Yonsei University

About: Yonsei University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Cancer. The organization has 50162 authors who have published 106172 publications receiving 2279044 citations. The organization is also known as: Yonsei.

Simulation in healthcare education: A best evidence practical guide. AMEE Guide No. 82

Abstract: Over the past two decades, there has been an exponential and enthusiastic adoption of simulation in healthcare education internationally. Medicine has learned much from professions that have established programs in simulation for training, such as aviation, the military and space exploration. Increased demands on training hours, limited patient encounters, and a focus on patient safety have led to a new paradigm of education in healthcare that increasingly involves technology and innovative ways to provide a standardized curriculum. A robust body of literature is growing, seeking to answer the question of how best to use simulation in healthcare education. Building on the groundwork of the Best Evidence in Medical Education (BEME) Guide on the features of simulators that lead to effective learning, this current Guide provides practical guidance to aid educators in effectively using simulation for training. It is a selective review to describe best practices and illustrative case studies. This Guide is the second part of a two-part AMEE Guide on simulation in healthcare education. The first Guide focuses on building a simulation program, and discusses more operational topics such as types of simulators, simulation center structure and set-up, fidelity management, and scenario engineering, as well as faculty preparation. This Guide will focus on the educational principles that lead to effective learning, and include topics such as feedback and debriefing, deliberate practice, and curriculum integration – all central to simulation efficacy. The important subjects of mastery learning, range of difficulty, capturing clinical variation, and individualized learning are also examined. Finally, we discuss approaches to team training and suggest future directions. Each section follows a framework of background and definition, its importance to effective use of simulation, practical points with examples, and challenges generally encountered. Simulation-based healthcare education has great potential for use throughout the healthcare education continuum, from undergraduate to continuing education. It can also be used to train a variety of healthcare providers in different disciplines from novices to experts. This Guide aims to equip healthcare educators with the tools to use this learning modality to its full capability.

Understanding dynamics between initial trust and usage intentions of mobile banking

Abstract: Mobile banking is an emerging application of mobile commerce that could become an additional revenue source to both banks and telecom service providers. It is a form of service convergence enabled by innovative technologies. Despite the alleged benefits of mobile banking, its acceptance has been short of industry expectations. One plausible explanation may be consumers' initial lack of trust in available services. The objective of our research is to reveal the mechanisms associated with the initial formation of people's trust in mobile banking and intention to use the service. For this, we attempt to understand the effect of four antecedent variables (structural assurances, relative benefits, personal propensity to trust and firm reputation) on shaping a person's initial trust in mobile banking and its usage intention. They represent four types of trust-inducing forces: institutional offering (structural assurances), cognition (perceived benefits), personality (personal propensity) and firm characteristics (firm reputation). We examine individual significance of the selected antecedents and also their comparative reliability in explaining the two exogenous variables. The technical basis of our empirical research is the innovative mobile banking solution that uses cellphones with a built-in smart chipset. The survey data are analyzed using structural equation modelling. The analysis showed that three variables (relative benefits, propensity to trust and structural assurances) had a significant effect on initial trust in mobile banking. Also, the perception of initial trust and relative benefits was vital in promoting personal intention to make use of related services. However, contrary to our expectation, the reputation as a firm characteristics variable failed to attract people to mobile banking.

k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI.

Abstract: A model-based dynamic MRI called k-t BLAST/SENSE has drawn significant attention from the MR imaging community because of its improved spatio-temporal resolution. Recently, we showed that the k-t BLAST/SENSE corresponds to the special case of a new dynamic MRI algorithm called k-t FOCUSS that is optimal from a compressed sensing perspective. The main contribution of this article is an extension of k-t FOCUSS to a more general framework with prediction and residual encoding, where the prediction provides an initial estimate and the residual encoding takes care of the remaining residual signals. Two prediction methods, RIGR and motion estimation/compensation scheme, are proposed, which significantly sparsify the residual signals. Then, using a more sophisticated random sampling pattern and optimized temporal transform, the residual signal can be effectively estimated from a very small number of k-t samples. Experimental results show that excellent reconstruction can be achieved even from severely limited k-t samples without aliasing artifacts. Magn Reson Med 61:103–116, 2009.

Extreme Learning Machine

Abstract: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction.