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Sample records for artificial microbialite model

  1. Microconchids from microbialite ecosystem immediately after end-Permian mass extinction: ecologic selectivity and implications for microbialite ecosystem structure

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    Yang, H.; Chen, Z.; Wang, Y. B.; Ou, W.; Liao, W.; Mei, X.

    2013-12-01

    The Permian-Triassic (P-Tr) carbonate successions are often characterized by the presence of microbialite buildups worldwide. The widespread microbialites are believed as indication of microbial proliferation immediately after the P-Tr mass extinction. The death of animals representing the primary consumer trophic structure of marine ecosystem in the P-Tr crisis allows the bloom of microbes as an important primary producer in marine trophic food web structure. Thus, the PTB microbialite builders have been regarded as disaster taxa of the P-Tr ecologic crisis. Microbialite ecosystems were suitable for most organisms to inhabit. However, increasing evidence show that microbialite dwellers are also considerably abundant and diverse, including mainly foraminifers Earlandia sp. and Rectocornuspira sp., lingulid brachiopods, ostrocods, gastropods, and microconchids. In particular, ostracods are extremely abundant in this special ecosystem. Microconchid-like calcareous tubes are also considerably abundant. Here, we have sampled systematically a PTB microbialite deposit from the Dajiang section, southern Guizhou Province, southwest China and have extracted abundant isolated specimens of calcareous worm tubes. Quantitative analysis enables to investigate stratigraphic and facies preferences of microconchids in the PTB microbialites. Our preliminary result indicates that three microconchid species Microconchus sp., Helicoconchus elongates and Microconchus aberrans inhabited in microbialite ecosystem. Most microconchilds occurred in the upper part of the microbialite buildup and the grainstone-packstone microfacies. Very few microconchilds were found in the rocks bearing well-developed microbialite structures. Their stratigraphic and environmental preferences indicate proliferation of those metazoan organisms is coupled with ebb of the microbialite development. They also proliferated in some local niches in which microbial activities were not very active even if those

  2. Reconstruction of limnology and microbialite formation conditions from carbonate clumped isotope thermometry.

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    Petryshyn, V A; Lim, D; Laval, B L; Brady, A; Slater, G; Tripati, A K

    2015-01-01

    Quantitative tools for deciphering the environment of microbialite formation are relatively limited. For example, the oxygen isotope carbonate-water geothermometer requires assumptions about the isotopic composition of the water of formation. We explored the utility of using 'clumped' isotope thermometry as a tool to study the temperatures of microbialite formation. We studied microbialites recovered from water depths of 10-55 m in Pavilion Lake, and 10-25 m in Kelly Lake, spanning the thermocline in both lakes. We determined the temperature of carbonate growth and the (18)O/(16)O ratio of the waters that microbialites grew in. Results were then compared to current limnological data from the lakes to reconstruct the history of microbialite formation. Modern microbialites collected at shallow depths (11.7 m) in both lakes yield clumped isotope-based temperatures of formation that are within error of summer water temperatures, suggesting that clumped isotope analyses may be used to reconstruct past climates and to probe the environments in which microbialites formed. The deepest microbialites (21.7-55 m) were recovered from below the present-day thermoclines in both lakes and yield radioisotope ages indicating they primarily formed earlier in the Holocene. During this time, pollen data and our reconstructed water (18)O/(16)O ratios indicate a period of aridity, with lower lake levels. At present, there is a close association between both photosynthetic and heterotrophic communities, and carbonate precipitation/microbialite formation, with biosignatures of photosynthetic influences on carbonate detected in microbialites from the photic zone and above the thermocline (i.e., depths of generally <20 m). Given the deeper microbialites are receiving <1% of photosynthetically active radiation (PAR), it is likely these microbialites primarily formed when lower lake levels resulted in microbialites being located higher in the photic zone, in warm surface waters. © 2014 John

  3. Metagenomic analysis reveals that modern microbialites and polar microbial mats have similar taxonomic and functional potential

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    Richard Allen White III

    2015-09-01

    Full Text Available Within the subarctic climate of Clinton Creek, Yukon, Canada, lies an abandoned and flooded open-pit asbestos mine that harbors rapidly growing microbialites. To understand their formation we completed a metagenomic community profile of the microbialites and their surrounding sediments. Assembled metagenomic data revealed that bacteria within the phylum Proteobacteria numerically dominated this system, although the relative abundances of taxa within the phylum varied among environments. Bacteria belonging to Alphaproteobacteria and Gammaproteobacteria were dominant in the microbialites and sediments, respectively. The microbialites were also home to many other groups associated with microbialite formation including filamentous cyanobacteria and dissimilatory sulfate-reducing Deltaproteobacteria, consistent with the idea of a shared global microbialite microbiome. Other members were present that are typically not associated with microbialites including Gemmatimonadetes and iron-oxidizing Betaproteobacteria, which participate in carbon metabolism and iron cycling. Compared to the sediments, the microbialite microbiome has significantly more genes associated with photosynthetic processes (e.g., photosystem II reaction centers, carotenoid and chlorophyll biosynthesis and carbon fixation (e.g., CO dehydrogenase. The Clinton Creek microbialite communities had strikingly similar functional potentials to non-lithifying microbial mats from the Canadian High Arctic and Antarctica, but are functionally distinct, from non-lithifying mats or biofilms from Yellowstone. Clinton Creek microbialites also share metabolic genes (R2 0.900. These metagenomic profiles from an anthropogenic microbialite-forming ecosystem provide context to microbialite formation on a human-relevant timescale.

  4. Modern carbonate microbialites from an asbestos open pit pond, Yukon, Canada.

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    Power, I M; Wilson, S A; Dipple, G M; Southam, G

    2011-03-01

    Microbialites were discovered in an open pit pond at an abandoned asbestos mine near Clinton Creek, Yukon, Canada. These microbialites are extremely young and presumably began forming soon after the mine closed in 1978. Detailed characterization of the periphyton and microbialites using light and scanning electron microscopy was coupled with mineralogical and isotopic analyses to investigate the mechanisms by which these microbialites formed. The microbialites are columnar in form (cm scale), have an internal spherulitic fabric (mm scale), and are mostly made of aragonite, which is supersaturated in the subsaline pond water. Initial precipitation is seen as acicular aragonite crystals nucleating onto microbial biomass and detrital particles. Continued precipitation entombs benthic diatoms (e.g. Brachysira vitrea), filamentous algae (e.g. Oedogonium sp.), dinoflagellates, and cyanobacteria. The presence of phototrophs at spherulite centers strongly suggests that these microbes play an important initial role in aragonite precipitation. Substantial growth of individual spherulites occurs abiotically through periodic precipitation of aragonite that forms concentric laminations around spherulite centers while pauses in spherulite growth allow for colonization by microbes. Aragonite associated with biomass (δ(13)C = -4.6‰ VPDB) showed a (13)C-enrichment of 0.8‰ relative to aragonite exhibiting no biomass (δ(13)C = -5.4‰ VPDB), which suggests a modest removal of isotopically light dissolved inorganic carbon by phototrophs. The combination of a low sedimentation rate, high calcification rate, and low microbial growth rate appears to result in the formation of these microbialites. The formation of microbialites at an historic mine site demonstrates that an anthropogenically constructed environment can foster microbial carbonate formation. © 2011 Blackwell Publishing Ltd.

  5. Microbial diversity and biomarker analysis of modern freshwater microbialites from Laguna Bacalar, Mexico.

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    Johnson, D B; Beddows, P A; Flynn, T M; Osburn, M R

    2018-05-01

    Laguna Bacalar is a sulfate-rich freshwater lake on the Yucatan Peninsula that hosts large microbialites. High sulfate concentrations distinguish Laguna Bacalar from other freshwater microbialite sites such as Pavilion Lake and Alchichica, Mexico, as well as from other aqueous features on the Yucatan Peninsula. While cyanobacterial populations have been described here previously, this study offers a more complete characterization of the microbial populations and corresponding biogeochemical cycling using a three-pronged geobiological approach of microscopy, high-throughput DNA sequencing, and lipid biomarker analyses. We identify and compare diverse microbial communities of Alphaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria that vary with location along a bank-to-bank transect across the lake, within microbialites, and within a neighboring mangrove root agglomeration. In particular, sulfate-reducing bacteria are extremely common and diverse, constituting 7%-19% of phylogenetic diversity within the microbialites, and are hypothesized to significantly influence carbonate precipitation. In contrast, Cyanobacteria account for less than 1% of phylogenetic diversity. The distribution of lipid biomarkers reflects these changes in microbial ecology, providing meaningful biosignatures for the microbes in this system. Polysaturated short-chain fatty acids characteristic of cyanobacteria account for Bacalar microbialites. By contrast, even short-chain and monounsaturated short-chain fatty acids attributable to both Cyanobacteria and many other organisms including types of Alphaproteobacteria and Gammaproteobacteria constitute 43%-69% and 17%-25%, respectively, of total abundance in microbialites. While cyanobacteria are the largest and most visible microbes within these microbialites and dominate the mangrove root agglomeration, it is clear that their smaller, metabolically diverse associates are responsible for significant biogeochemical cycling in this

  6. Textural and geochemical features of freshwater microbialites from Laguna Bacalar, Quintana Roo, Mexico

    OpenAIRE

    Castro-Contreras, Set I.; Gingras, Murray K.; Pecoits, Ernesto; Aubet, Natalie R.; Petrash, Daniel; Castro-Contreras, Saulo M.; Dick, Gregory; Planavsky, Noah J.; Konhauser, Kurt O.

    2014-01-01

    Microbialites provide some of the oldest direct evidence of life on Earth. They reached their peak during the Proterozoic and declined afterward. Their decline has been attributed to grazing and/or burrowing by metazoans, to changes in ocean chemistry, or to competition with other calcifying organisms. The freshwater microbialites at Laguna Bacalar (Mexico) provide an opportunity to better understand microbialite growth in terms of interaction between grazing organisms versus calcium carb...

  7. Microbes and mass extinctions: paleoenvironmental distribution of microbialites during times of biotic crisis.

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    Mata, S A; Bottjer, D J

    2012-01-01

    Widespread development of microbialites characterizes the substrate and ecological response during the aftermath of two of the 'big five' mass extinctions of the Phanerozoic. This study reviews the microbial response recorded by macroscopic microbial structures to these events to examine how extinction mechanism may be linked to the style of microbialite development. Two main styles of response are recognized: (i) the expansion of microbialites into environments not previously occupied during the pre-extinction interval and (ii) increases in microbialite abundance and attainment of ecological dominance within environments occupied prior to the extinction. The Late Devonian biotic crisis contributed toward the decimation of platform margin reef taxa and was followed by increases in microbialite abundance in Famennian and earliest Carboniferous platform interior, margin, and slope settings. The end-Permian event records the suppression of infaunal activity and an elimination of metazoan-dominated reefs. The aftermath of this mass extinction is characterized by the expansion of microbialites into new environments including offshore and nearshore ramp, platform interior, and slope settings. The mass extinctions at the end of the Triassic and Cretaceous have not yet been associated with a macroscopic microbial response, although one has been suggested for the end-Ordovician event. The case for microbialites behaving as 'disaster forms' in the aftermath of mass extinctions accurately describes the response following the Late Devonian and end-Permian events, and this may be because each is marked by the reduction of reef communities in addition to a suppression of bioturbation related to the development of shallow-water anoxia. © 2011 Blackwell Publishing Ltd.

  8. The microbial mats of Pavilion Lake microbialites: examining the relationship between photosynthesis and carbonate precipitation

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    Lim, D. S. S.; Hawes, I.; Mackey, T. J.; Brady, A. L.; Biddle, J.; Andersen, D. T.; Belan, M.; Slater, G.; Abercromby, A.; Squyres, S. W.; Delaney, M.; Haberle, C. W.; Cardman, Z.

    2014-12-01

    Pavilion Lake in British Columbia, Canada is an ultra-oligotrophic lake that has abundant microbialite growth. Recent research has shown that photoautotrophic microbial communities are important to modern microbialite development in Pavilion Lake. However, questions remain as to the relationship between changing light levels within the lake, variation in microbialite macro-structure, microbial consortia, and the preservation of associated biosignatures within the microbialite fabrics. The 2014 Pavilion Lake Research Project (PLRP) field program was focused on data gathering to understand these complex relationships by determining if a) light is the immediate limit to photosynthetic activity and, if so, if light is distributed around microbialites in ways that are consistent with emergent microbialite structure; and b) if at more local scales, the filamentous pink and green cyanobacterial nodular colonies identified in previous PLRP studies are centers of photosynthetic activity that create pH conditions suitable for carbonate precipitation. A diver-deployed pulse-amplitude modulated (PAM) fluorometer was used to collect synoptic in situ measurements of fluorescence yield and irradiance and across microbialites, focusing on comparing flat and vertical structural elements at a range of sites and depths. As well, we collected time series measurements of photosynthetic activity and irradiance at a set depth of 18 m across three different regions in Pavilion Lake. Our initial findings suggest that all microbialite surfaces are primarily light-limited regardless of depth or location within the lake. Shore based PAM fluorometry and microelectrode profiling of diver-collected samples suggest that pink and green nodules have different photosynthetic properties and pH profiles, and that nodular growth is likely to be the primary route of calcification due to the gelatinous covering the nodule creates. On-going tests for molecular signatures and isotopic shifts will allow for

  9. Exploring Biogeochemistry and Microbial Diversity of Extant Microbialites in Mexico and Cuba

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    Valdespino-Castillo, Patricia M.; Hu, Ping; Merino-Ibarra, Martín; López-Gómez, Luz M.; Cerqueda-García, Daniel; González-De Zayas, Roberto; Pi-Puig, Teresa; Lestayo, Julio A.; Holman, Hoi-Ying; Falcón, Luisa I.

    2018-01-01

    Microbialites are modern analogs of ancient microbial consortia that date as far back as the Archaean Eon. Microbialites have contributed to the geochemical history of our planet through their diverse metabolic capacities that mediate mineral precipitation. These mineral-forming microbial assemblages accumulate major ions, trace elements and biomass from their ambient aquatic environments; their role in the resulting chemical structure of these lithifications needs clarification. We studied the biogeochemistry and microbial structure of microbialites collected from diverse locations in Mexico and in a previously undescribed microbialite in Cuba. We examined their structure, chemistry and mineralogy at different scales using an array of nested methods including 16S rRNA gene high-throughput sequencing, elemental analysis, X-Ray fluorescence (XRF), X-Ray diffraction (XRD), Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS), Fourier Transformed Infrared (FTIR) spectroscopy and Synchrotron Radiation-based Fourier Transformed Infrared (SR-FTIR) spectromicroscopy. The resulting data revealed high biological and chemical diversity among microbialites and specific microbe to chemical correlations. Regardless of the sampling site, Proteobacteria had the most significant correlations with biogeochemical parameters such as organic carbon (Corg), nitrogen and Corg:Ca ratio. Biogeochemically relevant bacterial groups (dominant phototrophs and heterotrophs) showed significant correlations with major ion composition, mineral type and transition element content, such as cadmium, cobalt, chromium, copper and nickel. Microbial-chemical relationships were discussed in reference to microbialite formation, microbial metabolic capacities and the role of transition elements as enzyme cofactors. This paper provides an analytical baseline to drive our understanding of the links between microbial diversity with the chemistry of their lithified precipitations. PMID

  10. Questioning the Origin of the Great Salt Lake "Microbialites"

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    Frantz, C.; Matyjasik, M.; Newell, D. L.; Vanden Berg, M. D.; Park, C.

    2017-12-01

    The Great Salt Lake (GSL) of Utah contains abundant carbonate mounds that have been described in the literature as "biostromes", "bioherms", "stromatolites", and "microbialites". The structures are commonly cited as being rare examples of modern lacustrine microbialites, which implies that they are actively-forming and biogenic. Indeed, at least in some regions of the lake, the mounds are covered in a mixed community of cyanobacteria, algae, insect larval casings, microbial heterotrophs, and other organisms that is thought to contribute significantly to benthic primary productivity in GSL. However, the presence of a modern surface microbial community does not implicate a biogenic or modern origin for the mounds. The few studies to date GSL microbialites indicate that they are ancient, with radiocarbon calendar ages in the late Pleistocene and Holocene ( 13 - 3 cal ka). However, could they still be actively growing, and are the surface microbial communities playing a role? Here, we present results of a suite geochemical measurements used to constrain parameters—including groundwater seepage—influencing carbonate saturation and precipitation in the vicinity of one currently-submerged "microbialite reef" on the northern shore of Antelope Island in the South Arm of GSL. Our data suggests that calcium-charged brackish groundwater input to the lake through a permeable substratum in this location results in locally supersaturated conditions for aragonite, which could lead to modern, abiogenic mineralization. In addition, a series of laboratory experiments suggest that the modern surface microbial communities that coat the mounds do not appreciably facilitate carbonate precipitation in simulated GSL conditions, although they may serve as a template for precipitation when local waters become supersaturated.

  11. Permian-Triassic boundary microbialites at Zuodeng Section, Guangxi Province, South China: Geobiology and palaeoceanographic implications

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    Fang, Yuheng; Chen, Zhong-Qiang; Kershaw, Stephen; Yang, Hao; Luo, Mao

    2017-05-01

    A previously unknown microbialite bed in the Permian-Triassic (P-Tr) boundary beds of Zuodeng section, Tiandong County, Guangxi, South China comprises a thin (5 cm maximum thickness) stromatolite in the lower part and the remaining 6 m is thrombolite. The Zuodeng microbialite has a pronounced irregular contact between the latest Permian bioclastic limestone and microbialite, as in other sites in the region. The stromatolite comprises low-relief columnar and broad domal geometries, containing faint laminations. The thrombolite displays an irregular mixture of sparitic dark coloured altered microbial fabric and light coloured interstitial sediment in polished blocks. Abundant microproblematic calcimicrobe structures identified here as Gakhumella are preserved in dark coloured laminated areas of the stromatolite and sparitic areas in thrombolites (i.e. the calcimicrobial part, not the interstitial sediment) and are orientated perpendicular to stromatolitic laminae. Each Gakhumella individual has densely arranged segments, which form a column- to fan-shaped structure. Single segments are arch-shaped and form a thin chamber between segments. Gakhumella individuals in the stromatolite and thrombolite are slightly different from each other, but are readily distinguished from the Gakhumella- and Renalcis-like fossils reported from other P-Tr boundary microbialites in having a smaller size, unbranching columns and densely arranged, arch-shaped segments. Renalcids usually possess a larger body size and branching, lobate outlines. Filament sheath aggregates are also observed in the stromatolite and they are all orientated in one direction. Both Gakhumella and filament sheath aggregates may be photosynthetic algae, which may have played an important role in constructing the Zuodeng microbialites. Other calcimicrobes in the Zuodeng microbialite are spheroids, of which a total of five morphological types are recognized from both stromatolite and thrombolite: (1) sparry calcite

  12. Les microbialites de l'Anti-Atlas occidental (Maroc) : marqueurs stratigraphiques et témoins des changements environnementaux au Cambrien inférieur Stratigraphic and environmental significance of the Lower-Cambrian western Anti-Atlasic microbialites (Morocco)

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    Benssaou, Mohammed; Hamoumi, Naima

    2004-02-01

    Three microbialite forms are recognized in the Lower-Cambrian succession of Irherm area in the western Anti-Atlas (Morocco). Stromatolites, which correspond to non-calcified shallow marine laminated microbialites, are well developed in the basal Lower-Cambrian succession. Occurrence of calcified microbial thrombolites, in the middle part of this succession, reflects an increasing sea level from the peritidal zone to the subtidal environment. In the upper part of this succession, a second increasing water depth event and the development of branching archaeocyathan reefal framework lead to dendritic microbialite emergence. To cite this article: M. Benssaou, N. Hamoumi, C. R. Geoscience 336 (2004).

  13. Geomicrobiological study of modern microbialites from Mexico: towards a better understanding of the ancient fossil record

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    Benzerara K.

    2014-02-01

    Full Text Available Microbialites are sedimentary formations that are found throughout the geological record and are usually considered as one of the oldest traces of life on Earth. Although they have been known for more than a century and hold as an emblematic object in Earth Sciences, we yet do not understand in details how they form and how microbial processes impact their chemistry, structure and macroscopic morphology. Here, we show recent advances achieved owing to funding provided by the EPOV program on the formation of modern microbialites in the crater Lake Alchichica (Mexico. We first show that very diverse microbial communities populate these microbialites, including diverse microbial groups able to induce carbonate precipitation. We demonstrate that this microbial diversity can be preserved for several years in laboratory aquaria offering a nice opportunity for future studies to assess the relationships between biodiversity and microbialite formation. We then detail the textural modifications affecting cyanobacterial cells during the first steps of fossilization as captured in Alchichica microbialites. Finally, we report the discovery of a new deepbranching cyanobacterium species, Candidatus Gloeomargarita lithophora, able to form intracellular Ca-, Mg-, Sr- and Ba-rich carbonates and discuss the implications for the interpretation of the fossil record.

  14. Freshwater Microbialites of Pavilion Lake, British Columbia, Canada: A Limnological Investigation

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    Lim, D. S. S.; McKay, C. P.; Laval, B.; Bird, J.; Cady, S.

    2004-01-01

    Pavillion Lake is 5.7km long and an average of 0.8 km in width, and is located in Marble Canyon in the interior of British Columbia, Canada. It is a slightly alkaline, freshwater lake with a maximum-recorded depth of 65m. The basin walls of Pavilion Lake are lined with microbialite structures that are oriented perpendicularly to the shoreline, and which are found from depths of 5 meters to the bottom of the photic zone (light levels 1% of ambient; approximately 30m depth). These structures are speculated to have begun formation nearly 11,000 years ago, after the glacial retreat of the Cordilleran Ice Sheet. They are likely a distinctive assemblage of freshwater calcite microbialites, which display micromorphologies possibly related to the ancient Epiphyton and Girvanella classes of calcareous organosedimentary structures.

  15. Occurrence and genesis of Quaternary microbialitic tufa at Hammam Al Ali, Oman

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    Khalaf, Fikry I.

    2017-05-01

    Remnants of late Quaternary microbialitic tufa occurs within a shallow depression in the Hammam Al Ali hot spring area, which is located approximately 14.5 km to the southwest of Muscat, Oman. The tufa precipitated from hot spring water supersaturated with respect to calcium carbonate and is mostly of a porous phytogenic type, with occasional detrital and stromatolitic types. Microscopic and nanoscopic examination revealed that the tufa deposits developed through two successive processes of calcite precipitation, biotic and abiotic, preceded by limited precipitation of unstable aragonite. It is suggested that biologically mediated precipitation results in the construction of incomplete skeletal calcite crystals. The latter provide a base for classical physiochemical precipitation and, eventually, the development of complete sparry calcite crystals. The initiation of dendritic calcite crystals in the stromatolitic tufa as incomplete biogenic skeletal crystals and their characteristic growth pattern indicates that the tufa represents a clear example of hot spring calcitic microbialite.

  16. Structural parallels between terrestrial microbialites and Martian sediments: are all cases of `Pareidolia'?

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    Rizzo, Vincenzo; Cantasano, Nicola

    2017-10-01

    The study analyses possible parallels of the microbialite-known structures with a set of similar settings selected by a systematic investigation from the wide record and data set of images shot by NASA rovers. Terrestrial cases involve structures both due to bio-mineralization processes and those induced by bacterial metabolism, that occur in a dimensional field longer than 0.1 mm, at micro, meso and macro scales. The study highlights occurrence on Martian sediments of widespread structures like microspherules, often organized into some higher-order settings. Such structures also occur on terrestrial stromatolites in a great variety of `Microscopic Induced Sedimentary Structures', such as voids, gas domes and layer deformations of microbial mats. We present a suite of analogies so compelling (i.e. different scales of morphological, structural and conceptual relevance), to make the case that similarities between Martian sediment structures and terrestrial microbialites are not all cases of `Pareidolia'.

  17. Habitat conditions drive phylogenetic structure of dominant bacterial phyla of microbialite communities from different locations in Mexico.

