Proceedings OCEANS最新文献

{"title":"Time-dependent Effects On Ridge Initiation In Sea Ice Leads","authors":"M. Coon, G.S. Knoke, P. A. Lau","doi":"10.1109/OCEANS.1989.587528","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587528","url":null,"abstract":"Recent measurements of stresses in sea ice prior to and during ridge building indicate that large stress levels may be present in the ice for several hours before ridge building actually occurs. Therefore, it may be that the time dependence (viscosity) of the lead ice which forms the ridge may be important to the ridge initiation process. This effect is investigated in this paper by considering a simple model in which the lead ice is represented by a visco-elastic beam on an elastic foundation. Elapsed time from the time of load application until creep buckling failure is determined for a range of loadings and ice elastic moduli and viscosities. These properties were obtained during the Coordinated Eastern Arctic Experiment (CEAREX). These calculations provide a framework for planning field experiments to study lead ice failure and a starting point for analysis of lead ice failure mechanisms.","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115160191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Melville/Knorr Refit And Modernization: Capabilities For Science In The 1990's And Beyond","authors":"W. Hurley, D. H. Laible, J. Leiby","doi":"10.1109/OCEANS.1989.587099","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587099","url":null,"abstract":"The oceanographic research vessels RIV Melville and RIV Knorr were built in 1969, and although they have proved to be effective research platforms by demonstrating outstanding support to science, numerous problems have plagued and frustrated their operators and scientists. Consequently, repowering and modification studies were undertaken to redress the drawbacks of these ships. In addition, enhancements to their scientific capabilities were defined and incorporated into the project and a shipyard contract was awarded in February 1989 for their lengthening, refit and modernization. The desired capabilities for the modified vessels are significantly beyond those of the existing ships. This paper describes how these attributes were engineered into the refit. Habitability, laboratory and work spaces, storage capacities, speed and endurance, stationkeeping, maneuvering, and acoustic support improvements are discussed.","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132004296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neural Network Based Framework For Classification Of Oceanic Acoustic Signals","authors":"A. Maccato, R.J.P. de Figueii-edo","doi":"10.1109/OCEANS.1989.587491","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587491","url":null,"abstract":"This paper presents a new framework for intelligent acoustic signal processing by artificial neural networks. Problems addressed are the detection, classification, and estimation of signal parameters. The methodology consists of decomposing the above tasks into two stages. First, a highly structured, hierarchical/symbolic representation of the data is created using scale space algorithms. This calculation overcomes moderate noise and warping distortion present in the acoustic recording, and at the same time reduces the data to be processed. Second, neural network architectures are applied to the resulting symbolic structures to obtain the desired signal parameters. The use of neural network techniques allows training to be used in cases where the signals of interest are not easily characterized. Illustrations using simulated and real data will be presented. INTRODUCTION Artificial neural networks provide a new computational paradigm for solving a large class of signal recognition problems. In this paradigm, collections of elementary units called neurons work in parallel to perform a desired computational task. Each neuron performs a component of the overall calculation, and communicates its result to the other units in the network via neurosynaptic interconnections. The distinguishing characteristic of artificial neural networks, with respect to classical methods of computing, is that they can learn to perform a required calculation through training. Hence, rather than designing a procedure for computing the solution to a problem, a selection is made of a training set of exemplary input/output pairs. This is a very powerful property in problem instances for which the application data space is not characterized sufficiently well to allow explicit programming. This is often the case with oceanic acoustic signals, such as short duration transient signals resulting from spurious mechanical events in a vessel. Signal distortion caused by the water medium (amplitude and time warping effects), noise contamination, imprecise or unknown time of occurrence, and high nonstationarity are all difficulties to be confronted when trying to detect a signal, classify it, or estimate meaningful parameters of its source. Additionally, any