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1.
ABSTRACTThis paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance. 相似文献
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通过对接轨站设置位置和布置图型主要影响因素的分析。按照能够充分反映接轨站作业特点的交接方式。对接轨站所采用的布置图型进行了归纳和分类。 相似文献
4.
Mitsuo Miyazawa Hideki Kawazoe Mitsuro Hyakumachi 《Journal of chemical technology and biotechnology (Oxford, Oxfordshire : 1986)》2003,78(6):620-625
The microbial transformation of l‐menthol ( 1 ) was investigated by using 12 isolates of soil‐borne plant pathogenic fungi, Rhizoctonia solani (AG‐1‐IA Rs24, Joichi‐2, RRG97‐1; AG‐1‐IB TR22, R147, 110.4; AG‐1‐IC F‐1, F‐4, P‐1; AG‐1‐ID RCP‐1, RCP‐3, and RCP‐7) as a biocatalyst. Rhizoctonia solani F‐1, F‐4 and P‐1 showed 89.7–99.9% yields of converted product from 1 , RCP‐1, RCP‐3, and RCP‐7 26.0–26.9% and the other isolates 0.1–12.0%. In the cases of F‐1, F‐4 and P‐1, substrate 1 was converted to (?)‐(1S,3R,4S,6S)‐6‐hydroxymenthol ( 2 ), (?)‐(1S,3R,4S)‐1‐hydroxymenthol ( 3 ) and (+)‐(1S,3R,4R,6S)‐6,8‐dihydroxymenthol ( 4 ), which was a new compound. Substrate 1 was converted to 2 and/or 3 by RRG97‐1, 110.4, RCP‐1, RCP‐3 and RCP‐7. The structures of the metabolic products were elucidated on the basis of their spectral data. In addition, metabolic pathways of the biotransformation of 1 by Rhizoctonia solani are discussed. Finally, from the main component analysis and the differences in the yields of converted product from 1 , the 12 isolates of Rhizoctonia solani were divided into three groups based on an analysis of the metabolites. Copyright © 2003 Society of Chemical Industry 相似文献
5.
低温脂肪酶产生菌的筛选、鉴定及其部分酶学性质 总被引:15,自引:1,他引:14
从南极乔治王岛冻土来源的76株低温细菌中筛选到13株低温脂肪酶产生菌,对其中的BTsl0022菌株进行鉴定。通过生理生化特征、16s rDNA基因序列的同源性和系统发育分析发现,菌株RTsl0022属于假单胞菌属(Pseudomonas),但与已定名的假单胞菌有一定的差异,与未定名的Pseudomonas sp.PsB的亲缘关系最接近,故将其暂定名为Pseudomonas sp.BTsl0022。对该菌脂肪酶的酶学性质初步研究表明,酶的最适作用温度为24℃,对热敏感,60℃处理30min仅残留25%酶活性,酶的适宜作用pH范围在7.0~9.0,最适pH为8.0。 相似文献
6.
Chih-Ming Chen Hahn-Ming Lee Chia-Chen Tan 《Engineering Applications of Artificial Intelligence》2006,19(8):967-978
The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches. 相似文献
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1Introduction Reconstructivesurgeryisoneofthehottestre searchedsubjectsthatdealingwithbonedefects.Thetra ditionalmethodisimplantationoffreshautograftbonebe causeofitsnon immunoreactiveproperty.Butautologousbonewasnotabundantinsomecases.Sowefoundbeta trica… 相似文献
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就毛石砌体的拉接处理、现浇混凝土柱、墙拉结筋的留设、砖混结构中构造柱的质量预控等几个环节进行了分析,提出只有在施工中采取必要的措施,才能保证施工质量。 相似文献
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裂缝性油气藏采收率:100个裂缝性油气田实例的经验总结 总被引:5,自引:2,他引:3
通过对世界上100个裂缝性油气藏的综合评价,研究储集层及流体本身的性质(包括孔隙度、渗透率、黏度、可动油比例、含水饱和度、润湿性及裂缝分布特征等)和驱动机制及油藏管理战略(优化日产量和采用不同类型的提高采收率技术)对其最终采收率的影响。将裂缝性油气藏分为4类:I类的基质几乎没有孔隙度和渗透率,裂缝是储存空间和流体流动的通道;Ⅱ类的基质有较低的孔隙度和渗透率,基质提供储存空间,裂缝提供流动通道;Ⅲ类(微孔隙)的基质具有高孔隙度和低渗透率,基质提供储存空间,裂缝提供流动通道;Ⅳ类(大孔隙)的基质具有高的孔隙度和渗透率,基质提供储存空间和流动通道,裂缝仅增加渗透率。对26个Ⅱ类油气藏和20个Ⅲ类油气藏的开采历史的研究表明:Ⅱ类油气藏的采收率受水驱强度和最优日产量控制,日产量过高会很容易破坏Ⅱ类油气藏,一些Ⅱ类油气藏如果管理得当,采收率可以很高,不需要二次或三次采油;Ⅲ类油气藏的采收率主要受岩石和流体本身性质的影响,特别是基质渗透率、流体重度、润湿性以及裂缝强度等,不进行二次或三次采油不可能完全开采,往往需要采用一些提高采收率的专门技术。以往将Ⅱ类和Ⅲ类裂缝性油气藏归为一类,认识它们的区别将有助于选择更好的开发策略。 相似文献