• Mass Spectrometry Letters
• /
• v.5 no.4
• /
• pp.115-119
• /
• 2014

The Brazil nut (Bertholletia excelsa - Lecythidaceae) is considered a product with high economic value, being a food widely appreciated for its nutritional qualities. Although previous studies have reported the biochemical composition of Brazil nut oil, the knowledge regarding the phospholipid composition exhibits a disagreement: the composition of fatty acids present in the structures of phospholipids is reported as being different from the composition of the free fatty acids present in the oil. In this work, solid phase extraction (SPE) was employed to provide a fast extraction of the phospholipids from Brazil nuts, in order to compare the phospholipid profile of the in nature nuts and their fatty acids precursor present in the oil. The major phospholipids were characterized by mass spectrometry approach. Their fragmentation pattern through direct infusion electrospray ionization ion-trap tandem mass spectrometry ($ESI-IT-MS^2$) proved to be useful to unequivocal characterization of these substances. High resolution (HR) experiments through ESI using a quadruple time of flight mass spectrometry (QTOF) system were performed to reinforce the identifications.

• Journal of Creative Information Culture
• /
• v.5 no.2
• /
• pp.145-156
• /
• 2019

In this study, we have developed and evaluated an information extracting module based on the named entity recognition technique. For the given purpose in this paper, the module was designed to apply to the problem dealing with extraction of postal address information from arbitrary documents without any prior knowledge on the document layout. From the perspective of information technique practice, our approach can be said as a probabilistic n-gram (bi- or tri-gram) method which is a generalized technique compared with a uni-gram based keyword matching. It is the main difference between our approach and the conventional methods adopted in natural language processing that applying sentence detection, tokenization, and POS tagging recursively rather than applying the models sequentially. The test results with approximately two thousands documents are presented at this paper.

• Natural Product Sciences
• /
• v.29 no.2
• /
• pp.83-90
• /
• 2023

To our knowledge, this is the first study aiming to optimize the extraction conditions of total phenolic compounds (TPC) and total flavonoids contents (TFC) from Argania spinosa leaves using Response Surface Methodology (RSM) with a Box-Behnken design (BBD). The optimal conditions obtained were 5% (w/v) solvent-to-solid ratio, 72.33% ethanol concentration, and 10h ours as an extraction time, which resulted in an extract with maximum TPC (131.63 mg GAE/g dw) and TFC (10.66 mg QE/g dw). Under the optimal extraction conditions, the antioxidant activity of the extracts of leaves of argan tree showed a moderate antiradical capacity of DPPH (IC₅₀ = 0,130 mg/mL) and ABTS (IC₅₀ = 0.198 mg/mL). However, the leaves of argan tree showed a very interesting reducing power of Iron (IC₅₀ = 0.448 mg/ml) which is similar to that of the ascorbic acid (IC₅₀ = 0.371 mg/mL).

• Journal of Biomedical Engineering Research
• /
• v.29 no.4
• /
• pp.329-336
• /
• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• Journal of Computing Science and Engineering
• /
• v.2 no.2
• /
• pp.109-136
• /
• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1582-1587
• /
• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• Journal of the Korea Society of Computer and Information
• /
• v.5 no.2
• /
• pp.14-21
• /
• 2000

In the position of the users, it suggests the technology information space as a now paradigm, which supplement the function of science information DB. ICPIS which inputs described papers with keywords, offers the itemized summary of these contents, the visual indication and comparison of similar thesis. and it also supplises the abundant summary information, survey information, more than ten volumes of info communication thesis with starting the casual relation extraction for the users, playing a significant role in ICPIS is called KP, and it is package of domain knowledge that unifies the extraction and structure narration of the technology information. ICPIS extracts the technology information among the thesis that are deserved by the natual language treatment in the itemized KP described , and form the prescribed summary structure in KP.

• Annual Conference on Human and Language Technology
• /
• 2019.10a
• /
• pp.185-189
• /
• 2019

관계추출기의 학습을 위해서는 많은 학습 데이터가 필요한데, 사람이 모으게 되면 많은 비용이 필요하여 원격 지도 학습을 이용한 데이터 수집이 많은 연구에서 사용되고 있다. 원격 지도 학습은 지식베이스를 기반으로 학습 데이터를 자동으로 만들어 내는 방식이기에 비용이 거의 들지 않지만, 지식베이스의 질과 양에 영향을 받는다. 본 연구는 원격 지도 학습을 기본으로 관계추출기의 성능을 향상 시키고, 지식베이스를 확장하는 방안으로 반복학습을 제안한다. 실험을 적은 비용으로 빠르게 진행하기 위해 반복학습을 자동화 하는 시스템을 설계하여 실험을 하였고, 이 시스템으로 관계추출기의 성능이 향상 될 수 있는 가능성을 보였으며, 반복학습을 통한 지식베이스의 확장 방안을 제시한다.

• Journal of the Korea Society for Simulation
• /
• v.4 no.1
• /
• pp.1-11
• /
• 1995

The model of a FMS (Flexible Manufacturing System) admits to a natural hierarchical decomposition of highly decoupled units with similar structure and control. The FMS fractal architecture model represents a hierarchical structure built from elements of a single basic design. A SES (System Entity Structure) is a structural knowledge representation scheme that contains knowledge of decomposition, taxonomy, and coupling relationships of a system necessary to direct model synthesis. A substructure of a SES is extracted for use as the skeleton for a model. This substructure is called pruned SES and the extraction operation of a pruned SES from a SES is called pruning (or pruning operation). This paper presents a pruning operation called recursive pruning. It is applied to SES for generating a model structure whose sub-structure contains copies if itself as in FMS fractal architecture. Another pruning operation called delay pruning is also presented. Combined with recursive pruning the delay pruningis a useful tool for representing and constructing complex systems.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.101-112
• /
• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.