• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1732-1737
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• 2004

This paper proposes Internet-based checking technique for machine-tools with variant CNC (Computerized Numerical Controller). According to the architecture of CNC, CNC is classified into two types such as CAC (Closed Architecture Controller) which is conventional CNC, and OAC (Open Architecture Controller) which is a recently introduced PC-based controller. CAC has a closed architecture and it is dependent on CNC vender specification. Because of this, it has been very difficult for users to implement an application programs in CNC domain. Therefore, an additionally special module is required for Internet-based application such as remote checking. In this case, web I/O embedded module can be efficiently applied for Internet-based checking. The module is directly attached to TCP/IP network for communication. In order to obtain the monitoring data of CNC machines, the I/O signals of the module are assigned to PLC (Programmable Logic Controller) input and output (I/O) signals within CNC domain. On the other hand, OAC has a PC-based open architecture and an additional module is not necessary for the connection with external site. Because of this, a simple DAU is just used for signal sensing and data acquisition without additional communication modules. For Internet-based remote checking of machine-tools with OAC, a user-defined daemon and application programs are implemented as the form of internal function within the PC-based controller. Internet communication is performed between the daemon program in CNC domain and web script programs in external server. Checking points defined in this research are classified into two categories such as structured point and operational point. The formal includes the vibration of bearing, temperature of spindle unit and another periodical management. And the latter includes oil checking, clamp locking/unlocking and machining on/off status.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.35-43
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• 2013

We analyzed the relationships between backscattering coefficients of wheat measured by COSMO-SkyMed SAR and biophysical measurements such as biomass, vegetation water content, and soil moisture over an entire wheat growth period. Backscattering coefficients increased until DOY 129 and then decreased along with fresh weight, dry weight, and vegetation water content. Correlation analysis between backscattering and wheat growth parameters revealed that backscatter correlated well with fresh weight (r=0.88), vegetation water content (r=0.87), and dry weight (r=0.80), while backscatter did not correlated with soil moisture (r=0.18). Prediction equations for estimation of wheat growth parameters from the backscattering coefficients were developed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.97-106
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• 2012

Management of biosignal data in mobile devices causes many problems in real-time transmission of large volume of multimedia data or storage devices. Therefore, this research paper intends to suggest an m-Health system, a clinical data processing system using mobile in order to provide quick medical service. This system deployed health system on IP network, compounded outputs from many bio sensing in remote sites and performed integrated data processing electronically on various bio sensors. The m-health system measures and monitors various biosignals and sends them to data servers of remote hospitals. It is an Android-based mobile application which patients and their family and medical staff can use anywhere anytime. Medical staff access patient data from hospital data servers and provide feedback on medical diagnosis and prescription to patients or users. Video stream for patient monitoring uses a scalable transcoding technique to decides data size appropriate for network traffic and sends video stream, remarkably reducing loads of mobile systems and networks.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.1
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• pp.683-692
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• 2009

On-site remote sensing technique by using single lens reflex(SLR) digital camera will be expected as the useful tool for the vegetation measurement field such as a crop growth management, the monitoring of revegetation slope and the evaluation of environment. We reviewed the availability of the vegetation measurement using a digital camera which is sailed for general-purpose. As a result, we could analysis relationship with the illuminance of image plane and incidence energy of multitemporal observation images by doing gamma correction and exposure compensation. And also, we proposed the model formulas for the correction of influences of capturing angle and illuminance. In addition, we obtained high correlation of normalized difference vegetation index(NDVI) between digital camera and spectral photometer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.1010-1013
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• 2009

The youths of the farming village have moved to the metropolis, thus the most of the manpower which engages to a production remained in the village reaches layer old age and all thing sprouts long the whole and it is one. So to remove the waste of barn rather than to give feed to the livestock is hard that what step all automation of this part is necessary. Consequently we have developed the automation system in order to reduce the massive death of the livestock at the time of intense cold and hot. The system will be able to clean the waste of the barn and confront quickly in the change of temperature which is sudden it came. And we proposed also the system that will be able to watch at real-time and monitor the operational environment from a remote using CCD camera. In this paper, we proposed the remote control system which uses PDA in order to control the automation system of a stall while moving. The proposed system was embodied in order for the control and the monitor while the user is mobile using PDA screens. We also added a protection system in that system. The system sends the case warning and SMS while will have the fire and the intrusion from the outside and prevents a robbery.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.391-396
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• 2005

Red tide(harmful algae) in the Korean Coastal Waters has a given a great damage to the fishery every year. However, the aim of our study understands the influence of meteorological factors (air and water tempaerature, precipitation, sunshine, solar radiation, winds) relating to the mechanism of red tide occurrence and monitors red tide by satellite remote sensing, and analyzes the potential area for red tide occurrence by GIS. The meteorological factors have directly influenced on red tide formation. Thus, We want to predict and apply to red tide formation from statistical analyses on the relationships between red tide formation and meteorological factors. In future, it should be realized the near real time monitoring for red tide by the development of remote sensing technique and the construction of integrated model by the red tide information management system (the data base of red tide - meteorological informations).

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.37-43
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• 2022

Post-spill monitoring of hazardous noxious substances accidents is essential in the event of a spillage of significant quantities of pollutants and for the management of the marine environment resulting from the long-term effects of the persistent toxic substances. The accidental introduction of a sinker into the marine environment can create harmful anaerobic conditions in the benthic ecosystem and spread over the seafloor by the topography and currents. Through case studies, most post-spill monitoring includes modeling, remote sensing, and chemical analyses of the sediment and benthic organisms. The monitoring also evaluates the effectiveness of restoration and recovery activities and assesses damages and compensation.