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1. 发明授权

US5659487A Method for recovering from sun transit in communication of very small aperture terminal 失效
标题翻译：从非常小孔径终端的通信中恢复太阳运输的方法
公开(公告)号：US5659487A
公开(公告)日：1997-08-19
申请号：US463429
申请日：1995-06-05
申请人： Mi Sung Cho , Jong Soo Park
发明人： Mi Sung Cho , Jong Soo Park
IPC分类号： H04B7/14 , H04B7/185
CPC分类号： H04B7/18513
摘要： A method for recovering from a sun transit in a communication of a very small aperture terminal (VSAT) is disclosed, in which the sun transit outrage (STO) is prevented by using a sun transit recovering algorithm, thereby making it possible to apply the very small aperture terminal (VSAT) to the satellite communication. Generally, the communication system of the very small aperture terminal (VSAT) in which a still track satellite is used includes a VSAT central station (VCS), a network management system (NMS), and a VSAT remote station (VRS). In this communication system, when the sun reaches near the boresight axis of an antenna, an STO phenomenon occurs, with the result that the communication system is influenced by the additional noise power of the sun. Consequently, the reliability of the communication system drops to below an average quality which is tolerable in an antenna communication system. Particularly, in a communication system using a still track satellite, the STO phenomenon occurs once every day near the spring equinox and the autumnal equinox, and therefore, problems are encountered in carrying out the communications. The present invention provides a method for recovering from a sun transit in a communication of a very small aperture terminal, in which, when an STO phenomenon occurs in the communication system, this is predicted by a network management system (NMS), and is informed to the VCS and to the VRS, so that the communication can be halted during the occurrence of an STO phenomenon, and that the communication can be resumed after the termination of the STO phenomenon.
摘要翻译：公开了一种在非常小的孔径终端（VSAT）的通信中从太阳转运中恢复的方法，其中通过使用太阳运输恢复算法来防止太阳转运爆发（STO），从而使得可以非常适用小孔径终端（VSAT）到卫星通信。通常，使用静态轨道卫星的小孔径终端（VSAT）的通信系统包括VSAT中心站（VCS），网络管理系统（NMS）和VSAT远程站（VRS）。在该通信系统中，当太阳到达靠近天线的视轴时，发生STO现象，结果是通信系统受到太阳附加噪声的影响。因此，通信系统的可靠性降低到在天线通信系统中可容忍的平均质量以下。特别地，在使用静态轨道卫星的通信系统中，STO现象每天在春分和秋分上都发生一次，因此在进行通信时遇到问题。本发明提供一种在通信系统中发生STO现象的非常小的孔径终端的通信中从太阳转运中恢复的方法，这由网络管理系统（NMS）预测，并被通知到VCS和VRS，使得在STO现象发生期间可以停止通信，并且可以在STO现象终止之后恢复通信。

2. 发明授权

US11665281B2 Call recommendation system and call recommendation method based on artificial intelligence 有权
公开(公告)号：US11665281B2
公开(公告)日：2023-05-30
申请号：US17153054
申请日：2021-01-20
申请人： Byung Kwan Jung , Mi Sung Cho
发明人： Byung Kwan Jung , Mi Sung Cho
IPC分类号： H04M3/51 , H04M15/00 , G06N20/00 , G06N3/08
CPC分类号： H04M3/5175 , G06N3/08 , G06N20/00 , H04M15/49 , H04M15/8044 , H04M2215/745
摘要： A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

3. 发明申请

US20210274043A1 CALL RECOMMENDATION SYSTEM AND CALL RECOMMENDATION METHOD BASED ON ARTIFICIAL INTELLIGENCE 有权
公开(公告)号：US20210274043A1
公开(公告)日：2021-09-02
申请号：US17153054
申请日：2021-01-20
申请人： Byung Kwan Jung , Mi Sung Cho
发明人： Byung Kwan Jung , Mi Sung Cho
IPC分类号： H04M3/51 , H04M15/00 , G06N20/00 , G06N3/08
摘要： A call recommendation system based on artificial intelligence is provided. The call recommendation system includes a data collecting unit, a matching time predicting unit, a price determining unit, and a final ranking determining unit. When a service is requested from a service user, the data collecting unit collects first past data indicating a past location of the service user, first present data indicating a present location of the service user, second past data indicating a past location of a service provider, and second present data indicating a present location of the service provider. The matching time predicting unit inputs the first and second past data and the first and second present data to a recurrent neutral network (RNN) leaning model to predict a future location of the service user and a future location of the service provider and inputs first prediction data regarding the future location of the service user and second prediction data regarding the future location of the service provider to a prediction learning model to predict, when the service provider selects a service, a matching time required until the service provider is matched with a next service user after the service provider completes the service. The price determining unit determines a price for the service such that the price increases as the matching time increases. The final ranking determining unit determines a recommendation rating (or a recommendation priority) of a service among services required for the service provider based on preference data indicating preference of the service provider regarding a service and a price. The RNN learning model and the prediction learning model are based on a deep learning algorithm.

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