and applied algorithm were applied. As a result of this study, the RNN, GRU, and LSTM three algorithms with a cyclic structure achieved a minimum of 0.37% and a maximum of 1.25% inference performance improvement. The research results obtained through this study can accelerate the adoption of artificial intelligence chatbots in related industries. In addition, it can contribute to utilizing various RNN application algorithms. In future research, it will be necessary to study the effect of various activation functions on the performance improvement of artificial neural network algorithms.

Deep Learning-Based Prediction of the Quality of Multiple Concurrent Beams in mmWave Band (밀리미터파 대역 딥러닝 기반 다중빔 전송링크 성능 예측기법)

  • Choi, Jun-Hyeok;Kim, Mun-Suk
    • 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.

A research on the Construction and Sharing of Authority Record-focusing on the Case of Social Networks and Archival Context Project (전거레코드 구축 및 공유에 관한 연구 SNAC 프로젝트 사례를 중심으로)

  • Lee, Eun Yeong
    • The Korean Journal of Archival Studies
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    • no.71
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    • pp.49-89
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    • 2022
  • This study suggests the necessity and domestic application plan a national authority database that promotes an integrated access, richer search, and understanding of historical information sources and archival resources distributed among cultural heritage institutions through the "Social Networks and Archive Context" project case. As the SNAC project was transformed into an international cooperative organization led by NARA, it was possible to secure a sustainable operating system and realize cooperative authority control. In addition, SNAC authority records have the characteristics of providing richer contextual information about life and history and social and intellectual network information compared to libraries. Through case analysis, First, like SNAC, a cooperative body led by the National Archives and having joint ownership of the National Library of Korea should lead the development and expand the scope of participating institutions. Second, in the cooperative method, take a structure in which divisions are made for each field with special strengths, but the main decision-making is made through the administrative team in which the two organizations participate. Third, development of scalable open source software that can collect technical information in various formats when constructing authority data, designing with the structure and elements of archival authority records, designing functions to control the quality of authority records, and building user-friendly interfaces and the need for a platform design reflecting content elements.

A Comparative Analysis of Domestic and Foreign Docker Container-Based Research Trends (국내·외 도커 컨테이너 기반 연구 동향 비교 분석)

  • Bae, Sun-Young
    • The Journal of the Korea Contents Association
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    • v.22 no.10
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    • pp.742-753
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    • 2022
  • Cloud computing, which is rapidly growing as one of the core technologies of the 4th industrial revolution, has become the center of global IT trend change, and Docker, a container-based open source platform, is the mainstream for virtualization technology for cloud computing. Therefore, in this paper, research trends based on Docker containers were compared and analyzed, focusing on studies published from March 2013 to July 2022. As a result of the study, first, the number of papers published by year, domestic and foreign research were steadily increasing. Second, the keywords of the study, in domestic research, Docker, Docker Containers, and Containers were in the order, and in foreign research, Cloud Computing, Containers, and Edge Computing were in the order. Third, in the frequency of publishing institutions to estimate research trends, the utilization was the highest in two papers of the Korean Next Generation Computer Society and the Korean Computer Accounting Society. In the overseas research, IEEE Communications Surveys & Tutorials, IEEE Access, and Computer were in the order. Fourth, in the research method, experiments 78(26.3%) and surveys 32(10.8%) were conducted in domestic research. In foreign research, experiments 128(43.1%) and surveys 59(19.9%) were conducted. In the experiment of implementation research, In domestic research, System 25(8.4%), Algorithm 24(8.1%), Performance Measurement and Improvement 16(5.4%) were in the order, In foreign research, Algorithm 37(12.5%), Performance Measurement and Improvement 17(9.1%), followed by Framework 26(8.8%). Through this, it is expected that it will be used as basic data that can lead the research direction of Docker container-based cloud computing such as research methods, research topics, research fields, and technology development.

