• Journal of Life Science
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• v.29 no.10
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• pp.1126-1135
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• 2019

From a sample of bamboo byproduct, the protease-producing yeast strain CO-1 was newly isolated. Strain CO-1 is spherical to ovoid in shape and measures $3.1-4.0{\times}3.8-4.4{\mu}m$. For the growth of strain CO-1, the optimal temperature and initial pH were $30^{\circ}C$ and 4.0, respectively. The strain was able to grow in 0.0-15.0%(w/v) NaCl and 0.0-9.0%(v/v) ethanol. Based on a phylogenetic analysis of its 18S rDNA sequences, strain CO-1 was identified as Pichia anomala. The extracellular protease produced by P. anomala CO-1 was partially purified by ammonium sulfate precipitation, which resulted in a 14.6-fold purification and a yield of 7.2%. The molecular mass of the protease was recorded as approximately 30 kDa via zymogram. The protease activity reached its maximum when 1.0%(w/v) CMC was used as the carbon source, 1.0%(w/v) yeast extract was used as the nitrogen source, and 0.3%(w/v) $MnSO_4$ was used as the mineral source. The protease revealed the highest activity at pH 7.0 and $30^{\circ}C$. This enzyme maintained more than 75% of its stability at a pH range of 4.0-10.0. After heating at $65^{\circ}C$ for 1 hr, the neutral protease registered at 60% of its original activity. The protease production coincided with growth and attained a maximal level during the post-exponential phase.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.174-182
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• 2002

The object-oriented design methods and their CASE tools are widely used in practice by many real-time software developers. However, object-oriented CASE tools require an additional step of identifying tasks from a given design model. Unfortunately, it is difficult to automate this step for a couple of reasons: (1) there are inherent discrepancies between objects and tasks; and (2) it is hard to derive tasks while maximizing real-time schedulability since this problem makes a non-trivial optimization problem. As a result, in practical object-oriented CASE tools, task identification is usually performed in an ad-hoc manner using hints provided by human designers. In this paper, we present a systematic, schedulability-aware approach that can help mapping real-time object-oriented models to multi-threaded implementations. In our approach, a task contains a group of mutually exclusive transactions that may possess different periods and deadline. For this new task model, we provide a new schedulability analysis algorithm. We also show how the run-time system is implemented and how executable code is generated in our frame work. We have performed a case study. It shows the difficulty of task derivation problem and the utility of the automated synthesis of implementations as well as the Inappropriateness of the single-threaded implementations.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.137-146
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• 2012

In SCM(Supply Chain Management), a management paradigm where the customer satisfaction is to be achieved by minimizing the cost, reducing the uncertainty, and obtaining the overall optimization. As it performs the integrated operation of the paths of information, assets, and knowledge from the raw material providers to the retailers, the adoption of RFID(Radio Frequency Identification) in SCM could be expected to magnify the effectiveness of the system. However, there is a huge risk by deciding whether or not RFID system is adopted without the objective analysis under the uncertain circumstances. This research paper presents the statistical analysis methodologies for the comparison of RFID with Barcode on the aspect of utility and the statistical analysis tool, RUSAT, which was programmed for nonstatisticians' convenience. Assuming a pharmaceutical industry, this paper illustrates how the data were entered and analyzed in RUSAT. The results of this research are expected to be used not only for the pharmaceutical related company but also for the manufacturer, the whole-saler, and the retailer in the other logistic industries.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.69-80
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• 1999

Recently, social requirements of personal identification techniques are rapidly expanding in a number of new application ares. Especially fingerprint recognition is the most important technology. Fingerprint recognition technologies are well established, proven, cost and legally accepted. Therefore, it has more spot lighted among the any other biometrics technologies. In this paper we propose a new on-line fingerprint recognition algorithm for non-inked type live scanner to fit their increasing of security level under the computing environment. Fingerprint recognition system consists of two distinct structural blocks: feature extraction and feature matching. The main topic in this paper focuses on the feature matching using the fingerprint minutiae (ridge ending and bifurcation). Minutiae matching is composed in the alignment stage and matching stage. Success of optimizing the alignment stage is the key of real-time (on-line) fingerprint recognition. Proposed alignment algorithm using clique shows the strength in the search space optimization and partially incomplete image. We make our own database to get the generality. Using the traditional statistical discriminant analysis, 0.05% false acceptance rate (FAR) at 8.83% false rejection rate (FRR) in 1.55 second average matching speed on a Pentium system have been achieved. This makes it possible to construct high performance fingerprint recognition system.

