• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.35 no.3
• /
• pp.297-302
• /
• 2022

We investigated the origin of magnetic behaviors induced by an asymmetric spin exchange interaction in Fe-site engineered lead iron niobate [Pb(Fe_1/2Nb_1/2)O₃, PFN], which exhibits a room-temperature multiferroicity. The magnitude of spin exchange interaction was regulated by the introduced transition metals with a distinct Bohr magneton, i.e., Cr, Co, and Ni. All compositions were found to have a single-phase perovskite structure keeping their ferroelectric order except for Cr introduction. We discovered that the incorporation of each transition metal imposes a distinct magnetic behavior on the lead iron niobate system; antiferro-, hard ferro-, and soft ferromagnetism for Cr, Co, and Ni, respectively. This indicates that orbital occupancy and interatomic distance play key roles in the determination of magnetic behavior rather than the magnitude of the individual Bohr magneton. Further investigations are planned, such as X-ray absorption spectroscopy, to clarify the origin of magnetic properties in this system.

• Korean Journal of Ecology and Environment
• /
• v.54 no.4
• /
• pp.265-271
• /
• 2021

Intertidal mud crab (Macrophthalmus japonicus) is an organism with a hard chitinous exoskeleton and has function for an osmotic control in response to the salinity gradient of seawater. Crustacean exoskeletons change in their natural state in response to environmental factors, such as changes in the pH and water temperature, and the presence of pollutant substances and pathogen infection. In this study, the ecotoxicological effects of irgarol exposure and heavy metal distribution were presented by analyzing the surface roughness of the crab exoskeleton. The exoskeleton surface roughness and variation reduced in M. japonicus exposed to irgarol. In addition, it was confirmed that the surface roughness and variation were changed in the field M. japonicus crab according to the distribution of toxic heavy metals(Cd, Pb, Hg) in marine sediments. This change in the surface roughness of the exoskeleton represents a new end-point of the biological response of the crab according to external environmental stressors. This suggests that it may affect the functional aspects of exoskeleton protection, support, and transport. This approach can be utilized as a useful method for monitoring the aquatic environment as an integrated technology of mechanical engineering and biology.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.2
• /
• pp.65-70
• /
• 2023

Nanoimprint lithography (NIL) has attracted much attention due to its process simplicity, excellent patternability, process scalability, high productivity, and low processing cost for pattern formation. However, the pattern size that can be implemented on metal materials through conventional NIL technologies is generally limited to the micro level. Here, we introduce a novel hard imprint lithography method, extreme-pressure imprint lithography (EPIL), for the direct nano-to-microscale pattern formation on the surfaces of metal substrates with various thicknesses. The EPIL process allows reliable nanoscopic patterning on diverse surfaces, such as polymers, metals, and ceramics, without the use of ultraviolet (UV) light, laser, imprint resist, or electrical pulse. Micro/nano molds fabricated by laser micromachining and conventional photolithography are utilized for the nanopatterning of Al substrates through precise plastic deformation by applying high load or pressure at room temperature. We demonstrate micro/nanoscale pattern formation on the Al substrates with various thicknesses from 20 ㎛ to 100 mm. Moreover, we also show how to obtain controllable pattern structures on the surface of metallic materials via the versatile EPIL technique. We expect that this imprint lithography-based new approach will be applied to other emerging nanofabrication methods for various device applications with complex geometries on the surface of metallic materials.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.12
• /
• pp.51-57
• /
• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.31 no.4 s.54
• /
• pp.289-293
• /
• 2005

