• Journal of Korean Society on Water Environment
• /
• v.36 no.6
• /
• pp.621-635
• /
• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• International Journal of Advanced Culture Technology
• /
• v.9 no.1
• /
• pp.224-241
• /
• 2021

According to data from the National Police Agency, 75.5 percent of dead traffic accidents in Korea are truck accidents. About 1,000 people die in cargo truck accidents in Korea every year, and two to three people die in cargo truck accidents every day. In the survey, Korean cargo workers answer poor working conditions as an important cause of constant truck accidents. COVID 19 is increasing demand for non-face-to-face logistics. The inefficiency of the Korean transportation system is leading to excessive work burden for small logistics The inefficiency of the Korean transportation system is causing excessive work burden for small individual carriers. The inefficiency of the Korean transportation system is also evidenced by the number of deaths from logistics industry disasters that have risen sharply since 2020. Small and medium-sized Korean Enterprises located in CIPs (Connected Industrial Parks) often do not have smart access certification systems. And as a result, a lot of transportation time is wasted at the final destination stage. In the logistics industry, time is the cost and time is the revenue. The logistics industry is the representative industry in which time becomes money. The smart access authentication system architecture proposed in this paper allows small logistics private carriers to improve legal stability, and SMEs (Small and Medium-sized Enterprises) in CIPs to reduce logistics transit time. The CIPs smart access system proposed in this paper utilizes the currently active Mobile OTP (One Time Password), which can significantly reduce system design costs, significantly reduce the data capacity burden on individual cell phone terminals, and improve the response speed of individual cell phone terminals. It is also compatible with the OTP system, which was previously used in various ways, and the system reliability through the long period of use of the OTP system is also high. User customers can understand OTP access systems more easily than other smart access systems.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.5
• /
• pp.628-636
• /
• 2021

This paper contains the implementation and performance evaluation results of a system that collects environmental data at the time when the wild animal intrusion occurred at farms and then predicts future wild animal intrusions through a machine learning-based Vector Autoregression(VAR) model. To collect the data for intrusion prediction, an IoT-based hardware prototype was developed, which was installed on a small farm located near the school and simulated over a long period to generate intrusion events. The intrusion prediction service based on the implemented VAR model provides the date and time when intrusion is likely to occur over the next 30 days. In addition, the proposed system includes the function of providing real-time notifications to the farmers mobile device when wild animals intrusion occurs in the farm, and performance evaluation was conducted to confirm that the average response time was 7.89 seconds.

• Convergence Security Journal
• /
• v.20 no.5
• /
• pp.11-17
• /
• 2020

With the increase in cases of using Bluetooth devices used in the electric vehicle charging systems, security issues are also raised. Although various technical efforts have beed made to enhance security of bluetooth technology, various attack methods exist. In this paper, we propose an intelligent Bluetooth intrusion detection system based on a well-known machine learning method, Hidden Markov Model, for the purpose of detecting intelligently representative Bluetooth attack methods. The proposed approach combines packet types of H4, which is bluetooth transport layer protocol, and the transport directions of the packet firstly to represent the behavior of current traffic, and uses the temporal deployment of these combined types as the final input features for detecting attacks in real time as well as accurate detection. We construct the experimental environment for the data acquisition and analysis the performance of the proposed system against obtained data set.

• Journal of Information Technology Applications and Management
• /
• v.28 no.3
• /
• pp.49-58
• /
• 2021

Due to the increasing interest in wellness aroused by the aging population and the pursuing feature of active old age, Korean elderly set importance on long life with their healthy condition. Following the change in the paradigm of the medical delivery system from hospital-oriented, treatment-oriented to personal-centered and self-care, Service design application of Smart Healthcare for the elderly became valuable. Smart Healthcare is a healthcare service provided through the fusion of ICT technologies including mobile/wearable devices, IoT, big data, and information technology, and it is utilized to prevent diseases managing abundant health information and living habits. As a methodology for delivering such Smart Healthcare to the elderly, Service design can be adopted. Therefore, this study would like to present the perquisites of Smart Healthcare design for the elderly through analyzing the results from in-depth interview methods between the elderly and medical staff. As a result of this study, guidelines for Service design application of health vulnerability management for the elderly utilizing smart phones were presented. Therefore, this study presented four prerequisites composed of 'high level of supplementation and ethical decision making', 'improvement of inequality in accessibility and experience', 'resolving problems in policy implementation' and 'user-friendliness' for the Smart Healthcare service design for the elderly. Overall, Service design is expected to play an innovative role in improving the quality of life for the elderly through the process of collecting and delivering information on Smart Healthcare centered on the experience of the elderly.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.6
• /
• pp.749-756
• /
• 2021