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    Centeno, Carla M; Mejía, Omar; Falcón, Luisa I

    2016-09-01

    Community structure and composition are dictated by evolutionary and ecological assembly processes which are manifested in signals of, species diversity, species abundance and species relatedness. Analysis of species coexisting relatedness, has received attention as a tool to identify the processes that influence the composition of a community within a particular habitat. In this study, we tested if microbialite genetic composition is dependent on random events versus biological/abiotical factors. This study was based on a large genetic data set of two hypervariable regions (V5 and V6) from previously generated barcoded 16S rRNA amplicons from nine microbialite communities distributed in Northeastern, Central and Southeastern Mexico collected in May and June of 2009. Genetic data of the most abundant phyla (Proteobacteria, Planctomycetes, Verrucomicrobia, Bacteroidetes, and Cyanobacteria) were investigated in order to state the phylogenetic structure of the complete communities as well as each phylum. For the complete dataset, Webb NTI index showed positive and significant values in the nine communities analysed, where values ranged from 31.5 in Pozas Azules I to 57.2 in Bacalar Pirate Channel; meanwhile, NRI index were positive and significant in six of the nine communities analysed with values ranging from 18.1 in Pozas Azules I to 45.1 in Río Mesquites. On the other hand, when comparing each individual phylum, NTI index were positive and significant in all groups, except in Cyanobacteria for which positive and significant values were only found in three localities; finally, NRI index was significant in only a few of the comparisons performed. The results suggest that habitat filtering is the main process that drives phylogenetic structure in bacterial communities associated to microbialites with the exception of Cyanobacteria where different lineages can contribute to microbialite formation and growth.

  18. Carbonate microbialites and hardgrounds from Manito Lake, an alkaline, hypersaline lake in the northern Great Plains of Canada

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    Last, Fawn M.; Last, William M.; Halden, Norman M.

    2010-03-01

    Manito Lake is a large, perennial, Na-SO 4 dominated saline to hypersaline lake located in the northern Great Plains of western Canada. Significant water level decrease over the past several decades has led to reduction in volume and surface area, as well as an increase in salinity. The salinity has increased from 10 ppt to about 50 ppt TDS. This decrease in water level has exposed large areas of nearshore microbialites. These organogenic structures range in size from several cm to over a meter and often form large bioherms several meters high. They have various external morphologies, vary in mineralogical composition, and show a variety of internal fabrics from finely laminated to massive. In addition to microbiolities and bioherms, the littoral zone of Manito Lake contains a variety of carbonate hardgrounds, pavements, and cemented clastic sediments. Dolomite and aragonite are the most common minerals found in these shoreline structures, however, calcite after ikaite, monohydrocalcite, magnesian calcite, and hydromagnesite are also present. The dolomite is nonstoichiometric and calcium-rich; the magnesian calcite has about 17 mol% MgCO 3. AMS radiocarbon dating of paired organic matter and endogenic carbonate material confirms little or no reservoir affect. Although there is abundant evidence for modern carbonate mineral precipitation and microbialite formation, most of the larger microbialites formed between about 2300 and 1000 cal BP, whereas the hardgrounds, cements, and laminated crusts formed about 1000-500 cal BP.

  19. Structure, mineralogy and microbial diversity of geothermal spring microbialites associated with a deep oil drilling in Romania

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    Cristian eComan

    2015-03-01

    Full Text Available Modern mineral deposits play an important role in evolutionary studies by providing clues to the formation of ancient lithified microbial communities. Here we report the presence of microbialite-forming microbial mats in different microenvironments at 32ºC, 49ºC and 65ºC around the geothermal spring from an abandoned oil drill in Ciocaia, Romania. The mineralogy and the macro- and microstructure of the microbialites were investigated, together with their microbial diversity based on a 16S rRNA gene amplicon sequencing approach. The calcium carbonate is deposited mainly in the form of calcite. At 32ºC and 49ºC, the microbialites show a laminated structure with visible microbial mat-carbonate crystal interactions. At 65ºC, the mineral deposit is clotted, without obvious organic residues. Partial 16S rRNA gene amplicon sequencing showed that the relative abundance of the phylum Archaea was low at 32ºC (1%. The dominant bacterial groups at 32ºC were Cyanobacteria, Gammaproteobacteria, Firmicutes, Bacteroidetes, Chloroflexi, Thermi, Actinobacteria, Planctomycetes and Defferibacteres. At 49ºC, there was a striking dominance of the Gammaproteobacteria, followed by Firmicutes, Bacteroidetes, and Armantimonadetes. The 65ºC sample was dominated by Betaproteobacteria, Firmicutes, [OP1], Defferibacteres, Thermi, Thermotogae, [EM3] and Nitrospirae. Several groups from Proteobacteria and Firmicutes, together with Halobacteria and Melainabacteria were described for the first time in calcium carbonate deposits. Overall, the spring from Ciocaia emerges as a valuable site to probe microbes-minerals interrelationships along thermal and geochemical gradients.

  20. X-ray microtomography characterization of carbonate microbialites from a hypersaline coastal lagoon in the Rio de Janeiro State—Brazil

    Energy Technology Data Exchange (ETDEWEB)

    Machado, A.S., E-mail: alemachado@lin.ufrj.br [Laboratório de Geologia Sedimentar—IGEO, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil); Laboratório de Instrumentação Nuclear—COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil); Dal Bó, P.F.F. [Laboratório de Geologia Sedimentar—IGEO, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil); Lima, I. [Laboratório de Instrumentação Nuclear—COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil); Borghi, L. [Laboratório de Geologia Sedimentar—IGEO, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil); Lopes, R. [Laboratório de Instrumentação Nuclear—COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil)

    2015-06-01

    The objective of the present study is to apply the micro-CT technique to assess recent microbialite samples from a hypersaline coastal lagoon in the Rio de Janeiro State. The study comprises structural assessment, mineralogical characterization and porosity distribution of each sample. Micro-CT is increasingly present in geological reservoir analyses, and has advantages over other laboratory techniques since it is non-invasive and allows 2D/3D visualization of inner structures without previous preparation method, such as slabbing, polishing, thinning or impregnation. This technique renders structural analyses which can be spatially resolved to a scale of micrometers. Results show that micro-CT technique is also adequate for the characterization of carbonate microbialites, providing excellent high resolution 3D images, that enabled to distinguish different mineralogies and porosity distribution beyond it is inner structure.

  1. X-ray microtomography characterization of carbonate microbialites from a hypersaline coastal lagoon in the Rio de Janeiro State—Brazil

    International Nuclear Information System (INIS)

    Machado, A.S.; Dal Bó, P.F.F.; Lima, I.; Borghi, L.; Lopes, R.

    2015-01-01

    The objective of the present study is to apply the micro-CT technique to assess recent microbialite samples from a hypersaline coastal lagoon in the Rio de Janeiro State. The study comprises structural assessment, mineralogical characterization and porosity distribution of each sample. Micro-CT is increasingly present in geological reservoir analyses, and has advantages over other laboratory techniques since it is non-invasive and allows 2D/3D visualization of inner structures without previous preparation method, such as slabbing, polishing, thinning or impregnation. This technique renders structural analyses which can be spatially resolved to a scale of micrometers. Results show that micro-CT technique is also adequate for the characterization of carbonate microbialites, providing excellent high resolution 3D images, that enabled to distinguish different mineralogies and porosity distribution beyond it is inner structure

  2. Sulfur Isotope Trends in Archean Microbialite Facies Record Early Oxygen Production and Consumption

    Science.gov (United States)

    Zerkle, A.; Meyer, N.; Izon, G.; Poulton, S.; Farquhar, J.; Claire, M.

    2014-12-01

    The major and minor sulfur isotope composition (δ34S and Δ33S) of pyrites preserved in ~2.65-2.5 billion-year-old (Ga) microbialites record localized oxygen production and consumption near the mat surface. These trends are preserved in two separate drill cores (GKF01 and BH1-Sacha) transecting the Campbellrand-Malmani carbonate platform (Ghaap Group, Transvaal Supergroup, South Africa; Zerkle et al., 2012; Izon et al., in review). Microbialite pyrites possess positive Δ33S values, plotting parallel to typical Archean trends (with a Δ33S/δ34S slope of ~0.9) but enriched in 34S by ~3 to 7‰. We propose that these 34S-enriched pyrites were formed from a residual pool of sulfide that was partially oxidized via molecular oxygen produced by surface mat-dwelling cyanobacteria. Sulfide, carrying the range of Archean Δ33S values, could have been produced deeper within the microbial mat by the reduction of sulfate and elemental sulfur, then fractionated upon reaction with O2 produced by oxygenic photosynthesis. Preservation of this positive 34S offset requires that: 1) sulfide was only partially (50­­-80%) consumed by oxidation, meaning H2S was locally more abundant (or more rapidly produced) than O2, and 2) the majority of the sulfate produced via oxidation was not immediately reduced to sulfide, implying either that the sulfate pool was much larger than the sulfide pool, or that the sulfate formed near the mat surface was transported and reduced in another part of the system. Contrastingly, older microbialite facies (> 2.7 Ga; Thomazo et al., 2013) appear to lack these observed 34S enrichments. Consequently, the onset of 34S enrichments could mark a shift in mat ecology, from communities dominated by anoxygenic photosynthesizers to cyanobacteria. Here, we test these hypotheses with new spatially resolved mm-scale trends in sulfur isotope measurements from pyritized stromatolites of the Vryburg Formation, sampled in the lower part of the BH1-Sacha core. Millimeter

  3. Phylogenetic analysis of a microbialite-forming microbial mat from a hypersaline lake of the Kiritimati atoll, Central Pacific.

    Science.gov (United States)

    Schneider, Dominik; Arp, Gernot; Reimer, Andreas; Reitner, Joachim; Daniel, Rolf

    2013-01-01

    On the Kiritimati atoll, several lakes exhibit microbial mat-formation under different hydrochemical conditions. Some of these lakes trigger microbialite formation such as Lake 21, which is an evaporitic, hypersaline lake (salinity of approximately 170‰). Lake 21 is completely covered with a thick multilayered microbial mat. This mat is associated with the formation of decimeter-thick highly porous microbialites, which are composed of aragonite and gypsum crystals. We assessed the bacterial and archaeal community composition and its alteration along the vertical stratification by large-scale analysis of 16S rRNA gene sequences of the nine different mat layers. The surface layers are dominated by aerobic, phototrophic, and halotolerant microbes. The bacterial community of these layers harbored Cyanobacteria (Halothece cluster), which were accompanied with known phototrophic members of the Bacteroidetes and Alphaproteobacteria. In deeper anaerobic layers more diverse communities than in the upper layers were present. The deeper layers were dominated by Spirochaetes, sulfate-reducing bacteria (Deltaproteobacteria), Chloroflexi (Anaerolineae and Caldilineae), purple non-sulfur bacteria (Alphaproteobacteria), purple sulfur bacteria (Chromatiales), anaerobic Bacteroidetes (Marinilabiacae), Nitrospirae (OPB95), Planctomycetes and several candidate divisions. The archaeal community, including numerous uncultured taxonomic lineages, generally changed from Euryarchaeota (mainly Halobacteria and Thermoplasmata) to uncultured members of the Thaumarchaeota (mainly Marine Benthic Group B) with increasing depth.

  4. Phylogenetic analysis of a microbialite-forming microbial mat from a hypersaline lake of the Kiritimati atoll, Central Pacific.

    Directory of Open Access Journals (Sweden)

    Dominik Schneider

    Full Text Available On the Kiritimati atoll, several lakes exhibit microbial mat-formation under different hydrochemical conditions. Some of these lakes trigger microbialite formation such as Lake 21, which is an evaporitic, hypersaline lake (salinity of approximately 170‰. Lake 21 is completely covered with a thick multilayered microbial mat. This mat is associated with the formation of decimeter-thick highly porous microbialites, which are composed of aragonite and gypsum crystals. We assessed the bacterial and archaeal community composition and its alteration along the vertical stratification by large-scale analysis of 16S rRNA gene sequences of the nine different mat layers. The surface layers are dominated by aerobic, phototrophic, and halotolerant microbes. The bacterial community of these layers harbored Cyanobacteria (Halothece cluster, which were accompanied with known phototrophic members of the Bacteroidetes and Alphaproteobacteria. In deeper anaerobic layers more diverse communities than in the upper layers were present. The deeper layers were dominated by Spirochaetes, sulfate-reducing bacteria (Deltaproteobacteria, Chloroflexi (Anaerolineae and Caldilineae, purple non-sulfur bacteria (Alphaproteobacteria, purple sulfur bacteria (Chromatiales, anaerobic Bacteroidetes (Marinilabiacae, Nitrospirae (OPB95, Planctomycetes and several candidate divisions. The archaeal community, including numerous uncultured taxonomic lineages, generally changed from Euryarchaeota (mainly Halobacteria and Thermoplasmata to uncultured members of the Thaumarchaeota (mainly Marine Benthic Group B with increasing depth.

  5. Origin and diagenetic evolution of gypsum and microbialitic carbonates in the Late Sag of the Namibe Basin (SW Angola)

    Science.gov (United States)

    Laurent, Gindre-Chanu; Edoardo, Perri; Ian, Sharp R.; Peacock, D. C. P.; Roger, Swart; Ragnar, Poulsen; Hercinda, Ferreira; Vladimir, Machado

    2016-08-01

    Ephemeral evaporitic conditions developed within the uppermost part of the transgressive Late Sag sequence in the Namibe Basin (SW Angola), leading to the formation of extensive centimetre- to metre-thick sulphate-bearing deposits and correlative microbialitic carbonates rich in pseudomorphs after evaporite crystals. The onshore pre-salt beds examined in this study are located up to 25 m underneath the major mid-Aptian evaporitic succession, which is typified at the outcrop by gypsiferous Bambata Formation and in the subsurface by the halite-dominated Loeme Formation. Carbonate-evaporite cycles mostly occur at the top of metre-thick regressive parasequences, which progressively onlap and overstep landward the former faulted (rift) topography, or fill major pre-salt palaeo-valleys. The sulphate beds are made up of alabastrine gypsum associated with embedded botryoidal nodules, dissolution-related gypsum breccia, and are cross-cut by thin satin-spar gypsum veins. Nodular and fine-grained fabrics are interpreted as being diagenetic gypsum deposits resulting from the dissolution and recrystallisation of former depositional subaqueous sulphates, whereas gypsum veins and breccia result from telogenetic processes. The carbonates display a broader diversity of facies, characterised by rapid lateral variations along strike. Thin dolomitic and calcitic bacterial-mediated filamentous microbialitic boundstones enclose a broad variety of evaporite pseudomorphs and can pass laterally over a few metres into sulphate beds. Dissolution-related depositional breccias are also common and indicate early dissolution of former evaporite layers embedded within the microbialites. Sulphate and carbonate units are interpreted as being concomitantly deposited along a tide-dominated coastal supra- to intertidal- sabkha and constitute high-frequency hypersaline precursor events, prior to the accumulation of the giant saline mid-Aptian Bambata and Loeme Formations. Petrographic and geochemical

  6. The sedimentological characteristics of microbialites of the Cambrian in the vicinity of Beijing, China

    Directory of Open Access Journals (Sweden)

    Yin-Ye Wu

    2017-04-01

    Full Text Available With oil and gas exploration transferring to deeper and more ancient marine strata, more researches have been conducted about the Meso–Neoproterozoic and Cambrian microbial carbonate rocks by petroleum geologists. The Cambrian deposits experienced the first transgression of the Paleozoic, with shallow marine facies depositing in most areas, which are favorable for different kinds of biological reproduction. The Lower Cambrian in Beijing area is lithologically dominated by purple red shales interbedded with limestones, the Middle Cambrian is mainly composed of thick oolitic limestones, and the Upper Cambrian consists of thin limestones and flat-pebble conglomerates. Two beds of microbial carbonate rocks were discovered in the Cambrian outcrops in the vicinity of Beijing. One is from the Zhangxia Formation of Middle Cambrian, and the other is from the Gushan Formation of Upper Cambrian. The microbialites are characterized by combination of multiple stromatolites forming different bioherms. The bioherms are mostly in oval shape and with different sizes, which are 3–4 m long, and 1–3 m high. The surrounding strata beneath the bioherms are oolitic limestones. A central core of flat-pebble conglomerates occurred within each bioherm. Wavy or columnar stromatolites grow on the basis of flat-pebble conglomerates, with dentate erosional surfaces. The bioherm carbonate rocks are interpreted as products from a deep ramp sedimentary environment where potential oil and gas reservoirs can be found. The analysis of sedimentological characteristics of bioherm carbonate rocks and its lithofacies palaeogeography has significant implication for petroleum exploration. Research on geological record of microbialites is beneficial to investigating the Earth evolution, biodiversity, palaeoenvironment and palaeoclimate change, as well as biological extinction event during geological transitions. It also gives warning to human beings of modern biological crisis.

  7. Artificial Immune Networks: Models and Applications

    Directory of Open Access Journals (Sweden)

    Xian Shen

    2008-06-01

    Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

  8. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  9. An artificial ecosystem model used in the study of social, economic and technological dynamics: An artificial electrical energy market

    International Nuclear Information System (INIS)

    Arjona, D.

    1998-01-01

    This paper will present the artificial ecosystem as a tool, in the development of multi agent models for the simulation of economic and technological dynamics (as well as other possible applications). This tool is based on the mechanics of an artificial society and consists of autonomous artificial agents that interact with individuals that have different characteristics and behavior and other that have a similar conduct to their own. Initial conditions are assumed not to be controllable, however they can be influenced. The importance of the concept of the ecosystem is in understanding great units in the light of their own components which are relevant for the analysis and become interdependent among themselves and with other essential components that hold the total operation of the system. Ideas for the development of a simulation model based on autonomous intelligent agents are presented. These agents will have a brain that is based on artificial intelligence technologies. The Sand Kings Simulation Model, an artificial ecosystem model developed by the author, is described as well as the application of artificial intelligence to this artificial life model. An application to a real life problem is also offered as an artificial energy market that is currently being developed by the author is described

  10. Transport modeling: An artificial immune system approach

    Directory of Open Access Journals (Sweden)

    Teodorović Dušan

    2006-01-01

    Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

  11. Analysis of Mental Processes Represented in Models of Artificial Consciousness

    Directory of Open Access Journals (Sweden)

    Luana Folchini da Costa

    2013-12-01

    Full Text Available The Artificial Consciousness concept has been used in the engineering area as being an evolution of the Artificial Intelligence. However, consciousness is a complex subject and often used without formalism. As a main contribution, in this work one proposes an analysis of four recent models of artificial consciousness published in the engineering area. The mental processes represented by these models are highlighted and correlations with the theoretical perspective of cognitive psychology are made. Finally, considerations about consciousness in such models are discussed.

  12. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) ... (2006) applied rainfall–runoff modeling using ANN ... in artificial intelligence, engineering and science .... usually be estimated from a sample of observations.

  13. Natural vs. artificial groundwater recharge, quantification through inverse modeling

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2013-02-01

    Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.

  14. An Artificial Emotion Model For Visualizing Emotion of Characters

    OpenAIRE

    Junseok Ham; Chansun Jung; Junhyung Park; Jihye Ryeo; Ilju Ko

    2009-01-01

    It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emo...

  15. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  16. Artificial Neural Network Based Model of Photovoltaic Cell

    Directory of Open Access Journals (Sweden)

    Messaouda Azzouzi

    2017-03-01

    Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

  17. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two diff- erent ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods ...

  18. Artificial intelligence model for sustain ability measurement

    International Nuclear Information System (INIS)

    Navickiene, R.; Navickas, K.

    2012-01-01

    The article analyses the main dimensions of organizational sustain ability, their possible integrations into artificial neural network. In this article authors performing analyses of organizational internal and external environments, their possible correlations with 4 components of sustain ability, and the principal determination models for sustain ability of organizations. Based on the general principles of sustainable development organizations, a artificial intelligence model for the determination of organizational sustain ability has been developed. The use of self-organizing neural networks allows the identification of the organizational sustain ability and the endeavour to explore vital, social, antropogenical and economical efficiency. The determination of the forest enterprise sustain ability is expected to help better manage the sustain ability. (Authors)

  19. Modelling fuel cell performance using artificial intelligence

    Science.gov (United States)

    Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

  20. Modelling fuel cell performance using artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Ogaji, S.O.T.; Singh, R.; Pilidis, P.; Diacakis, M. [Power Propulsion and Aerospace Engineering Department, Centre for Diagnostics and Life Cycle Costs, Cranfield University (United Kingdom)

    2006-03-09

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed. (author)

  1. Lagoon microbialites on Isla Angel de la Guarda and associated peninsular shores, Gulf of California (Mexico)

    Science.gov (United States)

    Johnson, Markes E.; Ledesma-Vázquez, Jorge; Backus, David H.; González, Maria R.

    2012-07-01

    Examples of two closed lagoons with extensive growth of Recent microbialites showing variable surface morphology and internal structure are found on Isla Angel de la Guarda in the Gulf of California. Comparable lagoonal microbialites also occur ashore from Ensenada El Quemado on the adjacent peninsular mainland of Baja California. The perimeters of all three lagoons feature crusted structures indicative of thrombolites with a knobby surface morphology 2 cm to 3 cm in relief and internal clotting without any sign of laminations. Outward from this zone, thrombolitic construction thins to merge with a white calcified crust below which a soft substratum of dark organic material 4 cm to 6 cm in thickness is concealed. The substratum is laminated and heavily mucilaginous, as observed along the edges of extensive shrinkage cracks in the overlying crust. The thrombolitic crust is anchored to the shore, while the thinner crust and associated stromatolitic mats float on the surface of the lagoons. Laboratory cultures of the dark organic material yielded the solitary cyanobacterium Chroococcidiopsis as the predominant taxon interspersed with filamentous forms. In decreasing order of abundance, other morphotypes present include Phormidium, Oscillatoria, Geitlerinema, Chroococus, and probably Spirulina. The larger of the two island lagoons follows an east-west azimuth and covers 0.225 km2, while the smaller lagoon has a roughly north-south axis and covers only 0.023 km2. The salinity of water in the smaller lagoon was measured as148 ppt. Pliocene strata along the edge of the smaller modern lagoon include siltstone bearing calcified platelets suggestive of a microbial origin. Dry lagoons abandoned during the later Quaternary occur inland at higher elevations on the island, but retain no fossils except for sporadic white crusts cemented on cobbles around distinct margins. Raised Quaternary lagoons parallel to the big lagoon on Isla Angel de la Guarda are partly obscured by flood

  2. Neuro-Based Artificial Intelligence Model for Loan Decisions

    OpenAIRE

    Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

    2010-01-01

    Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpr...

  3. Artificial cilia : a physical model for ciliary propulsion

    OpenAIRE

    Babataheri , Avin

    2009-01-01

    Most microorganisms use cilia or flagella as a means of propulsion. These low Reynolds number swimming mechanisms have been studied theoretically and experimentally on living organisms. However, so far very few physical experimental models have been realised. We describe here the fabrication of microscopic artificial cilia, actuated by a magnetic field. These artificial cilia share with real cilia a large aspect ratio, great flexibility, and the actuation by a magnetic torque distributed alon...

  4. Mathematical problems in modeling artificial heart

    Directory of Open Access Journals (Sweden)

    Ahmed N. U.

    1995-01-01

    Full Text Available In this paper we discuss some problems arising in mathematical modeling of artificial hearts. The hydrodynamics of blood flow in an artificial heart chamber is governed by the Navier-Stokes equation, coupled with an equation of hyperbolic type subject to moving boundary conditions. The flow is induced by the motion of a diaphragm (membrane inside the heart chamber attached to a part of the boundary and driven by a compressor (pusher plate. On one side of the diaphragm is the blood and on the other side is the compressor fluid. For a complete mathematical model it is necessary to write the equation of motion of the diaphragm and all the dynamic couplings that exist between its position, velocity and the blood flow in the heart chamber. This gives rise to a system of coupled nonlinear partial differential equations; the Navier-Stokes equation being of parabolic type and the equation for the membrane being of hyperbolic type. The system is completed by introducing all the necessary static and dynamic boundary conditions. The ultimate objective is to control the flow pattern so as to minimize hemolysis (damage to red blood cells by optimal choice of geometry, and by optimal control of the membrane for a given geometry. The other clinical problems, such as compatibility of the material used in the construction of the heart chamber, and the membrane, are not considered in this paper. Also the dynamics of the valve is not considered here, though it is also an important element in the overall design of an artificial heart. We hope to model the valve dynamics in later paper.

