Magnetic control assembly qualification model

Fabrication and testing of the magnetic control assembly (MCA) are summarized. The MCA was designed as an add-on unit for certain existing components of the Nimbus and ERTS attitude control system. The MCA system consists of three orthogonal electromagnets; a magnetometer probe capable of sensing external fields in the X, Y, and Z axes; and the control electronics. An operational description of the system is given along with all major drawings and photographs. Manufacturing and inspection procedures are outlined and a chronological list of events is included with the fabrication summary

KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read

Enhanced phytoextraction of Cu, Pb, Zn and Cd with EDTA and EDDS

2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Plant uptake and the leaching of metals during the hot EDDS-enhanced phytoextraction process

Author name used in this publication: Chun-Ling LuoAuthor name used in this publication: Zhen-Guo ShenAuthor name used in this publication: Xiang-Dong Li2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Hot NTA application enhanced metal phytoextraction from contaminated soil

2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe