• Animal Bioscience
• /
• v.34 no.4
• /
• pp.575-583
• /
• 2021

Objective: The aim of the study was to determine the effect of parity and the stage of lactation on the metabolic profile of cows based on the basic chemical milk components and the blood parameters. Methods: The study material consisted of high-yielding Holstein-Friesian cows. In total, 473 cows were examined. According to the parity, cows were divided into four groups: primiparous (P), and multiparous in the second (M2), in the third (M3), and in subsequent lactations (M4). The feeding of cows was based on total mixed ration (TMR) ad libitum. Milk and blood samples were collected individually from each cow three times per standard lactation period. Results: Greater exacerbation of changes in the dynamics of the blood plasma parameters examined was proved for multiparous cows. The highest value of β-hydroxybutyrate acid (0.946 mmol/L) was found for multiparous cows from group M3 at the beginning of lactation. However, it was still in the normal range. The results showed aspartate aminotransferase, and gamma-glutamyl transferase (GGT) activities in dairy cows during lactation had significant variations taking in to account stage of lactation. The highest activity of GGT was found in the group of the oldest cows and measured from 26.36 U/L at the beginning of lactation to 48.75 U/L at the end of the lactation period. Conclusion: The time-related changes in the concentrations of the biochemical parameters described differ markedly among lactating cows, though the housing conditions on the research dairy farm are highly standardised. This indicates that the ability to cope with metabolic stress is mainly affected by the individual predispositions of cows and feed nutrient supply in different stage of lactation. Especially, the feed nutrient supply (in net energy for lactation), which was the best in TMR 1 in comparison TMR 3.

• Journal of Drive and Control
• /
• v.18 no.1
• /
• pp.1-8
• /
• 2021

This paper presents research on methods for the reduction of forklifts' noise level for the increased comfort and safety of its operator. A cooling fan with a high air volume flow rate installed in the forklift acts as an important design parameter which efficiently cools the heat exchanger system, helping to transfer internal heat from the engine room to the outdoors with both transmitted and diffracted opening noises. The cooling fan contributes significantly to both the forklift's emitted sound power and the operator room's noise level, thereby necessitating research on the forklift's reduction of acoustic power level and transmission. A noise analysis for various fan models with a biomimetic design based on eagle-wing geometry was conducted. In addition to the acoustic power generation, the aerodynamic performance of the cooling blade is also strongly influenced by the design of airfoil distribution, thereby requiring optimization. The cooling fans were fabricated and installed in the forklift in order to check the efficacy of the forklift engine's cooling, and the final version of the fan was measured for its ability to lower acoustic power level and cool the engine room. This study explains the aerodynamic and acoustic features of the designed fans with the use of BEM analysis and forklift test results.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.1
• /
• pp.539-544
• /
• 2021

The tactical data link is a military data network to improve the ability to recognize battlefield situations. The ROK military is promoting the tactical data link performance improvement programs. Tactical data link is essential to combine and integrate various platforms, sensor data, and command and control (C2) systems. Therefore, the research on related technical fields is required. However, the tactical data link has not disclosed detailed technical information due to the characteristics of military operation. In this paper, we propose a data-based automated analysis methodology using intellectual property information to understand the technology trend of tactical data link. In this paper, data related to intellectual property is automatically collected and pre-processed, and analyzed in terms of time series. In addition, the current status of each institution of patent technology information was generated, and the process of identifying key-researchers through network analysis was presented with providing results of our approach in this paper.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.1
• /
• pp.87-92
• /
• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• Land and Housing Review
• /
• v.11 no.4
• /
• pp.93-98
• /
• 2020

This study examined the particulate removal performance of three different types of air filters inside a heat exchanger. Of interest was the ability of each filter type in reducing the transmission of outdoor particulate matter of PM10 from entering an apartment while the heat exchanger was in operation. The study tested one commonly used medium filter (E11 grade) and two HEPA filters (H13 grade) of different thicknesses. Two different concentrations of particulate matter were used in the experiment to address different ambient air quality conditions in Korea, 32.75 ㎍/㎥ and 67.26 ㎍/㎥. Study results indicated that under the particulate matter concentration of 32.75 ㎍/㎥, all three filters were capable of removing more than 95% of the fine dust. However at a particulate matter concentration of 67.26 ㎍/㎥, the medium E11 grade filter was only able to remove about 90% of the particulates whereas the HEPA H13 grade filters were able to remove 95% or more of the particulates. The thicker HEPA filter (40T) was also more effective in removing particulates than the thinner HEPA filter (20T) by about 1.6 to 3 percentage points. Based on the findings of this study, it is recommended that HEPA filters of 20T thickness or greater be used during the high air pollution seasons of winter and spring in Korea while medium filters can be used during the other seasons to reduce outdoor air pollution transmission indoors.

