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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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control SYSTEM DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND control PERFORMANCE

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Asian Journal of control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： control system design LMI BMI tradeoff curves

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Abstracts

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Environmental Management 1977年第1期1卷 67-96页

作者： Frankenfeld, John W. Schulz, Wolfgang McMurty, George J. Petersen, Gary W. May, G. A. Hering, F. S. Schwartz, J. I. Heywood, J. B. Chigier, N. A. Grohse, E. W. Walker, J. D. Colwell, R. R. Petrakis, L. Pergament, H. S. Thorpe, R. D. Schoepf, Richard W. Krzyczkowski, Roman Henneman, Suzanne S. Hudson, Charles L. Putnam, Evelyn S. Thiesen, Donna J. Parks, G. A. McCarty, Perry L. Leckie, J. O. Schrumpf, Barry J. Simonson, G. H. Paine, D. P. Lawrence, R. D. Pyott, W. T. Leh, M. Elders, W. Combs, J. Caplen, T. Harrison, F. L. Wong, K. M. Heft., R. E. Charnell, Robert L. Lehmann, Edward J. Mallon, Lawrence G. Hatfield, Cecile Adams, Gerald H. Johanning, James Talvitie, Antti Noll, Kenneth E. Miller, Terry Smiarowski, Joseph F. Willis, Cleve E. Foster, John H. Schlesinger, Benjamin Daetz, Douglas Lear, Donald U. Smith, Mona F. Hundemann, Audrey S. Crockett, Pernell W. Werner, Kirk G. Carroll, Thomas E. Maase, David L. Genco, Joseph E. Ifeadi, Christopher N. Lowman, F. G. Christensen, S. W. Van Winkle, W. Mattice, J. S. Harrison, Elizabeth A. Barker, James C. Chesness, Jerry L. Smith, Ralph E. Shaheeen, Donald G. Raney, R. Keith Borton, T. Wezernak, C. T. Raney, R. K. Sherwani, Jabbor K. Moreau, David H. Eisenberg, Norman A. Lynch, Cornelius J. Breeding, Roger J. Johnson, J. D. Foster, K. E. Mouat, D. A. Clark, R. Hyden, John William Owen, Wilfred Bayfield, Neil G. Barrow, Graham C. Stolz, Stephanie B. Wienckowski, Louis A. Brown, Betram S. Keyfitz, Nathan Wilson, W. L. Newman, Peter W. G. Bammi, Deepak Bammi, Dalip Goddard, James E. Chisholm, Tony Walsh, Cliff Brennan, Geoffrey Thompson, K. S. Richardson, R. Jensen, Clayton E. Brown, Dail W. Mirabito, John A. Cowing, Thomas G. Binghamton, Suny Siehl, George H. Albrecht, O. W. Alexander, Ariel Barde, Jean -Philippe Darby, William P. McMichael, Francis Clay Dunlap, Robert W. Muckleston, Keith W. Frankenhoff, Charles A. Giulini, Lorenzo T. Wyatt, T. Black, Peter E. Keating, William Thomas Leonard, M. E. Fisher, E. L. Brunelle, M. F. Dickinson, J. E. Pethig, Rudiger Clapham Exxon Research & Engineering Co. Linden Government Research Lab Coast Guard Washington D.C. Department of Transportation Washington D.C. Office For Remote Sensing of Earth Resources. NASA Earth Resources Survey Program Pennsylvania State University Washington D.C. Department of the Navy Washington D.C. Cambridge. Dept. of Mechanical Engineering Massachusetts Inst. of Technology USA Huntsville. School of Graduate Studies and Research Alabama Univ. USA Dept. of Microbiology. Office of Naval Research Maryland Univ. Arlington Research and Development Co. Pittsburgh AeroChem Research Labs. Inc. Princeton Dept. of the Interior Office of Library Services. Bibliographic Series (Final) Washington D.C. Interplan Corp. Santa Barbara Urban Mass Transportation Adm. Washington D.C. Naval Underwater Systems Center Newport Calif. Mercury Project. National Science Found Stanford Univ Washington D.C. Div. of Advanced Environmental Research & Tech. USA Corvallis. NASA Earth Resources Survey Program Oregon State Univ. Washington D.C. Riverside. Inst. of Geophysics and Planetary Physics National Science Foundation California Univ. Washington D.C. Livermore Lawrence Livermore Lab. California Univ. USA Atlantic Oceanographic and Meteorological Labs. National Oceanic and Atmospheric Administration Miami National Technical Information Service Springfield Miami Univ. Coral Gables School of Law National Oceanic and Atmospheric Administration Rockville National Academy of Sciences Office of Sea Grant Washington D. C. School of Law National Oceanic and Atmospheric Administration Office of Sea Grant. Rockville Norman. Dept. of Civil Engineering and Environmental Science. Urban Mass Transportation Administration Oklahoma Univ. Washington D. C. Knoxville.Dept. of Civil Engineering. Federal Highway Administration Tennessee Univ. Washington D. C. Amherst.Water Resources Research Center. Office of Water Research and Technology Massachusetts Univ. Washington D. C. Calif. Dept. of Industria

