• 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.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.2
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• pp.81-88
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• 2021

In this paper, by applying LTCC substrate, the library of the passive elements is implemented. And it can be used in 24 GHz circuits. Depending on how to use it to the circuit, it is required large value by designing the basic structures such as electrode capacitor and spiral inductor. However they are not available in high-frequency domain, because their SRF(Self-Resonant Frequency) is lower than the frequency of 24-GHz. By solving the limit, this paper devised passive elements classified for the DC and the high-frequency domain. The basic structure is suitable for low frequency under 1~2 GHz like DC. The microstrip λ/8 length stub structure is proposed to use for high-frequency like 24-GHz. The open and short stub structure operate as a capacitor and inductor respectively, also they have their impedances. Through their impedances, we can extract the value with the impedance-related equation. In this paper, the proposed passive elements are produced with the permittivity 7.5 LTCC substrate, the basic structure which are available in the DC constituted a library of capacitance of 2.35 to 30.44 pF and inductance of 0.75 to 5.45 nH, measured respectively. The stub structure available in the high-frequency domain were built libraries of capacitance of 0.44 to 2.89 pF and inductance of 0.71 to 1.56 nH, calculated respectively. The measurements have proven how to diversify value, so libraries can be built more variously. It will be an alternative to the passive elements that it is possible to integrate with the operation circuit of radar module for the frequency 24-GHz.