• Korean Journal of Construction Engineering and Management
• /
• v.12 no.6
• /
• pp.151-159
• /
• 2011

Due to the Korean government's renewable energy support policy such as the renewable energy utilization building certificate and enlarging the compulsory ratio of investment on the public building, the rooftop photovoltaic projects are expanding rapidly. It is very important for the rooftop photovoltaic projects to analyze the shading effect of the adjacent structures or own facilities. But, the photovoltaic arrangements are planned by the experience of the designers or simple graphic tools. The purpose of this study is to build the process model for optimizing of rooftop photovoltaic arrangement based on three-dimensional geo-spatial information.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.4
• /
• pp.903-910
• /
• 2016

As smart mobile devices having touchscreens are growingly deployed, a pattern lock system, which is one of the graphical password systems, has become a major authentication mechanism. However, a user's unlocking behaviour leaves smudges on a touchscreen and they are vulnerable to the so-called smudge attacks. Smudges can help an adversary guess a secret pattern correctly. Several advanced pattern lock systems, such as TinyLock, have been developed to resist the smudge attacks. In this paper, we study an automated smudge attack that employs machine learning techniques and its effectiveness in comparison to the human-only smudge attacks. We also compare Android pattern lock and TinyLock schemes in terms of security. Our study shows that the automated smudge attacks are significantly advanced to the human-only attacks with regard to a success ratio, and though the TinyLock system is more secure than the Android pattern lock system.