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This work targets low-power portable electronic applications with PV power harvesting. A bi-directional dc-dc converter is required to interface the battery with the load and PV module. A new current-mode scheme is applied to the ...
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This work targets low-power portable electronic applications with PV power harvesting. A bi-directional dc-dc converter is required to interface the battery with the load and PV module. A new current-mode scheme is applied to the single-inductor three-port converter, where the peak and valley inductor current are controlled by two separate digital regulation loops. The controller is shown to regulate both the PV voltage and the load voltage in the presence of load and irradiance steps. In solar deficit mode, energy is transferred from the battery to the load indirectly through the PV node, eliminating the two input switches used in conventional dual-input, dual-output converters. The scheme is demonstrated on a digitally controlled 1 W harvester prototype.
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To improve the accuracy of conventional white light endoscopy in detecting the small lesion and identifying the margin of observable tumors, in the potential of light-induced fluorescence (LIF) spectroscopic imaging, using a gener...
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To improve the accuracy of conventional white light endoscopy in detecting the small lesion and identifying the margin of observable tumors, in the potential of light-induced fluorescence (LIF) spectroscopic imaging, using a general multivariate spectral classification algorithm, was evaluated. A conventional endoscopic system with a multiple channel spectrometer was used to measure the autofluorescence of nasopharyngeal tissue in vivo. Classification was based on the spectral difference between the carcinoma and normal tissue. A sophisticated algorithm based on Principal Component Analysis (PCA) was developed to differentiate between he nasopharyngeal carcinoma (NPC) from the normal tissue. Firstly, preprocessing was done to reduce noise and to calibrate the different measurement distances and geometry. Secondly, processing by PCA was done to effectively reduce the variable dimensions while maintaining useful information for analysis. Thirdly, various post-processing techniques were investigated and the classification performance was compared. Algorithms based on ratio of autofluorescence at two-wavelength and three-wavelength bands were used for comparison. The PCA based method shows a significant improvement over the two-wavelength and three-wavelength algorithm. Based on the entire spectra, the sensitivity of 92% and specificity of 96% were achieved using the PCA based algorithm for the detection of nasopharyngeal carcinomas. In conclusion, the PCA based statistical algorithm is efficient to achieve high spectral classification performance of NPC.
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Nowadays, web images are rapidly increasing with the development of internet technology. This situation leads to the difficulties on effective and efficient image retrieval from mass data under web environment. In this paper, we p...
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Nowadays, web images are rapidly increasing with the development of internet technology. This situation leads to the difficulties on effective and efficient image retrieval from mass data under web environment. In this paper, we propose a web images classification method by integrating SIFT features of the images with global features. First, Locality Sensitive Hashing (LSH) is adopted for local feature extraction by embedding the SIFT feature vector. Then, other global features, such as color, texture or shape feature, are extracted. Support Vector Machine (SVM) is employed for image classification by using these two types of features respectively. The two classification results are integrated by decision-level fusion to get the final classification result. Experimental results on a web image dataset show that the proposed method is able to improve the performance of web images classification.
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As frequently found in waster waters containing azo, to well reveal the effect ofsulfate on the azo reductive transformation-with acetate or propionate aspotential electron donors-in anaerobic systems, three laboratory-scalesequen...
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As frequently found in waster waters containing azo, to well reveal the effect ofsulfate on the azo reductive transformation-with acetate or propionate aspotential electron donors-in anaerobic systems, three laboratory-scalesequence batch reactors, one acetate (R1) and two propionate (R2, R3)laboratory-scale sequence batch reactors were set up. With riboflavin (RF) as aredox mediator, all experiments conditions were amended with the sameamount of acid orange 7 (AO7), one of the most common azo materials witha simple chemical structure, whereas with or without increasing sulfate orcorresponding sulfide concentrations. Results indicated that, high RFconcentration could facilitate the AO7 reduction through promoting propionatefermentation. As RF increased, the contribution chemical reduction andnonsulfate-reducing bacteria to AO7 reduction increased significantly, whilesulfate-reducing bacteria do the opposite.
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Effects of temperature (conventional (25℃) vs. mesophilic (35℃) vs. thermophilic (55℃)) on activated sludge properties (production and composition of EPS and interaction potential) and their roles in bioflocculation and settlin...
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Effects of temperature (conventional (25℃) vs. mesophilic (35℃) vs. thermophilic (55℃)) on activated sludge properties (production and composition of EPS and interaction potential) and their roles in bioflocculation and settling were studied using well-controlled sequencing batch reactors fed with a synthetic wastewater comprised of glucose and inorganic nutrients. The results show that thermophilic sludge had a poorer bioflocculation ability and settleability than that of conventional and mesophilic sludge. Analysis of extracellular polymeric substances (EPS) indicates that thermophilic sludge had a higher level of loosely bound EPS (LB-EPS) content than that of conventional and mesophilic sludge. The LB-EPS content of thermophilic sludge was ten times more than TB-EPS content of it, which coincided with higher supernatant turbidity. Therefore, the worse bioflocculation and settling ability of thermophilic sludge could be explained from the perspective of LB-EPS. Calculating the interaction energy of three kinds of sludge, the interaction barrier of thermophilic sludge disappeared which meant the attractive potential was dominant in the system. Thus, it should have led to a better flocculation, which did not agree with the actual performance. It indicates that the worse bioflocculation and settling ability of thermophilic sludge could be explained from the perspective of interaction energy.
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Effects of temperature (conventional (25°C) vs. mesophilic (35°C) vs. thermophilic (55°C)) on activated sludge properties (production and composition of EPS and interaction potential) and weir roles in bioflocculation and settl...
