• Journal of Digital Convergence
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• v.19 no.9
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• pp.217-228
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• 2021

The purpose of this study is to analyze drum performance techniques, focusing on the representative artists of funk music, who had a revival starting in the 1970s. The purpose of this study is to analyze the creativity shown in David Garibaldi's playing technique, which had a great influence on drum majors with his funk drum playing technique. As a research method, 'James Brown', 'Earth, Wind & Fire' and 'Tower of Power' with David Garibaldi as drummer were selected as representative bands. For the study period, the drum performance was examined by selecting two representative songs from among the songs released between 1965 and 1975, when the development of the corresponding funk music began. David Garibaldi's creative performance in this study is as follows. First, we tried to create a new rhythm in the form of changing the beat or resting the beat out of the frame of contemporary drum performance. Second, the 'Paradiddle', 'Accent', and 'Swiss Army triplet' rudiment techniques were applied to the entire drum kit, bringing innovation to the rhythm. Third, the samba pattern of Latin rhythm and the form of 'Afro-Cuban' were grafted onto funk music. Fourth, the idea of Unison Rhythm obtained from the structure of Latin music was applied to funk music. Based on this study, it is hoped that research on drum performance techniques of various genres will be conducted.

• KSBB Journal
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• v.23 no.3
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• pp.257-262
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• 2008

In this study, the reaction conditions for lipase-catalyzed resolution of racemic naproxen 2,2,2-trilfluoroethyl thioester were optimized, and the effect of surfactants was investigated. Among the organic solvents tested, the isooctane showed the highest conversion (92.19%) in a hydrolytic reaction of (S)-naproxen 2,2,2-trifluoroethyl thioester. In addition, the isooctane induced the highest initial reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=2.34{\times}10^{-2}mM/h$), the highest enantioselectivity (E = 36.12) and the highest specific activity ($V_s/(E_t)=7.80{\times}10^{-4}mmol/h{\cdot}g$) of lipase. Furthermore, reaction conditions such as temperature, concentration of the substrate and enzyme, and agitation speed were optimized using response surface methodology (RSM), and the statistical analysis indicated that the optimal conditions were $48.2^{\circ}C$, 3.51 mM, 30.11 mg/mL and 180 rpm, respectively. When the optimal reaction conditions were used, the conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester was 96.5%, which is similar to the conversion (94.6%) that was predicted by the model. After optimization of reaction conditions, the initial reaction rate, lipase specific activity and conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester increased by approximately 19.54%, 19.12% and 4.05%, respectively. The effect of surfactants such as Triton X-100 and NP-10 was also studied and NP-10 showed the highest conversion (89.43%), final reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=1.175{\times}10^{-2}mM/h$) and enantioselectivity (E = 59.24) of lipase.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.15 no.1
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• pp.113-125
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• 2004

Effect of microwave radiation on physico-chemical properties of cor'n starches was studied. Waxy com, com and high amylose com starches of varying moisture content(20～35%) were subjected to microwave processing(2450MHz) at $120^{\circ}$ and the experimental starch samples were examined by a X-ray diffractometry, rapid viscosity analyzer(RVA) and. with the samples in temperature was observed and the peaks of high amylose com starches at $2^{\circ}$=5.0, 15.0 and $23.0^{\circ}$, were disappeared indicating the melting of crystallines while those of com and waxy com had not changed. A change in gelatinization pattern was observed in the case of corn starches from type A with nearly no peak-viscosity and breakdown to type C. Except a decreased viscosity, no change was observed in those of waxy com starches.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.91-117
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• 2007

This study was to compare the contents and practical problems addressed, the process of teaching-learning method, and evaluation method of Korean Home Economics curriculum and of the Oregon and Ohio's Practical Problem Focused Family & Consumer Sciences Curricula. The results are as follows. First, contents of Korean curriculum are organized by major sub-concepts of Home Economics academic discipline whereas curricular of both Oregon and Ohio states are organized by practical problems. Oregon uses the practical problems which integrate multi-subjects and Ohio uses ones which are good for the contents of the module by integrating concerns or interests which are lower or detailed level (related interests). Since it differentiates interest and module and used them based on the basic concept of Family and Consumer Science, Ohio's approach could be easier for Korean teachers and students to adopt. Second, the teaching-learning process in Korean home economics classroom is mostly teacher-centered which hinders students to develop higher order thinking skills. It is recommended to use student-centered learning activities. State of Oregon and Ohio's teaching-learning process brings up the ability of problem-solving by letting students clearly analyze practical problems proposed, solve problems by themselves through group discussions and various activities, and apply what they learn to other problems. Third, Korean evaluation system is heavily rely on summative evaluation such as written tests. It is highly recommended to facilitate various performance assessment tools. Since state of Oregon and Ohio both use practical problems, they evaluate students mainly based on their activity rather than written tests. The tools for evaluation include project documents, reports of learning activity, self-evaluation, evaluation of discussion activity, peer evaluation in a group for each students for their performance, assessment about module, and written tests as well.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.149-162
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• 2014

