Browsing by Author "Chen, Jian-Ren"

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A Local Feature-based Human Motion Recognition Framework
(2009)
Lai, Yu-Chun
;
Liao, Hong-Yuan Mark
;
Lin, Cheng-Chung
;
Chen, Jian-Ren
;
Luo, Y. -F. Peter
In this paper, we propose a local feature-based human motion analysis framework Instead of using traditional analysis methods to characterize the global structure of human motion, we extract features directly from local regions that contain motion. To implement the above concept, we adopt the rules of visual attention theory, which assert that a human motion can be described simply by a set of local features comprised of spatial relationships rather than human postures. We select two kinds of features to represent the local variation of a human motion. First, we extract the long-term movement trend of the motion. The second feature is actually a set of rough features derived by sampling multi-scale moving edges. The two types of features are considered together during the recognition process. Our experiments demonstrate that the proposed approach can achieve very good recognition results.
Low density supercritical fluids precipitation of 9-cis and all trans-beta-carotenes enriched particulates from Dunaliella salina
(2013-07-19)
Chen, Jian-Ren
;
Wu, Jia-Jiuan
;
Lin, Justin Chun-Te
;
Wang, Yuan-Chuen
;
Young, Chiu-Chung
In this study, supercritical anti-solvent (SAS) pulverization coupled with reverse phase elution chromatography was employed to isolate 9-cis and trans-beta-carotenes from Dunaliella saline. Total concentration of 9-cis (134.7 mg/g) and trans-beta-carotene (204.2 mg/g) was increased from 338.9 mg/g of the ultrasonic extract to 859.7 mg/g (338.9 for 9-cis and 520.8 for trans) of the elution fraction. The SAS pulverization of the collected fraction further produced submicron-sized particulates containing 932.1 mg/g (355.6 for 9-cis and 576.5 for trans) of total beta-carotenes with a recovery of 86.3% (83.9% for cis and 87.8% for trans). Effects of two SAS operational conditions on the purity, recovery of total beta-carotenes, mean size and morphology of the precipitates were obtained from an experimentally designed method. Generation of micronized particulates enriched with 9-cis and trans-beta-carotenes by low-density SAS was proved to be feasible and environmental benign. (C) 2013 Elsevier B.V. All rights reserved.
Low density supercritical fluids precipitation of 9-cis and all trans-beta-carotenes enriched particulates from Dunaliella salina
(2013-07-19)
Chen, Jian-Ren
;
Wu, Jia-Jiuan
;
Lin, Justin Chun-Te
;
Wang, Yuan-Chuen
;
Young, Chiu-Chung
In this study, supercritical anti-solvent (SAS) pulverization coupled with reverse phase elution chromatography was employed to isolate 9-cis and trans-beta-carotenes from Dunaliella saline. Total concentration of 9-cis (134.7 mg/g) and trans-beta-carotene (204.2 mg/g) was increased from 338.9 mg/g of the ultrasonic extract to 859.7 mg/g (338.9 for 9-cis and 520.8 for trans) of the elution fraction. The SAS pulverization of the collected fraction further produced submicron-sized particulates containing 932.1 mg/g (355.6 for 9-cis and 576.5 for trans) of total beta-carotenes with a recovery of 86.3% (83.9% for cis and 87.8% for trans). Effects of two SAS operational conditions on the purity, recovery of total beta-carotenes, mean size and morphology of the precipitates were obtained from an experimentally designed method. Generation of micronized particulates enriched with 9-cis and trans-beta-carotenes by low-density SAS was proved to be feasible and environmental benign. (C) 2013 Elsevier B.V. All rights reserved.
Reconstruct Dynamic Systems from Large-Scale Open Data
(2015)
Tsai, Kun-Hung
;
Lin, Chia-Yu
;
Wang, Li-Chun
;
Chen, Jian-Ren
;
電機學院
With the prosperity of e-commerce, on-line vendors use recommendation systems in different fields. Classic recommendation algorithms are designed assuming that data is stationary and will not change over time. However, since the scale and variability of data are growing gradually, these methods will encounter the issues of the memory deficient and the out-of-date model, which degrade the recommendation accuracy intensively. In addition, retraining the whole model for every new arrival record results in high complexity. In this paper we propose a light-weight adaptive updating method to overcome these issues. Comparing with the explicit feedback recommendation, which asks the customers to express their opinions on the recommended items, the implicit feedback recommendation is easier to collect and non-intrusive way. However, the dynamic time-variant system with implicit feedback has not been seen in the literature. In this paper, we propose a real-time incremental updating algorithm (RI-SGD) to deal with time-variant systems based on the implicit feedback. We compare our method with methods that retraining the whole model and show that our method costs less than 1% of the retraining time with a competitive accuracy.