  5. Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

    Directory of Open Access Journals (Sweden)

    Rongjie Wang

    2015-07-01

    Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

  6. A multiscale approach for modeling actuation response of polymeric artificial muscles.

    Science.gov (United States)

    Sharafi, Soodabeh; Li, Guoqiang

    2015-05-21

    Artificial muscles are emerging materials in the field of smart materials with applications in aerospace, robotic, and biomedical industries. Despite extensive experimental investigations in this field, there is a need for numerical modeling techniques that facilitate cutting edge research and development. This work aims at studying an artificial muscle made of twisted Nylon 6.6 fibers that are highly cold-drawn. A computationally efficient phenomenological thermo-mechanical constitutive model is developed in which several physical properties of the artificial muscles are incorporated to minimize the trial-and-error numerical curve fitting processes. Two types of molecular chains are considered at the micro-scale level that control training and actuation processes viz. (a) helically oriented chains which are structural switches that store a twisted shape in their low temperature phase and restore their random configuration during the thermal actuation process, and (b) entropic chains which are highly drawn chains that could actuate as soon as the muscle heats up, and saturates when coil contact temperature is reached. The thermal actuation response of the muscle over working temperatures has been elaborated in the Modeling section. The performance of the model is validated by available experiments in the literature. The model may provide a design platform for future artificial muscle developments.

  7. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  8. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  9. Non-marine carbonate facies, facies models and palaeogeographies of the Purbeck Formation (Late Jurassic to Early Cretaceous) of Dorset (Southern England).

    Science.gov (United States)

    Gallois, Arnaud; Bosence, Dan; Burgess, Peter

    2015-04-01

    Non-marine carbonates are relatively poorly understood compared with their more abundant marine counterparts. Sedimentary facies and basin architecture are controlled by a range of environmental parameters such as climate, hydrology and tectonic setting but facies models are few and limited in their predictive value. Following the discovery of extensive Early Cretaceous, non-marine carbonate hydrocarbon reservoirs in the South Atlantic, the interest of understanding such complex deposits has increased during recent years. This study is developing a new depositional model for non-marine carbonates in a semi-arid climate setting in an extensional basin; the Purbeck Formation (Upper Jurassic - Lower Cretaceous) in Dorset (Southern England). Outcrop study coupled with subsurface data analysis and petrographic study (sedimentology and early diagenesis) aims to constrain and improve published models of depositional settings. Facies models for brackish water and hypersaline water conditions of these lacustrine to palustrine carbonates deposited in the syn-rift phase of the Wessex Basin will be presented. Particular attention focusses on the factors that control the accumulation of in-situ microbialite mounds that occur within bedded inter-mound packstones-grainstones in the lower Purbeck. The microbialite mounds are located in three units (locally known as the Skull Cap, the Hard Cap and the Soft Cap) separated by three fossil soils (locally known as the Basal, the Lower and the Great Dirt Beds) respectively within three shallowing upward lacustrine sequences. These complex microbialite mounds (up to 4m high), are composed of tabular small-scale mounds (flat and long, up to 50cm high) divided into four subfacies. Many of these small-scale mounds developed around trees and branches which are preserved as moulds (or silicified wood) which are surrounded by a burrowed mudstone-wackestone collar. Subsequently a thrombolite framework developed on the upper part only within

  10. Microbialite Biosignature Analysis by Mesoscale X-ray Fluorescence (μXRF) Mapping.

    Science.gov (United States)

    Tice, Michael M; Quezergue, Kimbra; Pope, Michael C

    2017-11-01

    As part of its biosignature detection package, the Mars 2020 rover will carry PIXL, the Planetary Instrument for X-ray Lithochemistry, a spatially resolved X-ray fluorescence (μXRF) spectrometer. Understanding the types of biosignatures detectable by μXRF and the rock types μXRF is most effective at analyzing is therefore an important goal in preparation for in situ Mars 2020 science and sample selection. We tested mesoscale chemical mapping for biosignature interpretation in microbialites. In particular, we used μXRF to identify spatial distributions and associations between various elements ("fluorescence microfacies") to infer the physical, biological, and chemical processes that produced the observed compositional distributions. As a test case, elemental distributions from μXRF scans of stromatolites from the Mesoarchean Nsuze Group (2.98 Ga) were analyzed. We included five fluorescence microfacies: laminated dolostone, laminated chert, clotted dolostone and chert, stromatolite clast breccia, and cavity fill. Laminated dolostone was formed primarily by microbial mats that trapped and bound loose sediment and likely precipitated carbonate mud at a shallow depth below the mat surface. Laminated chert was produced by the secondary silicification of microbial mats. Clotted dolostone and chert grew as cauliform, cryptically laminated mounds similar to younger thrombolites and was likely formed by a combination of mat growth and patchy precipitation of early-formed carbonate. Stromatolite clast breccias formed as lag deposits filling erosional scours and interstromatolite spaces. Cavities were filled by microquartz, Mn-rich dolomite, and partially dolomitized calcite. Overall, we concluded that μXRF is effective for inferring genetic processes and identifying biosignatures in compositionally heterogeneous rocks. Key Words: Stromatolites-Biosignatures-Spectroscopy-Archean. Astrobiology 17, 1161-1172.

  11. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  12. Evolvable mathematical models: A new artificial Intelligence paradigm

    Science.gov (United States)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  13. Comparing Neural Networks and ARMA Models in Artificial Stock Market

    Czech Academy of Sciences Publication Activity Database

    Krtek, Jiří; Vošvrda, Miloslav

    2011-01-01

    Roč. 18, č. 28 (2011), s. 53-65 ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

  14. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Geem, Zong Woo

    2011-01-01

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  15. Modeling of Reservoir Inflow for Hydropower Dams Using Artificial ...

    African Journals Online (AJOL)

    The stream flow at the three hydropower reservoirs in Nigeria were modeled using hydro-meteorological parameters and Artificial Neural Network (ANN). The model revealed positive relationship between the observed and the modeled reservoir inflow with values of correlation coefficient of 0.57, 0.84 and 0.92 for Kainji, ...

  16. Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

    2003-01-01

    Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

  17. Teaching methodology for modeling reference evapotranspiration with artificial neural networks

    OpenAIRE

    Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

    2015-01-01

    [EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

  18. Standard representation and unified stability analysis for dynamic artificial neural network models.

    Science.gov (United States)

    Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

    2018-02-01

    An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

  19. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  20. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  1. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  2. Microbialites and global environmental change across the Permian-Triassic boundary: a synthesis.

    Science.gov (United States)

    Kershaw, S; Crasquin, S; Li, Y; Collin, P-Y; Forel, M-B; Mu, X; Baud, A; Wang, Y; Xie, S; Maurer, F; Guo, L

    2012-01-01

    Permian-Triassic boundary microbialites (PTBMs) are thin (0.05-15 m) carbonates formed after the end-Permian mass extinction. They comprise Renalcis-group calcimicrobes, microbially mediated micrite, presumed inorganic micrite, calcite cement (some may be microbially influenced) and shelly faunas. PTBMs are abundant in low-latitude shallow-marine carbonate shelves in central Tethyan continents but are rare in higher latitudes, likely inhibited by clastic supply on Pangaea margins. PTBMs occupied broadly similar environments to Late Permian reefs in Tethys, but extended into deeper waters. Late Permian reefs are also rich in microbes (and cements), so post-extinction seawater carbonate saturation was likely similar to the Late Permian. However, PTBMs lack widespread abundant inorganic carbonate cement fans, so a previous interpretation that anoxic bicarbonate-rich water upwelled to rapidly increase carbonate saturation of shallow seawater, post-extinction, is problematic. Preliminary pyrite framboid evidence shows anoxia in PTBM facies, but interbedded shelly faunas indicate oxygenated water, perhaps there was short-term pulsing of normally saturated anoxic water from the oxygen-minimum zone to surface waters. In Tethys, PTBMs show geographic variations: (i) in south China, PTBMs are mostly thrombolites in open shelf settings, largely recrystallised, with remnant structure of Renalcis-group calcimicrobes; (ii) in south Turkey, in shallow waters, stromatolites and thrombolites, lacking calcimicrobes, are interbedded, likely depth-controlled; and (iii) in the Middle East, especially Iran, stromatolites and thrombolites (calcimicrobes uncommon) occur in different sites on open shelves, where controls are unclear. Thus, PTBMs were under more complex control than previously portrayed, with local facies control playing a significant role in their structure and composition. © 2011 Blackwell Publishing Ltd.

  3. Position control of a single pneumatic artificial muscle with hysteresis compensation based on modified Prandtl-Ishlinskii model.

    Science.gov (United States)

    Zang, Xizhe; Liu, Yixiang; Heng, Shuai; Lin, Zhenkun; Zhao, Jie

    2017-01-01

    High-performance position control of pneumatic artificial muscles is limited by their inherent nonlinearity and hysteresis. This study aims to model the length/pressure hysteresis of a single pneumatic artificial muscle and to realize its accurate position tracking control with forward hysteresis compensation. The classical Prandtl-Ishlinskii model is widely used in hysteresis modelling and compensation. But it is only effective for symmetric hysteresis. Therefore, a modified Prandtl-Ishlinskii model is built to characterize the asymmetric length/pressure hysteresis of a single pneumatic artificial muscle, by replacing the classical play operators with two more flexible elementary operators to independently describe the ascending branch and descending branch of hysteresis loops. On the basis, a position tracking controller, which is composed of cascade forward hysteresis compensation and simple proportional pressure controller, is designed for the pneumatic artificial muscle. Experiment results show that the MPI model can reproduce the length/pressure hysteresis of the pneumatic artificial muscle, and the proposed controller for the pneumatic artificial muscle can track the reference position signals with high accuracy. By modelling the length/pressure hysteresis with the modified Prandtl-Ishlinskii model and using its inversion for compensation, precise position control of a single pneumatic artificial muscle is achieved.

  4. Biofilm exopolymers control microbialite formation at thermal springs discharging into the alkaline Pyramid Lake, Nevada, USA

    Science.gov (United States)

    Arp, Gernot; Thiel, Volker; Reimer, Andreas; Michaelis, Walter; Reitner, Joachim

    1999-07-01

    Calcium carbonate precipitation and microbialite formation at highly supersaturated mixing zones of thermal spring waters and alkaline lake water have been investigated at Pyramid Lake, Nevada. Without precipitation, pure mixing should lead to a nearly 100-fold supersaturation at 40°C. Physicochemical precipitation is modified or even inhibited by the properties of biofilms, dependent on the extent of biofilm development and the current precipitation rate. Mucus substances (extracellular polymeric substances, EPS, e.g., of cyanobacteria) serve as effective Ca 2+-buffers, thus preventing seed crystal nucleation even in a highly supersaturated macroenvironment. Carbonate is then preferentially precipitated in mucus-free areas such as empty diatom tests or voids. After the buffer capacity of the EPS is surpassed, precipitation is observed at the margins of mucus areas. Hydrocarbon biomarkers extracted from (1) a calcifying Phormidium-biofilm, (2) the stromatolitic carbonate below, and (3) a fossil `tufa' of the Pleistocene pinnacles, indicate that the cyanobacterial primary producers have been subject to significant temporal changes in their species distribution. Accordingly, the species composition of cyanobacterial biofilms does not appear to be relevant for the formation of microbial carbonates in Pyramid Lake. The results demonstrate the crucial influence of mucus substances on carbonate precipitation in highly supersaturated natural environments.

  5. Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

    OpenAIRE

    Jun Wang; Song Gao; Shimin Zhao; Guang Hu; Xiaoli Zhang; Guowang Xie

    2018-01-01

    Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows tha...

  6. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  7. A self-taught artificial agent for multi-physics computational model personalization.

    Science.gov (United States)

    Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

    2016-12-01

    Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

  8. A photoactivated artificial muscle model unit: reversible, photoinduced sliding of nanosheets.

    Science.gov (United States)

    Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Shimada, Tetsuya; Takagi, Shinsuke; Tachibana, Hiroshi; Masui, Dai; Tong, Zhiwei; Inoue, Haruo

    2011-11-02

    A novel photoactivated artificial muscle model unit is reported. Here we show that organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale and the interlayer spaces in the layered hybrid structure shrink and expand vertically by photoirradiation. The sliding movement of the system on a giant scale is the first example of an artificial muscle model unit having much similarity with that in natural muscle fibrils. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) relative to the molecular size of ~1 nm by means of a reversible sliding mechanism.

  9. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  10. Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

    Science.gov (United States)

    2008-09-01

    The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

  11. Artificial neural network models for biomass gasification in fluidized bed gasifiers

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles

    2013-01-01

    Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...

  12. A classical Master equation approach to modeling an artificial protein motor

    International Nuclear Information System (INIS)

    Kuwada, Nathan J.; Blab, Gerhard A.; Linke, Heiner

    2010-01-01

    Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

  13. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

    2009-01-01

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  14. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  15. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  16. Modelling of artificial radioactivity migration in environment: a survey

    International Nuclear Information System (INIS)

    Bignoli, G.; Bertozzi, G.

    1979-01-01

    The aim of this report is to present a compilation and description of models to assess the environmental behaviour and effects of accidental and routine releases of artificial radioactivity from nuclear power facilities. About 60 models are described and a card is given for each one, to indicate in summarized form its features and data content. This collection is intended to help in developing specific personal models by assembling different parts chosen among the most suitable ones of different models of various degrees of sophistication

  17. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  18. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  19. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Federico Nuñez-Piña

    2018-01-01

    Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

  20. Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2018-01-01

    Full Text Available Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows that the networking structure of artificial spider web is better than that of traditional networks in terms of improving the overall reliability and invulnerability of communication system. A comprehensive study on the advantage characteristics of spider web has important theoretical and practical significance for promoting the invulnerability research of farmland wireless sensor network.

  1. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  2. Evaluation of articulation simulation system using artificial maxillectomy models.

    Science.gov (United States)

    Elbashti, M E; Hattori, M; Sumita, Y I; Taniguchi, H

    2015-09-01

    Acoustic evaluation is valuable for guiding the treatment of maxillofacial defects and determining the effectiveness of rehabilitation with an obturator prosthesis. Model simulations are important in terms of pre-surgical planning and pre- and post-operative speech function. This study aimed to evaluate the acoustic characteristics of voice generated by an articulation simulation system using a vocal tract model with or without artificial maxillectomy defects. More specifically, we aimed to establish a speech simulation system for maxillectomy defect models that both surgeons and maxillofacial prosthodontists can use in guiding treatment planning. Artificially simulated maxillectomy defects were prepared according to Aramany's classification (Classes I-VI) in a three-dimensional vocal tract plaster model of a subject uttering the vowel /a/. Formant and nasalance acoustic data were analysed using Computerized Speech Lab and the Nasometer, respectively. Formants and nasalance of simulated /a/ sounds were successfully detected and analysed. Values of Formants 1 and 2 for the non-defect model were 675.43 and 976.64 Hz, respectively. Median values of Formants 1 and 2 for the defect models were 634.36 and 1026.84 Hz, respectively. Nasalance was 11% in the non-defect model, whereas median nasalance was 28% in the defect models. The results suggest that an articulation simulation system can be used to help surgeons and maxillofacial prosthodontists to plan post-surgical defects that will be facilitate maxillofacial rehabilitation. © 2015 John Wiley & Sons Ltd.

  3. Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

    Science.gov (United States)

    González-Álvarez, Julio; Cervera-Crespo, Teresa

    2018-01-01

    Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

  4. Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

    Directory of Open Access Journals (Sweden)

    Piteľ Ján

    2016-01-01

    Full Text Available For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different approximations of static characteristics of artificial muscle were used.

  5. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  6. Pneumatic Artificial Muscle Actuation and Modeling

    Science.gov (United States)

    Leephakpreeda, Thananchai; Wickramatunge, Kanchana C.

    2009-10-01

    A Pneumatic Artificial Muscle (PAM) yields a natural muscle-like actuator with a high force to weight ratio, a soft and flexible structure, and adaptable compliance for a humanoid robot, rehabilitation and prosthetic appliances to the disabled, etc. To obtain optimum design and usage, the mechanical behavior of the PAM need to be understood. In this study, observations of experimental results reveal an empirical model for relations of physical variables, contraction and air pressure within the PAM, as compared to mechanical characteristics, such as stiffness or/and pulling forces of the PAM available now in market.

  7. A review on integration of artificial intelligence into water quality modelling.

    Science.gov (United States)

    Chau, Kwok-wing

    2006-07-01

    With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

  8. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk [Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Choi, Yong Suk; Hwang, Eui Hwan [Department of Oral and Maxillofacial Radiology, Institute of Oral Biology, School of Dentistry, Kyung Hee University, Seoul (Korea, Republic of)

    2009-03-15

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 {mu}m and the range of lesional area was 36.95-138.52 mm{sup 2}. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  9. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    International Nuclear Information System (INIS)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk; Choi, Yong Suk; Hwang, Eui Hwan

    2009-01-01

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 μm and the range of lesional area was 36.95-138.52 mm 2 . The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  10. Transport modeling of sorbing tracers in artificial fractures

    International Nuclear Information System (INIS)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo.

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs

  11. Transport modeling of sorbing tracers in artificial fractures

    Energy Technology Data Exchange (ETDEWEB)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs.

  12. Artificial life models in lung CTS

    International Nuclear Information System (INIS)

    Sorin, Cheran

    2006-01-01

    A new method for the analysis of 3D medical images is introduced. The algorithm is based on Biological Models of ants known as Artificial Life models. Test images (lung Computed Tomographies) undergo a 3D region growing procedure for the identification of the ribs cage. Active Contour Models (snakes) are used in order to build a confined area where ants are deployed. The ant-based approach, in which steps are allowed in any direction with different probabilities, allows a kind of tunneling effect for the successful identification of small 3D structures that are not clearly connected to the rest of the tree. The best approach is based on a gradient rule for the release of pheromone. A possible application, as part of a Computer Assisted Detection system for the identification of lung nodules, is the removal of the bronchial and vascular tree from lung CTs thus reducing the number of false positives a Nodule Hunter might report. (Full Text)

  13. Artificial neural network modelling of a large-scale wastewater treatment plant operation.

    Science.gov (United States)

    Güçlü, Dünyamin; Dursun, Sükrü

    2010-11-01

    Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

  14. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  15. MODELLING OF THIN LAYER DRYING KINETICS OF COCOA BEANS DURING ARTIFICIAL AND NATURAL DRYING

    Directory of Open Access Journals (Sweden)

    C.L. HII

    2008-04-01

    Full Text Available Drying experiments were conducted using air-ventilated oven and sun dryer to simulate the artificial and natural drying processes of cocoa beans. The drying data were fitted with several published thin layer drying models. A new model was introduced which is a combination of the Page and two-term drying model. Selection of the best model was investigated by comparing the determination of coefficient (R2, reduced chi-square (2 and root mean square error (RMSE between the experimental and predicted values. The results showed that the new model was found best described the artificial and natural drying kinetics of cocoa under the conditions tested.

  16. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  17. Artificial intelligence and exponential technologies business models evolution and new investment opportunities

    CERN Document Server

    Corea, Francesco

    2017-01-01

    Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

  18. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-03

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  19. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  20. Super capacitor modeling with artificial neural network (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)

    2004-07-01

    This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)

  1. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  2. EMG analysis and modelling of flat bench press using artificial ...

    African Journals Online (AJOL)

    The objective of this study was to evaluate the contribution of particular muscle groups during the Flat Bench Press (FBP) with different external loads. Additionally, the authors attempted to determine whether regression models or Artificial Neural Networks (ANNs) can predict FBP results more precisely and whether they can ...

  3. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  4. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

    International Nuclear Information System (INIS)

    Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

    2010-01-01

    Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

  5. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  6. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  7. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Zahra Pezeshki

    2016-02-01

    Full Text Available Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  8. Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

    Science.gov (United States)

    Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

    2017-09-01

    Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

  9. Calcium dynamics in microbialite-forming exopolymer-rich mats on the atoll of Kiritimati, Republic of Kiribati, Central Pacific.

    Science.gov (United States)

    Ionescu, D; Spitzer, S; Reimer, A; Schneider, D; Daniel, R; Reitner, J; de Beer, D; Arp, G

    2015-03-01

    Microbialite-forming microbial mats in a hypersaline lake on the atoll of Kiritimati were investigated with respect to microgradients, bulk water chemistry, and microbial community composition. O2, H2S, and pH microgradients show patterns as commonly observed for phototrophic mats with cyanobacteria-dominated primary production in upper layers, an intermediate purple layer with sulfide oxidation, and anaerobic bottom layers with sulfate reduction. Ca(2+) profiles, however, measured in daylight showed an increase of Ca(2+) with depth in the oxic zone, followed by a sharp decline and low concentrations in anaerobic mat layers. In contrast, dark measurements show a constant Ca(2+) concentration throughout the entire measured depth. This is explained by an oxygen-dependent heterotrophic decomposition of Ca(2+)-binding exopolymers. Strikingly, the daylight maximum in Ca(2+) and subsequent drop coincides with a major zone of aragonite and gypsum precipitation at the transition from the cyanobacterial layer to the purple sulfur bacterial layer. Therefore, we suggest that Ca(2+) binding exopolymers function as Ca(2+) shuttle by their passive downward transport through compression, triggering aragonite precipitation in the mats upon their aerobic microbial decomposition and secondary Ca(2+) release. This precipitation is mediated by phototrophic sulfide oxidizers whose action additionally leads to the precipitation of part of the available Ca(2+) as gypsum. © 2014 John Wiley & Sons Ltd.

  10. 3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

    Science.gov (United States)

    Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

    2014-09-10

    The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

  11. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  12. Artificial intelligence and finite element modelling for monitoring flood defence structures

    NARCIS (Netherlands)

    Pyayt, A.L.; Mokhov, I.I.; Kozionov, A.; Kusherbaeva, V.; Melnikova, N.B.; Krzhizhanovskaya, V.V.; Meijer, R.J.

    2011-01-01

    We present a hybrid approach to monitoring the stability of flood defence structures equipped with sensors. This approach combines the finite element modelling with the artificial intelligence for real-time signal processing and anomaly detection. This combined method has been developed for the

  13. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  14. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

    2014-01-01

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  15. A Model of Artificial Genotype and Norm of Reaction in a Robotic System

    OpenAIRE

    Durán Bosch, Ángel Juan; Pascual del Pobil Ferré, Ángel

    2016-01-01

    The genes of living organisms serve as large stores of information for replicating their behavior and morphology over generations. The evolutionary view of genetics that has inspired artificial systems with a Mendelian approach does not take into account the interaction between species and with the environment to generate a particular phenotype. In this paper, a genotype model is suggested to shape the relationship with the phenotype and the environment in an artificial system. A method to ob...

  16. Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

    NARCIS (Netherlands)

    Paudel, S.; Elmtiri, M.; Kling, W.L.; Corre, le O.; Lacarriere, B.

    2014-01-01

    This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider

  17. Artificial organ engineering

    CERN Document Server

    Annesini, Maria Cristina; Piemonte, Vincenzo; Turchetti, Luca

    2017-01-01

    Artificial organs may be considered as small-scale process plants, in which heat, mass and momentum transfer operations and, possibly, chemical transformations are carried out. This book proposes a novel analysis of artificial organs based on the typical bottom-up approach used in process engineering. Starting from a description of the fundamental physico-chemical phenomena involved in the process, the whole system is rebuilt as an interconnected ensemble of elemental unit operations. Each artificial organ is presented with a short introduction provided by expert clinicians. Devices commonly used in clinical practice are reviewed and their performance is assessed and compared by using a mathematical model based approach. Whilst mathematical modelling is a fundamental tool for quantitative descriptions of clinical devices, models are kept simple to remain focused on the essential features of each process. Postgraduate students and researchers in the field of chemical and biomedical engineering will find that t...

  18. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  19. Unsteady flamelet modelling of spray flames using deep artificial neural networks

    Science.gov (United States)

    Owoyele, Opeoluwa; Kundu, Prithwish; Ameen, Muhsin; Echekki, Tarek; Som, Sibendu

    2017-11-01

    We investigate the applicability of the tabulated, multidimensional unsteady flamelet model and artificial neural networks (TFM-ANN) to lifted diesel spray flame simulations. The tabulated flamelet model (TFM), based on the widely known flamelet assumption, eliminates the use of a progress variable and has been shown to successfully model global diesel spray flame characteristics in previous studies. While the TFM has shown speed-up compared to other models and predictive capabilities across a range of ambient conditions, it involves the storage of multidimensional tables, requiring large memory and multidimensional interpolation schemes. This work discusses the implementation of deep artificial neural networks (ANN) to replace the use of large tables and multidimensional interpolation. The proposed framework is validated by applying it to an n-dodecane spray flame (ECN Spray A) at different conditions using a 4 dimensional flamelet library. The validations are then extended for the simulations using a 5-dimensional flamelet table applied to the combustion of methyl decanoate in a compression ignition engine. Different ANN topologies, optimization algorithms and speed-up techniques are explored and details of computational resources required for TFM-ANN and the TFM are also presented. The overall tools and algorithms used in this study can be directly extended to other multidimensional tabulated models.

