• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• Journal of Information Processing Systems
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• 제18권1호
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4385-4399
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• 2021

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

• 한국환경보건학회지
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• 제48권3호
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• pp.131-137
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• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• 드라이브 ㆍ 컨트롤
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• 제20권1호
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• pp.41-47
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• 2023

The aim of this study was to develop a method for installing strain gauges on moving, lubricating oil-filled, and sealed parts, such as drive shafts of equipment, including construction machinery. A measuring device was constructed using an embedded CPU and an IoT sensor to measure the strain of the strain gauge, which allowed for the measurement of axial torque and axial force, and subsequent analysis. To verify the performance of the developed device, the axial torque and axial force of the forklift were measured during operation using a strain gauge attached to the inside of the drive shaft. This study confirmed the possibility of measuring and analyzing the strain of a moving part, such as the inside of a drive shaft, which is sealed and filled with hydraulic oil.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.71-78
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• 2022

Electronic measuring devices have an important role in agricultural projects and in various fields. Electronic measuring devices play a vital role in controlling and saving soil information. They are designed to measure the temperature, acidity and moisture of the soil. In this paper, a new methodology to manage irrigation and soil fertility using an electronic system is proposed. This is designed to operate the electronic irrigation and adds inorganic fertilizers automatically. This paper also explains the concept of remote management and control of agricultural projects using electronic soil measurement devices. The proposed methodology is aimed at managing the electronic irrigation process, reading the moisture percentage, elements of soil and controlling the addition of inorganic fertilizers. The system also helps in sending alert messages to the user when an error occurs in measuring the percentage of soil moisture specified for crop and a warning message when change happens to the fertility of soil as many workers find difficulty in daily checking of soil and operating agricultural machines such as irrigation machine and soil fertilizing machine, especially in large projects.

• 자동차안전학회지
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• 제14권1호
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• pp.45-54
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• 2022

Recently, the introduction of Cooperative Intelligent Transport Systems (C-ITS) has been attempted to solve the limitation of only the sensor of the vehicle itself. For example, vehicles traveling at intersections can drive more safely through C-ITS. By using V2X communication of WAVE and LTE, the driver can receive the status and time of traffic lights. However, LTE has a larger transmission delay time than WAVE, so timimg data may not match in real time. In this paper, using the SPaT message, it was confirmed that LTE has a larger C-ITS service transmission delay time than WAVE. Finally, it was confirmed that the timing data of SPaT provided by LTE corrected by the algorithm is similar to SPaT provided by WAVE. It was confirmed that safer intersection driving is possible based on real-time.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.265-266
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• 2021

본 논문은 사물인터넷을 이용한 노지 농작물 재배 시스템 구축 사례를 설명하고 노지 작물 재배 시스템에서의 환경 변수를 정의하였다. 다양한 센서을 통해서 토양의 온도, 함수율, 전기전도도, 산성도를 측정하여 LoRa 통신 모듈을 이용하여 서버로 정보를 전달하고, 서버는 이 데이터를 바탕으로 시비량 및 관수량을 제어한다. 노지 농작물 재배에 적합한 통신 방식인 LoRa 기술을 이용하여 넓은 노지를 관리하고 생산량 및 판매실적까지 관리하는 시스템을 개발하였다.

• Smart Structures and Systems
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• 제32권3호
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.