• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.5 no.1
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• pp.13-24
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• 2006

This paper proposes a wireless management system for a pet dog using wireless sensor network. The developed intelligent wireless management system is compose of a central control system, auto-feeder, miniguidance robot, and wireless sensing devices. The developed system uses three types of sensed data such as light, temperature, md sounds from a pet dog and surrounded environment respectively. The presented design method using these data provides an efficient way to controlling and monitoring the pet dog. The implemented system can be used as a design framework of portable device for the pet management.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.7 no.1
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• pp.1-13
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• 2007

This paper proposes a wireless management system for a pet dog using wireless sensor network. The developed intelligent wireless management system is compose of a central control system, auto-feeder, mini-guidance robot, and wireless sensing devices. The developed system uses three types of sensed data such as light, temperature, and sounds from a pet dog and surrounded environment respectively. The presented design method using these data provides an efficient way to controlling and monitoring the pet dog. The implemented system can be used as a design framework of portable device for the pet management.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.