Hsu-Yang Kung

Bio: Hsu-Yang Kung is an academic researcher from National Pingtung University of Science and Technology. The author has contributed to research in topics: Wireless sensor network & Middleware. The author has an hindex of 13, co-authored 69 publications receiving 659 citations. Previous affiliations of Hsu-Yang Kung include National Cheng Kung University.

A Synchronization Infrastructure For Multicast Multimedia At The Presentation Layer

Abstract: In order to have computers successfully become consumer electronics products, an infrastructure of multicast multimedia networking presentation architecture that allows low-end computer, e.g., a set-top-box or a diskless networking PC, having isochronous multimedia presentations is urgently required. The paper describes a feasible multicast multimedia presentation network architecture and the corresponding presentation control mechanisms. The network architecture is based on a lower layer multicast communication network, e.g. MBONE, and provides the corresponding management of multicast multimedia presentations which include multimedia resource management, admission management, multicast communication management, and multimedia presentation management. Based on the proposed network architecture, we present the corresponding media transmission methods, presentation control schemes, and synchronization control schemes for multicast multimedia presentations in detail.

Ubiquitous Healthcare Service System with Context-awareness Capability: Design and Implementation

Abstract: The rises of the life index quality together with the medical technology improvement lead to a longer life expectancy. Thus a better health care program, especially for elderly, is needed. The common health problems facing those senior citizens are changed from acute diseases to chronic diseases, such as diabetes, hypertension, etc. Along with these changes, medical tourism is becoming the trend of the future. In this paper, we propose a decision support systems, the Ubiquitous Context-aware Healthcare Service System (UCHS), which uses micro sensors integrate RFID to sense user's life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), and temperature and light. The UCHS is composted of Situation-Aware Medical Tourism Service Search Subsystem (SAMTS^3), Healthy-life Map Guiding Subsystem (HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user. The UCHS built upon an integrated service platform in which medical experts' knowledge and all position and negative influence of the proposed therapy are inferred by using semantic network.

An intelligent slope disaster prediction and monitoring system based on WSN and ANP

Abstract: Taiwan generally has large-scale landslides and torrential rainfall during the typhoon season. As Wireless Sensor Networks (WSN) and mobile communication technologies advance rapidly, state-of-the-art technologies are adopted to build a model to reliably predict and monitor disasters, as well as accumulate environmental variation-related information. By integrating WSN and Analytic Network Process (ANP), this study evaluates the weight of disaster factors that adopt the consistency index of pair comparisons on hillslopes. The weight estimation and classification of disaster factors are based on the K-means model to build the hillslope prediction model. The Portrait-based Disaster Alerting System (PDAS) is designed and implemented using the proposed disaster prediction model. The PDAS adopts Web-GIS to visualize the environmental information. Evaluation results of the system indicate that the proposed prediction model achieves more accurate disaster determination than the conventional method.

Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan

Abstract: Effective disaster prediction relies on using correct disaster decision model to predict the disaster occurrence accurately. This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow occurrence in Taiwan. The proposed prediction models are based on linear regression, multivariate analysis, and back-propagation networks. To create a practical simulation environment, the decision database is the pre-analyzed 181 potential debris-flows in Taiwan. According to the simulation results, the prediction model based on back-propagation networks predicted the debris flow most accurately. Moreover, a Real-timeMobileDebrisFlowDisasterForecastSystem (RM(DF)^2) was implemented as a three-tier architecture consisting of mobile appliances, intelligent situation-aware agents and decision support servers based on the wireless/mobile Internet communications. The RM(DF)^2 system provides real-time communication between the disaster area and the rescue-control center, and effectively prevents and manages debris-flow disasters.

Machine Learning in Agriculture: A Review.

Abstract: Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

System and method for synchronizing operations among a plurality of independently clocked digital data processing devices

Abstract: A system is described for maintaining synchrony of operations among a plurality of devices having independent clocking arrangements. A task distribution device is to distribute tasks to a synchrony group comprising a plurality of devices to perform tasks distributed by the task distribution device in synchrony. The task distribution device distributes each task to synchrony group members over a network. Each task is associated with a time stamp that indicates a time, relative to a clock maintained by the task distribution device, at which synchrony group members are to execute the task. Each synchrony group member periodically obtains from the task distribution device an indication of current time indicated by its clock, determines a time differential between the task distribution device's clock and its respective clock and determines therefrom a time at which, according to its respective clock, the time stamp indicates that it is to execute the task.

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Abstract: Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.