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1.
在对氯乙烯精馏这个复杂生产过程工艺及控制的分析基础上,提出了低沸塔回流、低塔釜加热、中间过料、高塔回流、高塔釜加热、成品冷凝等回路的控制模型。并介绍了用开放的DCS进行的组态、调试过程。因其图形化组态工具的直观、灵活、功能强,使复杂的控制问题简单化,且控制对工况变化的适应性强。经生产实际运行表明,产品质量、生产能力均超过预期值。 相似文献
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为保障供港蔬菜的安全,提高供港蔬菜的安全监管能力,本文从检验检疫部门监管工作实际出发,提出了基于射频识别技术的供港蔬菜安全监管溯源系统.首先介绍了RFID技术及其主要功能,通过检验检疫角度提出供港蔬菜的业务框架结构、技术框架结构和安全保障体系结构,在此基础上设计溯源系统种植基地、加工厂、口岸和超市四个点的功能需求,并规定了溯源系统与RFID的接口标准.实践证明,该系统能够实现供港蔬菜流转中的每一管理节点的上下游追溯,实现管理者对蔬菜从原料、生产、运输到销售全过程的了解和把握,使企业的生产、仓储、通关、配送过程更加便捷,全面保证了蔬菜集装箱在运输过程的完好性,满足了供港蔬菜的安全溯源监管体系需求.该系统对食品的安全监管、产品的伪劣甄别有着重要的启示作用和意义. 相似文献
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基于6种语体的句法树库构建了6个依存句法网络,对这些网络的边数、节点数、节点平均度、聚类系数、平均最短路径长度、网络中心势、直径、节点度幂律分布的幂指数、度分布与幂律拟合的决定系数等整体特征进行了对比分析。以这些整体特征为变量,采用欧几里德的“最短距离”法,对这6种语体的句法网络进行了聚类分析。研究结果显示,通过一些网络的主要参数,即网络节点数、聚集系数、平均路径长度、中心势以及节点度幂律分布的幂指数,可以对所研究的文本进行分类。与传统的文本聚类方法相比,其结果更容易从语言学的角度进行合理的解释。 相似文献
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黄晓莹 《计算机测量与控制》2017,25(6):5-5
医学图像常规检查中,需要对目标图像进行测量以获取相关数据。测量主要集中在对距离,面积,角度以及周长等数据的获取。由于医学检测的严谨性,数据准确,方法简便是必不可少的要求。随着计算机技术的发展,其在医学中的应用也得到了广泛的推崇。计算机辅助的医学图像测量不但替代了传统的手工测量方式,也极大地提高了测量的精度与效率。分析医学图像测量算法,针对如何确定目标区域的问题设计了颜色填充的方法,可有效区分出目标区域图像。为了提高该算法的效率,进一步提出了其改进算法,降低了算法执行次数,提高了系统性能。 相似文献
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为了解决当不完备混合决策系统中数据动态增加时,静态属性约简方法的计算复杂度高的问题,提出变精度下不完备混合数据的增量式属性约简方法。首先,在变精度模型下给出了利用条件熵度量属性的重要性程度;然后,详细分析和设计了当数据动态增加时条件熵的增量式更新变化情况和属性约简的更新机制;在此基础上,利用启发式贪心策略构造了增量式的属性约简算法,实现了不完备的数值型和符号型混合数据下属性约简的动态更新。通过UCI数据集中五个真实的混合型数据集的实验比较和分析,在约简效果方面,利用增量式属性约简算法处理Echocardiogram、Hepatitis、Autos、Credit和Dermatology数据集的增量规模为90%+10%时,数据集的原属性个数分别由12、19、25、17和34个约简至6、7、10、11和13个,分别占原属性集的50.0%、36.8%、40.0%、64.7%和38.2%;在执行时间方面,增量式算法在五个数据集的平均耗时分别为2.99 s、3.13 s、9.70 s、274.19 s和50.87 s,静态算法的平均耗时分别为284.92 s、302.76 s、1062.23 s、3510.79 s和667.85 s,且增量式算法的耗时与数据集的实例规模、属性个数和属性值类型的分布相关。实验结果表明,增量式属性约简算法在计算耗时方面要显著优于静态算法,且能有效剔除数据中的冗余属性。 相似文献
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In this paper, we suggested and studied the inverse length biased Maxell distribution (ILBMD) as a new continuous distribution of one parameter. The ILBMD is obtained by considering the inverse transformation technique of the Maxwell length biased distribution. Statistical characteristics of the ILBMD such as the moments, moment generating function, mode, quantile function, the coefficient of variation, coefficient of skewness, Moors and Bowley measures of kurtosis and skewness , stochastic ordering, stress-strength reliability, and mean deviations are obtained. In addition, the Bonferroni and Lorenz curves, Gini index, the reliability function, the hazard rate function, the reverse hazard rate function, the odds function, and the distributions of order statistics for the ILBMD, are presented. The ILBMD parameter is estimated using the maximum likelihood method, the method of moments, the maximum product of spacing technique, the ordinary and weight least square procedures, and the Cramer-Von-Mises methods. The Fishers information, as well as the Rényi and q-entropies, are derived. To investigate the usefulness of the proposed lifetime distribution and to illustrate the purpose of the study, a real dataset of the relief times of 20 patients receiving an analgesic is used. 相似文献