• Microbiology and Biotechnology Letters
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• v.40 no.3
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• pp.243-249
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• 2012

This study was conducted to screen an alginate-degrading microorganism and to investigate the characteristics of the alginate-degrading activity of its crude enzyme. A marine bacterium which produces extracellular alginate-degrading enzymes was isolated from the brown alga Sargassum thunbergii. 16S rRNA sequence analysis and physiological profiling resulted in the bacterium's identification as a Vibrio crassostreae strain, named Vibrio crassostreae PKA 1002. Its optimal culture conditions for growth were pH 9, 2% NaCl, $30^{\circ}C$ and a 24 hr incubation time. The optimal conditions for the alginate degrading ability of the crude enzyme produced by V. crassostreae PKA 1002 were pH 9, $30^{\circ}C$, a 48 hr incubation time and 8% alginic acid. The alginate degrading crude enzyme produced 3.035 g of reducing sugar per liter in 4% (w/v) alginate over 1 hr.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.251-275
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• 2011

In this paper, we propose a novel kernel called a semantic parse tree kernel that extends the parse tree kernel previously studied to extract protein-protein interactions(PPIs) and shown prominent results. Among the drawbacks of the existing parse tree kernel is that it could degenerate the overall performance of PPI extraction because the kernel function may produce lower kernel values of two sentences than the actual analogy between them due to the simple comparison mechanisms handling only the superficial aspects of the constituting words. The new kernel can compute the lexical semantic similarity as well as the syntactic analogy between two parse trees of target sentences. In order to calculate the lexical semantic similarity, it incorporates context-based word sense disambiguation producing synsets in WordNet as its outputs, which, in turn, can be transformed into more general ones. In experiments, we introduced two new parameters: tree kernel decay factors, and degrees of abstracting lexical concepts which can accelerate the optimization of PPI extraction performance in addition to the conventional SVM's regularization factor. Through these multi-strategic experiments, we confirmed the pivotal role of the newly applied parameters. Additionally, the experimental results showed that semantic parse tree kernel is superior to the conventional kernels especially in the PPI classification tasks.

• Korean Journal of Food Science and Technology
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• v.39 no.4
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• pp.438-446
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• 2007

The fermented soybean product, cheonggukjang, is favored by many people, partly due to its bio-functional ingredients. Since the fermentation process of cheonggukjang is mediated by enzymes, including proteases, produced by microbes, analysis of the proteome profile changes in cheonggukjang during fermentation would provide us with valuable information for fermentation optimization, as well as a better understanding of the formation mechanisms of the bio-functional substances. The soluble proteins from cheonggukjang were prepared by a phenol/chloroform extraction method, in order to remove interfering molecules for high resolution 2-D gel analysis. Proteomic analysis of the cheonggukjang different fermentation periods suggested that most of the soluble soy proteins were degraded into smaller forms within 20hr, and many microbial proteins, such as mucilage proteins, dominated the soluble protein fraction. The proteomic profile of cheonggukjang was very different from natto, in terms of the 2-D gel protein profile. Among the separated protein spots on the 2-D gels, 50 proteins from each gel were analyzed by MALDI-TOF MS and PMF for protein identification. Due to database limitations with regard to soy proteins and microbial proteins, identification of the changed proteins during fermentation was restricted to 9 proteins for cheonggukjang and 15 for natto. From de novo sequencing of the proteins by a tandem MS/MS, as well as by database searches using BLASTP, a limited number of proteins were identified with low reliability. However, the 2-D gel analysis of proteins, including protein preparation methods, remains a valuable tool to analyze complex mixtures of proteins entirely. Also, for intensive mass spectrometric analysis, it is also advisable to focus on a few of the interestingly changed proteins in cheonggukjang.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.8
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• pp.449-455
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• 2017

Process input data including material and energy, process output data including product, co-product and its environmental emissions of the reference and target processes were collected and analyzed to evaluate the process performance. Environmentally problematic input/environmental emissions of the manufacturing processes were identified using these data. Significant process inputs contributing to each of the environmental emissions were identified using multiple regression analysis between the process inputs and environmental emissions. Optimum combination of the end-of-pipe technologies for treating the environmental emissions considering economic aspects was made using the linear programming technique. The cement manufacturing processes in Korea and the EU producing same type of cement were chosen for the case study. Environmentally problematic input/environmental emissions of the domestic cement manufacturing processes include coal, dust, and $SO_x$. Multiple regression analysis among the process inputs and environmental emissions revealed that $CO_2$ emission was influenced most by coal, followed by the input raw materials and gypsum. $SO_x$ emission was influenced by coal, and dust emission by gypsum followed by raw material. Optimization of the end-of-pipe technologies treating dust showed that a combination of 100% of the electro precipitator and 2.4% of the fiber filter gives the lowest cost. The $SO_x$ case showed that a combination of 100% of the dry addition process and 25.88% of the wet scrubber gives the lowest cost. Salient feature of this research is that it proposed a method for identifying environmentally problematic input/environmental emissions of the manufacturing processes, in particular, cement manufacturing process. Another feature is that it showed a method for selecting the optimum combination of the end-of-pipe treatment technologies.