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Measurements of power and current components in unbalanced and distorted three‐phase systems

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European Transactions on Electrical Power 1993年第1期3卷 75-83页

作者： Andria, G. Salvatore, L. Savino, M. Trotta, A. Dr. Gregorio Andria (1956) AEI received the M. S. degree in Electrical Engineering from the State University of Bari/Italy in 1981 and the Ph. D. degree in Electrical Engineering in 1987 from the same University. From 1981 to 1983 he was working in the Electrotechnics and Electronics Department of the University of Bari as a member of the research team on electrical measurements. From 1984 to 1986 he was a Doctoral Fellow and currently he is a researcher in the same department. His research interests are in the fields of electrical and electronic measurements on components and systems including digital measurements for the analysis of electrical quantities in non-sinusoidal systems and the design of integrated optical sensors for measurement and control of non-electrical physical quantities. (Department of Electrotechnics and Electronics Faculty of Engineering polytechnic of Bovia E. Orabona 4 1-70125 Bari. Italy T +3980/242266 Fax + 3980/242410) Prof. Luigi Salvatore (1945) AEI received the degree in electrical engineering from the University of Bari/Italy in 1970. Since 1976 he has worked in the Electrotechnical and Electronic Department of the same University as a member of the research team on electrical machines. From 1983 to 1987 he was a researcher of electrical machines in the same department. Since 1987 he has been an Associate Professor of electrical machines at the University of Bari. At the present time his research interests include the control monitoring and diagnostics of AC drives and the areas of signal processing anddigital measurements on power electronics systems. (Department of Electrotechnics and Electronics Faculty of Engineering Polytechnic of Bari via E. Orabona 4 I-70125 Bari Italy T + 3980/242258 Fax + 3980/242410) Prof. Mario Savino (1947) AEI received the degree in Electrical Engineering from the University of Barif Italy in 1971. Since then he has been working in the Electrotechnical Institute of Bari until 1973 as researcher from 1973 to 1982 as Assistant Profe

The paper deals with the instantaneous power theory in three‐phase circuits by using the instantaneous time phasors of voltage and current. Particularly it is shown that the instantaneous components of the current time‐phasor in a rotating reference frame p‐q, oriented in such a way that the instantaneous voltage time‐phasor points to the p‐axis direction, represent the active and reactive currents. These components give rise to instantaneous real and imaginary powers, respectively. The spectral analysis of the above‐mentioned time‐phasors gives the harmonic symmetrical components of positive and negative sequence of voltage and current three‐phase systems. The line‐by‐line product of the voltage sequence spectrum by the complex conjugate of the current sequence spectrum gives the DC components of the instantaneous real and imaginary powers. The paper shows also that the instantaneous active and reactive currents are different from those defined on grounds of the Fryze's theory, which can be determined from the voltage and current sequence spectra. Copyright © 1993 John Wiley & Sons, Ltd.

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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COMMERCIAL APPLICATIONS OF ADVANCED MARINE VEHICLES FOR EXPRESS SHIPPING

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NAVAL ENGINEERS JOURNAL 1983年第3期95卷 283-300页

