• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.368-373
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• 2003

This paper presents a study of low frequencies volume velocity vibration control of a smart panel in order to reduce sound transmission. A distributed piezoelectric quadratically shaped polyvinylidene fluoride (PVDF) polymer film is used as a uniform force actuator and an array of 4$\times$4 accelerometer is used as a volume velocity sensor for the implementation of a single-input single-output con rot system. The theoretical and experimental study of sensor-actuator frequency response function sho vs that this sensor-actuator arrangement provides a required strictly positive real frequency response function below about 900Hz. Direct velocity feedback could therefore be implemented with a limited gain which gives reductions of about 15㏈ in vibration level and about 8 ㏈ in acoustic power level at the (1, 1) mode of the smart Panel. It has been also shown that the shaping error of PVDF actuator could limit he stability and performance of the control system.

• Smart Structures and Systems
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• v.5 no.1
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• pp.43-54
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• 2009

This study introduces a novel approach for locating damage in a structure using wireless sensor system with local level computational capability to alleviate data traffic load on the centralized computation. Smart wireless sensor systems, capable of iterative damage-searching, mimic an optimization process in a decentralized way. The proposed algorithm tries to detect damage in a structure by monitoring abnormal increases in strain measurements from a group of wireless sensors. Initially, this clustering technique provides a reasonably effective sensor placement within a structure. Sensor clustering also assigns a certain number of master sensors in each cluster so that they can constantly monitor the structural health of a structure. By adopting a voting system, a group of wireless sensors iteratively forages for a damage location as they can be activated as needed. Since all of the damage searching process occurs within a small group of wireless sensors, no global control or data traffic to a central system is required. Numerical simulation demonstrates that the newly developed searching algorithm implemented on wireless sensors successfully localizes stiffness damage in a plate through the local level reconfigurable function of smart sensors.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.209-215
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• 2019

In order to provide a safe free-weight-training environment to people without workout trainers, we suggest a smart bench press machine with an automatic weight control system sensitive to user tiredness. Physical weight plates on the machine are replaced with a hydraulic cylinder as a press load and the cylinder knob is coupled with a step motor to change its tensile force automatically in-between lifting exercises. Three subjects participated to verify the usability of the smart bench press machine. They were asked to lift a 6-RM press load 10 times with 3 different lifting conditions: 1) no assistance, 2) a human assistance, and 3) the automatic weight control. All subjects were not able to complete the 10 sets without assistance due to tiredness, but they finished the full sets under the two assistive conditions. Average lifting speeds under the automatic weight control condition showed the most consistent level. Normalized quasi-tension data based on surface electromyogram signals of both Pectoralis Majors revealed that the subjects maintained the target muscle activation level above 50% but not more than 80% throughout the 10 sets. Therefore, the smart bench press machine is expected to both keep pace with the lifting exercise and reduce risk of injuries due to excessive muscle tensions.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.43-54
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• 2021

Currently, citizens' expectations and concerns on utilizing artificial intelligence (AI) technologies in the public sector are widening with the rapid digital transformation. Furthermore the level of global acceptance on the AI and other intelligent digital technologies is augmenting with the needs of non-face-to-face types of public services more than ever due to the unforeseen and unpredictable pandemic, COVID-19. Thus, this study intended to empirically examine what policy directions for the public should be considered to provide well-designed services as well as to promote the evidence-based public policies in terms of Al speaker technology as a non-contact smart city service. Based on the survey of senior citizens' perceptions on AI (AI Speaker technology), this study conducted structure equation modeling analyses to identify whether technology acceptance models on to the varied dependent variables such as actual use, perception, attitude, and brand royalty. The Results of the empirical analyses showed that AI increased the positive level of citizens' perception, attitude and brand royalty on non-contact public services (smart city services) which are becoming more crucial for developing AI oriented government and providing intelligent public services effectively. In addition, theoretical and practical implications are discussed for understanding the changes of public service in the post-corona.