Nowadays there are many sun protection cosmetics including organic or inorganic UV filter as an active ingredient. Chemically stable inorganic sunsEreen agents, usually metal oxides, we widely employed in high SPF products. Titanium dioxide is one of the most frequently used inorganic UV filters. It has been used as pigments for a long period of cosmetic history. With the development of micronization techniques, it becomes possible to incorporate titanium dioxide in sunscreen formulations without whitening effect and it becomes an important research topic. However, there are very few works related to quantitations of titanium dioxide in sunscreen products. In this research, we analyzed amounts of titanium dioxide in sunscreen cosmetics by adapting redof titration, reduction of Ti(IV) to Ti(III) and reoxidation to Ti(IV). After calcification of other organic ingredients of cosmetics, titanium dioxide is dissolved by hot sulfuric acid. The dissolved Ti(IV) is reduced to the Ti(III) by adding aluminum metals. The reduced Ti(III) is titrated against a standard oxidizing agent, Fe(III) (ammonium iron(III) sulfate), with potassium thiocyanate as an indicator In order to test accuracy and applicability of the proposed method, we analyzed the amounts of titanium dioxide in four types of sunscreen cosmetics, such as cream, make-up base, foundation and powder, after adding known amounts of titanium dioxide $(1{\sim}25w/w%)$. The percent recoveries of the titanium dioxide in four types of formulations were in the range between 96 and 105%. We also analyzed 7 commercial cosmetic products labeled titanium dioxide as an ingredient and compared the results with those of obtained from ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry), one of the most powerful atomic analysis techniques. The results showed that the titrated amounts were well coincided with the analyzed amounts of titanium dioxide by ICP-AES. Although instrumental analytical methods, ICP-MS (Inductively Coupled Plasma-Mass Spectrometry) and ICP-AES, are the best for the analysis of titanium, it is hard to adopt because of their high prices for small cosmetic companies. It was found that the volumetric method presented here gat e quantitative and reliable results with routine lab-wares and chemicals.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.127-146
• /
• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Preventive Medicine and Public Health
• /
• v.29 no.1 s.52
• /
• pp.15-26
• /
• 1996

It has been studied that a variety of fauna and flora are sensitive biological indicators which reflect the severity of regional pollution of heavy metals, but in the center of part of Taegu City the controversial issue of lead poisoning attributable to the atmosphere which contains an increased concentrations of lead has been raised recently, it is usually hard to find suitable plants or animal in the areas with heavy traffic. Pigeons are ubiquitous in and around Taegu City area, inhabiting even the most densely populated areas with heavy traffic. With its small body size, high metabolic turnover, and rather limited mobility, a pigeon, as a biological indicator is expected. This study was conducted to monitor lead pollution in the Taegu and Kyongju City in Korea. We measured the lead content of the various tissue of three groups of feral pigeon(Columba livia) and soil and atmospheric lead concentration. First group was obtained in heavy traffic area in Taegu City, the second group was obtained a park in Taegu City and the third group was obtained light traffic area in Kyongju City. The air and soil lead concentration of heavy traffic area in Taegu City was $0.11{\mu}g/m^3,\;4.96{\mu}g/g$, that of park in Taegu City was $0.05{\mu}g/m^3,\;2.65{\mu}g/g$ and that of light traffic area in Kyongju City was $0.03{\mu}g/m^3,\;0.01{\mu}g/g$. The lead content of lung, blood, kidney, femur and liver of feral pigeons in heavy traffic area in Taegu City was significantly higher than pigeons obtained in a park in Taegu City and low traffic density area in Kyongju City(p<0.01). But stomach lead content of three group did not reflect a significant difference. In this study positive correlation was found between atmospheric lead concentrations and the concentration of lead in the pigeon's lung(r=0.5040, p<0.001), blood(r=0.3322, p<0.01), kidney(r=0.4824, p<0.001), femur(r=0.7214, p<0.001) and liver(r=0.4836, p<0.01). We can also found positive correlation between soil lead concentrations and the concentration of lead in the pigeon's femur(r=0.4850, p<0.001), kidney(r=0.4850, p<0.001) and liver(r=0.4386, p<0.01). In the pigeon's tissue there were significant correlations between concentration of lead in the blood and kidney(r=4818, p<0.001), femur(r=0.6157, p<0.001) and liver(r=0.3889, p<0.001). In conclusion, at the heavy traffic area in Taegu City, lead concentrations found in the atmosphere and soil are reflected in the lead concentrations of different tissue of urban pigeons. It is suggested that the tissue of pigeons can be good biological indicators of environmental lead pollution.