  20. Recent development in artificial photosynthetic model; Jinko kogosei no moderu ka kenkyu saikin no shinpo

    Energy Technology Data Exchange (ETDEWEB)

    Kaneko, M [Ibaraki Univ., Ibaraki (Japan). Faculty of Engineering

    1996-03-01

    In the conversion from solar energy into chemical energy (fuels) by photochemical conversion, an electron donor is necessary since all the fuels are reductive compounds. From the viewpoint of economic profit, water is the only one candidate as a cheap compound and existing impartially. In this paper, photosynthesis as well as the realization of its artificial model, and the relevant basic research executed recently aiming at the construction of an artificial photosynthetic system are explained. The main reaction of photosynthesis is the generation of carbohydrates by the reduction reaction of carbon dioxide with water as an electron donor and solar visual light as an energy resource. As a special example thereof, the UV photolysis of water due to the photocatalysis of a micro-particle system is introduced. The method of using a semiconductor and the method of using sensitizes are described as the photo excitation system when designing the artificial model. Additionally, as the research with respect to the construction of an artificial photosynthetic system, a photo-exciting charge transfer system is introduced. 27 refs., 1 fig.

  1. Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

    Science.gov (United States)

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

  2. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    Science.gov (United States)

    Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods. PMID:24883365

  3. Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

    Energy Technology Data Exchange (ETDEWEB)

    Saffer, Shelley (Sam) I.

    2014-12-01

    This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

  4. Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

    Science.gov (United States)

    Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

    2018-03-01

    The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

  5. Development of surrogate models using artificial neural network for building shell energy labelling

    NARCIS (Netherlands)

    Melo, A.P.; Costola, D.; Lamberts, R.; Hensen, J.L.M.

    2014-01-01

    Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of

  6. A review on the integration of artificial intelligence into coastal modeling.

    Science.gov (United States)

    Chau, Kwokwing

    2006-07-01

    With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

  7. Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

    Science.gov (United States)

    Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

    2016-08-01

    Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

  8. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

    1998-01-01

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  9. Soft computing in artificial intelligence

    CERN Document Server

    Matson, Eric

    2014-01-01

    This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...

  10. Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Sara Martina Maffioletti

    2018-04-01

    Full Text Available Summary: Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. : Maffioletti et al. generate human 3D artificial skeletal muscles from healthy donors and patient-specific pluripotent stem cells. These human artificial muscles accurately model severe genetic muscle diseases. They can be engineered to include other cell types present in skeletal muscle, such as vascular cells and motor neurons. Keywords: skeletal muscle, pluripotent stem cells, iPS cells, myogenic differentiation, tissue engineering, disease modeling, muscular dystrophy, organoids

  11. Intelligence: Real or artificial?

    OpenAIRE

    Schlinger, Henry D.

    1992-01-01

    Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally r...

  12. Towards artificial tissue models: past, present, and future of 3D bioprinting.

    Science.gov (United States)

    Arslan-Yildiz, Ahu; El Assal, Rami; Chen, Pu; Guven, Sinan; Inci, Fatih; Demirci, Utkan

    2016-03-01

    Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research.

  13. Perception of Sexual Orientation from Facial Structure: A Study with Artificial Face Models.

    Science.gov (United States)

    González-Álvarez, Julio

    2017-07-01

    Research has shown that lay people can perceive sexual orientation better than chance from face stimuli. However, the relation between facial structure and sexual orientation has been scarcely examined. Recently, an extensive morphometric study on a large sample of Canadian people (Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015) identified three (in men) and four (in women) facial features as unique multivariate predictors of sexual orientation in each sex group. The present study tested the perceptual validity of these facial traits with two experiments based on realistic artificial 3D face models created by manipulating the key parameters and presented to Spanish participants. Experiment 1 included 200 White and Black face models of both sexes. The results showed an overall accuracy (0.74) clearly above chance in a binary hetero/homosexual judgment task and significant differences depending on the race and sex of the face models. Experiment 2 produced five versions of 24 artificial faces of both sexes varying the key parameters in equal steps, and participants had to rate on a 1-7 scale how likely they thought that the depicted person had a homosexual sexual orientation. Rating scores displayed an almost perfect linear regression as a function of the parameter steps. In summary, both experiments demonstrated the perceptual validity of the seven multivariate predictors identified by Skorska et al. and open up new avenues for further research on this issue with artificial face models.

  14. An artificial pancreas provided a novel model of blood glucose level variability in beagles.

    Science.gov (United States)

    Munekage, Masaya; Yatabe, Tomoaki; Kitagawa, Hiroyuki; Takezaki, Yuka; Tamura, Takahiko; Namikawa, Tsutomu; Hanazaki, Kazuhiro

    2015-12-01

    Although the effects on prognosis of blood glucose level variability have gained increasing attention, it is unclear whether blood glucose level variability itself or the manifestation of pathological conditions that worsen prognosis. Then, previous reports have not been published on variability models of perioperative blood glucose levels. The aim of this study is to establish a novel variability model of blood glucose concentration using an artificial pancreas. We maintained six healthy, male beagles. After anesthesia induction, a 20-G venous catheter was inserted in the right femoral vein and an artificial pancreas (STG-22, Nikkiso Co. Ltd., Tokyo, Japan) was connected for continuous blood glucose monitoring and glucose management. After achieving muscle relaxation, total pancreatectomy was performed. After 1 h of stabilization, automatic blood glucose control was initiated using the artificial pancreas. Blood glucose level varied for 8 h, alternating between the target blood glucose values of 170 and 70 mg/dL. Eight hours later, the experiment was concluded. Total pancreatectomy was performed for 62 ± 13 min. Blood glucose swings were achieved 9.8 ± 2.3 times. The average blood glucose level was 128.1 ± 5.1 mg/dL with an SD of 44.6 ± 3.9 mg/dL. The potassium levels after stabilization and at the end of the experiment were 3.5 ± 0.3 and 3.1 ± 0.5 mmol/L, respectively. In conclusion, the results of the present study demonstrated that an artificial pancreas contributed to the establishment of a novel variability model of blood glucose levels in beagles.

  15. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    OpenAIRE

    Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

    2007-01-01

    Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

  16. A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

    DEFF Research Database (Denmark)

    Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

    2017-01-01

    in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns...

  17. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Artificial neural network models for prediction of intestinal permeability of oligopeptides

    Directory of Open Access Journals (Sweden)

    Kim Min-Kook

    2007-07-01

    Full Text Available Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC curve (the ROC score. The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

  19. A Quantum Implementation Model for Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ammar Daskin

    2018-02-01

    Full Text Available The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase estimation algorithm is known to provide speedups over the conventional algorithms for the eigenvalue-related problems. Combining the quantum amplitude amplification with the phase estimation algorithm, a quantum implementation model for artificial neural networks using the Widrow–Hoff learning rule is presented. The complexity of the model is found to be linear in the size of the weight matrix. This provides a quadratic improvement over the classical algorithms. Quanta 2018; 7: 7–18.

  20. Artificial Systems and Models for Risk Covering Operations

    Directory of Open Access Journals (Sweden)

    Laurenţiu Mihai Treapăt

    2017-04-01

    Full Text Available Mainly, this paper focuses on the roles of artificial intelligence based systems and especially on risk-covering operations. In this context, the paper comes with theoretical explanations on real-life based examples and applications. From a general perspective, the paper enriches its value with a wide discussion on the related subject. The paper aims to revise the volatilities’ estimation models and the correlations between the various time series and also by presenting the Risk Metrics methodology, as explained is a case study. The advantages that the VaR estimation offers, consist of its ability to quantitatively and numerically express the risk level of a portfolio, at a certain moment in time and also the risk of on open position (in titles, in FX, commodities or granted loans, belonging to an economic agent or even individual; hence, its role in a more efficient capital allocation, in the assumed risk delimitation, and also as a performance measurement instrument. In this paper and the study case that completes our work, we aim to prove how we can prevent considerable losses and even bankruptcies if VaR is known and applied accordingly. For this reason, the universities inRomaniashould include or increase their curricula with the study of the VaR model as an artificial intelligence tool. The simplicity of the presented case study, most probably, is the strongest argument of the current work because it can be understood also by the readers that are not necessarily very experienced in the risk management field.

  1. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.

  2. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  3. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  4. Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

    Science.gov (United States)

    Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

    2004-02-01

    To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal

  5. Variable recruitment fluidic artificial muscles: modeling and experiments

    International Nuclear Information System (INIS)

    Bryant, Matthew; Meller, Michael A; Garcia, Ephrahim

    2014-01-01

    We investigate taking advantage of the lightweight, compliant nature of fluidic artificial muscles to create variable recruitment actuators in the form of artificial muscle bundles. Several actuator elements at different diameter scales are packaged to act as a single actuator device. The actuator elements of the bundle can be connected to the fluidic control circuit so that different groups of actuator elements, much like individual muscle fibers, can be activated independently depending on the required force output and motion. This novel actuation concept allows us to save energy by effectively impedance matching the active size of the actuators on the fly based on the instantaneous required load. This design also allows a single bundled actuator to operate in substantially different force regimes, which could be valuable for robots that need to perform a wide variety of tasks and interact safely with humans. This paper proposes, models and analyzes the actuation efficiency of this actuator concept. The analysis shows that variable recruitment operation can create an actuator that reduces throttling valve losses to operate more efficiently over a broader range of its force–strain operating space. We also present preliminary results of the design, fabrication and experimental characterization of three such bioinspired variable recruitment actuator prototypes. (paper)

  6. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J

    1998-01-01

    kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained...

  7. Ground Motion Prediction Model Using Artificial Neural Network

    Science.gov (United States)

    Dhanya, J.; Raghukanth, S. T. G.

    2018-03-01

    This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

  8. A Developed Artificial Bee Colony Algorithm Based on Cloud Model

    Directory of Open Access Journals (Sweden)

    Ye Jin

    2018-04-01

    Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

  9. Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

    Science.gov (United States)

    Shao, Q; Rowe, R C; York, P

    2007-06-01

    This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

  10. Artificial neural network models' application for radioactive substances' migration forecasting in soil

    International Nuclear Information System (INIS)

    Kovalenko, V.I.; Khil'ko, O.S.; Kundas, S.P.

    2009-01-01

    The work is indicated to the use of artificial neural network (ANN) models in program complex SPS for radioactive substances' migration forecasting in soil. For the problem solution two ANN models are used. One of them forecasts radioactive substances' migration, another carries out forecasting of physical and chemical soil properties. Program complex SPS allows to achieve a low error of forecasting (no more than 5 %) and high training speed. (authors)

  11. Artificial Intelligence (AI) Studies in Water Resources

    OpenAIRE

    Ay, Murat; Özyıldırım, Serhat

    2018-01-01

    Artificial intelligence has been extensively used in many areas such as computer science,robotics, engineering, medicine, translation, economics, business, and psychology. Variousstudies in the literature show that the artificial intelligence in modeling approaches give closeresults to the real data for solution of linear, non-linear, and other systems. In this study, wereviewed the current state-of-the-art and progress on the modelling of artificial intelligence forwater variables: rainfall-...

  12. A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

    Science.gov (United States)

    Barrett, Anthony M.; Baum, Seth D.

    2017-03-01

    An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

  13. Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

    Science.gov (United States)

    Arif, N.; Danoedoro, P.; Hartono

    2017-12-01

    Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

  14. Narrative theories as computational models: reader-oriented theory and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Galloway, P.

    1983-12-01

    In view of the rapid development of reader-oriented theory and its interest in dynamic models of narrative, the author speculates in a serious way about what such models might look like in computational terms. Researchers in artificial intelligence (AI) have already begun to develop models of story understanding as the emphasis in ai research has shifted toward natural language understanding and as ai has allied itself with cognitive psychology and linguistics to become cognitive science. Research in ai and in narrative theory share many common interests and problems and both studies might benefit from an exchange of ideas. 11 references.

  15. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  16. Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

    Directory of Open Access Journals (Sweden)

    Tolga Kaya

    2010-11-01

    Full Text Available The purpose of this study is to compare the performances of Artificial Neural Networks (ANN and Multinomial Probit (MNP approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

  17. Development of Urban Inundation Warning Model at Cyclic Artificial Water Way in Song-do International City, Republic of Korea

    Science.gov (United States)

    Lee, T.; Lee, C.; Kim, H.

    2016-12-01

    Abstract Song-do international city was constructed by reclaiming land from the coastal waters of Yeonsu-gu, Incheon Metropolitan City, Republic of Korea. The □-shaped cyclic artificial water way has been considered for improving water quality, waterfront and internal drainage in Song-do international city. By improving water quality, various marine facilities, such as marina, artificial beach, marine terminal, and so on, will be set up around the artificial water way for the waterfront. Since the water stage of the artificial water way changes depending on water gates operations, it is necessary to develop an urban inundation warning model to evaluate safeties of the waterfront facilities and its passengers. By considering characteristics of urban watershed, we calculate discharge flowing into the water way using XP-SWMM model. As a result of estimating 100-year flood frequency, although there are slight differences in drainage sections, the maximum flood discharge occurs in 90-min rainfall duration. In order to consider impacts of tide and hydraulic structure, we establish Inland drainage plans through the analysis of unsteady flow using HEC-RAS. The urban inundation warning model is configured to issue a warning when the water plain elevation exceeds EL. 1.5m which is usually managed at EL. 1.0m. In this study, the design flood stage of artificial water way and urban inundation warning model are developed for Song-do international city, and therefore it is expected that a reliability of management and operation of the waterfront facilities is improved. Keywords : Artificial Water Way; Waterfront; Urban Inundation Warning Model. Acknowlegement This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

  18. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

    Directory of Open Access Journals (Sweden)

    Changqing Meng

    2016-09-01

    Full Text Available A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC model with artificial neural networks (ANNs. In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation. The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.

  19. Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

    Science.gov (United States)

    Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

    2016-01-01

    Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

  20. A DISTRIBUTED SMART HOME ARTIFICIAL INTELLIGENCE SYSTEM

    DEFF Research Database (Denmark)

    Lynggaard, Per

    2013-01-01

    A majority of the research performed today explore artificial intelligence in smart homes by using a centralized approach where a smart home server performs the necessary calculations. This approach has some disadvantages that can be overcome by shifting focus to a distributed approach where...... the artificial intelligence system is implemented as distributed as agents running parts of the artificial intelligence system. This paper presents a distributed smart home architecture that distributes artificial intelligence in smart homes and discusses the pros and cons of such a concept. The presented...... distributed model is a layered model. Each layer offers a different complexity level of the embedded distributed artificial intelligence. At the lowest layer smart objects exists, they are small cheap embedded microcontroller based smart devices that are powered by batteries. The next layer contains a more...

  1. Development and validation of deterioration models for concrete bridge decks - phase 1 : artificial intelligence models and bridge management system.

    Science.gov (United States)

    2013-06-01

    This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge invento...

  2. Spatially Resolved Artificial Chemistry

    DEFF Research Database (Denmark)

    Fellermann, Harold

    2009-01-01

    Although spatial structures can play a crucial role in chemical systems and can drastically alter the outcome of reactions, the traditional framework of artificial chemistry is a well-stirred tank reactor with no spatial representation in mind. Advanced method development in physical chemistry has...... made a class of models accessible to the realms of artificial chemistry that represent reacting molecules in a coarse-grained fashion in continuous space. This chapter introduces the mathematical models of Brownian dynamics (BD) and dissipative particle dynamics (DPD) for molecular motion and reaction...

  3. Spatially Resolved Artificial Chemistry

    DEFF Research Database (Denmark)

    Fellermann, Harold

    2009-01-01

    made a class of models accessible to the realms of artificial chemistry that represent reacting molecules in a coarse-grained fashion in continuous space. This chapter introduces the mathematical models of Brownian dynamics (BD) and dissipative particle dynamics (DPD) for molecular motion and reaction......Although spatial structures can play a crucial role in chemical systems and can drastically alter the outcome of reactions, the traditional framework of artificial chemistry is a well-stirred tank reactor with no spatial representation in mind. Advanced method development in physical chemistry has...

  4. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Ramaiah, Gurumurthy B.; Chennaiah, Radhalakshmi Y.; Satyanarayanarao, Gurumurthy K.

    2010-01-01

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  5. Artificial Neural Network Modelling of the Energy Content of Municipal Solid Wastes in Northern Nigeria

    Directory of Open Access Journals (Sweden)

    M. B. Oumarou

    2017-12-01

    Full Text Available The study presents an application of the artificial neural network model using the back propagation learning algorithm to predict the actual calorific value of the municipal solid waste in major cities of the northern part of Nigeria, with high population densities and intense industrial activities. These cities are: Kano, Damaturu, Dutse, Bauchi, Birnin Kebbi, Gusau, Maiduguri, Katsina and Sokoto. Experimental data of the energy content and the physical characterization of the municipal solid waste serve as the input parameter in nature of wood, grass, metal, plastic, food remnants, leaves, glass and paper. Comparative studies were made by using the developed model, the experimental results and a correlation which was earlier developed by the authors to predict the energy content. While predicting the actual calorific value, the maximum error was 0.94% for the artificial neural network model and 5.20% by the statistical correlation. The network with eight neurons and an R2 = 0.96881 in the hidden layer results in a stable and optimum network. This study showed that the artificial neural network approach could successfully be used for energy content predictions from the municipal solid wastes in Northern Nigeria and other areas of similar waste stream and composition.

  6. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  7. On prognostic models, artificial intelligence and censored observations.

    Science.gov (United States)

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  8. Application of artificial intelligence to the management of urological cancer.

    Science.gov (United States)

    Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

    2007-10-01

    Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

  9. Prediction of pork loin quality using online computer vision system and artificial intelligence model.

    Science.gov (United States)

    Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

    2018-06-01

    The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. DEVELOPING A HUMAN CONTROLLED MODEL FOR SAFE ARTIFICIAL INTELLIGENCE SYSTEMS

    OpenAIRE

    KÖSE, Utku

    2018-01-01

    Artificial Intelligence is known as one of the most effective research field of nowadays and the future. But rapid rise of Artificial Intelligence and its potential to solve all real world problems autonomously, it has caused also several anxieties. Some scientists think that intelligent systems can reach to a level, which is dangerous for the humankind so because of that some precautions should be taken. So, many sub-research fields like Machine Ethics or Artificial Intelligence Safety have ...

  11. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  12. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  13. Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

    Science.gov (United States)

    Yousif, Adil; Elfaki, Faiz

    2017-12-01

    The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

  14. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    Full Text Available A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  15. An analysis of urban collisions using an artificial intelligence model.

    Science.gov (United States)

    Mussone, L; Ferrari, A; Oneta, M

    1999-11-01

    Traditional studies on road accidents estimate the effect of variables (such as vehicular flows, road geometry, vehicular characteristics), and the calculation of the number of accidents. A descriptive statistical analysis of the accidents (those used in the model) over the period 1992-1995 is proposed. The paper describes an alternative method based on the use of artificial neural networks (ANN) in order to work out a model that relates to the analysis of vehicular accidents in Milan. The degree of danger of urban intersections using different scenarios is quantified by the ANN model. Methodology is the first result, which allows us to tackle the modelling of urban vehicular accidents by the innovative use of ANN. Other results deal with model outputs: intersection complexity may determine a higher accident index depending on the regulation of intersection. The highest index for running over of pedestrian occurs at non-signalised intersections at night-time.

  16. Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

    Directory of Open Access Journals (Sweden)

    Beigi Mohsen

    2017-01-01

    Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

  17. Artificial muscle for reanimation of the paralyzed face: durability and biocompatibility in a gerbil model.

    Science.gov (United States)

    Ledgerwood, Levi G; Tinling, Steven; Senders, Craig; Wong-Foy, Annjoe; Prahlad, Harsha; Tollefson, Travis T

    2012-11-01

    Current management of permanent facial paralysis centers on nerve grafting and muscle transfer; however, limitations of those procedures call for other options. To determine the durability and biocompatibility of implanted artificial muscle in a gerbil model and the degree of inflammation and fibrosis at the host tissue-artificial muscle interface. Electroactive polymer artificial muscle (EPAM) devices engineered in medical-grade silicone were implanted subcutaneously in 13 gerbils. The implanted units were stimulated with 1 kV at 1 Hz, 24 h/d via a function generator. Electrical signal input/output was recorded up to 40 days after implantation. The animals were euthanized between 23 and 65 days after implantation, and the host tissue-implant interface was evaluated histologically. The animals tolerated implantation of the EPAM devices well, with no perioperative deaths. The muscle devices created motion for a mean of 30.3 days (range, 19-40 days), with a mean of 2.6 × 106 cycles (range, 1.6 × 106 to 3.5 × 106 cycles). Histologic examination of the explanted devices revealed the development of a minimal fibrous capsule surrounding the implants, with no evidence of bacterial infection or inflammatory infiltrate. No evidence of device compromise, corrosion, or silicone breakdown was noted. Artificial muscle implanted in this short-term animal model was safe and functional in this preliminary study. We believe that EPAM devices will be a safe and viable option for restoration of facial motions in patients with irreversible facial paralysis.

  18. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  19. Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

    OpenAIRE

    Piteľ Ján; Tóthová Mária

    2016-01-01

    For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different appr...

  20. What Is an Artificial Muscle? A Systemic Approach.

    OpenAIRE

    Bertrand Tondu

    2015-01-01

    Artificial muscles define a large category of actuators we propose to analyze in a systemic framework by considering any artificial muscle as an open-loop stable system for any output which represents an artificial muscle dimension resulting from its “contraction”, understood in a broad meaning. This approach makes it possible to distinguish the artificial muscle from other actuators and to specify an original model for a linear artificial muscle, according to the theory of linear systems. Su...

  1. Artificial magnetic metamaterial design by using spiral resonators

    OpenAIRE

    Baena, J.D.; Marqués Sillero, Ricardo; Medina Mena, Francisco; Martel Villagrán, Jesús

    2004-01-01

    A metallic planar particle, that will be called spiral resonator (SR), is introduced as a useful artificial atom for artificial magnetic media design and fabrication. A simple theoretical model which provides the most relevant properties and parameters of the SR is presented. The model is validated by both electromagnetic simulation and experiments. The applications of SR's include artificial negative magnetic permeability media (NMPM) and left-handed-media (LHM) design. The main advantages o...

  2. Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

    NARCIS (Netherlands)

    Rezaeianzadeh, M.; Stein, A.; Tabari, H.; Abghari, H.; Jalalkamali, N.; Hosseinipour, E.Z.; Singh, V.P.

    2013-01-01

    Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied

  3. Artificial Consciousness or Artificial Intelligence

    OpenAIRE

    Spanache Florin

    2017-01-01

    Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus a...

  4. Metagenome-based diversity analyses suggest a significant contribution of non-cyanobacterial lineages to carbonate precipitation in modern microbialites

    Directory of Open Access Journals (Sweden)

    Purificacion eLopez-Garcia

    2015-08-01

    Full Text Available Cyanobacteria are thought to play a key role in carbonate formation due to their metabolic activity, but other organisms carrying out oxygenic photosynthesis (photosynthetic eukaryotes or other metabolisms (e.g. anoxygenic photosynthesis, sulfate reduction, may also contribute to carbonate formation. To obtain more quantitative information than that provided by more classical PCR-dependent methods, we studied the microbial diversity of microbialites from the Alchichica crater lake (Mexico by mining for 16S/18S rRNA genes in metagenomes obtained by direct sequencing of environmental DNA. We studied samples collected at the Western (AL-W and Northern (AL-N shores of the lake and, at the latter site, along a depth gradient (1, 5, 10 and 15 m depth. The associated microbial communities were mainly composed of bacteria, most of which seemed heterotrophic, whereas archaea were negligible. Eukaryotes composed a relatively minor fraction dominated by photosynthetic lineages, diatoms in AL-W, influenced by Si-rich seepage waters, and green algae in AL-N samples. Members of the Gammaproteobacteria and Alphaproteobacteria classes of Proteobacteria, Cyanobacteria and Bacteroidetes were the most abundant bacterial taxa, followed by Planctomycetes, Deltaproteobacteria (Proteobacteria, Verrucomicrobia, Actinobacteria, Firmicutes and Chloroflexi. Community composition varied among sites and with depth. Although cyanobacteria were the most important bacterial group contributing to the carbonate precipitation potential, photosynthetic eukaryotes, anoxygenic photosynthesizers and sulfate reducers were also very abundant. Cyanobacteria affiliated to Pleurocapsales largely increased with depth. Scanning electron microscopy (SEM observations showed considerable areas of aragonite-encrusted Pleurocapsa-like cyanobacteria at microscale. Multivariate statistical analyses showed a strong positive correlation of Pleurocapsales and Chroococcales with aragonite formation at

  5. NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Yao Yevenyo Ziggah

    Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural network (GRNN were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

  6. STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

    Science.gov (United States)

    ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

  7. Mechanical Characterization and Numerical Modelling of Rubber Shockpads in 3G Artificial Turf

    Directory of Open Access Journals (Sweden)

    David Cole

    2018-02-01

    Full Text Available Third generation (3G artificial turf systems use in sporting applications is increasingly prolific. These multi-component systems are comprised of a range of polymeric and elastomeric materials that exhibit non-linear and strain rate dependent behaviours under the complex loads applied from players and equipment. To further study and better understand the behaviours of these systems, the development of a numerical model to accurately predict individual layers’ behaviour as well as the overall system response under different loading conditions is necessary. The purpose of this study was to characterise and model the mechanical behaviour of a rubber shockpad found in 3G artificial surfaces for vertical shock absorption using finite element analysis. A series of uniaxial compression tests were performed to characterise the mechanical behaviour of the shockpad. Compression loading was performed at 0.9 Hz to match human walking speeds. A Microfoam material model was selected from the PolyUMod library and optimised using MCalibration software before being imported into ABAQUS for analysis. A finite element model was created for the shockpad using ABAQUS and a compressive load applied to match that of the experimental data. Friction coefficients were altered to view the effect on the loading response. The accuracy of the model was compared using a series of comparative measures including the energy loss and root mean square error.

