• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1032-1037
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• 2019

In the existing pharmacy automation system, quantity control was carried out only in the memory without first measuring the quantity of the medicine and during the operation of the facility. As a result, the number of drugs that have already been deducted during operation at the time of a facility error has not been managed. Serious problems are caused by important medicines that need to be managed because of lack of quantity control. In addition, the user had to refill the drug when it was exhausted because the user could not know the amount of the drug outside the facility. When the drug was out of stock, the facility was operating with loss of time to restart and the time to replenish the drug. Thus, in this paper, we designed and applied system to control the quantity of medicines frequently by adding displacement sensors to the robot of the facility.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.509-514
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• 2021

The pandemic of COVID-19 is driving the demand for non-face-to-face diagnosis, observation, and treatment in the healthcare environment, which has led to increased interest in helathcare robots. The authors intend to predict the direction in which the quarantine healthcare robots should be utilized in the post-corona era through analysis of national agency reports, on-offline press reports, and domestic and foreign robot company press releases. The COVID-19 pandemic has raised interest in medical robots. And there is a need to apply healthcare robots that can perform tasks such as disinfection, logistics transfer, screening tests, monitoring of patients, remote medical treatment support for isolated patients, and video calls with family members. Therefore, it is considered that future correct development and application of healthcare robots and empirical research to verify them should be continued based on sufficient consideration for various problems associated with the practical application of robots.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.349-356
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• 2022

Currently, the agricultural population in Korea indicates a decreasing and aging orientation. As the population of farm labor continues to decline, so farmers are feeling the pressure to be stable crop production. To solve the problem caused by the decreasing of farm labor, it is necessary to change over to "Digital agriculture". Digital agriculture is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture, and aims to integrate the several digital technologies into crop and livestock management and other processes in agriculture fields. In addition, digital agriculture can offer the opportunity to increase crop production, save costs for farmer. Therefore, in this study, for data-based Digital Agriculture, a greenhouse environment monitoring system for crop growth monitoring based on Node-RED, which even beginners can use easily, was developed, and the implemented system was verified in a hydroponic greenhouse. Several sensors, such as temperature, humidity, atmospheric pressure, CO₂, solar radiation, were used to obtain the environmental data of the greenhouse. And the environmental data were processed and visualized using Node-RED and MariaDB installed in rule.box digital. The environment monitoring system proposed in this study was installed in a hydroponic greenhouse and obtained the environmental data for almost two weeks. As a result, it was confirmed that all environmental data were obtained without data loss from sensors. In addition, the dashboard provides the names of installed sensors, real time environmental data, and changes in the last three days for each environmental data. Therefore, it is considered that farmers will be able to easily monitor the greenhouse environment using the developed system in this study.

• Journal of the Korean institute of surface engineering
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• v.56 no.4
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• pp.233-242
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• 2023

In recent years, energy-management studies in buildings have proven useful for energy savings. Typically, during heating and cooling, the energy from a given building is lost through its windows. Generally, to block the entry of ultraviolet (UV) and infrared (IR) rays, thin films of deposited metals or metal oxides are used, and the blocking of UV and IR rays by these thin films depends on the materials deposited on them. Therefore, by controlling the thicknesses and densities of the thin films, improving the transmittance of visible light and the blocking of heat rays such as UV and IR may be possible. Such improvements can be realized not only by changing the two-dimensional thin films but also by altering the zero-dimensional (0-D) nanostructures deposited on the films. In this study, 0-D nanoparticles were synthesized using a sol -gel procedure. The synthesized nanoparticles were deposited as deep coatings on polymer and glass substrates. Through spectral analysis in the UV-visible (vis) region, thin-film layers of deposited zinc oxide nanoparticles blocked >95 % of UV rays. For high transmittance in the visible-light region and low transmittance in the IR and UV regions, hybrid multiple layers of silica nanoparticles, zinc oxide particles, and fluorine-doped tin oxide nanoparticles were formed on glass and polymer substrates. Spectrophotometry in the UV-vis-near-IR regions revealed that the substrates prevented heat loss well. The glass and polymer substrates achieved transmittance values of 80 % in the visible-light region, 50 % to 60 % in the IR region, and 90 % in the UV region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.241-250
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• 2023

Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies. Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis. Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword "unmanned cafe" was at the center of the keyword cluster. Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.32-42
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• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Journal of Sensor Science and Technology
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• v.7 no.2
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• pp.124-130
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• 1998

High-speed 3D vision systems are essential for autonomous robot or vehicle control applications. In our study, a stereo vision process has been developed. It consists of three steps : extraction of edges in right and left images, matching corresponding edges and calculation of the 3D map. This process is implemented in a VME 150/40 Imaging Technology vision system. It is a modular system composed by a display, an acquisition, a four Mbytes image frame memory, and three computational cards. Programmable accelerator computational modules are running at 40 MHz and are based on TMS320C31 DSP with a $64{\times}32$ bit instruction cache and two $1024{\times}32$ bit internal RAMs. Each is equipped with 512 Kbytes static RAM, 4 Mbytes image memory, 1 Mbytes flash EEPROM and a serial port. Data transfers and communications between modules are provided by three 8 bit global video bus, and three local configurable pipeline 8 bit video bus. The VME bus is dedicated to system management. Tasks between DSPs are distributed as follows: two DSPs are used to edges detection, one for the right image and the other for the left one. The last processor computes the matching process and the 3D calculation. With $512{\times}512$ pixels images, this sensor generates dense 3D maps at a rate of about 1 Hz depending of the scene complexity. Results can surely be improved by using a special suited multiprocessors cards.

• Korean Journal of Construction Engineering and Management
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• v.1 no.1 s.1
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• pp.87-94
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• 2000

Crack sealing, a routine and necessary part of pavement maintenance, is a dangerous, costly, and labor-intensive operation. Within the North America, about ${\$}200$ million is spent annually on crack sealing, with the Texas Department of Transportation (TxDOT) spending about ${\$}7$ million annually (labor alone accounts for over 50 percent of these costs). Prompted by concerns of safety and cost, the University of Texas at Austin, in cooperation with TxDOT and the Federal Highway Administration (FHWA) has developed a unique computer-guided Automated Road Maintenance Machine (ARMM) for pavement crack sealing. In 1999, successful field tests have been undertaken in 8 States around the U.S. This paper first describes significance of the automated crack sealing and technical advances in automated crack sealers including the ARMM, developed in the U.S. It then discusses the ARMM's field implementation and performance evaluation results, and improvements and modifications suggested through the technology evaluation during the field trials. Current research efforts and future work plans in its further development are also presented in this paper.

• Knowledge Management Research
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• v.22 no.3
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• pp.235-252
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• 2021

The development of information and communication technology and COVID-19 have caused an unusual change in the hotel industry, and the demand for the contactless services such as service robots from hotel customers has surged. Therefore, this study investigates the perception of hotel customers on contactless services by applying a mixed-method analysis. Specifically, this study identified the causal correlations between variables through the structural equation model, and further applied the fuzzy set qualitative comparison analysis to derive patterns of variables that form the intention to use the non-face-to-face services. As a result of the analysis, it was shown that service experience co-creation, palyfulness, personalization, and trust had a significant effect on intention to use through the contactless service use desire. On the other hand, in the results of fuzzy-set qualitative comparison analysis, playfulness was derived as a core factor in all patterns. Based on these analysis results, this study provides academic basis for in-depth understanding of hotel customers' perception of contactless service and specific guidelines for hotel managers on the contactless service strategies in the era of COVID-19 pandemic.