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在智能制造系统中,工业物联网通过先进的管理技术将制造设备互连,实现了信息的实时传输、设备的范在化感知和数据的快速分析处理。但是由于制造设备的异构性、物联网网关(IoT网关)数据分析能力的有限性、制造设备的存储力低下,设备和数据的低安全性等缺陷严重阻碍了智能制造的发展。BEIIoT架构从制造企业的实际生产过程与应用角度出发,将区块链技术与边缘计算相结合,通过对服务器进行P2P组网以实现对设备去中心化管理;通过对边缘设备进行服务化封装,增强设备的安全性与实时分析能力,降低设备的异构性;使用DAG双链式数据存储结构,提高数据的冗余度与安全性,实现生产线数据的异步并发备份存储。BEIIoT架构为智能制造的实施提供了体系支持。 相似文献
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In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers.
The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for
ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method
with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index
ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems.
The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7].
Shi-Jay Chen was born in 1972, in Taipei, Taiwan, Republic of China. He received the B.S. degree in information management from the Kaohsiung
Polytechnic Institute, Kaohsiung, Taiwan, and the M.S. degree in information management from the Chaoyang University of Technology,
Taichung, Taiwan, in 1997 and 1999, respectively. He received the Ph.D. degree at the Department of Computer Science and Information
Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, in October 2004. His research interests
include fuzzy systems, multicriteria fuzzy decisionmaking, and artificial intelligence.
Shyi-Ming Chen was born on January 16, 1960, in Taipei, Taiwan, Republic of China. He received the Ph.D. degree in Electrical Engineering
from National Taiwan University, Taipei, Taiwan, in June 1991. From August 1987 to July 1989 and from August 1990 to July
1991, he was with the Department of Electronic Engineering, Fu-Jen University, Taipei, Taiwan. From August 1991 to July 1996,
he was an Associate Professor in the Department of Computer and Information Science, National Chiao Tung University, Hsinchu,
Taiwan. From August 1996 to July 1998, he was a Professor in the Department of Computer and Information Science, National
Chiao Tung University, Hsinchu, Taiwan. From August 1998 to July 2001, he was a Professor in the Department of Electronic
Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. Since August 2001, he has been a Professor
in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei,
Taiwan. He was a Visiting Scholar in the Department of Electrical Engineering and Computer Science, University of California,
Berkeley, in 1999. He was a Visiting Scholar in the Institute of Information Science, Academia Sinica, Republic of China,
in 2003. He has published more than 250 papers in referred journals, conference proceedings and book chapters. His research
interests include fuzzy systems, information retrieval, knowledge-based systems, artificial intelligence, neural networks,
data mining, and genetic algorithms.