• The Research Journal of the Costume Culture
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• v.30 no.2
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• pp.319-329
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• 2022

This study started from the practical need for a career experience program in the fashion major that is creative and responds to current methodologies. The purpose of this study is to propose a fashion work experience program that combines digital technology and practical experience. The research methods and procedures were as follows: fashion items and wearable devices were selected, the LED smart bag program was developed, and it was executed. A total of 123 students participated in the program, and a satisfaction survey was conducted after observation and oral evaluation. All of the participants completed the LED smart bag processes of design ideation, material selection, production, and styling using an eco-bag (one of the fashion items and as an LED wearable device). As a result of the participants' satisfaction (on a 5-point scale), most items showed a high level of satisfaction of 4.39 points or more. The smart bag program was evaluated to increase interest based on the time allotted and the students' level and to bolster their understanding of, and interest in, the fashion major. Therefore, this study is expected to be baseline to explore diversification of the fashion major work experience program, in order to create interest in the fashion major based on creative convergence competency.

• Journal of Technology Innovation
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• v.32 no.2
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• pp.59-82
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• 2024

This study estimates the impact of smart factories on workload, a direct indicator of labor demand, using information on smart factories from the Korea Labor Institute's Workplace Panel Survey. Overall, we find that the workload of production workers decreases as the level of smartification increases. Our heterogeneity analysis shows that the effect is concentrated among establishments producing the same product repeatedly. In contrast, we find that workload increases for managerial and technical occupations who need to put more effort into implementing more intelligent and connected production, and production workers at establishments pursuing product variety. These results are broadly consistent with the existing literature.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2322-2326
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• 2011

General cryptosystem uses differently the data and key value for the increment of security level, processes the repetition of limited number and increases the periodic feature of LFSR similar infinite series. So, it cause the efficiency of the cryptosystem. In this thesis, proposed algorithm is composed of reformat, permutation, data cipher block and key scheduler which is applied the new function by mixed symmetric cryptography and asymmetric cryptography. We design the cryptosystem of smart card using the common Synopsys and simulate by ALTERA MAX+PLUS II at 40MHz. Consequently, we confirm the 52% increment of processing rate and the security level of 16 rounds.

• Journal of Internet Computing and Services
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• v.19 no.2
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• pp.33-41
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• 2018

In recent years, technology is rapidly changing to support new service consumption and distribution models in multimedia service systems and hybrid delivery of media services is a key factor for enabling next generation multimedia services. This phenomenon can lead to rapidly increasing network traffic and ultimately has a direct and aggravating effect on the user's quality of service (QOS). To address the issue, we propose a novel system architecture to provide smart hybrid media services efficiently. The architecture is designed to apply the software-defined networking (SDN) method, detect changes in traffic, and combine the data, including user data, service features, and computation node status, to provide a service schedule that is suitable for the current state. To this end, the proposed architecture is based on 2-level scheduling, where Level-1 scheduling is responsible for the best network path and a computation node for processing the user request, whereas Level-2 scheduling deals with individual service requests that arrived at the computation node. This paper describes the overall concept of the architecture, as well as the functions of each component. In addition, this paper describes potential scenarios that demonstrate how this architecture could provide services more efficiently than current media-service architectures.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.69-80
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• 2023

Carbon neutrality in agriculture can be derived from systematic GHG reduction policies based on quantitative environmental impact analysis of GHG-emitting activities. This study is to explore how to advance the calculation of carbon emissions from agricultural activities to the detailed spatial level to a spatial Tier 3 level (Tier 2.5 level), methodologically beyond the Tier 2 approach. To estimate the GHG emissions beyond the Tier 2.5 level by region for detailed spatial units, we constructed available activity data on carbon emission impact factors such as rice cultivation, agricultural land use, and livestock. We also built and verified detailed data on emission activities at the field level through field surveys. The GHG emissions were estimated by applying the latest national emission factors and regional emission factors according to the IPCC 2019 GL based on the field-level activity data. This study has significance that it explored ways to build activity data and calculate GHG emissions through statistical data and field surveys based on parcels, one of the smallest spatial units for regional carbon reduction strategies. It is expected that by utilizing the activity data surveyed for each field and the emission factor considering the activity characteristics, it will be possible to improve the accuracy of GHG emission calculation and quantitatively evaluate the effect of applying reduction policies.