Deep Learning Based Rescue Requesters Detection Algorithm for Physical Security in Disaster Sites (재난 현장 물리적 보안을 위한 딥러닝 기반 요구조자 탐지 알고리즘)

  • Kim, Da-hyeon;Park, Man-bok;Ahn, Jun-ho
    • Journal of Internet Computing and Services
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    • v.23 no.4
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    • pp.57-64
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    • 2022
  • If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

EC-RPL to Enhance Node Connectivity in Low-Power and Lossy Networks (저전력 손실 네트워크에서 노드 연결성 향상을 위한 EC-RPL)

  • Jeadam, Jung;Seokwon, Hong;Youngsoo, Kim;Seong-eun, Yoo
    • Journal of Korea Society of Industrial Information Systems
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    • v.27 no.6
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    • pp.41-49
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    • 2022
  • The Internet Engineering Task Force (IETF) has standardized RPL (IPv6 Routing Protocol for Low-power Lossy Network) as a routing protocol for Low Power and Lossy Networks (LLNs), a low power loss network environment. RPL creates a route through an Objective Function (OF) suitable for the service required by LLNs and builds a Destination Oriented Directed Acyclic Graph (DODAG). Existing studies check the residual energy of each node and select a parent with the highest residual energy to build a DODAG, but the energy exhaustion of the parent can not avoid the network disconnection of the children nodes. Therefore, this paper proposes EC-RPL (Enhanced Connectivity-RPL), in which ta node leaves DODAG in advance when the remaining energy of the node falls below the specified energy threshold. The proposed protocol is implemented in Contiki, an open-source IoT operating system, and its performance is evaluated in Cooja simulator, and the number of control messages is compared using Foren6. Experimental results show that EC-RPL has 6.9% lower latency and 5.8% fewer control messages than the existing RPL, and the packet delivery rate is 1.7% higher.

Characterization of few-layered reduced graphene oxide (rGO) for standardization (소수의 층을 갖는 환원 graphene oxide(rGO) 표준화를 위한 물성분석)

  • Ahn, Hae Jun;Huh, Seung Hun;Jee, Youngho;Lee, Byeong Woo
    • Journal of the Korean Crystal Growth and Crystal Technology
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    • v.32 no.6
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    • pp.239-245
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    • 2022
  • Reduced graphene oxide (rGO) has attracted many attention and applications due to its excellent electrochemical ability. Therefore, standardization of rGO through structural and thermal analysis facilitates quality improvement and management, enabling users to increase efficiency and reduce relevant costs. For rGO and graphene-related materials, it is very important to determine the number of layers and define the resulting difference in physical properties. In this study, 3~4 layers of rGO-1 and 9~10 layers of rGO-2 were obtained from graphene oxide (GO) through a hydrazine reduction process. For the prepared rGOs, X-ray diffraction (XRD) pattern obtained a diffraction peak at 2θ≈25° related to (002) reflection was used to calculate the layer numbers by determining interlayer distance and FWHM value. To reduce the angular uncertainty, XRD data analysis was performed with angle correction using standard reference materials for X-ray powder diffraction analysis. Precise interlayer distance and number of layers were determined using OriginLab and open-source XRD diffraction analysis programs using the angle-corrected diffraction data. TG-DSC thermal analysis was performed to further standardize the physical properties of rGO samples.

Sequential Use of COMSOL Multiphysics® and PyLith for Poroelastic Modeling of Fluid Injection and Induced Earthquakes (COMSOL Multiphysics®와 PyLith의 순차 적용을 통한 지중 유체 주입과 유발지진 공탄성 수치 모사 기법 연구)

  • Jang, Chan-Hee;Kim, Hyun Na;So, Byung-Dal
    • The Journal of Engineering Geology
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    • v.32 no.4
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    • pp.643-659
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    • 2022
  • Geologic sequestration technologies such as CCS (carbon capture and storage), EGS (enhanced geothermal systems), and EOR (enhanced oil recovery) have been widely implemented in recent years, prompting evaluation of the mechanical stability of storage sites. As fluid injection can stimulate mechanical instability in storage layers by perturbing the stress state and pore pressure, poroelastic models considering various injection scenarios are required. In this study, we calculate the pore pressure, stress distribution, and vertical displacement along a surface using commercial finite element software (COMSOL); fault slips are subsequently simulated using PyLith, an open-source finite element software. The displacement fields, are obtained from PyLith is transferred back to COMSOL to determine changes in coseismic stresses and surface displacements. Our sequential use of COMSOL-PyLith-COMSOL for poroelastic modeling of fluid-injection and induced-earthquakes reveals large variations of pore pressure, vertical displacement, and Coulomb failure stress change during injection periods. On the other hand, the residual stress diffuses into the remote field after injection stops. This flow pattern suggests the necessity of numerical modeling and long-term monitoring, even after injection has stopped. We found that the time at which the Coulomb failure stress reaches the critical point greatly varies with the hydraulic and poroelastic properties (e.g., permeability and Biot-Willis coefficient) of the fault and injection layer. We suggest that an understanding of the detailed physical properties of the surrounding layer is important in selecting the injection site. Our numerical results showing the surface displacement and deviatoric stress distribution with different amounts of fault slip highlight the need to test more variable fault slip scenarios.