Dr. Chen has received several honors and awards, including the 1994 Outstanding Paper Award o f the Journal of Information
and Education, the 1995 Outstanding Paper Award of the Computer Society of the Republic of China, the 1995 and 1996 Acer Dragon
Thesis Awards for Outstanding M.S. Thesis Supervision, the 1995 Xerox Foundation Award for Outstanding M.S. Thesis Supervision,
the 1996 Chinese Institute of Electrical Engineering Award for Outstanding M.S. Thesis Supervision, the 1997 National Science
Council Award, Republic of China, for Outstanding Undergraduate Student's Project Supervision, the 1997 Outstanding Youth
Electrical Engineer Award of the Chinese Institute of Electrical Engineering, Republic of China, the Best Paper Award of the
1999 National Computer Symposium, Republic of China, the 1999 Outstanding Paper Award of the Computer Society of the Republic
of China, the 2001 Institute of Information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision,
the 2001 Outstanding Talented Person Award, Republic of China, for the contributions in Information Technology, the 2002 Institute
of information and Computing Machinery Thesis Award for Outstanding M.S. Thesis Supervision, the Outstanding Electrical Engineering
Professor Award granted by the Chinese Institute of Electrical Engineering (CIEE), Republic of China, the 2002 Chinese Fuzzy
Systems Association Best Thesis Award for Outstanding M.S. Thesis Supervision, the 2003 Outstanding Paper Award of the Technological
and Vocational Education Society, Republic of China, the 2003 Acer Dragon Thesis Award for Outstanding Ph.D. Dissertation
Supervision, the 2005 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision, the 2005 Acer
Dragon Thesis Award for Outstanding Ph.D. Dissertation Supervision, the 2005 Taiwan Fuzzy Systems Association Award for Outstanding
Ph.D. Dissertation Supervision, and the 2006 “Operations Research Society of Taiwan” Award for Outstanding M.S. Thesis Supervision.
Dr. Chen is currently the President of the Taiwanese Association for Artificial Intelligence (TAAI). He is a Senior Member
of the IEEE, a member of the ACM, the International Fuzzy Systems Association (IFSA), and the Phi Tau Phi Scholastic Honor
Society. He was an administrative committee member of the Chinese Fuzzy Systems Association (CFSA) from 1998 to 2004. He is
an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics - Part C, an Associate Editor of the IEEE Computational
Intelligence Magazine, an Associate Editor of the Journal of Intelligent & Fuzzy Systems, an Editorial Board Member of the
International Journal of Applied Intelligence, an Editor of the New Mathematics and Natural Computation Journal, an Associate
Editor of the International Journal of Fuzzy Systems, an Editorial Board Member of the International Journal of Information
and Communication Technology, an Editorial Board Member of the WSEAS Transactions on Systems, an Editor of the Journal of
Advanced Computational Intelligence and Intelligent Informatics, an Associate Editor of the WSEAS Transactions on Computers,
an Editorial Board Member of the International Journal of Computational Intelligence and Applications, an Editorial Board
Member of the Advances in Fuzzy Sets and Systems Journal, an Editor of the International Journal of Soft Computing, an Editor
of the Asian Journal of Information Technology, an Editorial Board Member of the International Journal of Intelligence Systems
Technologies and Applications, an Editor of the Asian Journal of Information Management, an Associate Editor of the International
Journal of Innovative Computing, Information and Control, and an Editorial Board Member of the International Journal of Computer
Applications in Technology. He was an Editor of the Journal of the Chinese Grey System Association from 1998 to 2003. He is
listed in International Who's Who of Professionals, Marquis Who's Who in the World, and Marquis Who's Who in Science and Engineering. 相似文献