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Automatic Generation of HTML Code Based on Web Page Sketch
Bada Kim, Sangmin Park, Taeyeon Won, Junyung Heo
http://doi.org/10.5626/JOK.2019.46.1.9
Various studies have been conducted to automatically encode GUI designs in web application development. In the past study, the focus was on object region detection using computer vision and object detection based on deep-learning. The past reported study had the limitations of incorrect detection or no detection of the object. In the present work, two technologies were applied collectively to reduce the limitations of conventional object detection. The computer vision is used for layout detection, and deep-learning is used for GUI object detection. Based on these technologies, detected layouts and GUI objects were converted into HTML code. Consequently, the accuracy and recall rate of GUI object detection were 91% and 86%, respectively, and it was possible to convert into HTML code.
An Offloading Scheme for Reliable Data Processing of Swarm-drones
Hong Min, Bongjae Kim, Junyoung Heo, Jinman Jung
http://doi.org/10.5626/JOK.2018.45.10.990
With the developing drone-related technologies, autonomous drones have many applications. The offloading technique is used to execute high computational tasks that are stored in the cloud to preserve the limited resources of a drone. In this paper, we determine the effect of offloading by using cost analysis for swarm-drones considering task completion time and energy consumption. If the drones take more time and spend more energy while offloading their tasks to the cloud, drones divide a large task into small tasks. These tasks are run by using the drone’s own resources to process data reliably and efficiently. Our simulation results also show how the task completion time and the energy consumption infuence the offloading decision.
A Function Level Static Offloading Scheme for Saving Energy of Mobile Devices in Mobile Cloud Computing
Hong Min, Jinman Jung, Junyoung Heo
Mobile cloud computing is a technology that uses cloud services to overcome resource constrains of a mobile device, and it applies the computation offloading scheme to transfer a portion of a task which should be executed from a mobile device to the cloud. If the communication cost of the computation offloading is less than the computation cost of a mobile device, the mobile device commits a certain task to the cloud. The previous cost analysis models, which were used for separating functions running on a mobile device and functions transferring to the cloud, only considered the amount of data transfer and response time as the offloading cost. In this paper, we proposed a new task partitioning scheme that considers the frequency of function calls and data synchronization, during the cost estimation of the computation offloading. We also verified the energy efficiency of the proposed scheme by using experimental results.
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