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A Tracking System Using Location Prediction and Dynamic Threshold for Minimizing SMS Delivery
Lai, Yuan-Cheng,Lin, Jian-Wei,Yeh, Yi-Hsuan,Lai, Ching-Neng,Weng, Hui-Chuan The Korea Institute of Information and Commucation 2013 Journal of communications and networks Vol.15 No.1
In this paper, a novel method called location-based delivery (LBD), which combines the short message service (SMS) and global position system (GPS), is proposed, and further, a realistic system for tracking a target's movement is developed. LBD reduces the number of short message transmissions while maintaining the location tracking accuracy within the acceptable range. The proposed approach, LBD, consists of three primary features: Short message format, location prediction, and dynamic threshold. The defined short message format is proprietary. Location prediction is performed by using the current location, moving speed, and bearing of the target to predict its next location. When the distance between the predicted location and the actual location exceeds a certain threshold, the target transmits a short message to the tracker to update its current location. The threshold is dynamically adjusted to maintain the location tracking accuracy and the number of short messages on the basis of the moving speed of the target. The experimental results show that LBD, indeed, outperforms other methods because it satisfactorily maintains the location tracking accuracy with relatively fewer messages.
A Tracking System Using Location Prediction and Dynamic Threshold for Minimizing SMS Delivery
Yuan-Cheng Lai,Jian-Wei Lin,Yi-Hsuan Yeh,Ching-Neng Lai,Hui-Chuan Weng 한국통신학회 2013 Journal of communications and networks Vol.15 No.1
In this paper, a novel method called location-based delivery (LBD), which combines the short message service (SMS) and global position system (GPS), is proposed, and further, a realistic system for tracking a target’s movement is developed. LBD reduces the number of short message transmissions while maintaining the location tracking accuracy within the acceptable range. The proposed approach, LBD, consists of three primary features: Short message format, location prediction, and dynamic threshold. The defined short message format is proprietary. Location prediction is performed by using the current location, moving speed, and bearing of the target to predict its next location. When the distance between the predicted location and the actual location exceeds a certain threshold, the target transmits a short message to the tracker to update its current location. The threshold is dynamically adjusted to maintain the location tracking accuracy and the number of shortmessages on the basis of themoving speed of the target. The experimental results show that LBD, indeed, outperforms other methods because it satisfactorily maintains the location tracking accuracy with relatively fewer messages.
Cheng, Fu-Yuan,Wan, Tien-Chun,Liu, Yu-Tse,Lai, Kung-Ming,Lin, Liang-Chuan,Sakata, Ryoichi Asian Australasian Association of Animal Productio 2008 Animal Bioscience Vol.21 No.5
This study developed a natural ingredient as a functional food possessing properties of attenuation of hypertension and cardiovascular hypertrophy. In a previous study hydrolysates obtained from chicken leg bone protein using Alcalase strongly inhibited angiotensin I converting enzyme (ACE) in vitro. In particular, hydrolysate (A4H) from four hours of incubation exhibited the highest ACE inhibitory activity (IC50 = 0.545 mg/ml). A4H was selected as a potent ACE inhibitor and orally administrated to spontaneously hypertensive rats (SHR) for eight weeks to investigate attenuating effects on age-related development of hypertension and cardiovascular hypertrophy. Results showed that treatment with A4H of SHRs attenuated the development of hypertension as effectively as the clinical antihypertensive drug captopril. Moreover, a significantly lower heart to body weight ratio and thinness of coronary arterial wall was observed in SHRs that had been treated with A4H or captopril. The results suggest that A4H can be utilized in developing an ACE inhibitor as a potential ingredient of functional foods to alleviate hypertension and cardiovascular hypertrophy.
Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.