• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.83-92
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• 2019

This qualitative study employed focused group interviews to collect data on the forgetfulness experienced by elderly persons (over the age of 65) who suffer with subjective memory impairment. The participants were ten elderly persons who participated in the cognitive function improvement program at an elderly welfare center in D city. They were divided into three groups that were comprised of three/four people each, and each group was interviewed for 40~60 minutes. The results showed that "difficulties in human relations", "feeling uncomfortable in daily life", "exposure to a safety risk", "inexpressible ambivalence" and "family effort to overcome forgetfulness" were all expressed in the interviews. The elderly who participated in this study and who complained of subjective memory impairment had both feelings of anxiety and anxiety about non-dementia, and they were making efforts to overcome forgetfulness themselves. Based on this study, we suggest that interventions should be developed that reflect the individual needs of elderly people with subjective memory impairment.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.60-64
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• 2022

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.

• ETRI Journal
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• v.44 no.2
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• pp.300-312
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• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.3
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• pp.376-382
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• 2010

It has been shown that BF-7 enhances short- and long-term memory and attention in normal person. BF-7 was addressed to clinical study for children if BF-7 is also effective to children, since accumulated verification of safety and effectiveness is needed for young ages, in special. We administered BF-7 and a placebo control to two different groups of children (7-12 years old, 9.78 on averages). Their memory enhancement was tested with Rey-Kim Memory Test for Children before and after the administration of BF-7 and a placebo, in a double blinded way. The results showed that long- and short-term memories were significantly improved by the administration of BF-7. Interestingly, the degree of memory preservation, the ability of memory application and awareness of complex thing were also significantly improved. These results indicate that BF-7 is a promising substance from natural resource improving learning and memory of children as well as cognitive function of adults

• Journal of the Ergonomics Society of Korea
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• v.30 no.2
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• pp.339-347
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• 2011

Objective: The purpose of this study was to evaluate of various material of pillows by using biomechanical variables such as the cervical stability, head pressure distribution, and muscle activity. Method: Eight subjects participated in the experiment. Three different materials such as polyester sponge, memory foam and the buckwheat shell used for Korean traditional pillow were tested. Electro-goniometer, six channels of electromyography(EMG), ten channels of the head pressure sensors were used to measure the biomechanical responses. Surface electrodes were attached to the right/left semispinals capitis(RSC, LSC), the right/left sternocleidomastoid(RSM, LSM), the right/left upper trapezius(RUT, LUT). The cervical stability was evaluated by the angle deviated from the standing neck position. The head pressure distribution was evaluated by the pressure per unit area recorded on the sensors and the intensity of peak pressure. Electromyography(EMG) data were analyzed by using root mean square(RMS) and mean power frequency(MPF). Results: The buckwheat shell material showed a higher stability in the cervical spine then the other pillows during spine position. In terms of head pressure distribution, the memory form indicated the lowest pressure at supine position, buckwheat shell material indicated the lowest pressure during lying down to side, and polyester cushion recorded the highest pressure at all postures. Conclusion: The buckwheat shell material has a biomechanical advantage to maintain a healthy neck angle and reduce the pressure on the head, which means the buckwheat shell is a potential material for ergonomic pillow design. The pillow with memory form showed second best biomechanical performance in this study. Application: The shape of the buckwheat shell pillow and the characteristics of materials can be used to design the pillow preventing neck pain and cervical disk problems.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.497-507
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• 2017

During recent earthquakes, a significant number of concrete structures suffered extensive damage. Conventional reinforced concrete structures are designed for life-time safety that may see permanent inelastic deformation after severe earthquakes. Hence, there is a need to utilize adequate materials that have the ability to tolerate large deformation and get back to their original shape. Super-elastic shape memory alloy (SMA) is a smart material with unique properties, such as the ability to regain undeformed shape by unloading or heating. In this research, four different stories (three, five, seven and nine) of reinforced concrete (RC) buildings have been studied and subjected to near-field ground motions. For each building, two different types of reinforcement detailing are considered, including (1) conventional steel reinforcement (RC frame) and (2) steel-SMA reinforcement (SMA RC frame), with SMA bars being used at plastic zones of beams and steel bars in other regions. Nonlinear time history analyses have been performed by "SeismoStruct" finite element software. The results indicate that the application of SMA materials in plastic hinge regions of the beams lead to reduction of the residual displacement and consequently post-earthquake repairs. In general, it can be said that shape memory alloy materials reduce structural damage and retrofit costs.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.430-438
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• 2021

The spatial distribution of neutron flux or reaction rate was calculated by cell or mesh tally in traditional Monte Carlo simulation. However, either cell or mesh tally leads to the increase of memory consumption and simulation time. In this paper, the function expansion tally (FET) method was developed in Reactor Monte Carlo code RMC to solve this problem. The FET method was applied to the tallies of neutron flux distributions of uranium block and PWR fuel rod models. Legendre polynomials were used in the axial direction, while Zernike polynomials were used in the radial direction. The results of flux, calculation time and memory consumption of different expansion orders were investigated, and compared with the mesh tally. Results showed that the continuous distribution of flux can be obtained by FET method. The flux distributions were consistent with that of mesh tally, while the memory consumption and simulation time can be effectively reduced. Finally, the convergence analysis of coefficients of polynomials were performed, and the selection strategy of FET order was proposed based on the statistics uncertainty of the coefficients. The proposed method can help to determine the order of FET, which was meaningful for the efficiency and accuracy of FET method.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Smart Structures and Systems
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• v.33 no.5
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• pp.325-332
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• 2024

This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained time-series data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data.