• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of Adhesion and Interface
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• v.24 no.3
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• pp.100-104
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• 2023

Van der Waals heterostructures have garnered significant attention in recent research due to their excellent electronic characteristics arising from the absence of dangling bonds and the exclusive reliance on Van der Waals forces for interlayer coupling. However, most studies have been confined to fundamental research employing the Scotch tape (mechanical exfoliation) method. We fabricated Van der Waals vertical heterojunction transistors to advance this field using materials exclusively grown via chemical vapor deposition (CVD). CVDgrown graphene was patterned through photolithography to serve as electrodes, while CVD-grown MoSe₂ was employed as the pickup/transfer material, resulting in the realization of Van der Waals heterojunction transistors with interlayer charge transfer effects. The electrical characteristics of the fabricated devices were thoroughly examined. Additionally, we observed variations in the transistor's performance based on the presence of defects in MoSe₂ layer.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of Ginseng Research
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• v.47 no.5
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• pp.627-637
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• 2023

Background: Damage to the healthy intestinal epithelial layer and regulation of the intestinal immune system, closely interrelated, are considered pivotal parts of the curative treatment for inflammatory bowel disease (IBD). Plant-based diets and phytochemicals can support the immune microenvironment in the intestinal epithelial barrier for a balanced immune system by improving the intestinal microecological balance and may have therapeutic potential in colitis. However, there have been only a few reports on the therapeutic potential of plant-derived exosome-like nanoparticles (PENs) and the underlying mechanism in colitis. This study aimed to assess the therapeutic effect of PENs from Panax ginseng, ginseng-derived exosome-like nanoparticles (GENs), in a mouse model of IBD, with a focus on the intestinal immune microenvironment. Method: To evaluate the anti-inflammatory effect of GENs on acute colitis, we treated GENs in Caco2 and lipopolysaccharide (LPS) -induced RAW 264.7 macrophages and analyzed the gene expression of proinflammatory cytokines and anti-inflammatory cytokines such as TNF-α, IL-6, and IL-10 by real-time PCR (RT-PCR). Furthermore, we further examined bacterial DNA from feces and determined the alteration of gut microbiota composition in DSS-induced colitis mice after administration of GENs through 16S rRNA gene sequencing analysis. Result: GENs with low toxicity showed a long-lasting intestinal retention effect for 48 h, which could lead to effective suppression of pro-inflammatory cytokines such as TNF-α and IL-6 production through inhibition of NF-κB in DSS-induced colitis. As a result, it showed longer colon length and suppressed thickening of the colon wall in the mice treated with GENs. Due to the amelioration of the progression of DSS-induced colitis with GENs treatment, the prolonged survival rate was observed for 17 days compared to 9 days in the PBS-treated group. In the gut microbiota analysis, the ratio of Firmicutes/Bacteroidota was decreased, which means GENs have therapeutic effectiveness against IBD. Ingesting GENs would be expected to slow colitis progression, strengthen the gut microbiota, and maintain gut homeostasis by preventing bacterial dysbiosis. Conclusion: GENs have a therapeutic effect on colitis through modulation of the intestinal microbiota and immune microenvironment. GENs not only ameliorate the inflammation in the damaged intestine by downregulating pro-inflammatory cytokines but also help balance the microbiota on the intestinal barrier and thereby improve the digestive system.

• Advances in aircraft and spacecraft science
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• v.11 no.1
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• pp.83-103
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• 2024

Adhesive bonding is currently widely used in many industrial fields, particularly in the aeronautics sector. Despite its advantages over mechanical joints such as riveting and welding, adhesive bonding is mostly used for secondary structures due to its low peel strength; especially if it is simultaneously exposed to temperature and humidity; and often presence of bonding defects. In fact, during joint preparation, several types of defects can be introduced into the adhesive layer such as air bubbles, cavities, or cracks, which induce stress concentrations potentially leading to premature failure. Indeed, the presence of defects in the adhesive joint has a significant effect on adhesive stresses, which emphasizes the need for a good surface treatment. The research in this field is aimed at minimizing the stresses in the adhesive joint at its free edges by geometric modifications of the ovelapping part and/or by changing the nature of the substrates. In this study, the finite element method is used to describe the mechanical behavior of bonded joints. Thus, a three-dimensional model is made to analyze the effect of defects in the adhesive joint at areas of high stress concentrations. The analysis consists of estimating the different stresses in an adhesive joint between two 2024-T3 aluminum plates. Two types of single lap joints(SLJ) were analyzed: a standard SLJ and another modified by removing 0.2 mm of material from the thickness of one plate along the overlap length, taking into account several factors such as the applied load, shape, size and position of the defect. The obtained results clearly show that the presence of a bonding defect significantly affects stresses in the adhesive joint, which become important if the joint is subjected to a higher applied load. On the other hand, the geometric modification made to the plate considerably reduces the various stresses in the adhesive joint even in the presence of a bonding defect.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.1-10
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• 2024