Course guidance is a mentoring process which is performed before students register for coming classes. The course guidance plays a very important role to students in checking degree audits of students and mentoring classes which will be taken in coming semester. Also, it is intimately involved with a graduation assessment or a completion of ABEEK certification. Currently, course guidance is manually performed by some advisers at most of universities in Korea because they have no electronic systems for the course guidance. By the lack of the systems, the advisers should analyze each degree audit of students and curriculum information of their own departments. This process often causes the human error during the course guidance process due to the complexity of the process. The electronic system thus is essential to avoid the human error for the course guidance. If the relation data model-based system is applied to the mentoring process, then the problems in manual way can be solved. However, the relational data model-based systems have some limitations. Curriculums of a department and certification systems can be changed depending on a new policy of a university or surrounding environments. If the curriculums and the systems are changed, a scheme of the existing system should be changed in accordance with the variations. It is also not sufficient to provide semantic search due to the difficulty of extracting semantic relationships between subjects. In this paper, we model a course mentoring ontology based on the analysis of a curriculum of computer science department, a structure of degree audit, and ABEEK certification. Ontology-based course guidance system is also proposed to overcome the limitation of the existing methods and to provide the effectiveness of course mentoring process for both of advisors and students. In the proposed system, all data of the system consists of ontology instances. To create ontology instances, ontology population module is developed by using JENA framework which is for building semantic web and linked data applications. In the ontology population module, the mapping rules to connect parts of degree audit to certain parts of course mentoring ontology are designed. All ontology instances are generated based on degree audits of students who participate in course mentoring test. The generated instances are saved to JENA TDB as a triple repository after an inference process using JENA inference engine. A user interface for course guidance is implemented by using Java and JENA framework. Once a advisor or a student input student's information such as student name and student number at an information request form in user interface, the proposed system provides mentoring results based on a degree audit of current student and rules to check scores for each part of a curriculum such as special cultural subject, major subject, and MSC subject containing math and basic science. Recall and precision are used to evaluate the performance of the proposed system. The recall is used to check that the proposed system retrieves all relevant subjects. The precision is used to check whether the retrieved subjects are relevant to the mentoring results. An officer of computer science department attends the verification on the results derived from the proposed system. Experimental results using real data of the participating students show that the proposed course guidance system based on course mentoring ontology provides correct course mentoring results to students at all times. Advisors can also reduce their time cost to analyze a degree audit of corresponding student and to calculate each score for the each part. As a result, the proposed system based on ontology techniques solves the difficulty of mentoring methods in manual way and the proposed system derive correct mentoring results as human conduct.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.187-198
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• 2019

The purpose of this study is to seek ways of utilizing TLBMC by understanding business model of social ventures that are accompanied by social and environmental as well as economic missions. In order to achieve this, business models from economic, environmental and social perspectives will be analyzed, and we seek to enhance sustainability of social venture entrepreneurs. As the analysis tool, TLBMC(Triple Layered Business Model Canvas) expands upon the business model canvas that is widely utilized and recognizes economical terms. The TLBMC is proposed by Joyce, A., & Paquin, R. L.(2016) to help achieve a holistic view with horizontal and vertical associations. The study tries to overcome limitations of previous studies and observe a variety of economic, environmental, and social perspectives that social ventures should have with the TLBMC. As a result, it has the following implications; Firstly, creating separate social, environmental and economic business model canvas helps a business to have a holistic approach. Secondly, it was found that social venture characteristics of environmental and social perspectives were applied in practice. Lastly, we have established experience data on social venture business model. This study focuses on the opinions, the meanings and the subjective views of the participants. As a result, conclusions are drawn by the researchers ' assertions and has limitations as a research on case studies. However, this study will help people who are preparing or studying social ventures to have economic, environmental, and social perspectives. Also, redefinition of the direction and value of entrepreneurs operating social ventures, such as vision and mission, will help clarify the roles and responsibility of organizations. As a fundamental step for future empirical studies, this study could be the base for social venture business model studies.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.171-193
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• 2018

