• Bulletin of the Korean Chemical Society
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• v.19 no.6
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• pp.654-660
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• 1998

$[Fe^{II}Fe^{III}BPLMP(OAc)_2](BPh_4)_2$ (1), a new model for the reduced form of the purple acid phosphatases, has been synthesized by using a dinucleating ligand, 2,6-bis[((2-pyridylmethyl)(6-methyl-2-pyridylmethyl)amino) methyl]-4-methylphenol (HBPLMP). Complex I has been characterized by X-ray diffraction method as having (μ-phenoxo)bis(acetato)diiron core. Complex 1 was crystallized in the monoclinic space group C2/c with the following cell parameters: a=41.620(6) Å, b=14.020(3) Å, c=27.007(4) Å, β=90.60(2)°, and Z=8. The iron centers in the complex 1 are ordered as indicated by the difference in the Fe-O bond lengths which match well with typical $Fe^{III}-O\; and\; Fe^{II}-O$ bond lengths. Complex 1 has been studied by electronic spectral, NMR, EPR, SQUID, and electochemical methods. Complex 1 exhibits strong bands at 592 nm, 1380 nm in $CH_3CN$ (ε = 1.0 × 103 , 3.0 × 102). These are assigned to $phenolate-to-Fe^{III}$ and intervalence charge-transfer transitions, respectively. Its NMR spectrum exhibits sharp isotropically shifted resonances, which number half of those expected for a valence-trapped species, indicating that electron transfer between $Fe^{II}\;and\;Fe^{III}$ centers is faster than NMR time scale. This complex undergoes quasireversible one-electron redox processes. The $Fe^{III}_2/Fe^{II}Fe^{III}\;and\;Fe^{II}Fe^{III}/Fe^{II}_2$ redox couples are at 0.655 and -0.085 V vs SCE, respectively. It has $K_{comp}=3.3{\times}10^{12}$ representing that BPLMP/bis(acetate) ligand combination stabilizes a mixed-valence $Fe^{II}Fe^{III}$ complex in the air. Complex 1 exhibits a broad EPR signal centered near g=1.55 which is a characteristic feature of the antiferromagnetically coupled high-spin $Fe^{II}Fe^{III}$ system $(S_{total}=1/2)$. This is consistent with the magnetic susceptibility study showing the weak antiferromagnetic coupling $(J= - 4.6\;cm^{-1},\; H= - 2JS_1{\cdot}S2)$ between $Fe^{II}\; and \;Fe^{III}$center.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.301-307
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• 2018

The most of the IR focus on the method for searching the document, so the keyword-based IR system is not able to reflect the feature information of the image. In order to overcome these limitations, we have developed a system that can search similar images based on the vector information of images, and it can search for similar images based on sketches. The proposed system uses the GAN to up sample the sketch to the image level, convert the image to the vector through the CNN, and then retrieve the similar image using the vector space model. The model was learned using fashion image and the image retrieval system was developed. As a result, the result is showed meaningful performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1474-1480
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• 2018

When fires occur in high-rise buildings, it is difficult to determine whether each escape route is safe because of complex structure. Therefore, it is necessary to provide residents with escape routes quickly after determining their safety. We propose a method to measure the visibility of the escape route due to the smoke generated in the fire by analyzing the images. The visibility can be easily measured if the density of smoke detected in the input image is known. However, this approach is difficult to use because there are no suitable methods for measuring smoke density. In this paper, we use principal component analysis by extracting a background image from input images and making it training data. Background images and smoke images are extracted from images given as inputs, and then the learned principal component analysis is applied to map of as a new feature space, and the change is calculated and the visibility due to the smoke is measured.

