• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.3
• /
• pp.572-579
• /
• 2009

A monitoring system needs many parameters to increase devices for monitoring data and to support various services. In particular, monitoring the status of a device in a wireless mobile environment has a difficulty in displaying multi data in a limited screen size, and transfer of the status data of a device into a network is largely related with network traffic. The research aims at designing a control board that collects data in order to effectively manage a greenhouse environment system. Also, the research tries to appropriately operate devices, environment data monitoring, and the control of each device by realizing a multiplexed interface based on a web. Thus, in the case in which a distributed client was a computer, monitoring and control were obtained with a web browser through the Lab VIEW web server of a server or local control module in order to effectively monitor and control according to the status of a user. In the case in which a client was a PDA, application of a wireless mobile considering the scale and data processing capacity of a displayer was connected. As a result of the research, we could confirm a satisfactory outcome from the viewpoint of a human-centered design by supplying adaptability and mobility according to the environment of a user.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.1
• /
• pp.9-18
• /
• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.