• Proceedings of the Korea Society of Information Technology Applications Conference
• /
• 2005.11a
• /
• pp.243-246
• /
• 2005

Recent ubiquitous computing environments increasingly impact on our lives using the current technologies of sensor network and ubiquitous services. In this paper, we propose ubiquitous architectural framework for ubiquitous sleep aid service(UbiSAS) in the subset of ubiquitous computing for refreshing of human's sleep. And we examine technical feasibility. Human can recover his health through refreshing sleep from fatigue. Ubiquitous architectural framework for UbiSAS in digital home offers agreeable sleeping environment and improves recovery from fatigue. So we present new concept of ubiquitous architectural framework dissolving stress. Specially, we apply context to context-aware framework module. This context is transferred to context adaptive inference engine which has service invocation function in intelligent agent module. Ubiquitous architectural framework for UbiSAS using context adaptive rule inference engine without user intervention is technical issue. That is to say, we should take sleep comfortably during our sleeping. And sensed information during sleeping is changed to context-aware information. This presents significant information in context adaptive rule inference engine for UbiSAS. This information includes all sleeping state during sleeping in context-aware computing technique. So we propose more effective and most suitable ubiquitous architectural framework using context adaptive rule inference engine for refreshing sleep in this paper.

• Journal of Sensor Science and Technology
• /
• v.28 no.6
• /
• pp.341-344
• /
• 2019

Liquid crystal (LC) mesophase materials manifest a variety of phase transitions. The optical properties of LCs are highly dependent upon the phase and orientation of the optical axis with respect to the polarization of incoming light. Studying the LC phase transitions is significantly important for a wide range of scientific and industrial applications. In this study, we demonstrate the propagation velocity of the phase boundary between the nematic and isotropic phase of 4-Cyano-4-pentylbiphenyl (5CB) liquid crystal for different electric fields using a polarized optical microscope. The results demonstrate that the propagation velocity of the phase boundary exhibits a peak value for a specific voltage, attributed to the supercooling of the isotropic phase of the LC. The analysis of the propagation velocity for different electric fields also provides a simple optical platform to measure the thermal anisotropy and voltage dependent thermal properties of the homogeneously aligned LC.

• Journal of Satellite, Information and Communications
• /
• v.10 no.4
• /
• pp.113-116
• /
• 2015

The electric utility has the responsibility of reducing the impact of peaks on electricity demand and related costs. Therefore, they have introduced Direct Load Control System (DLCS) to automate the external control of shedding customer load that it controls. The existing DLCS have been operated only depend on On/Off signal from the electric utility. That kind of DLCS operating has been successfully used until now. But since the number of customer load participating in the DLC program are keep increasing, On/Off signal control from the electric utility is no longer meets the needs of many different kind of customers. Therefore, In this paper, the author suggest the design of direct load control system using weather sensors to meet the diversity of different customer needs.

• Nuclear Engineering and Technology
• /
• v.46 no.3
• /
• pp.373-380
• /
• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.6
• /
• pp.430-439
• /
• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Nuclear Engineering and Technology
• /
• v.48 no.3
• /
• pp.702-710
• /
• 2016

Global concern and interest in the safety of nuclear power plants have increased considerably since the Fukushima accident. In the event of a severe accident, the reactor vessel water level cannot be measured. The reactor vessel water level has a direct impact on confirming the safety of reactor core cooling. However, in the event of a severe accident, it may be possible to estimate the reactor vessel water level by employing other information. The cascaded fuzzy neural network (CFNN) model can be used to estimate the reactor vessel water level through the process of repeatedly adding fuzzy neural networks. The developed CFNN model was found to be sufficiently accurate for estimating the reactor vessel water level when the sensor performance had deteriorated. Therefore, the developed CFNN model can help provide effective information to operators in the event of a severe accident.

• Journal of Sensor Science and Technology
• /
• v.25 no.3
• /
• pp.184-188
• /
• 2016

Highly ordered $SnO_2$ and NiO nanocolumns have been successfully achieved by glancing-angle deposition (GLAD) using an electron beam evaporator. Nanocolumnar $SnO_2$ and NiO sensors exhibited high performance owing to the porous nanostructural effect with the formation of a double Schottky junction and high surface-to-volume ratios. When all gas sensors were exposed to various gases such as $C_2H_5OH$, $C_6H_6$, and $CH_3COCH_3$, the response of the highly ordered $SnO_2$ nanocolumn were over 50 times higher than that of the $SnO_2$ thin film. This work will bring broad interest and create a strong impact in many different fields owing to its particularly simple and reliable fabrication process.

• Journal of Sensor Science and Technology
• /
• v.16 no.3
• /
• pp.179-186
• /
• 2007

Uroflowmetry is non-invasive and easily performed to diagnose benign prostate hypertrophy (BPH) frequent in aged men. Weight change during urination is usually measured to estimate the urinary flow rate by a load cell, but sensitive to any impacts against the bottom of the container, leading to unnecessary noise generation. Moreover, load cells are relatively expensive raising the production cost. The present study proposed a new technique, measuring hydraulic pressure on the bottom of the urine container to evaluate the urinary flow rate. Low cost pressure transducer enabled almost perfectly linear relationship between the urine volume and the hydraulic pressure. During both the simulated and human urination experiment, variance of the pressure signal was more than 50 % smaller than the weight signal acquired by a load cell, which demonstrated that the impact noise was decreased to a great degree by pressure compared to weight measurement.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.40 no.7
• /
• pp.601-607
• /
• 2012

The main function of a landing gear is to absorb the impact energy during touchdown. It it occasionally required for landing gear to have crashworthiness for improving survivability and safety in case of emergency landing. This paper introduces the design concept, performance analysis and drop test procedures for the development of the crashworthy landing gear. The shock absorbing ability and the crash behavior are proved by analyzing various sensor data and video clips from high speed camera recording during drop tests.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.6
• /
• pp.637-650
• /
• 2014

Geospatial research on emergency response in multi-level micro-spatial environments (e.g., multi-story buildings) that aims at understanding and analyzing human movements at the micro level has increased considerably since 9/11. Past research has shown that reducing the time rescuers needed to reach a disaster site within a building (e.g., a particular room) can have a significant impact on evacuation and rescue outcomes in this kind of disaster situations. With the purpose developing emergency response systems that are capable of using complex real-time geospatial information to generate fast-changing scenarios, this study develops a Spatiotemporal Optimal Route Algorithm (SORA) for guiding rescuers to move quickly from various entrances of a building to the disaster site (room) within the building. It identifies the optimal route and building evacuation bottlenecks within the network in real-time emergency situations. It is integrated with a Ubiquitous Sensor Network (USN) based tracking system in order to monitor dynamic geospatial entities, including the dynamic capacities and flow rates of hallways per time period. Because of the limited scope of this study, the simulated data were used to implement the SORA and evaluate its effectiveness for performing 3D topological analysis. The study shows that capabilities to take into account detailed dynamic geospatial data about emergency situations, including changes in evacuation status over time, are essential for emergency response systems.