• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.97-110
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• 2021

Fog can cause large-scale human and economic damages, including traffic systems and agriculture. So, Korea Meteorological Administration is operating about 290 visibility meters to improve the observation level of fog. However, it is still insufficient to detect very localized fog. In this study, high-resolution grid-type visibility data were retrieved from irregularly distributed visibility data across the country. To this end, three objective analysis techniques (Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Universal Kriging (UK)) were used. To find the best method and parameters, sensitivity test was performed for the effective radius, power parameter and variogram model that affect the level of objective analysis. Also, the effect of data distribution characteristics (level of normality) on the performance level of objective analysis was evaluated. IDW showed a relatively high level of objective analysis in terms of bias, RMSE and correlation, and the performance is inversely proportional to the effective radius and power parameter. However, the two Krigings showed relatively low level of objective analysis, in particular, greatly weakened the variability of the variables, although the level of output was different depending on the variogram model used. As the level of objective analysis is greatly influenced by the distribution characteristics of data, power, and models used, care should be taken when selecting objective analysis techniques and parameters.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.689-692
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• 2021

This paper is a prior study to improve the efficiency of offloading based on mobile agents to optimize allocation of computing resources and reduce latency that support user proximity of application services in a Fog/Edge Computing (FEC) environment. We propose an algorithm that effectively reduces the execution time and the amount of memory required when extracting optimal moving patterns from the vast set of spatio-temporal movement history data of moving objects. The proposed algorithm can be useful for the distribution and deployment of computing resources for computation offloading in future FEC environments through frequency-based optimal path extraction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.881-902
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• 2024

Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy-hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

• Journal of Bio-Environment Control
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• v.25 no.1
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• pp.24-29
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• 2016

The objective of this study was to examine the changes in temperature drop and fruit production due to low pressure fog system in plastic greenhouses during summer cultivation of Korean melon. The indoor temperature of plastic house was dropped by $7.6^{\circ}C$ compared to control on July 26th, 2015 from 10:00 to 18:00. Fruit weight was smaller and lighter by 96g compared to control. The sugar content and color parameter were also enhanced due to application of low pressure fog system. The fraction of malformed fruits was decreased by 15.3% in plots where low pressure fog system was applied. The fraction of marketable fruit and yield were increased by 15.3% and 26% compared to control, respectively. As a result, high quality fruit production within plastic house of summer was increased by applying low pressure fog system and it is positively affected the drop of indoor temperature.

• Journal of Bio-Environment Control
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• v.24 no.1
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• pp.34-38
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• 2015

This research was conducted to establish efficient methods to overcome high temperature and low humidity with light selective shading agent and two-fluid fogging system in greenhouses in hot season. There were four experimental treatments; not treated (Non), fogging by two-fluid fogging system (Fog), spraying onto the greenhouse surface with shading coating agent (Coat), and using fogging and coating together (F&C). The amount of solar radiation entered into the greenhouses was higher in Non, and then Fog, Coat, and F&C in descending order. Fog was more efficient to lower the air temperature and also raise relative humidity than Coat treatment. The crop temperature was about $6^{\circ}C$ higher in Control than the other treatments. F&C revealed as the most efficient method to control the environment inside the greenhouse, but fogging system seemed to be more economic. In stand-alone greenhouses spraying coating agent may be the appropriate choice because of their structural limitations, mainly eave height.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.36-41
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• 1994

The major objective of this study is to develop a method of zone cooling during summer day using heat pump for year- round cultivation. The efficiency of cold water cooling and fog cooling was investigated. In order to prevent the occurrence of blossom - end rot in tomato, cooling was induced together with air flow of the fruit treatment as well as promoting air circulation in the plant treatment was induced. The following results were obtained : 1) The temperature in the cold water cooling district was 1$0^{\circ}C$ lower than greenhouse temperature and the temperature in the fo8 cooling district was about 5$^{\circ}C$ lower than the greenhouse. 2) Regardless of cooling method, the treatment of air flow on fruit did not affect the fruit but prevent blossom-end rot. There was 34.5% occurrence rate of blossom -end rot in non-air flow district of cold water cooling 54.5% in non-air flow district of fog cooling and 78% in fog circulation cooling district. The cooling efficiency using cold water cooling method induced enough cooling at critical temperature for growth and development and the occurrence of blossom -end rot was lower than fog cooling. Fog cooling in culture district with air circulation did not induce and difference in temperature but caused an Increase in humidity resulting in 24% increase in the occurrence of blossom-end rot. Thus the occurrence of blossom-end rot in tomato caused by environmental factors can be attributed more to humidity than to temperature.

• Tuberculosis and Respiratory Diseases
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• v.78 no.4
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• pp.326-335
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• 2015

Background: Asian dust is known to have harmful effects on the respiratory system. Respiratory conditions are also influenced by environmental conditions regardless of the presence of pollutants. The same pollutant can have different effects on the airway when the air is dry compared with when it is humid. We investigated hospital visits for chronic obstructive pulmonary disease (COPD) and asthma in relation to the environmental conditions. Methods: We conducted a retrospective study using the Korean National Health Insurance Service claims database of patients who visited hospitals in Chuncheon between January 2006 and April 2012. Asian dust, haze, mist, and fog days were determined using reports from the Korea Meteorological Administration. Hospital visits for asthma or COPD on the index days were compared with the comparison days. We used two-way case-crossover techniques with one to two matching. Results: The mean hospital visits for asthma and COPD were $59.37{\pm}34.01$ and $10.04{\pm}6.18$ per day, respectively. Hospital visits for asthma significantly increased at lag0 and lag1 for Asian dust (relative risk [RR], 1.10; 95% confidence interval [CI], 1.01-1.19; p<0.05) and haze (RR, 1.13; 95% CI, 1.06-1.22; p<0.05), but were significantly lower on misty (RR, 0.89; 95% CI, 0.80-0.99; p<0.05) and foggy (RR, 0.89; 95% CI, 0.84-0.93; p<0.05) days than on control days. The hospital visits for COPD also significantly increased on days with Asian dust (RR, 1.29; 95% CI, 1.05-1.59; p<0.05), and were significantly lower at lag4 for foggy days, compared with days without fog (RR, 0.85; 95% CI, 0.75-0.97; p<0.05). Conclusion: Asian dust showed an association with airway diseases and had effects for several days after the exposure. In contrast to Asian dust, mist and fog, which occur in humid air conditions, showed the opposite effects on airway diseases, after adjusting to the pollutants. It would require more research to investigate the effects of various air conditions on airway diseases.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.3
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• pp.257-266
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• 2021

This study investigated the understanding of pre-service teachers in relation to the 'fog experiment' in the 5th grade 'Weather and Our Daily Life' unit of the 2009 and 2015 revised elementary school science textbooks. Pre-service teachers who participated in this study were 100 students, who are attending the university of education and taking courses in teaching research. After pre-learning about the 2009 and 2015 revised elementary school science textbooks and guide book, pre-service teachers conducted the experiment in groups. After that, the pre-service teachers individually presented answers to three questions, and the results of analyzing the answers are as follows. First, there were 24 (24%) preservice teachers who explained the difference in fog generation in the 2009 and 2015 revised curriculum with related scientific concepts such as condensation and water vapor, and only 1 (1%) of them explained the difference using the concept of saturation. Second, there were 48 (48%) pre-service teachers who found out the reason for the change in the fog experiment method according to the change in the curriculum. Third, pre-service teachers valued the reproduction and success of experiments rather than the importance of scientific knowledge, and such pre-service teachers suggested the use of alternative experiments or website.