• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.83-88
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• 2022

Internet of Things (hereinafter referred to as IoT) related technologies are continuously developing in line with the recent development of information and communication technologies. IoT system sends and receives unique data through network based on various sensors. Data generated by IoT systems can be defined as big data in that they occur in real time, and that the amount is proportional to the amount of sensors installed. Until now, IoT systems have applied data storage, processing and computation through centralized processing methods. However, existing centralized processing servers can be under load due to bottlenecks if the deployment grows in size and a large amount of sensors are used. Therefore, in this paper, we propose a distributed processing system for applying a data importance-based algorithm aimed at the high availability of the system to efficiently handle real-time sensor data arising in IoT environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.43-48
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• 2022

Although the distribution system has been structured as complicated as a mesh in the past, the connection points for each line are always kept open, so that it is operated as a radial distribution system (RDS). For RDS, the line utilization rate is determined according to the maximum load on the line, and the utilization rate is usually kept low. In addition, when a fault occurs in the RDS, a power outage of about 3 to 5 minutes occurs until the fault section is separated, and the healthy section is transferred to another line. To improve the disadvantages of the RDS, research on the construction of a networked distribution system (NDS) that linking multiple lines is in progress. Compared to the RDS, the NDS has advantages such as increased facility utilization, load leveling, self-healing, increased capacity connected to distributed generator, and resolution of terminal voltage drop. However, when a fault occurs in the network distribution system, fault current can flow in from all connected lines, and the direction of fault current varies depending on the fault point, so a high-precision fault current direction determination method and high-speed communication are required. Therefore, in this paper, we propose an accurate fault current direction determination method by comparing the peak value polarity of the fault current in the event of a fault, and a communication-based protection coordination method using this method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.201-211
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• 2022

According to the recent change in the cybersecurity paradigm, research on anomaly detection methods using machine learning and deep learning techniques, which are AI implementation technologies, is increasing. In this study, a comparative study on data preprocessing techniques that can improve the anomaly detection performance of a GRU (Gated Recurrent Unit) neural network-based intrusion detection model using NGIDS-DS (Next Generation IDS Dataset), an open dataset, was conducted. In addition, in order to solve the class imbalance problem according to the ratio of normal data and attack data, the detection performance according to the oversampling ratio was compared and analyzed using the oversampling technique applied with DCGAN (Deep Convolutional Generative Adversarial Networks). As a result of the experiment, the method preprocessed using the Doc2Vec algorithm for system call feature and process execution path feature showed good performance, and in the case of oversampling performance, when DCGAN was used, improved detection performance was shown.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• Journal of the Korean institute of surface engineering
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• v.56 no.1
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• pp.104-114
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• 2023

Three dimensional (3D) porous structures consisting of Cu@CoO core-shell-type nano-dendrites were synthesized and tested as the anode materials in lithium secondary batteries. For this purpose, first, the 3D porous films comprising Cu@Co core-shell-type nano-dendrites with various thicknesses were fabricated through the electrochemical co-deposition of Cu and Co. Then the Co shells were selectively anodized to form Co hydroxides, which was finally dehydrated to get Cu@CoO nanodendrites. The resulting electrodes exhibited very high reversible specific capacity almost 1.4~2.4 times the theoretical capacity of commercial graphite, and excellent capacity retention (~90%@50^th cycle) as compared with those of the existing transition metal oxides. From the analysis of the cumulative irreversible capacity and morphology change during charge/discharge cycling, it proved that the excellent capacity retention was attributed to the unique structural feature of our core-shell structure where only the thin CoO shell participates in the lithium storage. In addition, our electrodes showed a superb rate performance (70.5%@10.8 C-rate), most likely due to the open porous structure of 3D films, large surface area thanks to the dendritic structure, and fast electron transport through Cu core network.

• The Journal of Korean Academic Society of Nursing Education
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• v.28 no.4
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• pp.400-410
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• 2022

Purpose: This study aimed to understand the keywords and major topics of the educational goals and objectives of nursing educational institutions in South Korea. Methods: From May 10 to May 20, 2022, the educational goals and objectives of all 201 nursing educational institutions in South Korea were collected. Using the NetMiner program, degree and degree centrality, semantic structure, and topic modeling were analyzed. Results: The top keywords and semantic structures of educational goals included 'respect for human (life)-spirit-science-based on, global-competency-professional nurse-nursing personnel-training, professional-science-knowledge-skills, and patients-therapeutic care-relationship.' The educational goals' major topics were clients well-being based on science and respect for human life, a practicing nurse with capabilities and spirit, fostering a nursing personnel with creativity and professionalism, and training of global nurses. The top keywords and semantic structures of the educational objectives included 'holistic care-nursing-research-action-capability, critical thinking-health-problem solving-capability, and efficiency-communication-collaboration-capability.' The educational objectives' major topics were 'nursing professionalism, communication and problem-solving capability; a change of healthcare environments and a progress of nursing practices; fostering professional nurses with creativity and global capability; and clients' health and nursing practice.' Conclusion: Educational goals in nursing presented specific nursing values and concepts, such as respect for human life, therapeutic care relationships, and the promotion of well-being. Educational objectives in nursing presented the competencies of nurses as defined by the Korean Accreditation Board of Nursing Education (KABONE). Recently, the KABONE announced new program outcomes and competencies, which will require the revision of educational goals. To achieve those educational objectives, it is suggested that the expected level of competencies be clearly defined for nursing graduates.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.453-453
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• 2023

