• Analytical Science and Technology
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• v.24 no.6
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• pp.503-509
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• 2011

Specific surface area is becoming a very important factor when newly developed advanced nano-materials are evaluated. But there have been many differences in results when measuring specific surface areas, depending on the measuring equipments and analysis method. To verify the reliability of the specific surface area measurement device supplied within the country, Round Robin Test (RRT) has been done at 21 affiliated research institutes. As a result, it was found that several institute had problems in measuring of gas adsorption amount in measuring equipment, and this proved the need for certified reference material (CRM). Furthermore, it was also found that the results from BET analysis is easily swayed by the analyst's subjectivism, and the calculated results may differ up to 16% in case of CRM I depending on the selection range of BET analysis. So this showed that a standard guideline for BET constant C value and fitting correlation coefficient R is needed, to properly select range in BET analysis. The experience in RRT, distribution of CRM, and standardized procedure would result in improved reliability in industrial processes, and thus, would contribute to the quality management, the productivity improvement, the safety evaluation, and the new material development.

• Journal of Radiation Protection and Research
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• v.40 no.3
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• pp.162-167
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• 2015

Although the perfomance indicators of the nuclear power plants in Korea show optimal, it requires detailed analysis and discussion centered on the radiation dose. As analysis methods, analysis on the radiation dose of nuclear power plants over the past five years was assessed by comparing the relevant radiation dose of radiation workers and per capita average annual radiation dose of the world's major nuclear power stations was also analyzed. The radiation workers over the annual radiation dose limit of 50 mSv were not. The contrast ratio of the radiation exposure according to the reactor type was the normal operation of PHWR was 6.2% higher than those of the PWR. This shows the radiation work of PHWR during normal driving operation is much more than those of PWR. According to the Performance Indicators of the World Association of Nuclear Operator, the annual radiation dose per unit in 2013 showed 527 man-mSv of Korea is the best country among the major nuclear power generating states, the world average was 725 man-mSv. The annual per capita radiation dose is about 80% less than 1 mSv of the public dose limit and also the average per capita dose showed a very low level as 0.82 mSv. Workers in related organizations showed 1.07 mSv, the non-destructive inspection agency workers showed 3.87 mSv. The remarkable results were due to radiation reduced program such as development of radiation shielding and radiation protection. In conclusion, the radiation exposured dose of nuclear power plants workers in Korea showed a trend which is ideally reduced. But more are expected to be difficul and the psychological insecurity against the operation of the nuclear power plants is existed to the residents near the nuclear power plants. So the radiation dose reduction policy and radiation dose follow up study of nuclear power plants will be continously excuted.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.55-78
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• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.139-148
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• 2020

The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.183-192
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• 2012

Although underground water resource has relatively less pollution rate compared with surface water, its recovery faces many difficulties due to poor management. Our country monitors underground water to manage it effectively through auxiliary observation network for underground water. In this paper, we suggest water-well auto measure system based on Geosensor for business efficiency increase of water-well management and realtime monitering. In this system is consist of user GUI(Graphic User Interface) composed with water-well information and movement sub-observation network prototype system composed with GPS(Global Positioning System) and wireless sensor node such as water temperature, water level, electrical conductivity. In this system is using the light of the sun for self-power, variety water-well information collected wireless sensor node was a wireless transmitting/receiving a using CDMA(Code Division Multiple Access) module. Also, for promote with user ease, user GUI express that water-well collected in GIS(Geographic Information System) map. For performance evaluation of the proposed system, we perform experiment using sensing information through designed sub-observation network. And we was proved superiority of the proposed system through qualitative evaluation with other paper.

• Membrane Journal
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• v.19 no.2
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• pp.165-171
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• 2009

This study was for the evaluation of adaptability of the fiber filter as the pre-treatment of the RO membrane through SDI (Silt Density Index) measurement. The turbidity of raw waters were $0.76{\sim}1.6$ NTU for the effluent of sewer treatment plants (STP) and $2.2{\sim}3.3$ NTU for sea waters and 100 NTU for the surface water. The turbidity of the $2^{nd}$ filtrate of the serially connected two fiber filters was $0.07{\sim}0.25$ NTU and $SDI_{15}$ was $1.4{\sim}2.8$ when the 17% PAC was dosed $10{\sim}30ppm$. Results of the turbidity and $SDI_{15}$ of the $2^{nd}$ filtrate of the fiber filter which were compared with them of the lab scale MF/UF disc filter for the same STP's effluents showed that filtrate quality were enhanced with a little on the order of two stage fiber filter>MF>UF, the difference in $SDI_{15}$ was only $0.7{\sim}1.0$. So, the filtrate of the serially connected two stage fiber filter could satisfy $SDI_{15}$ 5.0 safely which was normally required for the feed water by the RO membrane supplier and it means the serially connected two stage fiber filter could be applied as the pre-treatment process of the RO membrane.

• Journal of Korean Society of Transportation
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• v.36 no.2
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• pp.67-85
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• 2018

Vehicle platooning through wireless communication and automated driving technology has become realized. Platooning is a technique in which several vehicles travel at regular intervals while maintaining a minimum safety distance. Truck platooning is of keen interest because it contributes to preventing truck crashes and reducing vehicle emissions, in addition to the increase in truck flow capacity. However, it should be noted that interactions between vehicle platoons and adjacent manually-driven vehicles (MV) significantly give an impact on the performance of traffic flow. In particular, when vehicles entering from on-ramp attempt to merge into the mainstream of freeway, proper interactions by adjusting platoon size and inter-platoon spacing are required to maximize traffic performance. This study developed a methodology for establishing operational strategies for truck platoonings on freeway on-ramp areas. Average speed and conflict rate were used as measure of effectiveness (MOE) to evaluate operational efficiency and safety. Microscopic traffic simulation experiments using VISSIM were conducted to evaluate the effectiveness of various platooning scenarios. A decision making process for selecting better platoon operations to satisfy operations and safety requirements was proposed. It was revealed that a platoon operating scenario with 50m inter-platoon spacing and the platoon consisting of 6 vehicles outperformed other scenarios. The proposed methodology would effectively support the realization of novel traffic management concepts in the era of automated driving environments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.6
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• pp.501-506
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• 2017

An infrared seeking missile does not emit any signal by itself as it is guided by passive heat signature from an aircraft. Therefore, it is difficult for the target aircraft to notice the existence of incoming missile, making it a serious threat. The usage of MAW(missile approach warning) that can notify the approaching infrared seeking missile is currently limited due to its high cost. Furthermore, effectiveness of MAW against infrared seeking missile is not available in open literature. Therefore, effect of evasive maneuver by MAW on the survivability of the aircraft is simulated to evaluate the benefit of the MAW in this research. The lethal range is used as a measure of aircraft survivability. An aircraft flying at an altitude of 5km with Mach 0.9 being tracked by air-launched AIM-9 infrared seeking missile is considered in this research. As a variable for the evasive maneuver, the MAW recognition distance of 5~7km and the G-force of 3~7G that limits maximum directional change of the aircraft are considered. Simulation results showed that the recognition of incoming missile by MAW and following evasive maneuver can reduce the lethal range considerably. Maximum reduction in lethal range is found to be 29.4%. Also, the MAW recognition distance have a greater importance than the aircraft maneuverability that is limited by structural limit of the aircraft.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.