Tainan University of Technology

About: Tainan University of Technology is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Password & Authentication. The organization has 311 authors who have published 641 publications receiving 10826 citations.

Magneto-optical Selection Rules in Bilayer Bernal Graphene

Abstract: The low-frequency magneto-optical properties of bilayer Bernal graphene are studied by the tight-binding model with four most important interlayer interactions taken into account. Since the main features of the wave functions are well depicted, the Landau levels can be divided into two groups based on the characteristics of the wave functions. These Landau levels lead to four categories of absorption peaks in the optical absorption spectra. Such absorption peaks own complex optical selection rules and these rules can be reasonably explained by the characteristics of the wave functions. In addition, twin-peak structures, regular frequency-dependent absorption rates and complex field-dependent frequencies are also obtained in this work. The main features of the absorption peaks are very different from those in monolayer graphene and have their origin in the interlayer interactions.

A novel two‐factor forecasting model for fuzzy time series

Abstract: The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improving forecasting accuracy, however, the issue of partitioning intervals has rarely been investigated. Recently, we proposed a novel deterministic forecasting model to eliminate the major overhead of determining the k-order issue in high-order models. This paper presents a continued work with focusing on handling the interval partitioning issue by applying the fuzzy c-means technology, which can take the distribution of data points into account and produce unequal-sized intervals. In addition, the forecasting model is extended to allow process twofactor problems. The accuracy superiority of the proposed model is demonstrated by conducting two empirical experiments and comparison to other existing models. The reliability of the forecasting model is further justified by using a Monte Carlo simulation and box plots. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Research on GP-GC Intergeneration Affective Interaction Product Design: Analysis from Chinese Social Perspective to Value Proposition

Abstract: Aging and baby bust are the problems that many countries are facing. Estranged intergenerational relationship has generally weakened family functions. How does grandparents-grandchildren intergenerational emotions interact and how to build sustainable relationships between grandparents and grandchildren have aroused great scientific interest in recent years. With the purpose of providing references for product design in promoting Intergenerational GP/GC relations, this paper, from the perspective of Chinese social thinking, extracted the needs and value propositions of GP/GC affective interaction that accords with China’s national conditions through literature review and user study on Chinese Intergenerational GP/GC relations. Research findings showed that design requirements and propositions can be defined as: activities that increase GP/GC physical contacts or interesting joint tasks that require manual operations by both generations, providing opportunities that give play to creativity, meeting demands of intergenerational outdoor leisure activities and family trips, and creating memories of the younger generation’s growth and happy time spent together.

Comprehensible Knowledge Discovery Using Particle Swarm Optimization with Monotonicity Constraints

Abstract: Due to uncertain data quality, knowledge extracted by methods merely focusing on gaining high accuracy might result in contradiction to experts’ knowledge or sometimes even common sense. In many application areas of data mining, taking into account the monotonic relations between the response variable and predictor variables could help extracting rules with better comprehensibility. This study incorporates Particle Swarm Optimization (PSO), which is a competitive heuristic technique for solving optimization tasks, with constraints of monotonicity for discovering accurate and comprehensible rules from databases. The results show that the proposed constraints-based PSO classifier can exploit rules with both comprehensibility and justifiability.