  8. Optimization of the artificial urinary sphincter: modelling and experimental validation

    International Nuclear Information System (INIS)

    Marti, Florian; Leippold, Thomas; John, Hubert; Blunschi, Nadine; Mueller, Bert

    2006-01-01

    The artificial urinary sphincter should be long enough to prevent strangulation effects of the urethral tissue and short enough to avoid the improper dissection of the surrounding tissue. To optimize the sphincter length, the empirical three-parameter urethra compression model is proposed based on the mechanical properties of the urethra: wall pressure, tissue response rim force and sphincter periphery length. In vitro studies using explanted animal or human urethras and different artificial sphincters demonstrate its applicability. The pressure of the sphincter to close the urethra is shown to be a linear function of the bladder pressure. The force to close the urethra depends on the sphincter length linearly. Human urethras display the same dependences as the urethras of pig, dog, sheep and calf. Quantitatively, however, sow urethras resemble best the human ones. For the human urethras, the mean wall pressure corresponds to (-12.6 ± 0.9) cmH 2 O and (-8.7 ± 1.1) cmH 2 O, the rim length to (3.0 ± 0.3) mm and (5.1 ± 0.3) mm and the rim force to (60 ± 20) mN and (100 ± 20) mN for urethra opening and closing, respectively. Assuming an intravesical pressure of 40 cmH 2 O, and an external pressure on the urethra of 60 cmH 2 O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm

  9. Artificial cell mimics as simplified models for the study of cell biology.

    Science.gov (United States)

    Salehi-Reyhani, Ali; Ces, Oscar; Elani, Yuval

    2017-07-01

    Living cells are hugely complex chemical systems composed of a milieu of distinct chemical species (including DNA, proteins, lipids, and metabolites) interconnected with one another through a vast web of interactions: this complexity renders the study of cell biology in a quantitative and systematic manner a difficult task. There has been an increasing drive towards the utilization of artificial cells as cell mimics to alleviate this, a development that has been aided by recent advances in artificial cell construction. Cell mimics are simplified cell-like structures, composed from the bottom-up with precisely defined and tunable compositions. They allow specific facets of cell biology to be studied in isolation, in a simplified environment where control of variables can be achieved without interference from a living and responsive cell. This mini-review outlines the core principles of this approach and surveys recent key investigations that use cell mimics to address a wide range of biological questions. It will also place the field in the context of emerging trends, discuss the associated limitations, and outline future directions of the field. Impact statement Recent years have seen an increasing drive to construct cell mimics and use them as simplified experimental models to replicate and understand biological phenomena in a well-defined and controlled system. By summarizing the advances in this burgeoning field, and using case studies as a basis for discussion on the limitations and future directions of this approach, it is hoped that this minireview will spur others in the experimental biology community to use artificial cells as simplified models with which to probe biological systems.

  10. Space Environment Modelling with the Use of Artificial Intelligence Methods

    Science.gov (United States)

    Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

    1996-12-01

    Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

  11. Growing Rocks: Implications of Lithification for Microbial Communities and Nutrient Cycling

    Science.gov (United States)

    Corman, J. R.; Poret-Peterson, A. T.; Elser, J. J.

    2014-12-01

    Lithifying microbial communities ("microbialites") have left their signature on Earth's rock record for over 3.4 billion years and are regarded as important players in paleo-biogeochemical cycles. In this project, we study extant microbialites to understand the interactions between lithification and resource availability. All microbes need nutrients and energy for growth; indeed, nutrients are often a factor limiting microbial growth. We hypothesize that calcium carbonate deposition can sequester bioavailable phosphorus (P) and expect the growth of microbialites to be P-limited. To test our hypothesis, we first compared nutrient limitation in lithifying and non-lithifying microbial communities in Río Mesquites, Cuatro Ciénegas. Then, we experimentally manipulated calcification rates in the Río Mesquites microbialites. Our results suggest that lithifying microbialites are indeed P-limited, while non-lithifying, benthic microbial communities tend towards co-limitation by nitrogen (N) and P. Indeed, in microbialites, photosynthesis and aerobic respiration responded positively to P additions (Pbacterial community composition based on analysis of 16S rRNA genes. Unexpectedly, calcification rates increased with OC additions (P<0.05), but not with P additions, suggesting that sulfate reduction may be an important pathway for calcification. Experimental reductions in calcification rates caused changes to microbial biomass OC and P concentrations (P<0.01 and P<0.001, respectively), although shifts depended on whether calcification was decreased abiotically or biotically. These results show that resource availability does influence microbialite formation and that lithification may promote phosphorus limitation; however, further investigation is required to understand the mechanism by which the later occurs.

  12. Development of a hybrid system of artificial neural networks and ...

    African Journals Online (AJOL)

    Development of a hybrid system of artificial neural networks and artificial bee colony algorithm for prediction and modeling of customer choice in the market. ... attempted to present a new method for the modeling and prediction of customer choice in the market using the combination of artificial intelligence and data mining.

  13. FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

    Directory of Open Access Journals (Sweden)

    Dinu Cristian

    2017-09-01

    Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

  14. Radioecological aspects in artificial groundwater recharge

    Energy Technology Data Exchange (ETDEWEB)

    Matthess, G [Kiel Univ. (Germany, F.R.). Geologisch-Palaeontologisches Inst. und Museum; Neumayr, V [Institut fuer Wasser-, Boden- und Lufthygiene, Frankfurt am Main (Germany, F.R.)

    1980-01-01

    In increasing extent surface waters, especially those of rivers and streams, are contaminated by radionuclides. Therefore it is necessary to investigate the possibility of impairment of the quality of artificially recharged groundwater and drinking water by radionuclides. Hazards for man are possible by drinking water, that was affected by waste and during exposition to air, as well as indirectly by irrigation water and the food chain. In a model calculation using realistic conditions the order of magnitude of these hazards for man by incorporation of radioactively contaminated artificially recharged drinking water are to be assessed. Here the parameters are discussed which must be considered in such an assessment. The model includes the use of river water for artificial recharge. All models and assessments assume the most unfavourable preconditions, which may lead to an impact to man.

  15. COMPUTER MODELING IN THE DEVELOPMENT OF ARTIFICIAL VENTRICLES OF HEART

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-01-01

    Full Text Available In article modern researches of processes of development of artificial ventricles of heart are described. Advanta- ges of application computer (CAD/CAE technologies are shown by development of artificial ventricles of heart. The systems developed with application of the given technologies are submitted. 

  16. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  17. An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

    International Nuclear Information System (INIS)

    Linares-Rodriguez, Alvaro; Ruiz-Arias, José Antonio; Pozo-Vazquez, David; Tovar-Pescador, Joaquin

    2013-01-01

    An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected – eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery. - Highlights: • Daily solar radiation data are generated using an artificial neural network ensemble. • Eleven Meteosat channels observations and a clear sky term are used as model inputs. • Model exploits all information within infrared Meteosat channels. • Measured data for a year from 83 ground stations are used. • The proposed approach has better performance than existing models on daily basis

  18. Artificial vesicles as an animal cell model for the study of biological application of non-thermal plasma

    International Nuclear Information System (INIS)

    Ki, S H; Park, J K; Sung, C; Lee, C B; Uhm, H; Choi, E H; Baik, K Y

    2016-01-01

    Artificial cell-like model systems can provide information which is hard to obtain with real biological cells. Giant unilamellar vesicles (GUV) containing intra-membrane DNA or OH radical-binding molecules are used to visualize the cytolytic activity of OH radicals. Changes in the GUV membrane are observed by microscopy or flow cytometry as performed for animal cells after non-thermal plasma treatment. The experimental data shows that OH radicals can be detected inside the membrane, although the biological effects are not as significant as for H 2 O 2 . This artificial model system can provide a systemic means to elucidate the complex interactions between biological materials and non-thermal plasma. (paper)

  19. Artificial intelligence based models for stream-flow forecasting: 2000-2015

    Science.gov (United States)

    Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

    2015-11-01

    The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

  20. Natural - synthetic - artificial!

    DEFF Research Database (Denmark)

    Nielsen, Peter E

    2010-01-01

    The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life.......The terms "natural," "synthetic" and "artificial" are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life....

  1. Artificial Intelligence in Civil Engineering

    Directory of Open Access Journals (Sweden)

    Pengzhen Lu

    2012-01-01

    Full Text Available Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

  2. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  3. A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

    Directory of Open Access Journals (Sweden)

    Ozge Cagcag Yolcu

    2013-01-01

    Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

  4. The potential of artificial aging for modelling of natural aging processes of ballpoint ink.

    Science.gov (United States)

    Weyermann, Céline; Spengler, Bernhard

    2008-08-25

    Artificial aging has been used to reproduce natural aging processes in an accelerated pace. Questioned documents were exposed to light or high temperature in a well-defined manner in order to simulate an increased age. This may be used to study the aging processes or to date documents by reproducing their aging curve. Ink was studied especially because it is deposited on the paper when a document, such as a contract, is produced. Once on the paper, aging processes start through degradation of dyes, solvents drying and resins polymerisation. Modelling of dye's and solvent's aging was attempted. These processes, however, follow complex pathways, influenced by many factors which can be classified as three major groups: ink composition, paper type and storage conditions. The influence of these factors is such that different aging states can be obtained for an identical point in time. Storage conditions in particular are difficult to simulate, as they are dependent on environmental conditions (e.g. intensity and dose of light, temperature, air flow, humidity) and cannot be controlled in the natural aging of questioned documents. The problem therefore lies more in the variety of different conditions a questioned document might be exposed to during its natural aging, rather than in the simulation of such conditions in the laboratory. Nevertheless, a precise modelling of natural aging curves based on artificial aging curves is obtained when performed on the same paper and ink. A standard model for aging processes of ink on paper is therefore presented that is based on a fit of aging curves to a power law of solvent concentrations as a function of time. A mathematical transformation of artificial aging curves into modelled natural aging curves results in excellent overlap with data from real natural aging processes.

  5. Artificial light and quantum order in systems of screened dipoles

    International Nuclear Information System (INIS)

    Wen Xiaogang

    2003-01-01

    The origin of light is an unsolved mystery in nature. Recently, it was suggested that light may originate from a new kind of order, quantum order. To test this idea in experiments, we study systems of screened magnetic/electric dipoles in two-dimensional (2D) and 3D lattices. We show that our models contain an artificial light-a photonlike collective excitation. We discuss how to design realistic devices that realize our models. We show that the 'speed of light' and the 'fine-structure constant' of the artificial light can be tuned in our models. The properties of artificial atoms (bound states of pairs of artificial charges) are also discussed. The existence of artificial light (as well as artificial electron) in condensed-matter systems suggests that elementary particles, such as light and electron, may not be elementary. They may be collective excitations of quantum order in our vacuum. In our model, light is realized as a fluctuation of string-nets and charges as the ends of open strings (or nodes of string nets)

  6. Artificial intelligence-based modeling and control of fluidized bed combustion

    Energy Technology Data Exchange (ETDEWEB)

    Ikonen, E.; Leppaekoski, K. (Univ. of Oulu, Dept. of Process and Environmental Engineering (Finland)). email: enso.ikonen@oulu.fi

    2009-07-01

    AI-inspired techniques have a lot to offer when developing methods for advanced identification, monitoring, control and optimization of industrial processes, such as power plants. Advanced control methods have been extensively examined in the research of the Power Plant Automation group at the Systems Engineering Laboratory, e.g., in fuel inventory modelling, combustion power control, modelling and control of flue gas oxygen, drum control, modelling and control of superheaters, or in optimization of flue-gas emissions. Most engineering approaches to artificial intelligence (AI) are characterized by two fundamental properties: the ability to learn from various sources and the ability to deal with plant complexity. Learning systems that are able to operate in uncertain environments based on incomplete information are commonly referred to as being intelligent. A number of other approaches exist, characterized by these properties, but not easily categorized as AI-systems. Advanced control methods (adaptive, predictive, multivariable, robust, etc.) are based on the availability of a model of the process to be controlled. Hence identification of processes becomes a key issue, leading to the use of adaptation and learning techniques. A typical learning control system concerns a selection of learning techniques applied for updating a process model, which in turn is used for the controller design. When design of learning control systems is complemented with concerns for dealing with uncertainties or vaguenesses in models, measurements, or even objectives, particularly close connections exist between advanced process control and methods of artificial intelligence and machine learning. Needs for advanced techniques are typically characterized by the desire to properly handle plant non-linearities, the multivariable nature of the dynamic problems, and the necessity to adapt to changing plant conditions. In the field of fluidized bed combustion (FBC) control, the many promising

  7. Artificial Intelligence in Civil Engineering

    OpenAIRE

    Lu, Pengzhen; Chen, Shengyong; Zheng, Yujun

    2012-01-01

    Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applicati...

  8. Use of artificial neural networks for transport energy demand modeling

    International Nuclear Information System (INIS)

    Murat, Yetis Sazi; Ceylan, Halim

    2006-01-01

    The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

  9. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

  10. Variable camber wing based on pneumatic artificial muscles

    Science.gov (United States)

    Yin, Weilong; Liu, Libo; Chen, Yijin; Leng, Jinsong

    2009-07-01

    As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, a variable camber wing with the pneumatic artificial muscle is developed. Firstly, the experimental setup to measure the static output force of pneumatic artificial muscle is designed. The relationship between the static output force and the air pressure is investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. Secondly, the finite element model of the variable camber wing is developed. Numerical results show that the tip displacement of the trailing-edge increases linearly with increasing external load and limited with the maximum static output force of pneumatic artificial muscles. Finally, the variable camber wing model is manufactured to validate the variable camber concept. Experimental result shows that the wing camber increases with increasing air pressure and that it compare very well with the FEM result.

  11. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  12. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Kalogirou, S.A. [Higher Technical Inst., Nicosia, Cyprus (Greece). Dept. of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. Al systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how Al techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of Al as a design tool in many areas of combustion engineering. (author)

  13. Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

    Science.gov (United States)

    Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

    2016-11-01

    This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Ancient sedimentary structures in the Mars, that resemble macroscopic morphology, spatial associations, and temporal succession in terrestrial microbialites.

    Science.gov (United States)

    Noffke, Nora

    2015-02-01

    Sandstone beds of the Mars have been interpreted as evidence of an ancient playa lake environment. On Earth, such environments have been sites of colonization by microbial mats from the early Archean to the present time. Terrestrial microbial mats in playa lake environments form microbialites known as microbially induced sedimentary structures (MISS). On Mars, three lithofacies of the Gillespie Lake Member sandstone display centimeter- to meter-scale structures similar in macroscopic morphology to terrestrial MISS that include "erosional remnants and pockets," "mat chips," "roll-ups," "desiccation cracks," and "gas domes." The microbially induced sedimentary-like structures identified in Curiosity rover mission images do not have a random distribution. Rather, they were found to be arranged in spatial associations and temporal successions that indicate they changed over time. On Earth, if such MISS occurred with this type of spatial association and temporal succession, they would be interpreted as having recorded the growth of a microbially dominated ecosystem that thrived in pools that later dried completely: erosional pockets, mat chips, and roll-ups resulted from water eroding an ancient microbial mat-covered sedimentary surface; during the course of subsequent water recess, channels would have cut deep into the microbial mats, leaving erosional remnants behind; desiccation cracks and gas domes would have occurred during a final period of subaerial exposure of the microbial mats. In this paper, the similarities of the macroscopic morphologies, spatial associations, and temporal succession of sedimentary structures on Mars to MISS preserved on Earth has led to the following hypothesis: The sedimentary structures in the Mars are ancient MISS produced by interactions between microbial mats and their environment. Proposed here is a strategy for detecting, identifying, confirming, and differentiating possible MISS during current and future Mars missions.

  15. An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor

    Science.gov (United States)

    Ahmed, Riaz; Banerjee, Sourav

    2018-02-01

    In this article, an extremely versatile predictive model for a newly developed Basilar meta-Membrane (BM2) sensors is reported with variable engineering parameters that contribute to it's frequency selection capabilities. The predictive model reported herein is for advancement over existing method by incorporating versatile and nonhomogeneous (e.g. functionally graded) model parameters that could not only exploit the possibilities of creating complex combinations of broadband frequency sensors but also explain the unique unexplained physical phenomenon that prevails in BM2, e.g. tailgating waves. In recent years, few notable attempts were made to fabricate the artificial basilar membrane, mimicking the mechanics of the human cochlea within a very short range of frequencies. To explain the operation of these sensors a few models were proposed. But, we fundamentally argue the "fabrication to explanation" approach and proposed the model driven predictive design process for the design any (BM2) as broadband sensors. Inspired by the physics of basilar membrane, frequency domain predictive model is proposed where both the material and geometrical parameters can be arbitrarily varied. Broadband frequency is applicable in many fields of science, engineering and technology, such as, sensors for chemical, biological and acoustic applications. With the proposed model, which is three times faster than its FEM counterpart, it is possible to alter the attributes of the selected length of the designed sensor using complex combinations of model parameters, based on target frequency applications. Finally, the tailgating wave peaks in the artificial basilar membranes that prevails in the previously reported experimental studies are also explained using the proposed model.

  16. A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

  17. An artificial neural network model for periodic trajectory generation

    Science.gov (United States)

    Shankar, S.; Gander, R. E.; Wood, H. C.

    A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

  18. Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

    Science.gov (United States)

    Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

    2010-05-01

    This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

  19. Time-lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone

    Science.gov (United States)

    Kennedy, Jeffrey R.; Ferre, Ty P.A.; Creutzfeldt, Benjamin

    2016-01-01

    Groundwater-level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high-frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water-content change. Gravity data are also useful for constraining parameters in a coupled groundwater-unsaturated zone model (Modflow-NWT model with the Unsaturated Zone Flow (UZF) package).

  20. Modeling mechanical properties of cast aluminum alloy using artificial neural network

    International Nuclear Information System (INIS)

    Jokhio, M.H.; Panhwar, M.I.

    2009-01-01

    Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

  1. Artificial intelligence

    CERN Document Server

    Hunt, Earl B

    1975-01-01

    Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field.Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction bet

  2. Microbialites in the shallow-water marine environments of the Holy Cross Mountains (Poland) in the aftermath of the Frasnian-Famennian biotic crisis

    Science.gov (United States)

    Rakociński, Michał; Racki, Grzegorz

    2016-01-01

    Microbial carbonates, consisting of abundant girvanellid oncoids, are described from cephalopod-crinoid and crinoid-brachiopod coquinas (rudstones) occurring in the lowermost Famennian of the Holy Cross Mountains, Poland. A Girvanella-bearing horizon (consist with numerous girvanellid oncoids) has been recognised at the Psie Górki section, and represents the northern slope succession of the drowned Dyminy Reef. This occurrence of microbialites in the aftermath of the Frasnian-Famennian event is interpreted as the result of opportunistic cyanobacteria blooms, which, as 'disaster forms', colonised empty shallow-water ecological niches during the survival phase following the Frasnian metazoan reef collapse, due to collapsed activity of epifaunal, grazing, and/or burrowing animals. The anachronistic lithofacies at Psie Górki is linked with catastrophic mass mortality of the cephalopod and crinoid-brachiopod communities during the heavy storm events. This mass occurrence of girvanellid oncoids, along with Frutexites-like microbial shrubs and, at least partly, common micritisation of some skeletal grains, records an overall increase in microbial activity in eutrophic normal marine environments. Microbial communities in the Holy Cross Mountains are not very diverse, being mainly represented by girvanellid oncoids, and stand in contrast to the very rich microbial communities known from the Guilin area (China), Canning Basin (Australia) and the Timan-northern Ural area (Russia). The association from Poland is similar to more diverse microbial communities represented by oncoids, trombolites and stromatolites, well known from the Canadian Alberta basin.

  3. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  4. Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition

    Directory of Open Access Journals (Sweden)

    Pierre Parrend

    2018-03-01

    Full Text Available The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.

  5. Artificial Consciousness or Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Spanache Florin

    2017-05-01

    Full Text Available Artificial intelligence is a tool designed by people for the gratification of their own creative ego, so we can not confuse conscience with intelligence and not even intelligence in its human representation with conscience. They are all different concepts and they have different uses. Philosophically, there are differences between autonomous people and automatic artificial intelligence. This is the difference between intelligence and artificial intelligence, autonomous versus automatic. But conscience is above these differences because it is neither conditioned by the self-preservation of autonomy, because a conscience is something that you use to help your neighbor, nor automatic, because one’s conscience is tested by situations which are not similar or subject to routine. So, artificial intelligence is only in science-fiction literature similar to an autonomous conscience-endowed being. In real life, religion with its notions of redemption, sin, expiation, confession and communion will not have any meaning for a machine which cannot make a mistake on its own.

  6. Braided artificial muscles: modeling and experimental validation

    Science.gov (United States)

    Dragan, Liliana; Cioban, Horia

    2009-01-01

    The paper presents a few graphical modalities for constructing the double helical braid, which is the basis for the braided artificial pneumatic muscles, by using specialized software applications. This represents the first stage in achieving the method of finite element analysis of this type of linear pneumatic actuator.

  7. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

  8. Rolling force prediction for strip casting using theoretical model and artificial intelligence

    Institute of Scientific and Technical Information of China (English)

    CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

    2010-01-01

    Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

  9. An experimental biomimetic platform for artificial olfaction.

    Directory of Open Access Journals (Sweden)

    Corrado Di Natale

    Full Text Available Artificial olfactory systems have been studied for the last two decades mainly from the point of view of the features of olfactory neuron receptor fields. Other fundamental olfaction properties have only been episodically considered in artificial systems. As a result, current artificial olfactory systems are mostly intended as instruments and are of poor benefit for biologists who may need tools to model and test olfactory models. Herewith, we show how a simple experimental approach can be used to account for several phenomena observed in olfaction. An artificial epithelium is formed as a disordered distributed layer of broadly selective color indicators dispersed in a transparent polymer layer. The whole epithelium is probed with colored light, imaged with a digital camera and the olfactory response upon exposure to an odor is the change of the multispectral image. The pixels are treated as olfactory receptor neurons, whose optical properties are used to build a convergence classifier into a number of mathematically defined artificial glomeruli. A non-homogenous exposure of the test structure to the odours gives rise to a time and spatial dependence of the response of the different glomeruli strikingly similar to patterns observed in the olfactory bulb. The model seems to mimic both the formation of glomeruli, the zonal nature of olfactory epithelium, and the spatio-temporal signal patterns at the glomeruli level. This platform is able to provide a readily available test vehicle for chemists developing optical indicators for chemical sensing purposes and for biologists to test models of olfactory system organization.

  10. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  11. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  12. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  13. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Directory of Open Access Journals (Sweden)

    Lakshmi Pathak

    Full Text Available Cholesterol oxidase (COD is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM, artificial neural network (ANN and genetic algorithm (GA have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  14. Artificial intelligence for the modeling and control of combustion processes: a review

    Energy Technology Data Exchange (ETDEWEB)

    Soteris A. Kalogirou, [Higher Technical Institute, Nicosia (Cyprus). Department of Mechanical Engineering

    2003-07-01

    Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. AI systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how AI techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of AI as a design tool in many areas of combustion engineering. 109 refs., 31 figs., 11 tabs.