In a previous study, a structure of a superplastic yoke consisting of a thin FR4 layer laminated with viscoelastic tape on both sides of a shape memory alloy (SMA) was proposed to reduce residual vibration generated by a deployable solar panel during high motion of a satellite. Damping properties of viscoelastic tapes will change with temperature, which can directly affect vibration reduction performance of the yoke. To check damping performance of the yoke at different temperatures, free damping tests were performed under various temperature conditions to identify the temperature range where the damping performance was maximized. Based on above temperature test results, this paper predicts temperature of the yoke through orbital thermal analysis so that the yoke can have effective damping performance even if it is exposed to an orbital thermal environment. In addition, the thermal design method was described so that the yoke could have optimal vibration reduction performance.

• Journal of Food Hygiene and Safety
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• v.20 no.4
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• pp.258-266
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• 2005

Licorice Extract and Erythritol, food additives used in korea, are widely used in foods as sweetener. Its application for use in food is regulated by the standard and specification for food additives but official analytical method far determination of these sweetener in food has not been established. Accordingly, we has been carried out to set up analytical method of the glycyrrhizic acid in several foods by the way of thin layer chromatography and high performance liquid chromatography glycyrrhizic acid is qualitative anaylsis technique consists of clean-up with a sep-pak $C_{18}$ cartridge, separation of the sweeteners by Silica gel 60 F254 TLC plate using 1-butanol:4Nammonia solution:ethanol (50:20:10) as mobile solvent. Also, the quantitative analysis for glycyrrhizic acid, was performed using Capcell prk $C_{18}$ column at wavelength 254nm and DW:Acetonitrile (62:38 (pH2.5)) as mobile phase. and we has been carried out to set up analytical method of the erythritol in several foods by the way of high performance liquid chromatography. erythritol is qualitative anaylsis technique consists of clean-up with a DW and hexane. The quantitative analysis for erythritol, was performed using Asahipak NH2P-50 column, Rl and DW:Acetonitrile (25:75) as mobile phase. The glycyrrhizic acid results determined as glycyrrhizic acid in 105 items were as follows; N.D$\∼$48.7ppm for 18 items in soy sauce, N.D$\∼$5.3ppm for 12 items in sauce, N.D$\∼$988.93ppm for 15 items in health food, N.D$\∼$180.7ppm for 26 items in beverages, N.D$\∼$2.6ppm for 8 items in alcoholic beverages repectively and ND for 63 items in the ethers. The erythritol results determined as erythritol in 52 items were as follows; N.D$\∼$155.6ppm for 13 items in gm, N.D$\∼$398.1ppm for 12 items in health foods repectively and ND for 45 items in the others.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.47
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• pp.15-32
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• 2002

