• 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.

• Journal of the Microelectronics and Packaging Society
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• v.22 no.4
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• pp.83-89
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• 2015

In this paper, we propose a fabrication technique for enhanced electrical properties of piezoelectric thick films with excellent patterning property using sol-infiltration and a direct-patterning process. To achieve the needs of high-density and direct-patterning at a low sintering temperature (< $850^{\circ}C$), a photosensitive lead zirconate titanate (PZT) solution was infiltrated into a screen-printed thick film. The direct-patterned PZT films were clearly formed on a locally screen-printed thick film, using a photomask and UV light. Because UV light is scattered in the screen-printed thick film of a porous powder-based structure, there are needs to optimize the photosensitive PZT sol infiltration process for obtaining the enhanced properties of PZT thick film. By optimizing the concentration of the photosensitive PZT sol, UV irradiation time, and solvent developing time, the hybrid films prepared with 0.35 M of PZT sol, 4 min of UV irradiation and 15 sec solvent developing time, showed a very dense with a large grain size at a low sintering temperature of $800^{\circ}C$. It also illustrated enhanced electrical properties (remnant polarization, $P_r$, and coercive field, $E_c$). The $P_r$ value was over four times higher than those of the screen-printed films. These films integrated on silicon wafer substrate could give a potential of applications in micro-sensors and -actuators.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.333-338
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• 2019

In the field of radiotherapy, the Quality Assurance(QA) procedure to verify the safety of treatment is considered to be very important. However, due to various problems of the conventional dosimeters used for the QA, researches on these dosimeters have been actively carried out to replace them. In this study, to maximize the sensitivity by visible light(VL) emitted from phosphors, blended hybrid sensors were fabricated by blending various weight percent(wt%) of $Gd_2O_2S:Tb$ which is a phosphor with excellent fluorescence efficiency into $PbI_2$. Then, the electrical properties to high energy radiation from the blended sensors and the pure $PbI_2$ sensor were compared and evaluated. As a result of the sensitivity evaluation, the sensor of 3wt% showed the highest value with more than 40% difference from the other sensors, and gradual decreasing in sensitivity was observed with increasing wt% except for the sensor of 3wt%. Also, in the reproducibility evaluation, the pure $PbI_2$ sensor exhibited a large variation in coefficient of variation(CV)>0.015, while all the blended sensors showed CV<0.015.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• Membrane Journal
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• v.34 no.2
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• pp.87-95
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• 2024

Recent studies explore a wide array of desalination and water treatment methods, encompassing membrane processes such as reverse osmosis (RO), nanofiltration (NF), and electrodialysis (ED) to advanced capacitive deionization (CDI) and its membrane variant (MCDI). Comparative analyses reveal ED's cost-effectiveness in low-salinity scenarios, while hybrid systems (NF-MCDI, RO-NF-MCDI) show improved salt removal and energy efficiency. Novel ion separation methods (NF-CDI, NF-FCDI) offer enhanced efficacy and energy savings. These studies also highlight the efficiency of these methods in treating complex wastewater specific to various industries. Environmental impact assessments emphasize the need for sustainability in system selection. Additionally, the integration of microfabricated sensors into membranes allows real-time monitoring, advancing technology development. These studies underscore the variety and promise of emerging desalination and water treatment technologies. They provide valuable insights for enhancing efficiency, minimizing energy usage, tackling industry-specific issues, and innovating to surpass conventional method limitations. The future of sustainable water treatment appears bright, with continual advancements focused on improving efficiency, minimizing environmental impact, and ensuring adaptability across diverse applications.