  15. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  16. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    International Nuclear Information System (INIS)

    Souto, Kelling C.; Nunes, Wallace W.; Machado, Marcelo D.

    2011-01-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  17. Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

    Science.gov (United States)

    Zhang, Jun; Simeonov, Anthony; Yip, Michael C.

    2018-03-01

    Robotic artificial muscles are compliant and can generate straight contractions. They are increasingly popular as driving mechanisms for robotic systems. However, their strain and tension force often vary simultaneously under varying loads and inputs, resulting in three-dimensional hysteretic relationships. The three-dimensional hysteresis in robotic artificial muscles poses difficulties in estimating how they work and how to make them perform designed motions. This study proposes an approach to driving robotic artificial muscles to generate designed motions and forces by modeling and compensating for their three-dimensional hysteresis. The proposed scheme captures the nonlinearity by embedding two hysteresis models. The effectiveness of the model is confirmed by testing three popular robotic artificial muscles. Inverting the proposed model allows us to compensate for the hysteresis among temperature surrogate, contraction length, and tension force of a shape memory alloy (SMA) actuator. Feedforward control of an SMA-actuated robotic bicep is demonstrated. This study can be generalized to other robotic artificial muscles, thus enabling muscle-powered machines to generate desired motions.

  18. Dynamic Analysis of a Phytoplankton-Fish Model with Biological and Artificial Control

    OpenAIRE

    Wang, Yapei; Zhao, Min; Pan, Xinhong; Dai, Chuanjun

    2014-01-01

    We investigate a nonlinear model of the interaction between phytoplankton and fish, which uses a pair of semicontinuous systems with biological and artificial control. First, the existence of an order-1 periodic solution to the system is analyzed using a Poincaré map and a geometric method. The stability conditions of the order-1 periodic solution are obtained by a theoretical mathematical analysis. Furthermore, based on previous analysis, we investigate the bifurcation in the order-1 periodi...

  19. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  20. Abstraction in artificial intelligence and complex systems

    CERN Document Server

    Saitta, Lorenza

    2013-01-01

    Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the K

  1. Towards Detecting Group Identities in Complex Artificial Societies

    DEFF Research Database (Denmark)

    Grappiolo, Corrado; Yannakakis, Georgios N.

    2012-01-01

    This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures and is compo......This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures...

  2. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Ali Hussein Khalaf

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARXand back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining  thirty percent are used for testing  and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  3. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid F. Ahmed

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  4. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  5. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2013-01-01

    Full Text Available Artificial neural network (ANN models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M, MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

  6. Artificial intelligence approach with the use of artificial neural networks for the creation of a forecasting model of Plasmopara viticola infection.

    Science.gov (United States)

    Bugliosi, R; Spera, G; La Torre, A; Campoli, L; Scaglione, M

    2006-01-01

    Most of the forecasting models of Plasmopara viticola infections are based upon empiric correlations between meteorological/environmental data and pathogen outbreak. These models generally overestimate the risk of infections and induce to treat the vineyard even if it should be not necessary. In rare cases they underrate the risk of infection leaving the pathogen to breakout. Starting from these considerations we have decided to approach the problem from another point of view utilizing Artificial Intelligence techniques for data elaboration and analysis. Meanwhile the same data have been studied with a more classic approach with statistical tools to verify the impact of a large data collection on the standard data analysis methods. A network of RTUs (Remote Terminal Units) distributed all over the Italian national territory transmits 12 environmental parameters every 15 minutes via radio or via GPRS to a centralized Data Base. Other pedologic data is collected directly from the field and sent via Internet to the centralized data base utilizing Personal Digital Assistants (PDAs) running a specific software. Data is stored after having been preprocessed, to guarantee the quality of the information. The subsequent analysis has been realized mostly with Artificial Neural Networks (ANNs). Collecting and analizing data in this way will probably bring us to the possibility of preventing Plasmospara viticola infection starting from the environmental conditions in this very complex context. The aim of this work is to forecast the infection avoiding the ineffective use of the plant protection products in agriculture. Applying different analysis models we will try to find the best ANN capable of forecasting with an high level of affordability.

  7. Fluid-driven origami-inspired artificial muscles

    Science.gov (United States)

    Li, Shuguang; Vogt, Daniel M.; Rus, Daniela; Wood, Robert J.

    2017-12-01

    Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ˜600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.

  8. Artificial Neural Networks and the Mass Appraisal of Real Estate

    Directory of Open Access Journals (Sweden)

    Gang Zhou

    2018-03-01

    Full Text Available With the rapid development of computer, artificial intelligence and big data technology, artificial neural networks have become one of the most powerful machine learning algorithms. In the practice, most of the applications of artificial neural networks use back propagation neural network and its variation. Besides the back propagation neural network, various neural networks have been developing in order to improve the performance of standard models. Though neural networks are well known method in the research of real estate, there is enormous space for future research in order to enhance their function. Some scholars combine genetic algorithm, geospatial information, support vector machine model, particle swarm optimization with artificial neural networks to appraise the real estate, which is helpful for the existing appraisal technology. The mass appraisal of real estate in this paper includes the real estate valuation in the transaction and the tax base valuation in the real estate holding. In this study we focus on the theoretical development of artificial neural networks and mass appraisal of real estate, artificial neural networks model evolution and algorithm improvement, artificial neural networks practice and application, and review the existing literature about artificial neural networks and mass appraisal of real estate. Finally, we provide some suggestions for the mass appraisal of China's real estate.

  9. Symbiodinium-Induced Formation of Microbialites: Mechanistic Insights From in Vitro Experiments and the Prospect of Its Occurrence in Nature

    Directory of Open Access Journals (Sweden)

    Jörg C. Frommlet

    2018-05-01

    Full Text Available Dinoflagellates in the genus Symbiodinium exhibit a variety of life styles, ranging from mutualistic endosymbioses with animal and protist hosts to free-living life styles. In culture, Symbiodinium spp. and naturally associated bacteria are known to form calcifying biofilms that produce so-called symbiolites, i.e., aragonitic microbialites that incorporate Symbiodinium as endolithic cells. In this study, we investigated (i how algal growth and the combined physiological activity of these bacterial-algal associations affect the physicochemical macroenvironment in culture and the microenvironment within bacterial-algal biofilms, and (ii how these interactions induce the formation of symbiolites. In batch culture, calcification typically commenced when Symbiodinium spp. growth approached stationary phase and when photosynthetic activity and its influence on pH and the carbonate system of the culture medium had already subsided, indicating that symbiolite formation is not simply a function of photosynthetic activity in the bulk medium. Physical disturbance of bacteria-algal biofilms, via repeated detaching and dispersing of the developing biofilm, generally impeded symbiolite formation, suggesting that the structural integrity of biofilms plays an important role in generating conditions conducive to calcification. Microsensor measurements of pH and O2 revealed a biofilm microenvironment characterized by high photosynthetic rates and by dynamic changes in photosynthesis and respiration with light intensity and culture age. Ca2+ microsensor measurements confirmed the significance of the biofilm microenvironment in inducing calcification, as photosynthesis within the biofilm induced calcification without the influence of batch culture medium and under environmentally relevant flow conditions. Furthermore, first quantitative data on calcification from 26 calcifying cultures enabled a first broad comparison of Symbiodinium-induced bacterial

  10. Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

    Directory of Open Access Journals (Sweden)

    Yueru Ma

    2014-01-01

    Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

  11. APPLYING TEACHING-LEARNING TO ARTIFICIAL BEE COLONY FOR PARAMETER OPTIMIZATION OF SOFTWARE EFFORT ESTIMATION MODEL

    Directory of Open Access Journals (Sweden)

    THANH TUNG KHUAT

    2017-05-01

    Full Text Available Artificial Bee Colony inspired by the foraging behaviour of honey bees is a novel meta-heuristic optimization algorithm in the community of swarm intelligence algorithms. Nevertheless, it is still insufficient in the speed of convergence and the quality of solutions. This paper proposes an approach in order to tackle these downsides by combining the positive aspects of TeachingLearning based optimization and Artificial Bee Colony. The performance of the proposed method is assessed on the software effort estimation problem, which is the complex and important issue in the project management. Software developers often carry out the software estimation in the early stages of the software development life cycle to derive the required cost and schedule for a project. There are a large number of methods for effort estimation in which COCOMO II is one of the most widely used models. However, this model has some restricts because its parameters have not been optimized yet. In this work, therefore, we will present the approach to overcome this limitation of COCOMO II model. The experiments have been conducted on NASA software project dataset and the obtained results indicated that the improvement of parameters provided better estimation capabilities compared to the original COCOMO II model.

  12. An artificial vector model for generating abnormal electrocardiographic rhythms

    International Nuclear Information System (INIS)

    Clifford, Gari D; Nemati, Shamim; Sameni, Reza

    2010-01-01

    We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods

  13. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  14. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  15. Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

    Science.gov (United States)

    Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

    2018-02-15

    Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

  16. Forecasting daily lake levels using artificial intelligence approaches

    Science.gov (United States)

    Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

    2012-04-01

    Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

  17. Electron beam lithographic modeling assisted by artificial intelligence technology

    Science.gov (United States)

    Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

    2017-07-01

    We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

  18. Evaluation of a Novel Artificial Tear in the Prevention and Treatment of Dry Eye in an Animal Model.

    Science.gov (United States)

    She, Yujing; Li, Jinyang; Xiao, Bing; Lu, Huihui; Liu, Haixia; Simmons, Peter A; Vehige, Joseph G; Chen, Wei

    2015-11-01

    To evaluate effects of a novel multi-ingredient artificial tear formulation containing carboxymethylcellulose (CMC) and hyaluronic acid (HA) in a murine dry eye model. Dry eye was induced in mice (C57BL/6) using an intelligently controlled environmental system (ICES). CMC+HA (Optive Fusion™), CMC-only (Refresh Tears(®)), and HA-only (Hycosan(®)) artificial tears and control phosphate-buffered saline (PBS) were administered 4 times daily and compared with no treatment (n = 64 eyes per group). During regimen 1 (prevention regimen), mice were administered artificial tears or PBS for 14 days (starting day 0) while they were exposed to ICES, and assessed on days 0 and 14. During regimen 2 (treatment regimen), mice exposed to ICES for 14 days with no intervention were administered artificial tears or PBS for 14 days (starting day 14) while continuing exposure to ICES, and assessed on days 0, 14, and 28. Corneal fluorescein staining and conjunctival goblet cell density were measured. Artificial tear-treated mice had significantly better outcomes than control groups on corneal staining and goblet cell density (P dry eye. Improvements observed for corneal fluorescein staining and conjunctival goblet cell retention suggest that this combination may be a viable treatment option for dry eye disease.

  19. MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

    OpenAIRE

    Alexandridis, Konstantinos T.; Pijanowski, Bryan C.

    2002-01-01

    The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving g...

  20. Artificial neural network model of pork meat cubes osmotic dehydratation

    Directory of Open Access Journals (Sweden)

    Pezo Lato L.

    2013-01-01

    Full Text Available Mass transfer of pork meat cubes (M. triceps brachii, shaped as 1x1x1 cm, during osmotic dehydration (OD and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w, temperature (20-50ºC, and immersion time (1-5 h in terms of water loss (WL, solid gain (SG, final dry matter content (DM, and water activity (aw, were investigated using experimental results. Five artificial neural network (ANN models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.

  1. Modeling and experiments on the drive characteristics of high-strength water hydraulic artificial muscles

    Science.gov (United States)

    Zhang, Zengmeng; Hou, Jiaoyi; Ning, Dayong; Gong, Xiaofeng; Gong, Yongjun

    2017-05-01

    Fluidic artificial muscles are popular in robotics and function as biomimetic actuators. Their pneumatic version has been widely investigated. A novel water hydraulic artificial muscle (WHAM) with high strength is developed in this study. WHAMs can be applied to underwater manipulators widely used in ocean development because of their environment-friendly characteristics, high force-to-weight ratio, and good bio-imitability. Therefore, the strength of WHAMs has been improved to fit the requirements of underwater environments and the work pressure of water hydraulic components. However, understanding the mechanical behaviors of WHAMs is necessary because WHAMs use work media and pressure control that are different from those used by pneumatic artificial muscles. This paper presents the static and dynamic characteristics of the WHAM system, including the water hydraulic pressure control circuit. A test system is designed and built to analyze the drive characteristics of the developed WHAM. The theoretical relationships among the amount of contraction, pressure, and output drawing force of the WHAM are tested and verified. A linearized transfer function is proposed, and the dynamic characteristics of the WHAM are investigated through simulation and inertia load experiments. Simulation results agree with the experimental results and show that the proposed model can be applied to the control of WHAM actuators.

  2. Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators.

    Science.gov (United States)

    Wickramatunge, Kanchana Crishan; Leephakpreeda, Thananchai

    2013-11-01

    Pneumatic Artificial Muscle (PAM) actuators yield muscle-like mechanical actuation with high force to weight ratio, soft and flexible structure, and adaptable compliance for rehabilitation and prosthetic appliances to the disabled as well as humanoid robots or machines. The present study is to develop empirical models of the PAM actuators, that is, a PAM coupled with pneumatic control valves, in order to describe their dynamic behaviors for practical control design and usage. Empirical modeling is an efficient approach to computer-based modeling with observations of real behaviors. Different characteristics of dynamic behaviors of each PAM actuator are due not only to the structures of the PAM actuators themselves, but also to the variations of their material properties in manufacturing processes. To overcome the difficulties, the proposed empirical models are experimentally derived from real physical behaviors of the PAM actuators, which are being implemented. In case studies, the simulated results with good agreement to experimental results, show that the proposed methodology can be applied to describe the dynamic behaviors of the real PAM actuators. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

    Science.gov (United States)

    Djurovic, Nevenka; Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

    2015-01-01

    Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  4. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

    Directory of Open Access Journals (Sweden)

    Nevenka Djurovic

    2015-01-01

    Full Text Available Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS and an artificial neural network (ANN model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  5. Folding pathways explored with artificial potential functions

    International Nuclear Information System (INIS)

    Ulutaş, B; Bozma, I; Haliloglu, T

    2009-01-01

    This paper considers the generation of trajectories to a given protein conformation and presents a novel approach based on artificial potential functions—originally proposed for multi-robot navigation. The artificial potential function corresponds to a simplified energy model, but with the novelty that—motivated by work on robotic navigation—a nonlinear compositional scheme of constructing the energy model is adapted instead of an additive formulation. The artificial potential naturally gives rise to a dynamic system for the protein structure that ensures collision-free motion to an equilibrium point. In cases where the equilibrium point is the native conformation, the motion trajectory corresponds to the folding pathway. This framework is used to investigate folding in a variety of protein structures, and the results are compared with those of other approaches including experimental studies

  6. Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering.

    Science.gov (United States)

    Maffioletti, Sara Martina; Sarcar, Shilpita; Henderson, Alexander B H; Mannhardt, Ingra; Pinton, Luca; Moyle, Louise Anne; Steele-Stallard, Heather; Cappellari, Ornella; Wells, Kim E; Ferrari, Giulia; Mitchell, Jamie S; Tyzack, Giulia E; Kotiadis, Vassilios N; Khedr, Moustafa; Ragazzi, Martina; Wang, Weixin; Duchen, Michael R; Patani, Rickie; Zammit, Peter S; Wells, Dominic J; Eschenhagen, Thomas; Tedesco, Francesco Saverio

    2018-04-17

    Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D) artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs) from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  7. Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

    Science.gov (United States)

    Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

    2018-02-01

    Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising

  8. Building Explainable Artificial Intelligence Systems

    National Research Council Canada - National Science Library

    Core, Mark G; Lane, H. Chad; van Lent, Michael; Gomboc, Dave; Solomon, Steve; Rosenberg, Milton

    2006-01-01

    As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of computer-controlled entities...

  9. Effects of artificial gravity on the cardiovascular system: Computational approach

    Science.gov (United States)

    Diaz Artiles, Ana; Heldt, Thomas; Young, Laurence R.

    2016-09-01

    Artificial gravity has been suggested as a multisystem countermeasure against the negative effects of weightlessness. However, many questions regarding the appropriate configuration are still unanswered, including optimal g-level, angular velocity, gravity gradient, and exercise protocol. Mathematical models can provide unique insight into these questions, particularly when experimental data is very expensive or difficult to obtain. In this research effort, a cardiovascular lumped-parameter model is developed to simulate the short-term transient hemodynamic response to artificial gravity exposure combined with ergometer exercise, using a bicycle mounted on a short-radius centrifuge. The model is thoroughly described and preliminary simulations are conducted to show the model capabilities and potential applications. The model consists of 21 compartments (including systemic circulation, pulmonary circulation, and a cardiac model), and it also includes the rapid cardiovascular control systems (arterial baroreflex and cardiopulmonary reflex). In addition, the pressure gradient resulting from short-radius centrifugation is captured in the model using hydrostatic pressure sources located at each compartment. The model also includes the cardiovascular effects resulting from exercise such as the muscle pump effect. An initial set of artificial gravity simulations were implemented using the Massachusetts Institute of Technology (MIT) Compact-Radius Centrifuge (CRC) configuration. Three centripetal acceleration (artificial gravity) levels were chosen: 1 g, 1.2 g, and 1.4 g, referenced to the subject's feet. Each simulation lasted 15.5 minutes and included a baseline period, the spin-up process, the ergometer exercise period (5 minutes of ergometer exercise at 30 W with a simulated pedal cadence of 60 RPM), and the spin-down process. Results showed that the cardiovascular model is able to predict the cardiovascular dynamics during gravity changes, as well as the expected

  10. NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Constantin Ilie

    2009-05-01

    Full Text Available The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial in

  11. Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wenqian Ruan

    2017-12-01

    Full Text Available The commercially available nanoscale zerovalent zinc (nZVZ was used as an adsorbent for the removal of malachite green (MG from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO and artificial neural network coupled with genetic algorithm (ANN-GA were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12% was compatible with the experimental value (90.72%. Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0 were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

  12. Fluid-driven origami-inspired artificial muscles.

    Science.gov (United States)

    Li, Shuguang; Vogt, Daniel M; Rus, Daniela; Wood, Robert J

    2017-12-12

    Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg-all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration. Copyright © 2017 the Author(s). Published by PNAS.

  13. Artificial Intelligence in Cardiology.

    Science.gov (United States)

    Johnson, Kipp W; Torres Soto, Jessica; Glicksberg, Benjamin S; Shameer, Khader; Miotto, Riccardo; Ali, Mohsin; Ashley, Euan; Dudley, Joel T

    2018-06-12

    Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  14. Molecular fossils of prokaryotes in ancient authigenic minerals: archives of microbial activity in reefs and mounds?

    Science.gov (United States)

    Heindel, Katrin; Birgel, Daniel; Richoz, Sylvain; Westphal, Hildegard; Peckmann, Jörn

    2016-04-01

    Molecular fossils (lipid biomarkers) are commonly used as proxies in organic-rich sediments of various sources, including eukaryotes and prokaryotes. Usually, molecular fossils of organisms transferred from the water column to the sediment are studied to monitor environmental changes (e.g., temperature, pH). Apart from these 'allochthonous' molecular fossils, prokaryotes are active in sediments and mats on the seafloor and leave behind 'autochthonous' molecular fossils in situ. In contrast to many phototrophic organisms, most benthic sedimentary prokaryotes are obtaining their energy from oxidation or reduction of organic or inorganic substrates. A peculiarity of some of the sediment-thriving prokaryotes is their ability to trigger in situ mineral precipitation, often but not only due to metabolic activity, resulting in authigenic rocks (microbialites). During that process, prokaryotes are rapidly entombed in the mineral matrix, where the molecular fossils are protected from early (bio)degradation. In contrast to other organic compounds (DNA, proteins etc.), molecular fossils can be preserved over very long time periods (millions of years). Thus, molecular fossils in authigenic mineral phases are perfectly suitable to trace microbial activity back in time. Among the best examples of molecular fossils, which are preserved in authigenic rocks are various microbialites, forming e.g. in phototrophic microbial mats and at cold seeps. Microbialite formation is reported throughout earth history. We here will focus on reefal microbialites form the Early Triassic and the Holocene. After the End-Permian mass extinction, microbialites covered wide areas on the ocean margins. In microbialites from the Griesbachian in Iran and Turkey (both Neotethys), molecular fossils of cyanobacteria, archaea, anoxygenic phototrophs, and sulphate-reducing bacteria indicate the presence of layered microbial mats on the seafloor, in which carbonate precipitation was induced. In association with

  15. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  16. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Shahram Paydar

    2016-01-01

    Full Text Available Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist’s skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80% have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient’s clinical variables, known as malignancy related risk factors. Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011. The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy. Results: We compared the preoperative

  17. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  18. Modeling of an industrial drying process by artificial neural networks

    Directory of Open Access Journals (Sweden)

    E. Assidjo

    2008-09-01

    Full Text Available A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN, precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

  19. Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

    OpenAIRE

    Ali Reza Zirak; Sobhan Roshani

    2016-01-01

    A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...

  20. Artificial skin and patient simulator comprising the artificial skin

    NARCIS (Netherlands)

    2011-01-01

    The invention relates to an artificial skin (10, 12, 14), and relates to a patient simulator (100) comprising the artificial skin. The artificial skin is a layered structure comprising a translucent cover layer (20) configured for imitating human or animal skin, and comprising a light emitting layer

  1. Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

    NARCIS (Netherlands)

    Bos, A.; Bos, A.; Bos, M.; van der Linden, W.E.

    1993-01-01

    The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value

  2. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump and a contin......Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump...... of the insulin pump and the CGM has paved the way for a fully automatic treatment regime, the artificial pancreas. The idea is to connect the CGM with the insulin pump via a control algorithm running on e.g. the patients smart phone. The CGM observations are sent to the smart phone and based on this information...... of the system directly. The purpose of this PhD-project was to investigate the potential of SDEs in the artificial pancreas development. Especially, the emerging continuous monitoring of glucose levels makes SDEs highly applicable to this field. The current thesis aims at demonstrating and discussing...

  3. Forecasting Monsoon Precipitation Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

  4. Artificial Lipid Membranes: Past, Present, and Future.

    Science.gov (United States)

    Siontorou, Christina G; Nikoleli, Georgia-Paraskevi; Nikolelis, Dimitrios P; Karapetis, Stefanos K

    2017-07-26

    The multifaceted role of biological membranes prompted early the development of artificial lipid-based models with a primary view of reconstituting the natural functions in vitro so as to study and exploit chemoreception for sensor engineering. Over the years, a fair amount of knowledge on the artificial lipid membranes, as both, suspended or supported lipid films and liposomes, has been disseminated and has helped to diversify and expand initial scopes. Artificial lipid membranes can be constructed by several methods, stabilized by various means, functionalized in a variety of ways, experimented upon intensively, and broadly utilized in sensor development, drug testing, drug discovery or as molecular tools and research probes for elucidating the mechanics and the mechanisms of biological membranes. This paper reviews the state-of-the-art, discusses the diversity of applications, and presents future perspectives. The newly-introduced field of artificial cells further broadens the applicability of artificial membranes in studying the evolution of life.

  5. Artificial Intelligence.

    Science.gov (United States)

    Information Technology Quarterly, 1985

    1985-01-01

    This issue of "Information Technology Quarterly" is devoted to the theme of "Artificial Intelligence." It contains two major articles: (1) Artificial Intelligence and Law" (D. Peter O'Neill and George D. Wood); (2) "Artificial Intelligence: A Long and Winding Road" (John J. Simon, Jr.). In addition, it contains two sidebars: (1) "Calculating and…

  6. Artificial organs: recent progress in artificial hearing and vision.

    Science.gov (United States)

    Ifukube, Tohru

    2009-01-01

    Artificial sensory organs are a prosthetic means of sending visual or auditory information to the brain by electrical stimulation of the optic or auditory nerves to assist visually impaired or hearing-impaired people. However, clinical application of artificial sensory organs, except for cochlear implants, is still a trial-and-error process. This is because how and where the information transmitted to the brain is processed is still unknown, and also because changes in brain function (plasticity) remain unknown, even though brain plasticity plays an important role in meaningful interpretation of new sensory stimuli. This article discusses some basic unresolved issues and potential solutions in the development of artificial sensory organs such as cochlear implants, brainstem implants, artificial vision, and artificial retinas.

