• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.217-226
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• 2009

Marine geological storage of $CO_2$ is regarded as one of the most promising options to response climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the marine geological structure such as deep sea saline aquifer. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_x$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to perform numerical process simulation using thermodynamic equation of state. The purpose of the present paper is to compare and analyse the relevant equations of state including PR, PRBM, RKS and SRK equation of state for $CO_2-N_2$ mixture. To evaluate the predictive accuracy of the equation of the state, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $N_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable equation of state and relevant binary parameter in designing the $CO_2-N_2$ mixture marine geological storage process.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Molecules and Cells
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• v.43 no.11
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• pp.909-920
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• 2020

Cytosolic Ca²⁺ levels ([Ca²⁺]_c) change dynamically in response to inducers, repressors, and physiological conditions, and aberrant [Ca²⁺]_c concentration regulation is associated with cancer, heart failure, and diabetes. Therefore, [Ca²⁺]_c is considered as a good indicator of physiological and pathological cellular responses, and is a crucial biomarker for drug discovery. A genetically encoded calcium indicator (GECI) was recently developed to measure [Ca²⁺]_c in single cells and animal models. GECI have some advantages over chemically synthesized indicators, although they also have some drawbacks such as poor signal-to-noise ratio (SNR), low positive signal, delayed response, artifactual responses due to protein overexpression, and expensive detection equipment. Here, we developed an indicator based on interactions between Ca²⁺-loaded calmodulin and target proteins, and generated an innovative GECI sensor using split nano-luciferase (Nluc) fragments to detect changes in [Ca²⁺]_c. Stimulation-dependent luciferase activities were optimized by combining large and small subunits of Nluc binary technology (NanoBiT, LgBiT:SmBiT) fusion proteins and regulating the receptor expression levels. We constructed the binary [Ca²⁺]_c sensors using a multicistronic expression system in a single vector linked via the internal ribosome entry site (IRES), and examined the detection efficiencies. Promoter optimization studies indicated that promoter-dependent protein expression levels were crucial to optimize SNR and sensitivity. This novel [Ca²⁺]_c assay has high SNR and sensitivity, is easy to use, suitable for high-throughput assays, and may be useful to detect [Ca²⁺]_c in single cells and animal models.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.59-68
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• 2011

The analysis of optimal infiltration path is one of the representative fields in which the GIS technology can be useful for the military purpose. Usually the analysis of the optimal path is done with network data. However, for military purpose, it often needs to be done with raster data. Because raster data needs far more computation than network data, it is difficult to apply the methods usually used in network data, such as Dijkstra algorithm. The genetic algorithm, which has shown great outcomes in optimization problems, was applied. It was used to minimize the detection probability of infiltration route. 2D binary array genes and its crossover and mutation were suggested to solve this problem with raster data. 30 tests were performed for each population size, 500, 1000, 2000, and 3000. With each generation, more adoptable routes survived and made their children routes. Results indicate that as the generations increased, average detection probability decreased and the routes converged to the optimal path. Also, as the population size increases, more optimal routes were found. The suggested genetic algorithm successfully finds the optimal infiltration route, and it shows better performance with larger population.

• Korean Journal of Food Science and Technology
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• v.30 no.5
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• pp.1114-1119
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• 1998

The stabilities of O/W emulsions (lipophilic core material:lipophobic wall material=3:2, w/w) containing various kinds of emulsifiers were compared to determine the optimal conditions of the HLB (hydrophilic lipophilic balance) value, the concentration and composition of emulsifier, the ratio of core material to the wall material, and the concentration and composition of polymers in the wall material. The effect of different chemical types of emulsifiers and the influence of single vs. binary emulsifier systems were compared with 13 kinds of emulsifier HLB values of $0.6{\sim}16.7$ at the concentration of 0.50%(w/w). The emulsion system was stable (more than 99.0 of ESI value) when the HLB value of the emulsifier was more than 11.0 or less than 2.8 of emulsifier HLB value. But it was unstable (less than 40.0 of ESI value) at the HLB value of the emulsifier between 3.4 and 8.6. Especially, we could find out the emulsion containing the emulsifier of polyglycerol polyricinoleate (PGPR, HLB 0.6) became stable creamy state. And, the ESI value of binary emulsifier system containing 0.25%(w/w) of PGPR and 0.25%(w/w) of polyoxyethylene sorbitan monolaurate (PSML, HLB 16.7) was higher than that of any single emulsifier system at the concentration of 0.50%(w/w). The highest emulsion stability was obtained in the liquefied wall material composed of 0.25%(w/v) of waxy corn starch and 0.50%(w/v) of agar.

• Korean Journal of Food Science and Technology
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• v.35 no.3
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• pp.423-428
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• 2003

Stabilities of W/O emulsions containing eugenolchitosan (EuCs) prepared from chitosan and eugenol were compared to determine the optimal conditions for the ratio of water (core phase) to corn oil (continuous phase), the concentration of EuCs, storage temperature, and the extent of homo-mixing. The optimal ratio of water to corn oil was 2:3 (w/w). The effects of EuC concentrations, and singular vs. binary system of emulsifiers on the storage stability of the emulsion were investigated with EuCs and polyoxyethylene sorbitan monolaurate. The emulsion was stable, showing more than 95% emulsion stability index (ESI) value, when the concentration of EuCs was more than 0.18% (w/v). ESI value of binary emulsifier system was almost equal to that of singular emulsifier system at the concentration of 0.18% (w/v). At this singular emulsifier system, the W/O emulsion formed by EuCs had ESI value of 100%. The optimal concentration of EuCs was determined as 0.18% (w/v). The highest stability of the emulsion was obtained from the homo-mixing at 11,000 rpm for 10 sec and the storage temperature ranging $25{\sim}65^{\circ}C$. EuCs produced from this study was mutagenecity-negative on Ames test and contained no heavy metal ions.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.555-560
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• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.841-853
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• 2024

This paper proposes an optimization method for the GF(2)[x] multiplication operation in HQC on AVX2. HQC is a candidate in NIST PQC standardization round 4 and is a binary code-based key exchange algorithm. The multiplication operation is one of the most time-complex operations in HQC, accounting for about 30% of the total clock cycles in the AVX2 environment. For the optimization, we used Karatsuba and Toom-Cook algorithms. Both algorithms are based on divide-and-conquer methods, which require multiplications of smaller order within them. We propose a method to optimize polynomial multiplication in HQC by finding the most efficient combination of Karatsuba and Toom-Cook algorithms, and compare the performance of the proposed method based on the implementation submitted to the PQC standardization. The results of the comparison demonstrate a performance improvement of 4.5%, 2.5%, and 30.3% over the GF(2)[x] multiplications of original hqc-128, -192, and -256. When applied to key generation, encapsulation, and decapsulation, the performance improvement over the original HQC is 2.2%, 2.4%, and 2.3% for hqc-128, 1.6%, 4.2%, and 2.6% for hqc-192, and 13.3%, 14.7%, and 13.3% for hqc-256, respectively.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.463-472
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• 2000

In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.538-542
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• 2001

DNA computing has been applied to the problem of getting an optimal soluting since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems This paper presents DNA coding method finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms(GA). GA searches efffectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses DNA molecules and four-type bases denoted by the A(Ademine) C(Gytosine);G(Guanine)and T(Thymine). The selection, crossover, mutation operators are applied to both DNA coding algorithm and genetic algorithms and the comparison has been performed. The results show that the DNA based algorithm performs better than GA.