The endeavors enhancing the grain quality of high-yielding japonica rice were steadily continued during 1980s-1990s along with the self-sufficiency of rice production and the increasing demands of high-quality rices. During this time, considerably great progress and success was obtained in development of high-quality japonica cultivars and quality evaluation techniques including the elucidation of interrelationship between the physicochemical properties of rice grain and the physical or palatability components of cooked rice. In 1990s, some high-quality japonica rice cultivars and special rices adaptable for food processing such as large kernel, chalky endosperm, aromatic and colored rices were developed and its objective preference and utility was also examined by a palatability meter, rapid-visco analyzer and texture analyzer, Recently, new special rices such as extremely low-amylose dull or opaque non-glutinous endosperm mutants were developed. Also, a high-lysine rice variety was developed for higher nutritional utility. The water uptake rate and the maximum water absorption ratio showed significantly negative correlations with the K/Mg ratio and alkali digestion value(ADV) of milled rice. The rice materials showing the higher amount of hot water absorption exhibited the larger volume expansion of cooked rice. The harder rices with lower moisture content revealed the higher rate of water uptake at twenty minutes after soaking and the higher ratio of maximum water uptake under the room temperature condition. These water uptake characteristics were not associated with the protein and amylose contents of milled rice and the palatability of cooked rice. The water/rice ratio (in w/w basis) for optimum cooking was averaged to 1.52 in dry milled rices (12% wet basis) with varietal range from 1.45 to 1.61 and the expansion ratio of milled rice after proper boiling was average to 2.63(in v/v basis). The major physicochemical components of rice grain associated with the palatability of cooked rice were examined using japonica rice materials showing narrow varietal variation in grain size and shape, alkali digestibility, gel consistency, amylose and protein contents, but considerable difference in appearance and texture of cooked rice. The glossiness or gross palatability score of cooked rice were closely associated with the peak, hot paste and consistency viscosities of viscosities with year difference. The high-quality rice variety "IIpumbyeo" showed less portion of amylose on the outer layer of milled rice grain and less and slower change in iodine blue value of extracted paste during twenty minutes of boiling. This highly palatable rice also exhibited very fine net structure in outer layer and fine-spongy and well-swollen shape of gelatinized starch granules in inner layer and core of cooked rice kernel compared with the poor palatable rice through image of scanning electronic microscope. Gross sensory score of cooked rice could be estimated by multiple linear regression formula, deduced from relationship between rice quality components mentioned above and eating quality of cooked rice, with high probability of determination. The $\alpha$-amylose-iodine method was adopted for checking the varietal difference in retrogradation of cooked rice. The rice cultivars revealing the relatively slow retrogradation in aged cooked rice were IIpumbyeo, Chucheongyeo, Sasanishiki, Jinbubyeo and Koshihikari. A Tonsil-type rice, Taebaegbyeo, and a japonica cultivar, Seomjinbyeo, showed the relatively fast deterioration of cooked rice. Generally, the better rice cultivars in eating quality of cooked rice showed less retrogradation and much sponginess in cooled cooked rice. Also, the rice varieties exhibiting less retrogradation in cooled cooked rice revealed higher hot viscosity and lower cool viscosity of rice flour in amylogram. The sponginess of cooled cooked rice was closely associated with magnesium content and volume expansion of cooked rice. The hardness-changed ratio of cooked rice by cooling was negatively correlated with solids amount extracted during boiling and volume expansion of cooked rice. The major physicochemical properties of rice grain closely related to the palatability of cooked rice may be directly or indirectly associated with the retrogradation characteristics of cooked rice. The softer gel consistency and lower amylose content in milled rice revealed the higher ratio of popped rice and larger bulk density of popping. The stronger hardness of rice grain showed relatively higher ratio of popping and the more chalky or less translucent rice exhibited the lower ratio of intact popped brown rice. The potassium and magnesium contents of milled rice were negatively associated with gross score of noodle making mixed with wheat flour in half and the better rice for noodle making revealed relatively less amount of solid extraction during boiling. The more volume expansion of batters for making brown rice bread resulted the better loaf formation and more springiness in rice breed. The higher protein rices produced relatively the more moist white rice bread. The springiness of rice bread was also significantly correlated with high amylose content and hard gel consistency. The completely chalky and large grain rices showed better suitability far fermentation and brewing. The glutinous rice were classified into nine different varietal groups based on various physicochemical and structural characteristics of endosperm. There was some close associations among these grain properties and large varietal difference in suitability to various traditional food processing. Our breeding efforts on improvement of rice quality for high palatability and processing utility or value-adding products in the future should focus on not only continuous enhancement of marketing and eating qualities but also the diversification in morphological, physicochemical and nutritional characteristics of rice grain suitable for processing various value-added rice foods.ice foods.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.2
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• pp.94-106
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• 2010

Understanding of the amount of energy resources and site selection are required prior to develop Ocean Thermal Energy (OTE). It is necessary to calculate the appearance probability of difference of seawater temperature(${\Delta}T$) between sea surface layer and underwater layers. This research mainly aimed to calculate the appearance probability of ${\Delta}T$ using frequency analysis(FA) and harmonic analysis(HA), and compare the advantages and weaknesses of those methods which has used in the South Sea of Korea. Spatial scale for comparison of two methods was divided into local and global scales related to the estimation of energy resources amount and site selection. In global scale, the Probability Differences(PD) of calculated ${\Delta}T$ from using both methods were created as spatial distribution maps, and compared areas of PD. In local scale, both methods were compared with not only the results of PD at the region of highest probability but also bimonthly probabilities in the regions of highest and lowest PD. Basically, the strong relationship(pearson r=0.96, ${\alpha}$=0.05) between probabilities of two methods showed the usefulness of both methods. In global scale, the area of PD more than 10% was less than 5% of the whole area, which means both methods can be applied to estimate the amount of OTE resources. However, in practice, HA method was considered as a more pragmatic method due to its capability of calculating under various ${\Delta}T$ conditions. In local scale, there was no significant difference between the high probability areas by both methods, showing difference under 5%. However, while FA could detect the whole range of probability, HA had a disadvantage of inability of detecting probability less than 10%. Therefore it was analyzed that the HA is more suitable to estimate the amount of energy resources, and FA is more suitable to select the site for OTE development.