• Wind and Structures
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• v.27 no.6
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• pp.381-397
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• 2018

Nonlinear behavior in fluid-structure interaction (FSI) of bridge decks becomes increasingly significant for modern bridges with increasing spans, larger flexibility and new aerodynamic deck configurations. Better understanding of the nonlinear aeroelasticity of bridge decks and further development of reduced-order nonlinear models for the aeroelastic forces become necessary. In this paper, the amplitude-dependent and neutral angle dependent nonlinearities of the motion-induced loads are further highlighted by series of computational fluid dynamics (CFD) simulations. An effort has been made to investigate a semi-analytical time-domain model of the nonlinear motion induced loads on the deck, which enables nonlinear time domain simulations of the aeroelastic responses of the bridge deck. First, the computational schemes used here are validated through theoretically well-known cases. Then, static aerodynamic coefficients of the Great Belt East Bridge (GBEB) cross section are evaluated at various angles of attack, leading to the so-called nonlinear backbone curves. Flutter derivatives of the bridge are identified by CFD simulations using forced harmonic motion of the cross-section with various frequencies. By varying the amplitude of the forced motion, it is observed that the identified flutter derivatives are amplitude-dependent, especially for $A^*_2$ and $H^*_2$ parameters. Another nonlinear feature is observed from the change of hysteresis loop (between angle of attack and lift/moment) when the neutral angles of the cross-section are changed. Based on the CFD results, a semi-analytical time-domain model for describing the nonlinear motion-induced loads is proposed and calibrated. This model is based on accounting for the delay effect with respect to the nonlinear backbone curve and is established in the state-space form. Reasonable agreement between the results from the semi-analytical model and CFD demonstrates the potential application of the proposed model for nonlinear aeroelastic analysis of bridge decks.

• Korean Journal of Heritage: History & Science
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• v.36
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• pp.69-86
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• 2003

This article introduce the manufacturing technique of Silla potteries based on the result excavated from Songogdong and Mulchunri site in Gyungju. As a result, we selected the kiln-site to produce Silla potteries and knew the feature which following to make them. 1. The Environmental elements to take a kiln-site were abundant fuel, plenty water and suitable soil. In particular, efficient usage of refracted winds and reserved space of forepart in the kiln-site were importantly applied to select place of kiln-site. 2. The structure of the kiln-body have been changing according to the time. It could be massproduced by produce-group from the middle and end of sixth centry which the fireplace-kiln was generalized. 3. The work center of equipments were related kiln-site. It consisted of mixed wheel, keepingpit and ditch. We knew that a look-out shed had been appeared according to utility purpose variously. 4. It sees as trimming trace of inner and outter aspects in excavated potteries and we knew that wheel had been turn to the contrast watch direction. For producing pottery of the good guality, various kiln-tools had been used already at Silla period and they used for the different purpose. 5. We intended to know method for laying the potteries in the kiln through the example of the adherent pottery to be melted. Finally, manufature and tomb-site are separated by the time through current situation of Songokdong and Mulchonri site. At the same time, we could know that group of Chounbuk kiln-site moved from the south to the north step by step.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.219-225
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• 2021

As for the battery package method, a prismatic package method is preferred for stability reasons, but it is rapidly expanding due to the stability verification of a pouch type package. The pouch type using the lamination process has an advantage of high battery energy density because it can reduce space waste, but has a disadvantage of low productivity. Therefore, in this paper, by extracting edge detection algorithm precision, pattern algorithm precision, and motion controller recall rate by improving backlight lighting fixtures to minimize light diffusion, securing standards for stereo camera position relationship displacement monitoring, and securing standards for lens release monitoring. We propose to implement a system that ensures accuracy and reliability in positioning. As a result of the experiment, the proposed system shows an average error range of 0.032mm for edge detection, 0.02mm for pattern algorithm, and 0.014mm for motion controller, thus ensuring the accuracy and reliability of the positioning mechanism.

• Design Convergence Study
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• v.19 no.2
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• pp.107-122
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• 2020

"lux et sonitus" is an audiovisual art exhibition series with an artistic combination of music and video at its center. Since its first introduction in 2013, the series have been held five times under the theme of "exhibition of music", presenting works featuring both audio and visual media in an effort to explore the key issues in the field of audiovisual art. In addition to the previous achievement of the exhibition, recent works from the series feature new concepts that explore the possibility of expanding the realm of synesthesia. In this paper, details of the entire series are summarized. In addition, theoretical background behind creative results of the series is analyzed in the context of music, synesthesia and space found in Nam June Paik's audiovisual artwork as a source of inspiration. This will contribute to establishing a vision for the creation/analysis of audiovisual art in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Smart Media Journal
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• v.11 no.4
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• pp.30-37
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• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.