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Effects of temperature (conventional (25°C) vs. mesophilic (35°C) vs. thermophilic (55°C)) on activated sludge properties (production and composition of EPS and interaction potential) and weir roles in bioflocculation and settling were studied using well-controlled sequencing batch ractors fed with a synthetic wastewater comprised of glucose and inorganic nutrients. The results show that thermophilic sludge had a poorer bioflocculation ability and settleability than that of conventional and mesophilic sludge. Analysis of extracellular polymeric substances (EPS) indicates that thermophilic sludge had a higher level of loosely bound EPS (LB-EPS) content than that of conventional and mesophilic sludge. The LB-EPS content of thermophilic sludge was ten times more than TB-EPS content of it, which coincided with higher supernatant turbidity. Therefore, the worse bioflocculation and settling ability of thermophilic sludge could be explained from the perspective of LB-EPS. Calculating the interaction energy of three kinds of sludge, the interaction barrier of thermophilic sludge disappeared which meant the attractive potential was dominant in the system.
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摘要 :
Effects of temperature (conventional (25°C) vs. mesophilic (35°C) vs. thermophilic (55°C)) on activated sludge properties (production and composition of EPS and interaction potential) and their roles in bioflocculation and sett...
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Effects of temperature (conventional (25°C) vs. mesophilic (35°C) vs. thermophilic (55°C)) on activated sludge properties (production and composition of EPS and interaction potential) and their roles in bioflocculation and settling were studied using well-controlled sequencing batch reactors fed with a synthetic wastewater comprised of glucose and inorganic nutrients. The results show that thermophilic sludge had a poorer bioflocculation ability and settleability than that of conventional and mesophilic sludge. Analysis of extracellular polymeric substances (EPS) indicates that thermophilic sludge had a higher level of loosely bound EPS (LB-EPS) content than that of conventional and mesophilic sludge. The LB-EPS content of thermophilic sludge was ten times more than TB-EPS content of it, which coincided with higher supernatant turbidity. Therefore, the worse bioflocculation and settling ability of thermophilic sludge could be explained from the perspective of LB-EPS. Calculating the interaction energy of three kinds of sludge, the interaction barrier of thermophilic sludge disappeared which meant the attractive potential was dominant in the system. Thus, it should have led to a better flocculation, which did not agree with the actual performance. It indicates that the worse bioflocculation and settling ability of thermophilic sludge could be explained from the perspective of interaction energy.
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Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environment...
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Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environments is an unsolved problem. Reinforcement learning (RL) is a naturally promising tool. For prosthesis control with a user in the loop, it is desirable that the controlled prosthesis can adapt to different task environments as quickly and smoothly as possible. However, most RL agents learn or relearn from scratch when the environment changes. To address this issue, we propose the knowledge-guided Q-learning (KG-QL) control method as a principled way for the problem. In this report, we collected and used data from two able-bodied (AB) subjects wearing a RL controlled robotic prosthetic limb walking on level ground. Our ultimate goal is to build an efficient RL controller with reduced time and data requirements and transfer knowledge from AB subjects to amputee subjects. Toward this goal, we demonstrate its feasibility by employing OpenSim, a well-established human locomotion simulator. Our results show the OpenSim simulated amputee subject improved control tuning performance over learning from scratch by utilizing knowledge transfer from AB subjects. Also in this paper, we will explore the possibility of information transfer from AB subjects to help tuning for the amputee subjects.
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Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environment...
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Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environments is an unsolved problem. Reinforcement learning (RL) is a naturally promising tool. For prosthesis control with a user in the loop, it is desirable that the controlled prosthesis can adapt to different task environments as quickly and smoothly as possible. However, most RL agents learn or relearn from scratch when the environment changes. To address this issue, we propose the knowledge-guided Q-learning (KG-QL) control method as a principled way for the problem. In this report, we collected and used data from two able-bodied (AB) subjects wearing a RL controlled robotic prosthetic limb walking on level ground. Our ultimate goal is to build an efficient RL controller with reduced time and data requirements and transfer knowledge from AB subjects to amputee subjects. Toward this goal, we demonstrate its feasibility by employing OpenSim, a well-established human locomotion simulator. Our results show the OpenSim simulated amputee subject improved control tuning performance over learning from scratch by utilizing knowledge transfer from AB subjects. Also in this paper, we will explore the possibility of information transfer from AB subjects to help tuning for the amputee subjects.
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This paper aims to develop an optimal controller that can automatically provide personalized control of robotic knee prosthesis in order to best support gait of individual prosthesis wearers. We introduced a new reinforcement lear...
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This paper aims to develop an optimal controller that can automatically provide personalized control of robotic knee prosthesis in order to best support gait of individual prosthesis wearers. We introduced a new reinforcement learning (RL) controller for this purpose based on the promising ability of RL controllers to solve optimal control problems through interactions with the environment without requiring an explicit system model. However, collecting data from a human-prosthesis system is expensive and thus the design of a RL controller has to take into account data and time efficiency. We therefore propose an offline policy iteration based reinforcement learning approach. Our solution is built on the finite state machine (FSM) impedance control framework, which is the most used prosthesis control method in commercial and prototypic robotic prosthesis. Under such a framework, we designed an approximate policy iteration algorithm to devise impedance parameter update rules for 12 prosthesis control parameters in order to meet individual users' needs. The goal of the reinforcement learning-based control was to reproduce near-normal knee kinematics during gait. We tested the RL controller obtained from offline learning in real time experiment involving the same able-bodied human subject wearing a robotic lower limb prosthesis. Our results showed that the RL control resulted in good convergent behavior in kinematic states, and the offline learning control policy successfully adjusted the prosthesis control parameters to produce near-normal knee kinematics in 10 updates of the impedance control parameters.
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