viable algorithm for recognizing acoustic signals in real-time must possess a fast implementation and, in the case of neural nets, a relatively short training time. To this end, we propose to decompose the recognition task into two stages. The acoustic signal is first preprocessed by a numerical transformation that partially removes noise and distortion effects present in the raw data. In the second stage the resulting information is fed to a neural network for final recognition. The overall method benefits from the filtering characteristics (and possibly data reduction properties) of the first stage, and also from the learning ability of the second stage. OUTLINE O F T H E ALGORITHM To illustrate this concept, we have selected a s","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130333602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development Of A Solid-State, 5 mS Dissolved Oxygen Sensor","authors":"R. D. Hudson, G.M. Eaton","doi":"10.1109/OCEANS.1989.587124","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587124","url":null,"abstract":"The d e v e l o p m e n t o f a. p a . t e n t e d , s o l i d s t a . t e d i s s o l v e d o x y g e n s e n s o r i s d e s c r i b e d . T e s t s u n d e r v a . r i o u s c o n d i t i o n s o f t e m p e r a t u r e , f l o w a.nd p r e s s u r e ha.ve s h o w n t h e s u i t a , b i l i t y o f t h e s e n s o r f o r u s e i n f r e s h w a . t e r a n d 0cea.n c o n d i t i o n s . The u n i t ha.s a. r e s p o n s e t ime of l e s s ; t h a n 10 m i l l i s e c o n d s t o l a . r g e o x y g e n c h a n g e s ( i n u n s t i r r e d c o n d i t i o n s w h e r e a. s : ~ . g n i f i c a . n t b o u n d a r y l a . y e r e x i s t s ) . When i m m e r s e d i n a. w e l l s t i r r e d e n v i r o n m e n t (a.ncl t h e b o u n d a . r y 1 a . y e r i s c o r r e s p o n d i n g l y small), r e s p o n s e t i m e s o f l e s s tha .n 4 m i l l i s e c o n d s 1la.ve b e e n o b t a i n e d , ma.king i t compa. ra .b le i n s p e e d t o t h e c o n d u c t i v i t y , s e n s o r s u s e d i n 0cea .n C T D s . The d e v i c e ’ s p e r f o r m a , n c e c o n t r a . s t s m o s t f a . v o r a . b l y w i t h e x i s t i n g c o m m e r c i a . 1 d i s s o l v e d o x y g e n d e v i c e s , w h i c h ha.ve t i m e c o n s t a , n t s o f t h e order o f I 5 s e c o n d s o r l o n g e r . A s e c o n d g e n e r a . t i o n s e n s o r ,, s p c c i f i c a . l l y t a . i l o r e d f o r o c e a n a , p p l i c a . t i o n s i s d e s c r i b e d . T h i c k f i l m a.nd h y b r i d c i r c u i t t e c h n i q u e s a . re e m p l o y e d i n i t s f a , b r i c a , t i o n . F i e l d ds . ta . a . r e p r e s e n t e d . t e m p e r a. t u r e a.n ci d e p t 11 I N T R O D U C T I O N O x y g e n i s t h e m o s t i m p o r t a . n t d i s s o l v e d g a s f o r a l l f o r m s o f l i f e i n w a t e r . M e a , s u r e m e n t o f d i s s o l v e d o x y g e n ( D O ) i n a.n a . q u e o u s medium i s s i g n i f i c a . n t i n medica.] . , b i o 1 o g i ca. 1, e n v i r o n m e n t a l a.nd o c e a . n o g r a p h i c s e t t i n g s . D e t e r m i n a t i o n o f o x y g e n ‘ c o n t e n t on a c o n t i n u o u s b a s i s h8.s u s u a . l l y b e e n d o n e u s i n g p o l a r o g r a . p h i c or g a , l v a . n i c m e t h o d s ( 1 , 2 ) . T h e i n t r o d u c t i o n o f a. m e m b r a n e t y p e s e n s o r i m p r o v e d t h e u s e f u l n e s s o f t h e t e c h n i q u e , when compa. red t o t h e i o d o m e t r i c t i t r a . t i o n ( W i n k l e r ) m e t h o d , w h i c h wa.s p i o n e e r e d a c e n t u r y a g o ; b u t t o d a t e , p o l a . r o g r a p h i c s e n s o : r s a . re s l o w t o r e s p o n d , a . re m a . i n t e n a , n c e i n t e n s i v e , p r o n e t o d r i f t , a.nd a r e t e m p e r a . t u r e a.nd p r e s s u r e s e n s i t i v e . The p o l a , r o g r a . p h i c t e c h n i q u e u s e s two n o b l e meta .1 e l e c t r o d e s , i m m e r s e d i n a.n e l e ","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Landlocked States' New Rights To Adjacent Coastal States ` Living Marine Resources: Is There Anything Left For Them?","authors":"F. Gable","doi":"10.1109/OCEANS.1989.592849","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.592849","url":null,"abstract":"","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic Positioning Using Multielement Array","authors":"A. Zielinski, Lixue Wu","doi":"10.1109/OCEANS.1989.587489","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587489","url":null,"abstract":"With these assumptions, problem of estimating the arrival angle of a plane wave at linear array is analogous to that encountered in spectral analysis. The well known applicable methods are maximum likelihood estimation [l], adaptive arrays [ 2 ] , maximum entropy estimation [3], and wavenumber spectral estimation [4]. This paper aims to give a straightforward real-time treatment of this estimation problem. A discrete Fourier transform (DFT) algorithm is employed. A chirp z-transform (CZT) algorithm and employment of two frequencies are suggested to improve the system resolution. The incident signals arriving from direction 0 are received by N (even) nondirectional, point sensors arranged along a straight line. If the acoustic pressure received by each sensor is simultaneously sampled at time t = t o and the samples are subjected to the discrete Fourier transform (DFT), we obtain the power-normalized beam pattern given by [5]","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acoustic Data Acquisition And Processing Systems","authors":"Grace R. Kamitakahara, R. Teichrob","doi":"10.1109/OCEANS.1989.587512","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.587512","url":null,"abstract":"Acoustic oceanography places special demands on high speed, high volume data collection and processing. Digital audio equipment provides opportunity for high quality digital recording of acoustic signals. Systems typically include one or more acoustic transducers from which signals are recorded through an audio PCM (pulse code modulation) unit onto a VCR (video cassette recorder), recording two 16-bit channels at 44.1 kHz each for up to 8 hours (Fig. la). System variations involve modifications to the PCM to allow input of additional digital data on the VCR recording and to extract the decoded serial data on playback in order to convert it to parallel for input to a computer. Different configurations may multiplex the analogue inputs (Fig. lb), or enter digital data directly into the encoder (Fig. IC). Playback systems allow the PCM to decode and error-correct the data for processing by computers (Fig 2a). The data is recovered from VCR in stream mode at the same data rate at which it was recorded (i.e. 176 K bytes per second). Real-time techniques to handle the data volume and rapid processing allow data to be stored onto 9 track tape (Fig. 3a); overviewed using histograms (Fig. 3b); windowed on selective events (Fig. 3c); averaged and processed in other ways using DSP (digital signal processing) hardware (Fig. 2b). These algorithms greatly compress the data, reducing it by 10 to 1000 times depending on the application. a n a l o g i n p u t s P C M V C R (a1","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123644362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ocean Dumping Risk Assessment: Analysis Of Uncertainties Imposed By Variability In Chemical Bioconcentration, Commercial Fish Landings, And Human Fish Consumption","authors":"J. Lipton","doi":"10.1109/OCEANS.1989.586859","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.586859","url":null,"abstract":"Human consumption of marine organisms that have been contaminated by municipal sewage sludges dumped of the U.S. mid-Atlantic coast may pose significant health risks. In order to protect human health, researchers have modeled these risks. Such models have failed to reflect uncertainties imposed by parameter variability. An analysis was conducted to examine the effects of variability in bioconcentration, commercial fish landings, and seafood consumption on risk estimates. Calculated human health risks varied by three orders of magnitude when variability in these parameters was included in the risk model. Published bioconcentration factors (BCF) for DDT and for PCBs in different fish species were found to be log-normally distributed ( v = 5 . 0 , 0 = . 6 for DDT and p=5.2, a=.8 for PCBs) . This relationship was used to model health risks stemming from consumption of species for which BCF are unknown. The probability of health risks exeeding critical regulatory threshold (e.g. 10 -6) was estimated. Risk \"contingency tables,\" indicating the probability of exceeding target risk thresholds under different model assumptions, were then compiled. Finally, distributions of BCF, commercial fish landings, and fish consumption were used in a Monte-Carlo simulation of human health risks. Results indicate that parameter variability can lead to a range of estimated health risks which straddles current risk-management thresholds. Thus, if not explicitly considered, this variability can lead to errors in risk-based decision-making.","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123821950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geosat Altimeter Observations of the Distribution and Movement of Sea Surface Height Anomalies in the North-East Pacific","authors":"J. Gower","doi":"10.1109/OCEANS.1989.586717","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.586717","url":null,"abstract":"","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123872041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indian River Inlet Field Measurement Program","authors":"D. Mcgehee, J. Lillycrop","doi":"10.1109/OCEANS.1989.592821","DOIUrl":"https://doi.org/10.1109/OCEANS.1989.592821","url":null,"abstract":"","PeriodicalId":331017,"journal":{"name":"Proceedings OCEANS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1989-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}