Thanks to PLC and IoT Sensor, huge amounts of data has been accumulated onto the companies' databases. Machine Learning Algorithms for the predictive model with good performance have been widely utilized in the manufacturing process. We present how to improve the performance of machine learning predictive models. To improve the performance of the predictive model, typical techniques such as increasing the sample size, optimizing the hyper parameters for the algorithm, and selecting a proper machine learning algorithm for the predictive model would be shown. We suggest some new ways to make the model performance much better. With the proposed methods, we can build a better predictive model for predicting and controlling product qualities and save incredibly large amount of quality failure cost.

• Journal of the Korea Convergence Society
• /
• v.13 no.3
• /
• pp.227-237
• /
• 2022

With the growing interest in convergence, there have been various attempts to measure convergence, but the definition of convergence is ambiguous and consensus on appropriate indicators has not been reached, so measurement of convergence is still at a rudimentary stage. In this study, using the KSIC-IPC linkage table developed by the Korean Intellectual Property Office to analyze the correlation and impact of patents, industry, economy, and population, we propose a new evaluation model that can evaluate industry convergence from patent data. In addition, it was verified whether the industry convergence derived from this properly reflects the corporate convergence characteristics. As a result of classifying the convergence of 39,740 patents owned by global major automobile companies, and evaluating the degree of convergence of each company, it was confirmed that the industry convergence derived using the KSIC-IPC linkage table better reflects the corporate convergence characteristics than the technology convergence classified by IPC co-classification. Therefore, the industry convergence data of automotive sector derived from the new industry convergence evaluation model using the KSIC-IPC linkage table is expected to be widely used for future convergence research.

• Journal of Information Technology Services
• /
• v.21 no.1
• /
• pp.127-144
• /
• 2022

Recently, Korea is promoting cooperation with various countries, centering on ASEAN countries, with the aim of exporting Korean smart cities for the globalization of smart cities. The purpose of this study is to select excellent smart city technologies through analysis of smart city technologies owned by domestic companies and company status, and to prepare a plan for revitalization of companies with smart city technologies. Through prior research, the implications were derived through research on the existing smart city. Next, established a smart city policy analysis and smart city technology classification criteria through Korea and Overseas smart city policy and Korea smart city technology status DB. And the big data of smart city technology possessed by Korea companies and a plan for selecting a smart city export technology was prepared through analysis by region and company. As a result, to activate the technology possessed by Korea companies and to export overseas, it seems to need financial support and tax incentives that secure a pathway to export specialized smart technologies of SMEs, along with citizen participation and institutional supplementation. The smart city technology fields with the highest utilization in Korea were traffic, green energy, e-government, crime prevention, and construction, and the service types were platform, IoT, AI, big data, and GIS/GPS. These technologies are expected to contribute to building a platform for overseas smart city technology exports.

• Nuclear Engineering and Technology
• /
• v.54 no.7
• /
• pp.2444-2452
• /
• 2022

The field-programmable gate array (FPGA) is gaining popularity in industrial automation such as nuclear power plant instrumentation and control (I&C) systems due to the benefits of having non-existence of operating system, minimum software errors, and minimum common reason failures. Separate functions can be processed individually and in parallel on the same integrated circuit using FPGAs in comparison to the conventional microprocessor-based systems used in any plant operations. The use of FPGAs offers the potential to minimize complexity and the accompanying difficulty of securing regulatory approval, as well as provide superior protection against obsolescence. Wireless sensor networks (WSNs) are a new technology for acquiring and processing plant data wirelessly in which sensor nodes are configured for real-time signal processing, data acquisition, and monitoring. ZigBee (IEEE 802.15.4) is an open worldwide standard for minimum power, low-cost machine-to-machine (M2M), and internet of things (IoT) enabled wireless network communication. It is always a challenge to follow the specific topology when different Zigbee nodes are placed in a large network such as a plant. The research article focuses on the hardware chip design of different topological structures supported by ZigBee that can be used for monitoring and controlling the different operations of the plant and evaluates the performance in Vitex-5 FPGA hardware. The research work presents a strategy for configuring FPGA with ZigBee sensor nodes when communicating in a large area such as an industrial plant for real-time monitoring.

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