• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.3-10
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• 2024

The purpose of this study is to analyze the effects of smart construction equipment applied at the construction site of the first smart city in the Korea and derive an application strategy for the utilization of smart construction equipment. To achieve this, aythors examined the practical effects and issues of safety systems and construction systems utilizing machine guidance (MG) technology, which is a representative smart construction equipment used in civil engineering construction sites. Both the MG safety system and MG construction system were found to be sufficiently effective in improving construction productivity. However, there are challenges that need to be addressed, such as the approval process for work results using MG systems, system changes due to frequent replacement of on-site equipment, and usability improvements for elderly on-site workers. The study presented some solutions that have been implemented on-site to address these issues. The utilization effects and issues presented in the study were analyzed through direct feedback from workers and managers who have utilized the MG technology on-site for a considerable period of time. These results can be used as preliminary data for the similar construction projects, considering the limited availability of empirical analysis data for equipment automation.

• ETRI Journal
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• v.36 no.5
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• pp.741-751
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• 2014

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.8
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• pp.872-878
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• 2008

Many industrial operations require continuous or nearly-continuous operation of machines, interruption of which can result in significant cost loss. The condition monitoring of these machines has received considerable attentions in recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is to develop a new type of smart sensor for on-line condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This system is capable for signal preprocessing task and analog to digital converter which is controlled by CPU. This sensor communicates with a remote site PC using TCP/IP protocols. The developed sensor executes performance tests for data acquisition accuracy estimations.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1107-1112
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• 2016

This study is the Smart Office features that take advantage of KEPCO eIoT(: energy Internet of Thing) platform, and it's Network configuration is composed of sensing device, gateway, platform, and the service server. The key features are parts for processing protocol data between the gateway and the device using LoRa(: Long Range) technology, Intelligent applications and public safety data connected to the PS-LTE(: Public Safety-Long-Term Evolution) system. And the resource tree provided Smart Office for the service, which commonly used in the application server and the device.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.850-852
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• 2014

Recently, the development of Machine to Machine (M2M) communication has been largely accomplished in a variety of fields including smart home. In M2M communication, the role of sensor node is only limited to gather data and send them to upper application layers. In this research, the limited role of the sensor node in traditional M2M communication is improved in order for the devices to make inference, which makes it possible to provide basic context-aware services within sensor node level. Therefore, implementation of rule-based inference system on microcontroller for smart home is proposed.

• Smart Media Journal
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• v.11 no.9
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• pp.47-55
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• 2022

Quality control in the smart factory manufacturing process is an important factor. Currently, quality inspection of home appliance manufacturing parts produced by the mold process is mostly performed with the naked eye of the operator, resulting in a high error rate of inspection. In order to improve the quality competition, an automatic defect detection system was designed and implemented. The proposed system acquires an image by photographing an object with a high-performance scan camera at a specific location, and reads defective products due to scratches, dents, and foreign substances according to the vision inspection algorithm. In this study, the depth-based branch decision algorithm (DBD) was developed to increase the recognition rate of defects due to scratches, and the accuracy was improved.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.1
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• pp.85-109
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• 2023

Laundry has traditionally been regarded as one of the most demanding household chores, but the introduction of smart washing machines is changing this perception. Although smart washing machines have been on the market for several years and consumers' perceptions of washing machines have changed, little is known about consumers' perceptions of smart washing machines. The purpose of this study is to determine what users expect from smart washing machines. We conducted two focus group interviews with sixteen participants who had used smart home appliances to acquire qualitative data. Stimuli created by the interviewees were applied in the focus group interviews to collect more insightful data. We analyzed the data using the three-step method and QSR NVivo. Analysis revealed ten categories of predictive expectations, including seven utilitarian attributes (i.e., smart functionality, smart user interface, reliability, controllability, interactivity, functional value, and economic value) and three hedonic attributes (i.e., fashionable value, psychological value, and social value). The results of this study have implications for the development of smart washing machines that would satisfy consumers by taking user expectations into account.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.523-529
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• 2009

Smart sensor is known as intelligent sensor, it is different with other conventional sensors in the case of intelligent system embedded on it. Smart sensor has many benefits e.g. low-cost in usage, self-decision and self-diagnosis abilities. This sensor consists of perception element(sensing element), signal processing and technology of communication. In this work, a bridge and structure of smart sensor has been investigated to be capable to condition monitoring routine. This investigation involves low power consumption, software programming, fast data acquisition ability, and authoritativeness warranty. Moreover, this work also develops smart sensor to be capable to perform high sampling rate, high resolution of ADC, high memory capacity, and good communication for data transfer. The result shows that the developed smart sensor is promising to be applied to various industrial fields.