LOD(Linked Open Data) cloud is a practical implementation of semantic web. We suggested a new method that provides identity links conveniently in LOD cloud. It also allows changes in LOD to be reflected to searching results without any omissions. LOD provides detail descriptions of entities to public in RDF triple form. RDF triple is composed of subject, predicates, and objects and presents detail description for an entity. Links in LOD cloud, named identity links, are realized by asserting entities of different RDF triples to be identical. Currently, the identity link is provided with creating a link triple explicitly in which associates its subject and object with source and target entities. Link triples are appended to LOD. With identity links, a knowledge achieves from an LOD can be expanded with different knowledge from different LODs. The goal of LOD cloud is providing opportunity of knowledge expansion to users. Appending link triples to LOD, however, has serious difficulties in discovering identity links between entities one by one notwithstanding the enormous scale of LOD. Newly added entities cannot be reflected to searching results until identity links heading for them are serialized and published to LOD cloud. Instead of creating enormous identity links, we propose LOD to prepare its own link policy. The link policy specifies a set of target LODs to link and constraints necessary to discover identity links to entities on target LODs. On searching, it becomes possible to access newly added entities and reflect them to searching results without any omissions by referencing the link policies. Link policy specifies a set of predicate pairs for discovering identity between associated entities in source and target LODs. For the link policy specification, we have suggested a set of vocabularies that conform to RDFS and OWL. Identity between entities is evaluated in accordance with a similarity of the source and the target entities' objects which have been associated with the predicates' pair in the link policy. We implemented a system "Change Acceptable In-Depth Searching System(CAIDS)". With CAIDS, user's searching request starts from depth_0 LOD, i.e. surface searching. Referencing the link policies of LODs, CAIDS proceeds in-depth searching, next LODs of next depths. To supplement identity links derived from the link policies, CAIDS uses explicit link triples as well. Following the identity links, CAIDS's in-depth searching progresses. Content of an entity obtained from depth_0 LOD expands with the contents of entities of other LODs which have been discovered to be identical to depth_0 LOD entity. Expanding content of depth_0 LOD entity without user's cognition of such other LODs is the implementation of knowledge expansion. It is the goal of LOD cloud. The more identity links in LOD cloud, the wider content expansions in LOD cloud. We have suggested a new way to create identity links abundantly and supply them to LOD cloud. Experiments on CAIDS performed against DBpedia LODs of Korea, France, Italy, Spain, and Portugal. They present that CAIDS provides appropriate expansion ratio and inclusion ratio as long as degree of similarity between source and target objects is 0.8 ~ 0.9. Expansion ratio, for each depth, depicts the ratio of the entities discovered at the depth to the entities of depth_0 LOD. For each depth, inclusion ratio illustrates the ratio of the entities discovered only with explicit links to the entities discovered only with link policies. In cases of similarity degrees with under 0.8, expansion becomes excessive and thus contents become distorted. Similarity degree of 0.8 ~ 0.9 provides appropriate amount of RDF triples searched as well. Experiments have evaluated confidence degree of contents which have been expanded in accordance with in-depth searching. Confidence degree of content is directly coupled with identity ratio of an entity, which means the degree of identity to the entity of depth_0 LOD. Identity ratio of an entity is obtained by multiplying source LOD's confidence and source entity's identity ratio. By tracing the identity links in advance, LOD's confidence is evaluated in accordance with the amount of identity links incoming to the entities in the LOD. While evaluating the identity ratio, concept of identity agreement, which means that multiple identity links head to a common entity, has been considered. With the identity agreement concept, experimental results present that identity ratio decreases as depth deepens, but rebounds as the depth deepens more. For each entity, as the number of identity links increases, identity ratio rebounds early and reaches at 1 finally. We found out that more than 8 identity links for each entity would lead users to give their confidence to the contents expanded. Link policy based in-depth searching method, we proposed, is expected to contribute to abundant identity links provisions to LOD cloud.