  7. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  8. Artificially Structured Semiconductors to Model Novel Quantum Phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Pinczuk, Aron [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics; Wind, Shalom J. [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics

    2018-01-13

    Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These

  9. Artificial Enzymes, "Chemzymes"

    DEFF Research Database (Denmark)

    Bjerre, Jeannette; Rousseau, Cyril Andre Raphaël; Pedersen, Lavinia Georgeta M

    2008-01-01

    Enzymes have fascinated scientists since their discovery and, over some decades, one aim in organic chemistry has been the creation of molecules that mimic the active sites of enzymes and promote catalysis. Nevertheless, even today, there are relatively few examples of enzyme models that successf......Enzymes have fascinated scientists since their discovery and, over some decades, one aim in organic chemistry has been the creation of molecules that mimic the active sites of enzymes and promote catalysis. Nevertheless, even today, there are relatively few examples of enzyme models...... that successfully perform Michaelis-Menten catalysis under enzymatic conditions (i.e., aqueous medium, neutral pH, ambient temperature) and for those that do, very high rate accelerations are seldomly seen. This review will provide a brief summary of the recent developments in artificial enzymes, so called...... "Chemzymes", based on cyclodextrins and other molecules. Only the chemzymes that have shown enzyme-like activity that has been quantified by different methods will be mentioned. This review will summarize the work done in the field of artificial glycosidases, oxidases, epoxidases, and esterases, as well...

  10. Modeling Common-Sense Decisions in Artificial Intelligence

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  11. Artificial intelligence in the diagnosis of low back pain.

    Science.gov (United States)

    Mann, N H; Brown, M D

    1991-04-01

    Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

  12. A conceptual and computational model of moral decision making in human and artificial agents.

    Science.gov (United States)

    Wallach, Wendell; Franklin, Stan; Allen, Colin

    2010-07-01

    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent's selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we

  13. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    Fadare, D.A.

    2009-01-01

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  14. Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

    Science.gov (United States)

    Rohrmeier, Martin A; Cross, Ian

    2014-07-01

    Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Linear and nonlinear ARMA model parameter estimation using an artificial neural network

    Science.gov (United States)

    Chon, K. H.; Cohen, R. J.

    1997-01-01

    This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

  16. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

    Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

  17. Modeling of biodistribution of 90 Y-DOTA-hR3 by using artificial intelligence techniques

    International Nuclear Information System (INIS)

    Ondarse, Dianelys; Quiza, Ramon; Leyva, Rene; Zamora, Minely; Ducat, Luis; Hernandez, Ignacio; Alonso, Luis Michel

    2011-01-01

    In this work the biodistribution of radioimmunoconjugate 9 0Y-DOTA-hR3 was modeled by using an artificial neural network. In vivo stability of 9 0Y-DOTA-hR3 was determined in healthy male Wistar rats at 4, 24 and 48 hours, in different organs. A model describing the relationship between, by one hand, the incorporated dose and, by the other hand, organ and time was developed by using a multilayer perceptron neural network. Adjusted model was analyzed by several statistical tests. Outcomes shown that proposed neural model describes the relationship between the studied variables in a proper way. (Author)

  18. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

    Science.gov (United States)

    Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

    2015-01-01

    The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

  19. Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

    Science.gov (United States)

    Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

    2017-07-03

    Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

  20. A comparative study of the antacid effect of raw spinach juice and spinach extract in an artificial stomach model.

    Science.gov (United States)

    Panda, Vandana Sanjeev; Shinde, Priyanka Mangesh

    2016-12-01

    BackgroundSpinacia oleracea known as spinach is a green-leafy vegetable consumed by people across the globe. It is reported to possess potent medicinal properties by virtue of its numerous antioxidant phytoconstituents, together termed as the natural antioxidant mixture (NAO). The present study compares the antacid effect of raw spinach juice with an antioxidant-rich methanolic extract of spinach (NAOE) in an artificial stomach model. MethodsThe pH of NAOE at various concentrations (50, 100 and 200 mg/mL) and its neutralizing effect on artificial gastric acid was determined and compared with that of raw spinach juice, water, the active control sodium bicarbonate (SB) and a marketed antacid preparation ENO. A modified model of Vatier's artificial stomach was used to determine the duration of consistent neutralization of artificial gastric acid for the test compounds. The neutralizing capacity of test compounds was determined in vitro using the classical titration method of Fordtran. Results NAOE (50, 100 and 200 mg/mL), spinach juice, SB and ENO showed significantly better acid-neutralizing effect, consistent duration of neutralization and higher antacid capacity when compared with water. Highest antacid activity was demonstrated by ENO and SB followed by spinach juice and NAOE200. Spinach juice exhibited an effect comparable to NAOE (200 mg/mL). ConclusionsThus, it may be concluded that spinach displays significant antacid activity be it in the raw juice form or as an extract in methanol.

  1. Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

    Directory of Open Access Journals (Sweden)

    Musztyfaga-Staszuk M.

    2015-09-01

    Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

  2. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  3. Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cédric Beaulac

    2017-01-01

    Full Text Available We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

  4. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  5. [Artificial organs].

    Science.gov (United States)

    Raguin, Thibaut; Dupret-Bories, Agnès; Debry, Christian

    2017-01-01

    Research has been fighting against organ failure and shortage of donations by supplying artificial organs for many years. With the raise of new technologies, tissue engineering and regenerative medicine, many organs can benefit of an artificial equivalent: thanks to retinal implants some blind people can visualize stimuli, an artificial heart can be proposed in case of cardiac failure while awaiting for a heart transplant, artificial larynx enables laryngectomy patients to an almost normal life, while the diabetic can get a glycemic self-regulation controlled by smartphones with an artificial device. Dialysis devices become portable, as well as the oxygenation systems for terminal respiratory failure. Bright prospects are being explored or might emerge in a near future. However, the retrospective assessment of putative side effects is not yet sufficient. Finally, the cost of these new devices is significant even if the advent of three dimensional printers may reduce it. © 2017 médecine/sciences – Inserm.

  6. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  7. Artificial heart

    Energy Technology Data Exchange (ETDEWEB)

    1984-10-18

    Super-pure plutonium-238 could use heat produced during fission to power an implanted artificial heart. Three model hearts have worked for some time. Concern that excess heat would make the procedure unsafe for humans has broadened the search for another energy source, such as electrohydraulic drive or an external power battery. A back pack approach may provide an interim solution until materials are developed which can withstand heart activity and be small enough for implantation.

  8. Classification system for rain fed wheat grain cultivars using artificial ...

    African Journals Online (AJOL)

    Artificial neural network (ANN) models have found wide applications, including ... of grains is essential for various applications as wheat grain industry and cultivation. In order to classify the rain fed wheat cultivars using artificial neural network ...

  9. Artificial intelligence and information-control systems of robots - 87

    International Nuclear Information System (INIS)

    Plander, I.

    1987-01-01

    Independent research areas of artificial intelligence represent the following problems: automatic problem solving and new knowledge discovering, automatic program synthesis, natural language, picture and scene recognition and understanding, intelligent control systems of robots equipped with sensoric subsystems, dialogue of two knowledge systems, as well as studying and modelling higher artificial intelligence attributes, such as emotionality and personality. The 4th Conference draws on the problems treated at the preceding Conferences, and presents the most recent knowledge on the following topics: theoretical problems of artificial intelligence, knowledge-based systems, expert systems, perception and pattern recognition, robotics, intelligent computer-aided design, special-purpose computer systems for artificial intelligence and robotics

  10. Simulation of nonlinear random vibrations using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  11. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    Energy Technology Data Exchange (ETDEWEB)

    Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)

    2017-09-15

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  12. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    International Nuclear Information System (INIS)

    Yildiz, Sayiter

    2017-01-01

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R"2 value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R"2 values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  13. Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

    Science.gov (United States)

    Buckland, Michael K.; Florian, Doris

    1991-01-01

    Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

  14. Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Mohsen Shanbeh

    2011-01-01

    Full Text Available One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV% of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.

  15. An artificial neural network model for rainfall forecasting in Bangkok, Thailand

    Directory of Open Access Journals (Sweden)

    N. Q. Hung

    2009-08-01

    Full Text Available This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness, the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

  16. Proof of concept of an artificial muscle: theoretical model, numerical model, and hardware experiment.

    Science.gov (United States)

    Haeufle, D F B; Günther, M; Blickhan, R; Schmitt, S

    2011-01-01

    Recently, the hyperbolic Hill-type force-velocity relation was derived from basic physical components. It was shown that a contractile element CE consisting of a mechanical energy source (active element AE), a parallel damper element (PDE), and a serial element (SE) exhibits operating points with hyperbolic force-velocity dependency. In this paper, the contraction dynamics of this CE concept were analyzed in a numerical simulation of quick release experiments against different loads. A hyperbolic force-velocity relation was found. The results correspond to measurements of the contraction dynamics of a technical prototype. Deviations from the theoretical prediction could partly be explained by the low stiffness of the SE, which was modeled analog to the metal spring in the hardware prototype. The numerical model and hardware prototype together, are a proof of this CE concept and can be seen as a well-founded starting point for the development of Hill-type artificial muscles. This opens up new vistas for the technical realization of natural movements with rehabilitation devices. © 2011 IEEE

  17. Application of two-barrier model of radioactive agent transport in sea water for analyzing artificial radionuclide release from containers with radioactive waste dumped in Kara Sea

    Energy Technology Data Exchange (ETDEWEB)

    Grishin, Denis S.; Laykin, Andrey I.; Kuchin, Nickolay L.; Platovskikh, Yuri A. [Krylov State Research Center, Saint Petersburg, 44 Moskovskoe shosse, 196158 (Russian Federation)

    2014-07-01

    Modeling of artificial radionuclide transport in sea water is crucial for prognosis of radioecological situation in regions where dumping of radioactive waste had been made and/or accidents with nuclear submarines had taken place. Distribution of artificial radionuclides in bottom sediments can be a detector of radionuclide release from dumped or sunk objects to marine environment. Proper model can determine the dependence between radionuclide distribution in sediments and radionuclide release. Following report describes two-barrier model of radioactive agent transport in sea water. It was tested on data from 1994 - 2013 expeditions to Novaya Zemlya bays, where regular dumping of solid radioactive waste was practiced by the former USSR from the early 1960's until 1990. Two-barrier model agrees with experimental data and allows more accurate determination of time and intensity of artificial radionuclide release from dumped containers. (authors)

  18. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    Energy Technology Data Exchange (ETDEWEB)

    Zarzalejo, L.F.; Ramirez, L.; Polo, J. [DER-CIEMAT, Madrid (Spain). Renewable Energy Dept.

    2005-07-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models. (author)

  19. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    International Nuclear Information System (INIS)

    Zarzalejo, Luis F.; Ramirez, Lourdes; Polo, Jesus

    2005-01-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models

  20. The Construction of Intelligent English Teaching Model Based on Artificial Intelligence

    Directory of Open Access Journals (Sweden)

    Xiaoguang Li

    2017-12-01

    Full Text Available In order to build a modernized tool platform that can help students improve their English learning efficiency according to their mastery of knowledge and personality, this paper develops an online intelligent English learning system that uses Java and artificial intelligence language Prolog as the software system. This system is a creative reflection of the thoughts of expert system in artificial intelligence. Established on the Struts Spring Hibernate lightweight JavaEE framework, the system modules are coupled with each other in a much lower degree, which is convenient to future function extension. Combined with the idea of expert system in artificial intelligence, the system developed appropriate learning strategies to help students double the learning effect with half the effort; Finally, the system takes into account the forgetting curve of memory, on which basis the knowledge that has been learned will be tested periodically, intending to spare students’ efforts to do a sea of exercises and obtain better learning results.

  1. Modeling a full-scale primary sedimentation tank using artificial neural networks.

    Science.gov (United States)

    Gamal El-Din, A; Smith, D W

    2002-05-01

    Modeling the performance of full-scale primary sedimentation tanks has been commonly done using regression-based models, which are empirical relationships derived strictly from observed daily average influent and effluent data. Another approach to model a sedimentation tank is using a hydraulic efficiency model that utilizes tracer studies to characterize the performance of model sedimentation tanks based on eddy diffusion. However, the use of hydraulic efficiency models to predict the dynamic behavior of a full-scale sedimentation tank is very difficult as the development of such models has been done using controlled studies of model tanks. In this paper, another type of model, namely artificial neural network modeling approach, is used to predict the dynamic response of a full-scale primary sedimentation tank. The neuralmodel consists of two separate networks, one uses flow and influent total suspended solids data in order to predict the effluent total suspended solids from the tank, and the other makes predictions of the effluent chemical oxygen demand using data of the flow and influent chemical oxygen demand as inputs. An extensive sampling program was conducted in order to collect a data set to be used in training and validating the networks. A systematic approach was used in the building process of the model which allowed the identification of a parsimonious neural model that is able to learn (and not memorize) from past data and generalize very well to unseen data that were used to validate the model. Theresults seem very promising. The potential of using the model as part of a real-time process control system isalso discussed.

  2. Artificial Neural Networks For Hadron Hadron Cross-sections

    International Nuclear Information System (INIS)

    ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

    2011-01-01

    In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

  3. Modeling the compliance of polyurethane nanofiber tubes for artificial common bile duct

    Science.gov (United States)

    Moazeni, Najmeh; Vadood, Morteza; Semnani, Dariush; Hasani, Hossein

    2018-02-01

    The common bile duct is one of the body’s most sensitive organs and a polyurethane nanofiber tube can be used as a prosthetic of the common bile duct. The compliance is one of the most important properties of prosthetic which should be adequately compliant as long as possible to keep the behavioral integrity of prosthetic. In the present paper, the prosthetic compliance was measured and modeled using regression method and artificial neural network (ANN) based on the electrospinning process parameters such as polymer concentration, voltage, tip-to-collector distance and flow rate. Whereas, the ANN model contains different parameters affecting on the prediction accuracy directly, the genetic algorithm (GA) was used to optimize the ANN parameters. Finally, it was observed that the optimized ANN model by GA can predict the compliance with high accuracy (mean absolute percentage error = 8.57%). Moreover, the contribution of variables on the compliance was investigated through relative importance analysis and the optimum values of parameters for ideal compliance were determined.

  4. An optimization on strontium separation model for fission products (inactive trace elements) using artificial neural networks

    International Nuclear Information System (INIS)

    Moosavi, K.; Setayeshi, S.; Maragheh, M.Gh.; Ahmadi, S.J.; Kardan, M.R.; Banaem, L.M.

    2009-01-01

    In this study, an experimental design using artificial neural networks for an optimization on the strontium separation model for fission products (inactive trace elements) is investigated. The goal is to optimize the separation parameters to achieve maximum amount of strontium that is separated from the fission products. The result of the optimization method causes a proper purity of Strontium-89 that was separated from the fission products. It is also shown that ANN may be establish a method to optimize the separation model.

  5. Tracer test modeling for characterizing heterogeneity and local scale residence time distribution in an artificial recharge site.

    Science.gov (United States)

    Valhondo, Cristina; Martinez-Landa, Lurdes; Carrera, Jesús; Hidalgo, Juan J.; Ayora, Carlos

    2017-04-01

    Artificial recharge of aquifers (AR) is a standard technique to replenish and enhance groundwater resources, that have widely been used due to the increasing demand of quality water. AR through infiltration basins consists on infiltrate surface water, that might be affected in more or less degree by treatment plant effluents, runoff and others undesirables water sources, into an aquifer. The water quality enhances during the passage through the soil and organic matter, nutrients, organic contaminants, and bacteria are reduced mainly due to biodegradation and adsorption. Therefore, one of the goals of AR is to ensure a good quality status of the aquifer even if lesser quality water is used for recharge. Understand the behavior and transport of the potential contaminants is essential for an appropriate management of the artificial recharge system. The knowledge of the flux distribution around the recharge system and the relationship between the recharge system and the aquifer (area affected by the recharge, mixing ratios of recharged and native groundwater, travel times) is essential to achieve this goal. Evaluate the flux distribution is not always simple because the complexity and heterogeneity of natural systems. Indeed, it is not so much regulate by hydraulic conductivity of the different geological units as by their continuity and inter-connectivity particularly in the vertical direction. In summary for an appropriate management of an artificial recharge system it is needed to acknowledge the heterogeneity of the media. Aiming at characterizing the residence time distribution (RTDs) of a pilot artificial recharge system and the extent to which heterogeneity affects RTDs, we performed and evaluated a pulse injection tracer test. The artificial recharge system was simulated as a multilayer model which was used to evaluate the measured breakthrough curves at six monitoring points. Flow and transport parameters were calibrated under two hypotheses. The first

  6. A comparative study of the antacid effect of some commonly consumed foods for hyperacidity in an artificial stomach model.

    Science.gov (United States)

    Panda, Vandana; Shinde, Priyanka; Deora, Jyoti; Gupta, Pankaj

    2017-10-01

    The incorporation of certain alkalinizing vegetables, fruits, milk and its products in the diet has been known to alleviate hyperacidity. These foods help to restore the natural gastric balance and function, curb acid reflux, aid digestion, reduce the burning sensation due to hyperacidity and soothe the inflamed mucosa of the stomach. The present study evaluates and compares the antacid effect of broccoli, kale, radish, cucumber, lemon juice, cold milk and curd in an artificial stomach model. The pH of the test samples and their neutralizing effect on artificial gastric acid was determined and compared with that of water, the active control sodium bicarbonate and a marketed antacid preparation ENO. A modified model of Vatier's artificial stomach was used to determine the duration of consistent neutralization of artificial gastric acid by the test samples. The neutralizing capacity of the test samples was determined in vitro using the classical titration method of Fordtran. All test samples except lemon showed significantly higher (p<0.05 for cucumber and p<0.001 for the rest) acid neutralizing effect than water. All test samples also exhibited a significantly (p<0.001) higher duration of consistent neutralization and higher antacid capacity than water. Highest antacid activity was demonstrated by cold milk and broccoli which was comparable with ENO and sodium bicarbonate. It may be concluded that the natural food ingredients used in this study exhibited significant antacid activity, justifying their use as essential dietary components to counter hyperacidity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm-Artificial Neural Network.

    Science.gov (United States)

    Ramadan Suleiman, Ahmed; Nehdi, Moncef L

    2017-02-07

    This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

  8. A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

    Science.gov (United States)

    Maschio, Célio; José Schiozer, Denis

    2015-01-01

    In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.

  9. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  10. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios

    Science.gov (United States)

    Sanikhani, Hadi; Kisi, Ozgur; Maroufpoor, Eisa; Yaseen, Zaher Mundher

    2018-02-01

    The establishment of an accurate computational model for predicting reference evapotranspiration (ET0) process is highly essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, six artificial intelligence (AI) models were investigated for modeling ET0 using a small number of climatic data generated from the minimum and maximum temperatures of the air and extraterrestrial radiation. The investigated models were multilayer perceptron (MLP), generalized regression neural networks (GRNN), radial basis neural networks (RBNN), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC), and gene expression programming (GEP). The implemented monthly time scale data set was collected at the Antalya and Isparta stations which are located in the Mediterranean Region of Turkey. The Hargreaves-Samani (HS) equation and its calibrated version (CHS) were used to perform a verification analysis of the established AI models. The accuracy of validation was focused on multiple quantitative metrics, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R 2), coefficient of residual mass (CRM), and Nash-Sutcliffe efficiency coefficient (NS). The results of the conducted models were highly practical and reliable for the investigated case studies. At the Antalya station, the performance of the GEP and GRNN models was better than the other investigated models, while the performance of the RBNN and ANFIS-SC models was best compared to the other models at the Isparta station. Except for the MLP model, all the other investigated models presented a better performance accuracy compared to the HS and CHS empirical models when applied in a cross-station scenario. A cross-station scenario examination implies the

  11. Wave functions and low-order density matrices for a class of two-electron 'artificial atoms' embracing Hookean and Moshinsky models

    International Nuclear Information System (INIS)

    Holas, A.; Howard, I.A.; March, N.H.

    2003-01-01

    A class of model two-electron 'artificial atoms' is proposed which embraces both Hookean and Moshinsky models. Particle densities and spinless first-order density matrices are obtained for this class of models. These quantities and the interacting system kinetic energy can be calculated using the ground-state solution of an explicit single-particle radial Schroedinger equation

  12. Artificial intelligence techniques in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Laughton, M.A.

    1997-12-31

    Since the early to mid 1980s much of the effort in power systems analysis has turned away from the methodology of formal mathematical modelling which came from the fields of operations research, control theory and numerical analysis to the less rigorous techniques of artificial intelligence (AI). Today the main AI techniques found in power systems applications are those utilising the logic and knowledge representations of expert systems, fuzzy systems, artificial neural networks (ANN) and, more recently, evolutionary computing. These techniques will be outlined in this chapter and the power system applications indicated. (Author)

  13. Porphyrin and fullerene-based artificial photosynthetic materials for photovoltaics

    International Nuclear Information System (INIS)

    Imahori, Hiroshi; Kashiwagi, Yukiyasu; Hasobe, Taku; Kimura, Makoto; Hanada, Takeshi; Nishimura, Yoshinobu; Yamazaki, Iwao; Araki, Yasuyuki; Ito, Osamu; Fukuzumi, Shunichi

    2004-01-01

    We have developed artificial photosynthetic systems in which porphyrins and fullerenes are self-assembled as building blocks into nanostructured molecular light-harvesting materials and photovoltaic devices. Multistep electron transfer strategy has been combined with our finding that porphyrin and fullerene systems have small reorganization energies, which are suitable for the construction of light energy conversion systems as well as artificial photosynthetic models. Highly efficient photosynthetic electron transfer reactions have been realized at ITO electrodes modified with self-assembled monolayers of porphyrin oligomers as well as porphyrin-fullerene linked systems. Porphyrin-modified gold nanoclusters have been found to have potential as artificial photosynthetic materials. These results provide basic information for the development of nanostructured artificial photosynthetic systems

  14. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  15. Artificial exomuscle investigations for applications-metal hydride

    International Nuclear Information System (INIS)

    Crevier, Marie-Charlotte; Richard, Martin; Rittenhouse, D Matheson; Roy, Pierre-Olivier; Bedard, Stephane

    2007-01-01

    In pursuing the development of bionic devices, Victhom identified a need for technologies that could replace current motorized systems and be better integrated into the human body motion. The actuators used to obtain large displacements are noisy, heavy, and do not adequately reproduce human muscle behavior. Subsequently, a project at Victhom was devoted to the development of active materials to obtain an artificial exomuscle actuator. An exhaustive literature review was done at Victhom to identify promising active materials for the development of artificial muscles. According to this review, metal hydrides were identified as a promising technology for artificial muscle development. Victhom's investigations focused on determining metal hydride actuator potential in the context of bionics technology. Based on metal hydride properties and artificial muscle requirements such as force, displacement and rise time, an exomuscle was built. In addition, a finite element model, including heat and mass transfer in the metal hydride, was developed and implemented in FEMLAB software. (review article)

  16. Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

    Science.gov (United States)

    Buch, A. M.; Narain, A.; Pandey, P. C.

    1994-01-01

    The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

  17. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  18. A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

    Science.gov (United States)

    Yorek, Nurettin; Ugulu, Ilker

    2015-01-01

    In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

  19. Biologically inspired technologies using artificial muscles

    Science.gov (United States)

    Bar-Cohen, Yoseph

    2005-01-01

    After billions of years of evolution, nature developed inventions that work, which are appropriate for the intended tasks and that last. The evolution of nature led to the introduction of highly effective and power efficient biological mechanisms that are scalable from micron to many meters in size. Imitating these mechanisms offers enormous potentials for the improvement of our life and the tools we use. Humans have always made efforts to imitate nature and we are increasingly reaching levels of advancement where it becomes significantly easier to imitate, copy, and adapt biological methods, processes and systems. Some of the biomimetic technologies that have emerged include artificial muscles, artificial intelligence, and artificial vision to which significant advances in materials science, mechanics, electronics, and computer science have contributed greatly. One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their operation mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the state-of-the-art and challenges to making artificial muscles and their potential biomimetic applications.

  20. An empirical model of the Earth's bow shock based on an artificial neural network

    Science.gov (United States)

    Pallocchia, Giuseppe; Ambrosino, Danila; Trenchi, Lorenzo

    2014-05-01

    All of the past empirical models of the Earth's bow shock shape were obtained by best-fitting some given surfaces to sets of observed crossings. However, the issue of bow shock modeling can be addressed by means of artificial neural networks (ANN) as well. In this regard, here it is presented a perceptron, a simple feedforward network, which computes the bow shock distance along a given direction using the two angular coordinates of that direction, the bow shock predicted distance RF79 (provided by Formisano's model (F79)) and the upstream alfvénic Mach number Ma. After a brief description of the ANN architecture and training method, we discuss the results of the statistical comparison, performed over a test set of 1140 IMP8 crossings, between the prediction accuracies of ANN and F79 models.

