• Parasites, Hosts and Diseases
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• v.57 no.4
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• pp.417-422
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• 2019

From October 2015 to August 2018, tapeworm proglottids were obtained from 10 patients who were residents of Daegu and Gyeongbuk provinces and had a history of raw beef consumption. Most of them had no overseas travel experience. The gravid proglottids obtained from the 10 cases had 15-20 lateral uterine branches. A part of internal transcribed spacer 1 (ITS1) DNA of the 10 cases, amplified by polymerase chain reaction (PCR) and digested with AleI restriction enzyme, produced the same band pattern of Taenia saginata, which differentiated from T. asiatica and T. solium. Sequences of ITS1 and cytochrome c oxidase subunit 1 (cox1) showed higher homology to T. saginata than to T. asiatica and T. solium. Collectively, these 10 cases were identified as T. saginata human infections. As taeniasis is one of the important parasitic diseases in humans, it is necessary to maintain hygienic conditions during livestock farming to avoid public health concerns.

• Journal of Aerospace System Engineering
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• v.13 no.1
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• pp.1-10
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• 2019

The flow characteristics in a stirred tank are very useful in a wide variety of industrial applications. Generally, the flow pattern, power consumption and mixing time in stirred vessels depend not only on the design of the impeller, but also on the tanks' geometry and internal structure. In this study, the analysis of an unstable and unsteady complicated flow characteristics generated by the interaction between the baffle shape and impeller were performed using the ANSYS FLUENT LES Turbulence Model. The study compared the predictions of CFD with the interaction between two types of rotating impellers (axial and radial flows) and the shapes of three baffles. The results of the comparison verified that the design model showed a relatively efficient trend in the mixing flow fields and characteristics around the impeller and baffles during agitation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.568-574
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• 2020

In this paper, we propose a method to reduce the square root computation having high computation complexity in Canny edge detection algorithm using image processing. The proposed method is to reduce the number of operation calculating gradient magnitude using pixel's continuity using make a specific pattern instead of square root computation in gradient magnitude calculating operation. Using various test images and changing number of hole pixels, we can check for calculate match rate about 97% for one hole, and 94%, 90%, 88% when the number of hole is increased and measure decreasing computation time about 0.2ms for one hole, and 0.398ms, 0.6ms, 0.8ms when the number of hole is increased. Through this method, we expect to implement low power embedded vision system through high accuracy and a reduced operation number using two-hole pixels.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.850-858
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• 2022

To deploy Gate Recurrent Units (GRU) on resource-constrained embedded devices, this paper presents a reconfigurable FPGA-based GRU accelerator that enables structured compression. Firstly, a dense GRU model is significantly reduced in size by hybrid quantization and structured top-k pruning. Secondly, the energy consumption on external memory access is greatly reduced by the proposed reuse computing pattern. Finally, the accelerator can handle a structured sparse model that benefits from the algorithm-hardware co-design workflows. Moreover, inference tasks can be flexibly performed using all functional dimensions, sequence length, and number of layers. Implemented on the Intel DE1-SoC FPGA, the proposed accelerator achieves 45.01 GOPs in a structured sparse GRU network without batching. Compared to the implementation of CPU and GPU, low-cost FPGA accelerator achieves 57 and 30x improvements in latency, 300 and 23.44x improvements in energy efficiency, respectively. Thus, the proposed accelerator is utilized as an early study of real-time embedded applications, demonstrating the potential for further development in the future.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1023-1035
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• 2021

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Structural Engineering and Mechanics
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• v.86 no.3
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• pp.385-397
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• 2023

This study presents an experiment and analysis to compare the seismic behavior of full-scale reinforced concrete beam-column joint strengthened by prestressed steel strips, externally bonded steel plate, and CFRP sheets. For experimental investigation, five specimens, including one joint without any retrofitting, one joint retrofitted by externally bonded steel plate, one joint retrofitted by CFRP sheets, and two joints retrofitted by prestressed steel strips, were tested under cyclic-reserve loading. The failure mode, strain response, shear deformation, hysteresis behavior, energy dissipation capacity, stiffness degradation and damage indexes of all specimens were analyzed according to experimental study. It was found that prestressed steel strips, steel plate and CFRP sheets improved shear resistance, energy dissipation capacity, stiffness degradation behavior and reduced the shear deformation of the joint core area, as well as changed the failure pattern of the specimen, which led to the failure mode changed from the combination of flexural failure of beams and shear failure of joints core to the flexural failure of beams. In addition, the beam-column joint retrofitted by steel plate exhibited a high bearing capacity, energy consumption capacity and low damage index compared with the joint strengthened by prestressed steel strip, and the prestressed steel strips reinforced joint showed a high strength, energy dissipation capacity and low shear deformation, stirrups strains and damage index compared to the CFRP reinforced joint, which indicated that the frame joints strengthened with steel plate exhibited the most excellent seismic behavior, followed by the prestressed steel strips.

• Clean Technology
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• v.16 no.3
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• pp.182-190
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• 2010

The feasibility of clean technology to minimize the $CO_2$ emission by recycling and reuse the waste materials and energy have been studied for the cement industry. A life cycle assessment (LCA) was performed for an alternative raw material-supply method to use the molted slag as the major raw material in the cement clinker manufacturing. Using this new method, a 60% of $CO_2$ could be reduced that comes out during the decarboxylation from the cement rotary kiln. The energy-efficiency improvement and the alternative energy methods that had been determined in our previous study through the environmental assessment of cement industry were applied to the study for the reduction of $CO_2$ emission. The natural gas, one of the fossil fuels, was also used as the first choice to get the result at the earliest time by the most economic and the most efficient green technology and to switch into the carbon neutral energy consumption pattern.

• Journal of Korea Trade
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• v.26 no.6
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• pp.83-95
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• 2022

Purpose - With the recent spread of COVID-19, U.S. consumers' consumption pattern is changing towards purchasing large-capacity products, as they stay at home longer. Thus, the current research investigates the effects of box shape and component diversity for large-sized products on product evaluation in logistic business. Moreover, this research examines that information-processing fluency mediates the moderating effects of box shape and product components on target evaluations to confirm psychological mechanism for generating this effect. Design/methodology - In order to examine the hypotheses, the current research conducts two online experiments. The 184 participants (Study 1), and 205 participants (Study 2) of U.S. nationality were recruited through Amazon Mechanical Turk. This research analyzes the data by using SPSS 25 and PROCESS macro 4.0. Findings - Study 1 demonstrates that when the height of a box is greater than its width, products with single components promote positive target evaluations, while when the width of box is greater than its height, products with a variety of components lead to positive target evaluations. Study 2 shows that the same results are replicated in other product categories and with different box shape ratios. Moreover, Study 2 also finds that the ease of information processing mediates the interaction effects of box shape and component diversity on U.S. consumers' target evaluations. Originality/value - The current research has originality in that it investigates the effect of box shape and product composition diversity on U.S. consumer product evaluation from the perspective of information-processing theory Moreover, this research has practical implications for global traders who prepare for entering the U.S. market.