• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1245-1251
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• 2012

Recently, needs of recreational marine industry are consistently increasing due to increasing of income and pursuit on well-being. For these needs, central government and local governments have tried to build tens of marinas to meet demands and promote related industries. Therefore, need of smart pedestal system has also increased. However, domestic companies have not manufactured smart pedestal systems, so many pedestal systems have been imported from advanced countries such as England, Denmark, and Italy. This research has developed a smart pedestal system using IT convergence technology. This system is assembled by each several module which has its own function and board. Each module is not developed on an embedded OS. Instead, it has its customized operational software based on its kernel. Thus, this system brings about cost reduction of productions and maintenance.

• Smart Structures and Systems
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• v.3 no.4
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• pp.405-422
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• 2007

A new hybrid damping technique for vibration reduction in flexible structures, wherein a combination of layers of hard passive damping alloys and active (smart) magnetostrictive material is used to reduce vibrations, is proposed. While most conventional vibration control treatments are based exclusively on either passive or active based systems, this technique aims to combine the advantages of these systems and simultaneously, to overcome the inherent disadvantages in the individual systems. Two types of combined damping systems are idealized and studied here, viz., the Noninteractive system and the Interactive system. Frequency domain studies are carried out to investigate their performance. Finite element simulations using previously developed smart beam elements are carried out on typical metallic and laminated composite cantilever beams treated with hybrid damping. The influence of various parameters like excitation levels, frequency (mode) and control gain on the damping performance is investigated. It is shown that the proposed system could be used effectively to dampen the structural vibration over a wide frequency range. The interaction between the active and passive damping layers is brought out by a comparative study of the combined systems. Illustrative comparisons with 'only passive' and 'only active' damping schemes are also made. The influence and the mode dependence of control gain in a hybrid system is clearly illustrated. This study also demonstrates the significance and the exploitation of strain dependency of passive damping on the overall damping of the hybrid system. Further, the influence of the depthwise location of damping layers in laminated structures is also investigated.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.199-203
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• 2019

In this paper, we propose the efficient impulsive noise mitigation scheme for power line communication (PLC) systems in smart grid applications. The proposed scheme estimates the channel impulsive noise information of receiver by applying machine learning. Then, the estimated impulsive noise is updated in data base. In the modulator, the impulsive noise which reduces the PLC performance is effectively mitigated through proposed technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the conventional model. As a result, the proposed noise mitigation improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC systems for smart grid.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.21-30
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• 2022

Renewable energy systems aim to achieve carbon neutrality and replace fossil fuels. Photovoltaic technologies are the most widely used renewable energy. However, they require a large operating area, thereby decreasing available farmland. Accordingly, agrivoltaic systems (AVSs)-innovative smart farm technologies that utilize solar energy for crop growth and electricity production-are attracting attention. Although several empirical studies on these systems have been conducted, comprehensive research on their design is lacking, and no standard model suitable for South Korea has been developed. Therefore, this study created an integral design of AVS reflecting domestic crop cultivation conditions and conducted a structural analysis for safety assessment. The shading ratio, planting distance, and agricultural machinery work of the system were determined. In addition, national construction standards were applied to evaluate their structural safety using a finite element analysis. Through this, the safety of this system was ensured, and structural considerations were put forward. It is expected that the AVS model will allow for a stable utilization of renewable energy and smart farm technologies in rural areas.

• Asia pacific journal of information systems
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• v.26 no.4
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• pp.561-576
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• 2016

This paper aims to develop a conceptual model of smart tourism destination competitiveness to provide implications in terms of smart tourism destination realization and smart tourism destination competitiveness development. A literature review on tourism destination competitiveness and smart tourism destination is performed. A conceptual model is suggested on the basis of the model of destination competitiveness developed by Crouch and Ritchie (1999). The suggested conceptual model integrates the traditional concepts of comparative advantages and competitive advantages, seven core resources and attractors, and five destination management factors. Smart technology is included as a new core resource and attractor in the model. This study is the first to comprehensively conceptualize smart tourism destination competitiveness. Moreover, this study has practical value in the sense that it focused on the convergence between smart technology and other factors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.457-468
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• 2023

The purpose of this study is to develop a high-speed combine harvester. The performance was evaluated by composing a dynamic simulation model of a threshing cylinder and analyzing the amount of threshed rice grain during threshing operations. The rotational speed of the threshing cylinder was set at 10 rpm intervals from 500 rpm until 540 rpm, based on the rated rotational speed of 507 rpm. The rice stem model was developed using the EDEM software using measured rice stem properties. Multibody dynamics software was utilized to model the threshing cylinder and tank comprising five sections below the threshing cylinder, and the threshing performance was evaluated by weighing the grain collected in the threshing tank during threshing simulations. The simulation results showed that section 1 and 2 threshed more grains compared to section 3 and 4. It was also found that when the threshing speed was higher, the larger number of grains were threshed. Only simulation was conducted in this study. Therefore, the validation of the simulation model is required. A comparative analysis to validate the simulation model by field experiment will be conducted in the future.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.251-259
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• 2017

Recently, the smart healthcare industry that grafted the Internet of Things (IoT) onto the healthcare and medical services is being highlighted. Smart healthcare refers to the healthcare and medical services offered on the basis of information communication technologies including application, wearable devices and platforms, etc. The number of next generation smart healthcare devices are increasing in the smart healthcare industry through the combination of information technology (IT) and Bio Technology (BT), which are the most active areas among the 6T, which are the areas of the next generation industry. With the emergence of a diverse range of smart healthcare systems or devices, whether the smart healthcare system can be linked with other systems organically and the security and quality of the system have become important elements of evaluation. In this Study, the characteristics and service types of smart healthcare systems were examined, and the trends in the technology and industry of the smart healthcare system were analyzed. Moreover, this Study aims to develop the evaluation method for security and standards for quality evaluation by deducing the factors for the evaluation of smart healthcare system on the basis of the results of aforementioned examination. It is anticipated that this can induce improvement of quality and development of highly reliable products of a smart healthcare system, and will become the core technology to substitute the technology protection barrier.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.