상수관망의 수질사고와 이상상황 발생시 대응을 위해서는 급수구역에 설치되어 있는 자동수질측정기, 정밀여과장치, 재염소주입설비, 자동드레인 등의 계측·제어설비들 간의 유기적인 정보공유를 통한 제어를 필요로 한다. 스마트 상수관망 운영플랫폼은 이러한 인프라 시설의 운영방안을 고려하여 분산되어 있는 계측데이터를 통합감시 및 제어하는 시스템으로 개발되었다. 상수관망 운영플랫폼은 능동형 분석 제어기술을 도입하여, 스마트 상수관망 인프라 설비를 최적제어할 수 있도록 구현하였다. 통합운영 플랫폼은 PostgreSQL, PostGIS, GeoServer, OpenLayers 등의 기술을 활용하여 개발하였다. 플랫폼은 계측감시, 시설관리, 운영제어 등의 기능으로 구성되며, 상수도 업무지원을 위한 관망해석 및 네트워크 분석 기능을 지원한다. 본 시스템은 스마트 상수도 구축사업을 통해 구축한 유량·수질모니터링 장비와 수질관리를 위해 도입된 재염소, 자동드레인 설비의 운영상태를 실시간 조회하는 모니터링 프로그램과, 관망해석 프로그램 그리고 대상설비의 최적제어를 위한 운영관리 프로그램으로 구성되어 있다. 모니터링 프로그램은 현장에서 측정되고 있는 유량, 수압, 수질, 펌프운전 등의 상태를 실시간으로 감시하고 클라우드 데이터베이스에 저장·관리하는 기능을 수행한다. 관망해석 프로그램은 EPA_Net모형과 연계되어 관망수리·수질해석을 수행하는 부분으로 재염소설비의 염소 추가주입이나 자동드레인을 통한 배제시 나타나게되는 관의 수리·수질변화를 클라우드 컴퓨팅 환경에서 분석하고 결과를 가시화 하는 기능을 갖고 있다. 운영관리 프로그램은 재염소 주입이 필요할 경우 주입량의 산정하는 부분과 관망 파손이나 수질사고 발생시 최적 단수예상지역을 도출하는 기능을 보유하고 있다. 향후 스마트 상수관망의 능동형 수질관리를 추진하는 지자체에 도입하여 인프라운영관리 기술 확보 및 수질관리 능력 개선과 실시간 감시 및 위기 대응능력 향상에 기여할 것으로 기대된다.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.83-90
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• 2009

Since wireless sensor networks are deployed in open environments, an attacker can physically capture some sensor nodes. Using information of compromised nodes, an attacker can launch false data injection attacks that report nonexistent events. False data can cause false alarms and draining the limited energy resources of the forwarding nodes. In order to detect and discard such false data during the forwarding process, various security solutions have been proposed. But since they are prevention-based solutions that involve additional operations, they would be energy-inefficient if the corresponding attacks are not launched. In this paper, we propose a detection method that can detect false data injection attacks without extra overheads. The proposed method is designed based on the signature of false data injection attacks that has been derived through simulation. The proposed method detects the attacks based on the number of reporting nodes, the correctness of the reports, and the variation in the number of the nodes for each event. We show the proposed method can detect a large portion of attacks through simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.249-251
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• 2022

In this paper, we present machine learning to control drone flight using EEG signals. We defined takeoff, forward, backward, left movement and right movement as control targets and measured EEG signals from the frontal lobe for controlling using Fp1. Fp2 Fp2 two-channel dry electrode (NeuroNicle FX2) measuring at 250Hz sampling rate. And the collected data were filtered at 6~20Hz cutoff frequency. We measured the motion image of the action associated with each control target open for 5.19 seconds. Using Matlab's classification learner for the measured EEG signal, the triple layer neural network, logistic regression kernel, nonlinear polynomial Support Vector Machine(SVM) learning was performed, logistic regression kernel was confirmed as the highest accuracy for takeoff and forward, backward, left movement and right movement of the drone in learning by class True Positive Rate(TPR).