Usual intake of dietary isoflavone and its major food sources in Koreans: Korea National Health and Nutrition Examination Survey 2016-2018 data

  • Kim, Yoona;Kim, Dong Woo;Kim, Kijoon;Choe, Jeong-Sook;Lee, Hae-Jeung
    • Nutrition Research and Practice
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    • v.16 no.sup1
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    • pp.134-146
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    • 2022
  • BACKGROUND/OBJECTIVES: Accumulating evidence has shown the beneficial effects of isoflavone on health. There is limited information on the usual isoflavone intake for Koreans. This study examined the usual intake of total isoflavone and its major food sources in Koreans according to age and gender. SUBJECTS/METHODS: The dietary intake data of 21,271 participants aged 1 yrs and older from the Korea National Health and Nutrition Examination Survey (KNHANES) VII 2016-2018 were analyzed. The average isoflavone intake was estimated based on the 24-h dietary recall data in KNHANES and the isoflavone database from the Korea Rural Development Administration (RDA) and literatures. The usual isoflavone intake was estimated by applying the ratio of within- and between-participant variance estimated from the 2009 KNHANES data to the 7th KNHANES (2016-2018) data. The variance of the isoflavone intake was calculated using MIXTRAN macro with intake data for two days in the 2009 KNHANES. Complex sample analysis with stratified variables and integrated weights was conducted. RESULTS: The mean total isoflavone intake in the Korean population aged 1 yrs and older (n = 21,271) was 139.27 mg/d, which was higher than the usual intake of 47.44mg/d. Legumes were a major contributing food group (91%), with arrowroot being a major individual contributor to the isoflavone intake (67.2%), followed by 21.3% of soybean, 5.4% of bean sprouts, and 2.1% of tofu. The usual isoflavone intake was highest in the participants aged 50 to 64 yrs old and increased with age until 50 to 64 yrs and then decreased with further increases in age. The usual isoflavone intake of participants aged 65 yrs and older was higher for men than for women, showing gender differences. CONCLUSIONS: The usual dietary intake of isoflavone varied according to age and gender in the Korean population. This study showed that the usual isoflavone intake was lower than the average isoflavone intake. The difference between percentiles of the usual isoflavone intake was similarly smaller than the average intake. An estimation of average intake can be hindered by the occasional consumption of foods high in isoflavones, suggesting that the usual intake estimation method can be more appropriate. Further research will be needed to establish isoflavone dietary guidelines regarding the effects of isoflavone intake on health outcomes.

A Study on the 3D Precise Modeling of Old Structures Using Merged Point Cloud from Drone Images and LiDAR Scanning Data (드론 화상 및 LiDAR 스캐닝의 정합처리 자료를 활용한 노후 구조물 3차원 정밀 모델링에 관한 연구)

  • Chan-hwi, Shin;Gyeong-jo, Min;Gyeong-Gyu, Kim;PuReun, Jeon;Hoon, Park;Sang-Ho, Cho
    • Explosives and Blasting
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    • v.40 no.4
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    • pp.15-26
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    • 2022
  • With the recent increase in old and dangerous buildings, the demand for technology in the field of structure demolition is rapidly increasing. In particular, in the case of structures with severe deformation of damage, there is a risk of deterioration in stability and disaster due to changes in the load distribution characteristics in the structure, so rapid structure demolition technology that can be efficiently dismantled in a short period of time is drawing attention. However, structural deformation such as unauthorized extension or illegal remodeling occurs frequently in many old structures, which is not reflected in structural information such as building drawings, and acts as an obstacle in the demolition design process. In this study, as an effective way to overcome the discrepancy between the structural information of old structures and the actual structure, access to actual structures through 3D modeling was considered. 3D point cloud data inside and outside the building were obtained through LiDAR and drone photography for buildings scheduled to be blasting demolition, and precision matching between the two spatial data groups was performed using an open-source based spatial information construction system. The 3D structure model was completed by importing point cloud data matched with 3D modeling software to create structural drawings for each layer and forming each member along the structure slab, pillar, beam, and ceiling boundary. In addition, the modeling technique proposed in this study was verified by comparing it with the actual measurement value for selected structure member.