• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.585-593
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• 2021

Background and objective: Illegal open-air incineration, which is criticized as a leading source of air pollutants among agricultural activities, currently requires constant effort and attention. Countries around the world have been undertaking studies on the emission of heavy metal substances in fine dust discharged during the incineration process. A precise analytical method is required to examine the harmful effects of particulate pollutants on the human body. Methods: In order to simulate open-air incineration, the infrastructure needed for incineration tests complying with the United States Environmental Protection Agency (EPA) Method 5G was built, and a large-area analysis was conducted on particulate pollutants through automated scanning electron microscopy (SEM)-energy-dispersive X-ray spectroscopy (EDS). For the test specimen, high-density polyethylene (HDPE) waste collected by the DangJin Office located in Choongcheongnam-do was used. To increase the identifiability of the analyzed particles, the incineration experiment was conducted in an incinerator three times after dividing the film waste into 200 g specimens. Results: Among the metal particulate matters detected in the HDPE waste incineration test, transition metals included C (20.8-37.1 wt%) and O (33.7-37.9 wt%). As for other chemical matters, the analysis showed that metal particulate matters such as metalloids, alkali metals, alkaline earth metals, and transition metals reacted to C and C-O. Si, a representative metalloid, was detected at 14.8-20.8 wt%, showing the highest weight ratio except for C and O. Conclusion: In this study, the detection of metal chemicals in incinerated particulate matters was effectively confirmed through SEM-EDS. The results of this study verified that HDPE waste adsorbs metal chemicals originating from soil due to its own properties and deterioration, and that when incinerated, it emits particulate matters containing transition metals and other metals that contribute to the excessive production and reduction of reactive oxygen species.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.25-30
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• 2024

Smart City, a key application area of the AIoT (Artificial Intelligence of Things), provides various services in safety, security, and healthcare sectors through location tracking and location-based services. an IPS (Indoor Positioning System) is required to implement location-based services, and wireless communication technologies such as WiFi, UWB (Ultra-wideband), and BLE (Bluetooth Low Energy) are being applied. BLE, which enables data transmission and reception with low power consumption, can be applied to various IoT devices such as sensors and beacons at a low cost, making it one of the most suitable wireless communication technologies for indoor positioning. BLE utilizes the RSSI (Received Signal Strength Indicator) to estimate the distance, but due to the influence of multipath fading, which causes variations in signal strength, it results in an error of several meters. In this paper, we conducted research on a path loss model that can be applied to BLE IPS for proximity services, and confirmed that optimizing the free space propagation loss coefficient can reduce the distance error between the Tx and Rx devices.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.3-11
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• 2018

The Construction Workers Mutual Aid Association is carrying out a number of strategic tasks with the goal of "contributing to improving employment and stabilizing the return of workers to construction workers through employment welfare and retirement deduction services." One of them is the retirement deduction system. The retirement allowance system is a system for the retirement income and livelihood security of construction workers who are not adequately protected by the Labor Standards Act, such as retirement allowances due to the nature of day labor. The Construction Workers Mutual Aid Association has promoted the introduction of electronic manpower management. For the efficient management of the pilot sites and the plans for the future, comprehensive evaluation of the pilot sites as well as the evaluation of the status and operation results of each pilot project site are needed. Therefore, in this study, we will develop performance indicators to evaluate the current state of electronic manpower pilot projects and analyze the actual situation of pilot project sites through actual application, and try to derive future implementation strategies.