2018, Vol.13, No.6

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

pISSN : 1975-7700 / eISSN : 2734-0570
https://journal.kci.go.kr/kkits

Many studies have been conducted to investigate into an university students to entrepreneurial activities. This paper aims to examine structural relationship between startup programs aided by university what reflected in GEM(Global Entrepreneurship Monitor) model and was establishing entrepreneurial intention of current university students. Total of 364 suitable sample data were used to empirical analysis hypotheses. The results of the hypothesis test through analysis of the structural model are summarized as follows. First, among the factors of universities’ startup support systems, entrepreneurial education, entrepreneurial consulting has a positive effect on entrepreneurial intention. But entrepreneurial fund had a negative influence upon entrepreneurial intention. Second, entrepreneurship has the partial mediating effect on the relationship between entrepreneurial education and entrepreneurial intention, and on the relationship between entrepreneurial consulting and entrepreneurial intention. Third, entrepreneurial education has more influence than entrepreneurial consulting. This implies that more attention needs to be paid to the new business, focusing on entrepreneurial education and strengthening entrepreneurial consulting for start-ups.

The immediate recognition of abnormal temperatures in the furnace system is very important for timely disaster control. Conventional abnormal temperature detection techniques typically use pre-programmed thresholds to identify abnormal patterns from a stream of temperature data in a statistical manner. Therefore, it is not suitable for use in a heating furnace system in which abnormal patterns must be detected in real time. Hierarchical Temporal Memory (HTM) is one of the machine learning models with excellent ability to analyze patterns of stream data. This model is a new machine intelligence technique that uses unsupervised continuous learning using Cortical Learning Algorithm (CLA), which is a time-based learning algorithm, and Spare Distributed Representation (SDR), which stores temporal and spatial patterns. This HTM, which is distinguished from existing deep learning or artificial neural network model, is capable of real-time continuous prediction from input of stream data and can be used to recognize abnormal states in various fields. HTM can be used as a robust model to detect abnormalities in successive temperature data in a heating furnace system because the furnace system continues to generate temperature stream data with a certain pattern over time. Here we developed an HTM-based, unsupervised, real-time anomaly detection system for heating furnaces. Performance evaluation shows that the suggested system can perform excellent abnormal real-time detection.

Continuous cyber threats are on the rise for critical information from public institutions and private companies. However, the attacks by hackers are not predictable and continue to evolve, and there is a limitation in current security test methods to develop a reliable information system because of time and cost issues. As a new way to strengthen information system security, this study suggests a security test method with utilizing crowd SW testing technique, which is attracting attention as a new software testing methodology. Crowd SW security testing has the advantage of detecting the fault and strengthening security verification on various systems through a large number of people’s participation. It can also be a solution to the time and cost problems posed by existing security testing. Utilizing the method in security test contribute to the improvement of information systems’ reliability and quality through checking security vulnerabilities. In order to activate the cloud SW security test, it is necessary to apply the security test for each stage of development and to test public and leakage organizations. If crowd SW security testers are fostered at the national level, it will contribute to strengthening SW security and solving the manpower shortage problem of SW testing industry.