• International journal of advanced smart convergence
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• v.12 no.4
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• pp.111-118
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• 2023

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

• Journal of the Korean Magnetic Resonance Society
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• v.14 no.2
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• pp.117-126
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• 2010

Hsp33 is a molecular chaperone achieving a holdase activity upon response to a dual stress by heat and oxidation. Despite several crystal structures available, the activation process is not clearly understood, because the structure inactive Hsp33 as its reduced, zinc-bound, monomeric form has not been solved yet. Thus, we initiated structural investigation of the reduced Hsp33 monomer by NMR. In this study, to overcome the high molecular weight (33 kDa), the protein was triply isotope-[$^{13}C$, $^{15}N$, $^2H$]-labeled and its inactive, monomeric state was ensured. 2D-[$^1H$, $^{15}N$]-TROSY and a series of triple resonance spectra could be successfully obtained on a high-field (900 MHz) NMR machine with a cryoprobe. However, under all of the different conditions tested, the number of resonances observed was significantly less than that expected from the amino acid sequence. Thus, a possible contribution of dynamic conformational exchange leading to a line broadening is suggested that might be important for activation process of Hsp33.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.112-124
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• 2008

RDF and OWL are the primary base technologies for implementing Semantic Web. Recently, many researches related with them, or applying them into the other application domains, have been introduced. However, relatively little work has been done for securing the RDF and OWL data. In this article, we briefly introduce an RDF triple based model for specifying RDF access authorization related with RDF security. Next, to efficiently find the authorization conflict by RDF inference, we introduce a method using prime number graph labeling in detail. The problem of authorization conflict by RDF inference is that although the lower concept is permitted to be accessed, it can be inaccessible due to the disapproval for the upper concept. Because by the RDF inference, the lower concept can be interpreted into the upper concept. Some experimental results show that the proposed method using the prime number graph labeling has better performance than the existing simple method for the detection of the authorization conflict.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.27-33
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• 2002

There are two significant drawbacks in triple image indexing; one is that is cannot support concept-based image retrieval and the other is that it fails to allow disjunctive labeling of images. To remedy the drawbacks, we propose a new technique supporting a concept-based retrieval against images indexed by indefinite fuzzy triples (I-fuzzy triples). The I-fuzzy triples allow not only a disjunctive image labeling, but also a concept-based matching against images labeled disjunctively. The disjunctive labeling is based on the expended closed world assumption and the concept-based image retrieval is based on fuzzy matching. In this paper, we also propose a concept-based query evaluation against the image database to extract desired answers with the degree of certainty $\alpha$$\in$[1,0].

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.422-433
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• 2009

As an effort to secure Semantic Web, in this paper, we introduce an RDF access authorization model based on an ontology hierarchy and an RDF triple pattern. In addition, we apply the authorization model to RDF query validation for approved access authorizations. A subscribed SPARQL or RQL query, which has RDF triple patterns, can be denied or granted according to the corresponding access authorizations which have an RDF triple pattern. In order to efficiently perform the query validation process, we first analyze some primary authorization conflict conditions under RDF subsumption inference, and then we introduce an efficient query validation algorithm using the conflict conditions and Dewey graph labeling technique. Through experiments, we also show that the proposed validation algorithm provides a reasonable validation time and when data and authorizations increase it has scalability.

• The Korean Journal of Nuclear Medicine
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• v.28 no.2
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• pp.167-171
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• 1994

Ethylcystein dimer (ECD) was synthesized by dimerizatlon of L-thiazolidine-4-carboxylic acid in liquid ammania with sodium metal and successive esterification in ethanolic solution of hydrogen chlorde. The purified product was labeled with $^{99m}Tc$ in the presence of sodium glucarate(pH= 5.6) and stannous chloride. Best result was obtained from the preparation con sisting of 0.1mg ECD, $40{\mu}l$ of 0.4M sodium glucarate (pH=5.6), and $20{\mu}g$ of stannous chloride. The labeling efficiency was 90% with previous condition. The labeled $^{99m}Tc$-ECD was stable at least for 3 hours in PBS(pH=7.4) at room temperature. About 10mCi of $^{99m}Tc$-ECD was injected to normal volunteer, and SPECT image of brain was obtained by triple head camera 10 minutes after inection. The image showed similar distribution of radioactivity in brain with that of HMPAO image.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.04a
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• pp.80-80
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• 1995

T4 Endonuclease V (Mw 16,000) acts as a repair enzyme for UV induced pyrimidine dimers in DNA. Many researchers have studied the biochemical characteristics of the enzyme. However the precise action mechanism of T4 endo V has not fully elucidated yet. In our laboratory NMR spectroscopy technique is being used for the structural study of T4 endo V. Because of its low temperature stability and high content of ${\alpha}$-helix, the conventional $^1$H NMR technique was inapplicable. Therefore we utilized stable isotope labeling technique and so far prepared about 10 amino acid specific labeled proteins. The HSQC spectra of amino acid specific labeled proteins will help us to interpret the triple resonance 3D, 4D data which are under processing, We also studied the behaviors of specific amino acid residues whose roles might be critical. When the enzyme labeled by $\^$15/N-Thr was mixed with the substrate oligonucleotide (semispecific -TT- sequence), one crosspeak in its HSQC spectrum was completely desappeared, which means that one of seven Thr residues is in the binding site of the enzyme with DNA, This result is well consistent with previous report that implicated the Thr 2 residue in the activity of the enzyme. Similar studies were carried on the behaviors of Arg and Tyr residues.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1000-1011
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• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.49-63
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• 2008

RDF is the base ontology model which is used in Semantic Web defined by W3C. OWL expands the RDF base model by providing various vocabularies for defining much more ontology relationships. Recently Jain and Farkas have suggested an RDF access control model based on RDF triple. Their research point is to introduce an authorization conflict problem by RDF inference which must be considered in RDF ontology data. Due to the problem, we cannot adopt XML access control model for RDF, although RDF is represented by XML. However, Jain and Farkas did not define the authorization propagation over the RDF upper/lower ontology concepts when an RDF authorization is specified. The reason why the authorization specification should be defined clearly is that finally, the authorizatin conflict is the problem between the authorization propagation in specifying an authorization and the authorization propagation in inferencing authorizations. In this article, first we define an RDF access authorization specification based on RDF triple in detail. Next, based on the definition, we analyze the authoriztion conflict problem by RDF inference in detail. Next, we briefly introduce a method which can quickly find an authorization conflict by using graph labeling techniques. This method is especially related with the subsumption relationship based inference. Finally, we present a comparison analysis with Jain and Farkas' study, and some experimental results showing the efficiency of the suggested conflict detection method.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.518-527
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• 2007

It is an important issue to find out the difference between RDF documents, because RDF documents are changed frequently. When RDF documents contain blank nodes, we need a matching technique for blank nodes in the change detection. Blank nodes have a nested form and they are used in most RDF documents. A RDF document can be modeled as a graph and it will contain many subtrees. We can consider a change detection problem as a minimum cost tree matching problem. In this paper, we propose a change detection technique for RDF documents using the labeling scheme for blank nodes. We also propose a method for improving the efficiency of general triple matching, which used predicate grouping and partitioning. In experiments, we showed that our approach was more accurate and faster than the previous approaches.