• Journal of KIISE:Software and Applications
• /
• v.37 no.2
• /
• pp.95-103
• /
• 2010

Improving the competitiveness of software products in the market involves procuring the means to design software architecture that deliver qualities necessitated by stakeholder requirements within allocated budget, thereby improving the cost-effectiveness of the end product. Currently, software architecture evaluation methods are used to predict and review qualities inherent in software architecture designs and to choose a candidate architecture that delivers desired qualities. Existing software architecture evaluation methods, however, fail to address the cost considerations dependent on the architecture chosen for product implementation. In this paper we suggest a cost estimation method for software architecture which adapts the cost drivers in the software cost estimation model COCOMO II to support cost estimation during architecture evaluation. The suggested method can be performed in coordination with existing software architecture evaluation efforts and supplements existing architecture evaluation techniques with guidelines for identifying and evaluating cost drivers in candidate software architectures without incurring extra overhead. The accuracy of the cost estimation using the suggested method is verified through application of the method to the architecture candidates used in RPS (Robot Patrol System), a surveillance embedded system.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.2
• /
• pp.363-368
• /
• 2021

Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1169-1170
• /
• 2023

본 논문에서는 험지 환경에서 순찰하는 모바일 로봇의 이동 가능성(traversability)을 수행하기 위해 로봇에 탑재되는 Jetson AGX Orin에서의 실시간 Semantic Segmentation을 달성하는 것을 목표로 하였다. 험지 환경을 위한 OFFSEG 모델을 활용하였으며, 다운샘플링, 파라미터 최적화 등 각종 경량화 기술을 적용하여 지연 시간을 단축시켰다. 또한 현장과 유사한 환경에서의 테스트를 통해 처리 시간을 목표로 하는 100ms에 근접한 시간으로 단축할 수 있었다.