• Applied Chemistry for Engineering
• /
• v.22 no.3
• /
• pp.332-335
• /
• 2011

As our interests in eco-friendly materials have been significantly increased, the utilization of porous zeolite concrete that has structural functionality and permeability has been increased. In this paper, the mixture of porous concrete and zeolite, which can be used as multirole boulders, was investigated for the suitability of an environment-friendly product by evaluating of the water purification ability. The contamination removal rates of BOD, TOC, T-N, and T-P in stagnant water tank were 70.6, 67.0, 57.7, and 50.6%, respectively. Also for the non-point source pollution with the inflow and the outflow, the removal rates of Zn, Pb, BOD, and COD were 99.9, 90.0, 69.2, and 33.5%, respectively. The performance of the heavy metal contamination removal for the porous zeolite showed better than that of stagnant system. Therefore, it is expected that the installation of the porous zeolite concrete can play a role as an eco-friendly products by its high contamination removal.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.4
• /
• pp.93-102
• /
• 2008

Fingerprint sensor interoperability refers to the ability of a system to compensate for the variability introduced in the finger data of individual due to the deployment of different sensors. The purpose of this paper is the development of a compensation algorithm by which the interoperability of fingerprint recognition can be improved among various different fingerprint sensors. In this paper we show that a simple transformation derived to form a Taylor series expansion can be used in conjunction with a set of corresponding minutia points to improve the correspondence of finer fingerprint details within a fingerprint image. This is demonstrated by an applying the transformation to a database of fingerprint images and examining the minutiae match scores with and without the transformation. The EER of the proposed method was improved by average 60.94% better than before compensation.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.5
• /
• pp.47-58
• /
• 2011

Recently, there are several technologies for protecting information in the lightweight device, One of them, the AES[1] algorithm and CRT mode, is used for numerous services(e,g, OMA DRM, VoIP, IPTV) as encryption technique for preserving confidentiality. Although it is possible that the AES algorithm CRT mode can parallel process transmitting data, IPTV Set-top Box or Mobile Device that uses these streaming service has limited computation-ability. So optimizing crypto algorithm and enhancing its efficiency for those environment have become an important issue. In this paper, we propose implementation method that can improve efficiency of the AES-CRT Mode by improving algorithm logics. Moreover, we prove the performance of our proposal on the mobile device which has limited capability.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.120-130
• /
• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.

• Animal Bioscience
• /
• v.34 no.3_spc
• /
• pp.354-362
• /
• 2021

Broiler chickens grow rapidly, and their nutrient requirements change daily. However, broilers are fed three to five diet phases, meaning nutrients are under or oversupplied throughout production. Increasing diet phases improves production efficiency as there is less time in the production cycle that nutrients are in under or over-supply. Nevertheless, the process of administering four or more diets is costly and often impractical. New technologies are now available to blend feed to match the daily nutrient requirements of broilers. Thus, the aim of this review is to evaluate previous studies measuring the impact of increasing feed phases on nutrient utilisation and growth performance, and review recent studies taking this concept to the extreme; precision nutrition - feeding a new diet for each day of the production cycle. This review will also discuss how modern precision feeding technologies have been utilised and the potential that new technologies may bring to the poultry industry. The development of a precision nutrition regime which targets daily requirements by blending dietary components on farm is anticipated to improve the efficiency of production, reduce production cost and therefore improve sustainability of the industry. There is also potential for precision feeding technology along with precision nutrition strategies to deliver a plethora of other management and economic benefits. These include increased fluidity to cope with sudden environmental or market changes, and the ability to alter diets on a farm by farm level in a large, integrated operation. Thus, the future possibilities and practical implications for such technologies to generate a paradigm shift in feed formulation within the poultry industry to meet the rising demand for animal protein is also discussed.