作者： LUEDEKE, G FARNHAM, RB JR. George Luedeke Jr.: received his BS degree in Mechanical Engineering from Massachusetts Institute of Technology and his MS degree in Product Design from Illinois Institute of Technology. Early in his career Mr. Luedeke joined General Motors Corporation as a designer responsible for development of people mover and rail rapid transit systems. From 1964 to 1974 he was with Hughes Aircraft Company. At Hughes he performed analyses and developed designs for a wide variety of program and proposal efforts such as: High Speed Ground Transportation (DOT) Task Force Command Center (NAVY) Panama Canal Marine Traffic Control Center (Panama Canal Co.) Royal Iranian Navy Command Center (Iran) Tactical Information Processing and Interpretation Center (Air Force) and WALLEYE CONDOR and PHOENIX Missile Systems (NAVY). He also had marketing development responsibilities related to the diversification of Hughes resources in civil business areas such as: Automatic train control (WMATA BARTD SCRTD) water/sewage treatment plant automation (Santa Clara County) Aqueduct Control (SWR) Hydrometeorological data collection (BPA WMO) and Salton Sea basin systems analysis (Dept. of the Interior). He was responsible for combat system integration for the Hughes 2000T Surface Effect Ship (SES) proposal. He also conducted detailed studies concerning ship flexure for the Improved Point Defense Target Acquisition System Program and for the definition of operational High Energy Laser weapon installations on a series of conventional monohulls (DLG DD and CVN). Since 1974 Mr. Luedeke has been employed at RMI Inc. (formerly Rohr Marine Inc.). During this time he has held several positions. His responsibilities have included directing a number of studies on advanced SES concepts managing activities defining mission/cost effectiveness of military and commercial SES's including defining the operational benefits and enhanced survivability characteristics of cargo SES's for high speed military sealiftfor NA TO and Southeast Asia

This paper will present the results of a marketing, engineering, and economic analysis of advanced marine vehicles done by IMA Resources, Inc. and RMI, Inc., in support of a Maritime Administration project to study “Multimode Express Shipping”. The study was conducted in 1981 and examined the economic benefits of using advanced marine vehicles as express cargo vessels in domestic and international service. Commodity characteristics, desirable express carrier rates, and potential high payoff service and route alternatives were identified. Advanced marine vehicles were surveyed and sized to meet desirable deadweight and block speed objectives. The costs of operating these craft on a variety of trade routes were calculated using an advanced marine vehicle economic analysis program. Revenues, expenses, break-even, profit and loss, cash flow requirements, tax summary and economic indicators (i.e., cost/ton – mile, etc.) were projected over the expected life of the vehicles as was return on investment. Traffic density and market penetration considerations narrowed the field of choice to smaller sized advanced marine vehicle carriers (i.e., 50 and 250 ton deadweight) and to three international and five domestic routes.

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EMERGING ROLES OF NIH AND EPA IN THE REGULATION OF RDNA TECHNOLOGY

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BIO-TECHNOLOGY 1983年第9期1卷 757-768页

作者： KORWEK, EL Edward L. Korwek Ph.D. J.D. is associated with the law offices of Keller and Heckman 1150 17th St. N.W. Washington D.C. 20036.REFERENCES Committee on Recombinant DNA "Potential Biohazards of Recombinant DNA Molecules" Nature250: 175 (1974) Proc. Nat. Acad. Sci.71: 2593 (1974) Science185: 303 (1974).|Article|Fed. Regist.48: 24556 (1983).Milewski E. Editor's Note. Recombinant DNA Tech. Bull.4: i (1981).Inside EPA 4 1 (1983). EPA has already held a meeting and published a draft report on the subject of its regulation of this area under the TSCA. EPA "Administrator's Toxic Substances Advisory Committee Meeting" Fed. Regist.48: 8342 (1983) Regulation of Genetically Engineered Substances Under TSCA Chemical Control Division Office of Toxic Substances Office of Pesticides and Toxic Substances Environmental Protection Agency Washington D.C. (March 1982). Congress also recently held a hearing on the subject of existing federal authority over the release of R-DNA-containing organisms into environment. M. Sun Science221: 136 (1983).Sects. 2-30 15 U.S. Code sects. 2601-2629 (1976 and Supp. V 1981). Hereinafter all references in the text to TSCA refer to the section numbers as enacted and not to the corresponding U.S. Code sections.The Administrative Procedure Act specifically states that the reviewing court shall "hold unlawful and set aside agency action findings and conclusions found to be hellip in excess of statutory jurisdiction authority or limitations or short of statutory right. hellip " 5 U.S. Code sect. 706(2)(C) (1976).PHS Act 42 U.S. Code sects. 217a and 241 (1976) Charter Recombinant DNA Advisory Committee Department of Health and Human Services (1982).Korwek E. Food Drug and Cosm. L. J.35: 633 (1980) p. 636.Although DHHS has some authority under Section 361 of the PHS Act to regulate R-DNA materials that cause human disease and are communicable most types of experimentation would not fall into this category. Because of this limitation the Sub committee of the Federal

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