  1. Intelligible Artificial Intelligence

    OpenAIRE

    Weld, Daniel S.; Bansal, Gagan

    2018-01-01

    Since Artificial Intelligence (AI) software uses techniques like deep lookahead search and stochastic optimization of huge neural networks to fit mammoth datasets, it often results in complex behavior that is difficult for people to understand. Yet organizations are deploying AI algorithms in many mission-critical settings. In order to trust their behavior, we must make it intelligible --- either by using inherently interpretable models or by developing methods for explaining otherwise overwh...

  2. Integration of Artificial Neural Network Modeling and Genetic Algorithm Approach for Enrichment of Laccase Production in Solid State Fermentation by Pleurotus ostreatus

    OpenAIRE

    Potu Venkata Chiranjeevi; Moses Rajasekara Pandian; Sathish Thadikamala

    2014-01-01

    Black gram husk was used as a solid substrate for laccase production by Pleurotus ostreatus, and various fermentation conditions were optimized based on an artificial intelligence method. A total of six parameters, i.e., temperature, inoculum concentration, moisture content, CuSO4, glucose, and peptone concentrations, were optimized. A total of 50 experiments were conducted, and the obtained data were modeled by a hybrid of artificial neural network (ANN) and genetic algorithm (GA) approaches...

  3. Artificial intelligence

    CERN Document Server

    Ennals, J R

    1987-01-01

    Artificial Intelligence: State of the Art Report is a two-part report consisting of the invited papers and the analysis. The editor first gives an introduction to the invited papers before presenting each paper and the analysis, and then concludes with the list of references related to the study. The invited papers explore the various aspects of artificial intelligence. The analysis part assesses the major advances in artificial intelligence and provides a balanced analysis of the state of the art in this field. The Bibliography compiles the most important published material on the subject of

  4. Artificial Neural Networks for Thermochemical Conversion of Biomass

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Bruno, Joan Carles

    2015-01-01

    Artificial neural networks (ANNs), extensively used in different fields, have been applied for modeling biomass gasification processes in fluidized bed reactors. Two ANN models are presented, one for circulating fluidized bed gasifiers and another for bubbling fluidized bed gasifiers. Both models...

  5. Using global analysis models of water resources as an initial measure in management proposals concerning the artificial recharge of aquifers; Empleo de modelos de analisis global de recursos hidricos como primera actuacion a emprender en propuestas de gestion que contemplen operaciones de recarga artificial de acuiferos

    Energy Technology Data Exchange (ETDEWEB)

    Murillo, J. M.; Navarro, J. A.

    2008-07-01

    This paper discusses artificial recharge not as an individual component disconnected from the other elements that make up a system of water resources, but as an integrated part of such a system, one that is interrelated with all the others, such that any action affecting a given element may affect the recharge operation, and vice versa. The methodology applied throughout this study is based on the technique of systems analysis, and makes use of the AQUATOOL software package with respect to assessing guarantees, water availability for the artificial recharge operation and the suitability of the host aquifer. The results obtained show that it is necessary, in the first place, to draw up a global model of water resources, incorporating all the elements that constitute the system; then, taking into account the results obtained, a viability analysis should be made of the artificial recharge operation, by means of a numerical model of the relevant parameters for the aquifer(s) in question. This model should specify in detail the infiltration operation proposed. If deemed appropriate, and either before or after drawing up the parameter model, a pilot artificial recharge plant can be constructed, so that a small-scale assay may be made of specific aspects of the artificial recharge; in any case, such a pilot plant should always be constructed after obtaining the global analysis model of water resources. The practical application described in this paper refers to the Quiebrajano-Viboras water exploitation system, which is located in the province of Jaen (Spain). (Author) 43 refs.

  6. Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

    Science.gov (United States)

    Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

    2013-01-01

    As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

  7. Evapotranspiration Modeling by Linear, Nonlinear Regression and Artificial Neural Network in Greenhouse (Case study Reference Crop, Cucumber and Tomato

    Directory of Open Access Journals (Sweden)

    vahid Rezaverdinejad

    2017-01-01

    Full Text Available Introduction: Greenhouse cultivation is a steadily developing agricultural sector throughout the world. In addition, it is known that water is a major issue almost all part of the world especially for countries which have insufficient water source. With this great expansion of greenhouse cultivation, the need of appropriate irrigation management has a great importance. Accurate determination of irrigation scheduling (irrigation timing and frequency is one of the main factors in achieving high yields and avoiding loss of quality in greenhouse tomato and cucumber. To do this, it is fundamental to know the crop water requirements or real evapotranspiration. Accurate estimation on crop water requirement is needed to avoid the excess or deficit water application, with consequent impacts on nutrient availability for plants. This can be done by using appropriate method to determine the crop evapotranspiration (ETc. In greenhouse cultivation, crop transpiration is the most important energy dissipation mechanisms that influence ETc rate. There are a large number of literatures on methods to estimate ETc in greenhouses. ETc can be measured or estimated by direct or indirect methods. The most common direct method estimates ETc from measurements with weighing lysimeters. Thisalsoincludes the evaporation measuring equipment, class A pan, Piche atmometer and modified atmometer. Indirect method includes the measurement of net radiation, temperature, relative humidity, and air vapour pressure deficit. A large number of models have been developed from these measurements to estimate ETc. Due to the fast development of under greenhouse cultivation all around the world, the needs of information on how it affects ETc in greenhouses has to be known and summarized. The existing models for ETc calculation have to be studied to know whether it is reliable for greenhouse climate (hereafter, microclimate or not. Regression and artificial neural network models are two

  8. Simulation of Blood flow in Artificial Heart Valve Design through Left heart

    Science.gov (United States)

    Hafizah Mokhtar, N.; Abas, Aizat

    2018-05-01

    In this work, an artificial heart valve is designed for use in real heart with further consideration on the effect of thrombosis, vorticity, and stress. The design of artificial heart valve model is constructed by Computer-aided design (CAD) modelling and simulated using Computational fluid dynamic (CFD) software. The effect of blood flow pattern, velocity and vorticity of the artificial heart valve design has been analysed in this research work. Based on the results, the artificial heart valve design shows that it has a Doppler velocity index that is less than the allowable standards for the left heart with values of more than 0.30 and less than 2.2. These values are safe to be used as replacement of the human heart valve.

  9. Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

    International Nuclear Information System (INIS)

    Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian

    2015-01-01

    Highlights: • A novel Short Term Medium Voltage (MV) Load Forecasting (STLF) model is presented. • A knowledge-based STLF error control mechanism is implemented. • An Artificial Neural Network (ANN)-based optimum tuning is applied on STLF. • The relationship between load profiles and operational conditions is analysed. - Abstract: This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

  10. Proactive learning for artificial cognitive systems

    Science.gov (United States)

    Lee, Soo-Young

    2010-04-01

    The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

  11. Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

    Science.gov (United States)

    Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

    2017-11-20

    A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

  12. Artificial snowmaking possibilities and climate change based on regional climate modeling in the Southern Black Forest

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Philipp; Matzarakis, Andreas [Freiburg Univ. (Germany). Meteorological Inst.; Steiger, Robert [alpS - Centre for Climate Change Adaptation Technologies, Innsbruck (Austria)

    2012-04-15

    Winter sport, especially ski tourism - is one of those sectors of tourism that will be affected by climate change. Ski resorts across the Alps and in the adjacent low mountain ranges react to warm winter seasons by investing in artificial snowmaking. But snowmaking in warm winter seasons is fraught with risk, because sufficiently low air temperature will become less frequent in the future. The present study deals with the ski resort Feldberg, which has 14 ski lifts and 16 ski slopes which is the biggest ski resort in the German Federal state Baden-Wuerttemberg. The impact of climate change in this region is extraordinary important because winter tourism is the main source of revenue for the whole area around the ski resort. The study area is in altitudinal range of 850 to 1450 meters above sea level. At the moment, it is possible to supply one third of the whole area with artificial snow, but there is plan for artificial snowmaking of the whole Feldberg area by the year 2020. Based on this, more detailed investigations of season length and the needed volume of produced snow are necessary. A ski season simulation model (SkiSim 2.0) was applied in order to assess potential impacts of climate change on the Feldberg ski area for the A1B and B1 emission scenarios based on the ECHAM5 GCM downscaled by the REMO RCM. SkiSim 2.0 calculates daily snow depth (natural and technically produced snow) and the required amount of artificial snow for 100 m altitudinal bands. Analysing the development of the number of potential skiing days, it can be assessed whether ski operation is cost covering or not. Model results of the study show a more pronounced and rapid shortening of the ski season in the lower ranges until the year 2100 in each climate scenario. In both the A1B and B1 scenario runs of REMO, a cost-covering ski season of 100 days cannot be guaranteed in every altitudinal range even if snowmaking is considered. In this context, the obtained high-resolution snow data can

  13. Nuclear-powered artificial heart system

    International Nuclear Information System (INIS)

    Pouchot, W.D.; Lehrfeld, D.

    1976-01-01

    As reported to the 9th IECEC, a bench model version of a nuclear-powered artificial heart system to be used as a replacement for the natural heart was constructed and tested as part of a broader U.S. ERDA program. A report is given of the system design and integration, bench testing, and field support equipment of an implantable and advanced version of the bench model incorporating some of the component developments reported to the 10th IECEC. The basic elements of the system are a 32-watt Pu-238 heat source, a Stirling engine thermal converter, a coupling mechanism, and a mechanical blood pump drive actuating, alternatively, two artificial ventricles of polymeric material. As tested on the bench using a mock circulation, the system provides approximately 9 liters/minute at 120/80 mm Hg aortic pressure. At 190/145 mm Hg aortic pressure, the maximum flow decreases to about 7 liters/minute

  14. A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

    Science.gov (United States)

    Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

  15. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  16. Artificial force fields for multi-agent simulations of maritime traffic and risk estimation

    NARCIS (Netherlands)

    Xiao, F.; Ligteringen, H.; Van Gulijk, C.; Ale, B.J.M.

    2012-01-01

    A probabilistic risk model is designed to estimate probabilities of collisions for shipping accidents in busy waterways. We propose a method based on multi-agent simulation that uses an artificial force field to model ship maneuvers. The artificial force field is calibrated by AIS data (Automatic

  17. Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network

    Science.gov (United States)

    Mandal, Sumantra; Sivaprasad, P. V.; Dube, R. K.

    2007-12-01

    An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.

  18. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  19. Modelling 2,4-dichlorophenol bioavailability and bioaccumulation by the freshwater fingernail clam Sphaerium corneum using artificial particles and humic acids

    International Nuclear Information System (INIS)

    Verrengia Guerrero, N.R.; Taylor, M.G.; Simkiss, K.

    2007-01-01

    The complex and variable composition of natural sediments makes it very difficult to predict the bioavailability and bioaccumulation of sediment-bound contaminants. Several approaches have been proposed to overcome this problem, including an experimental model using artificial particles with or without humic acids as a source of organic matter. For this work, we have applied this experimental model, and also a sample of a natural sediment, to investigate the uptake and bioaccumulation of 2,4-dichlorophenol (2,4-DCP) by Sphaerium corneum. Additionally, the particle-water partition coefficients (K d ) were calculated. The results showed that the bioaccumulation of 2,4-DCP by clams did not depend solely on the levels of chemical dissolved, but also on the amount sorbed onto the particles and the characteristics and the strength of that binding. This study confirms the value of using artificial particles as a suitable experimental model for assessing the fate of sediment-bound contaminants. - An experimental model is proposed to better understand the bioavailability and bioaccumulation of sediment-bound 2,4-dichlorophenol

  20. The deconvolution of complex spectra by artificial immune system

    Science.gov (United States)

    Galiakhmetova, D. I.; Sibgatullin, M. E.; Galimullin, D. Z.; Kamalova, D. I.

    2017-11-01

    An application of the artificial immune system method for decomposition of complex spectra is presented. The results of decomposition of the model contour consisting of three components, Gaussian contours, are demonstrated. The method of artificial immune system is an optimization method, which is based on the behaviour of the immune system and refers to modern methods of search for the engine optimization.

  1. Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production

    Science.gov (United States)

    2017-06-30

    collect light energy and separate charge for developing new types of nanobiodevices to construct ”artificial leaf” from solar to fuel. or Concept of...AFRL-AFOSR-JP-TR-2017-0054 Artificial Leaf Based on Artificial Photosynthesis for Solar Fuel Production Mamoru Nango NAGOYA INSTITUTE OF TECHNOLOGY...display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY)      30-06-2017 2

  2. [Experimental study on novel hybrid artificial trachea transplantation].

    Science.gov (United States)

    Liu, Wenliang; Xiao, Peng; Liang, Hengxing; An, Ran; Cheng, Gang; Yu, Fenglei

    2014-04-01

    We developed and designed a new type of artificial trachea. The basic structure of the artificial trachea was polytetrafluoroethylene vascular prosthesis linked with titanium rings on both sides. Dualmesh was sutured on titanium rings. This experimentation follows the replacement of trachea in dogs with a combined artificial trachea to investigate the feasibility of this type of prosthesis. Sixteen dogs were implanted with the combined artificial trachea after resection of 5 cm of cervical trachea. The 5 cm-long trachea of dogs on the necks were resected and the reconstruction of the defect of the trachea was performed with trachea prosthesis. According to the method of trachea reconstruction, the models were divided into 2 groups, artificial trachea implantation group (the control group, n = 8) and group of artificial trachea implantation with growth factor (the experimental group, n = 8). Then computer tomography scan (CT), bronchoscope and pathologic examination were conducted periodically to observe the healing state of the hybrid artificial trachea. None of the dogs died during operation of cervical segmental trachea construction. But four dogs in the control group died of apnea in succession because artificial trachea was displaced and the lumen was obstructed, while 2 dogs died in the experimental group. In the first month there was granulation around anastomosis with slight stenosis. The rest of dogs were well alive until they were sacrificed 14 months later. The mean survival time of the experimental group was longer than that of the control group. The rate of infection, anastomotic dehiscence, severe stenosis and accidental death in the experimental group were lower than the control group (P anastomosis effectively but infections and split or displacement of the artificial trachea are still major problems affecting long-term survival of the animals. Application of growth factors to a certain extent promotes tissue healing by changing the local environment.

  3. Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

    Science.gov (United States)

    Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

    2017-01-01

    In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

  4. Applications of artificial intelligence technology to wastewater treatment fields in China

    Institute of Scientific and Technical Information of China (English)

    QING Xiao-xia; WANG Bo; MENG De-tao

    2005-01-01

    Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.

  5. Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

    Science.gov (United States)

    Wang, Geng; Zhou, Kexin; Zhang, Yeming

    2018-04-01

    The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

  6. Generative Artificial Intelligence : Philosophy and Theory of Artificial Intelligence

    NARCIS (Netherlands)

    van der Zant, Tijn; Kouw, Matthijs; Schomaker, Lambertus; Mueller, Vincent C.

    2013-01-01

    The closed systems of contemporary Artificial Intelligence do not seem to lead to intelligent machines in the near future. What is needed are open-ended systems with non-linear properties in order to create interesting properties for the scaffolding of an artificial mind. Using post-structuralistic

  7. Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

    Science.gov (United States)

    Alagha, Jawad S.; Seyam, Mohammed; Md Said, Md Azlin; Mogheir, Yunes

    2017-12-01

    Artificial intelligence (AI) techniques have increasingly become efficient alternative modeling tools in the water resources field, particularly when the modeled process is influenced by complex and interrelated variables. In this study, two AI techniques—artificial neural networks (ANNs) and support vector machine (SVM)—were employed to achieve deeper understanding of the salinization process (represented by chloride concentration) in complex coastal aquifers influenced by various salinity sources. Both models were trained using 11 years of groundwater quality data from 22 municipal wells in Khan Younis Governorate, Gaza, Palestine. Both techniques showed satisfactory prediction performance, where the mean absolute percentage error (MAPE) and correlation coefficient ( R) for the test data set were, respectively, about 4.5 and 99.8% for the ANNs model, and 4.6 and 99.7% for SVM model. The performances of the developed models were further noticeably improved through preprocessing the wells data set using a k-means clustering method, then conducting AI techniques separately for each cluster. The developed models with clustered data were associated with higher performance, easiness and simplicity. They can be employed as an analytical tool to investigate the influence of input variables on coastal aquifer salinity, which is of great importance for understanding salinization processes, leading to more effective water-resources-related planning and decision making.

  8. Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

    Energy Technology Data Exchange (ETDEWEB)

    Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

    2011-03-15

    An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  9. Artificial Intelligence and Science Education.

    Science.gov (United States)

    Good, Ron

    1987-01-01

    Defines artificial intelligence (AI) in relation to intelligent computer-assisted instruction (ICAI) and science education. Provides a brief background of AI work, examples of expert systems, examples of ICAI work, and addresses problems facing AI workers that have implications for science education. Proposes a revised model of the Karplus/Renner…

  10. A Game Theoretic Framework for Incentive-Based Models of Intrinsic Motivation in Artificial Systems

    Directory of Open Access Journals (Sweden)

    Kathryn Elizabeth Merrick

    2013-10-01

    Full Text Available An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players’ optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  11. A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

    Science.gov (United States)

    Merrick, Kathryn E; Shafi, Kamran

    2013-01-01

    An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

  12. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  13. Artificial life and Piaget.

    Science.gov (United States)

    Mueller, Ulrich; Grobman, K H.

    2003-04-01

    Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.

  14. Modeling and analysis of a meso-hydraulic climbing robot with artificial muscle actuation.

    Science.gov (United States)

    Chapman, Edward M; Jenkins, Tyler E; Bryant, Matthew

    2017-07-10

    This paper presents a fully coupled electro-hydraulic model of a bio-inspired climbing robot actuated by fluidic artificial muscles (FAMs). This analysis expands upon previous FAM literature by considering not only the force and contraction characteristics of the actuator, but the complete hydraulic and electromechanical circuits as well as the dynamics of the climbing robot. This analysis allows modeling of the time-varying applied pressure, electrical current, and actuator contraction for accurate prediction of the robot motion, energy consumption, and mechanical work output. The developed model is first validated against mechanical and electrical data collected from a proof-of-concept prototype robot. The model is then employed to study the system-level sensitivities of the robot locomotion efficiency and average climbing speed to several design and operating parameters. The results of this analysis demonstrate that considering only the transduction efficiency of the FAM actuators is insufficient to maximize the efficiency of the complete robot, and that a holistic approach can lead to significant improvements in performance. © 2017 IOP Publishing Ltd.

  15. Anachronistic facies from a drowned Lower Triassic carbonate platform: Lower member of the Alwa Formation (Ba'id Exotic), Oman Mountains

    Science.gov (United States)

    Woods, Adam D.; Baud, Aymon

    2008-09-01

    The lower member of the Alwa Formation (Lower Olenekian), found within the Ba'id Exotic in the Oman Mountains (Sultanate of Oman), consists of ammonoid-bearing, pelagic limestones that were deposited on an isolated, drowned carbonate platform on the Neotethyan Gondwana margin. The strata contain a variety of unusual carbonate textures and features, including thrombolites, Frutexites-bearing microbialites that contain synsedimentary cements, matrix-free breccias surrounded by isopachous calcite cement, and fissures and cavities filled with large botryoidal cements. Thrombolites are found throughout the study interval, and occur as 0.5-1.0 m thick lenses or beds that contain laterally laterally-linked stromatactis cavities. The Frutexites-bearing microbialites occur less frequently, and also form lenses or beds, up to 30 cm thick; the microbialites may be laminated, and often developed on hardgrounds. In addition, the Frutexites-bearing microbialites also contain synsedimentary calcite cement crusts and botryoids (typically fracturing of the limestone and the precipitation of large, botryoidal aragonite cements in fissures that cut across the primary fabric. Environmental conditions, specifically palaeoxygenation and the degree of calcium carbonate supersaturation, likely controlled whether the thrombolites (high level of calcium carbonate supersaturation associated with vertical mixing of water masses and dysoxic conditions) or Frutexites-bearing microbialites (low level of calcium carbonate supersaturation associated with anoxic conditions and deposition below a stable chemocline) formed. The results of this study point to continued environmental stress in the region during the Early Triassic that likely contributed to the uneven recovery from the Permian-Triassic mass extinction.

  16. Artificial immune system algorithm in VLSI circuit configuration

    Science.gov (United States)

    Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

    2017-08-01

    In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

  17. Artificial intelligence approach to accelerator control systems

    International Nuclear Information System (INIS)

    Schultz, D.E.; Hurd, J.W.; Brown, S.K.

    1987-01-01

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms

  18. Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

    Science.gov (United States)

    2013-01-01

    Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

  19. Load Forecasting with Artificial Intelligence on Big Data

    OpenAIRE

    Glauner, Patrick; State, Radu

    2016-01-01

    In the domain of electrical power grids, there is a particular interest in time series analysis using artificial intelligence. Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self-learn feature hierarchies from the data rather than modeling hand-crafted features. I...

  20. Biologically inspired technologies using artificial muscles

    Science.gov (United States)

    Bar-Cohen, Yoseph

    2005-01-01

    One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their response mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the current state of- the-art and challenges to making artificial muscles and their potential biomimetic applications.

  1. A study of groundwater monitoring data analysis using Artificial Neural Network model

    International Nuclear Information System (INIS)

    Watanabe, Kunio; Gautam, M.R.; Saegusa, Hiromitsu

    2003-05-01

    The results of groundwater flow modeling are to be justified using groundwater monitoring data in the hydrogeological characterization. On the other hand, hydraulic continuities of the geological structures, all of which are considered to have great effect on groundwater flow and/or groundwater quality, are to be estimated using the groundwater flow monitoring data with hydraulic response to some impacts such as borehole drilling, pumping test and so on. Therefore, the groundwater monitoring is important for characterizing the geological and hydrogeological environments. In order to characterize of hydrogeological environment using the monitoring data, it is important to evaluate the influence of artificial and natural impact on the monitoring data. In this study, the following three research works are carried out based on the groundwater monitoring data collected at the Tono area. Artificial Neural Network (ANN) was adopted as the tool for monitoring data analysis. Runoff analysis for assessment of importance of soil moisture on runoff estimation in a catchment. Analysis of water level fluctuation for determination influence factors in the water level fluctuation and for filtering out the influence factors from the water level data . Analysis of hydraulic pressure fluctuation in deep geological formations for hydrogeological characterization and assessment of human influence on the pore pressure in deep formation. Through this study, applicability of ANN for analysis and interpretation of the groundwater monitoring data could be confirmed and methodology for utilization the monitoring data for understanding and characterization of hydrogeological environment could be developed. (author)

  2. Moderate hypothermia technique for chronic implantation of a total artificial heart in calves.

    Science.gov (United States)

    Karimov, Jamshid H; Grady, Patrick; Sinkewich, Martin; Sunagawa, Gengo; Dessoffy, Raymond; Byram, Nicole; Moazami, Nader; Fukamachi, Kiyotaka

    2017-06-01

    The benefit of whole-body hypothermia in preventing ischemic injury during cardiac surgical operations is well documented. However, application of hypothermia during in vivo total artificial heart implantation has not become widespread because of limited understanding of the proper techniques and restrictions implied by constitutional and physiological characteristics specific to each animal model. Similarly, the literature on hypothermic set-up in total artificial heart implantation has also been limited. Herein we present our experience using hypothermia in bovine models implanted with the Cleveland Clinic continuous-flow total artificial heart.

  3. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  4. A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

    OpenAIRE

    Khatib, Tamer; Elmenreich, Wilfried

    2015-01-01

    This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that...

  5. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude....

  6. Virtual Reality for Artificial Intelligence: human-centered simulation for social science.

    Science.gov (United States)

    Cipresso, Pietro; Riva, Giuseppe

    2015-01-01

    There is a long last tradition in Artificial Intelligence as use of Robots endowing human peculiarities, from a cognitive and emotional point of view, and not only in shape. Today Artificial Intelligence is more oriented to several form of collective intelligence, also building robot simulators (hardware or software) to deeply understand collective behaviors in human beings and society as a whole. Modeling has also been crucial in the social sciences, to understand how complex systems can arise from simple rules. However, while engineers' simulations can be performed in the physical world using robots, for social scientist this is impossible. For decades, researchers tried to improve simulations by endowing artificial agents with simple and complex rules that emulated human behavior also by using artificial intelligence (AI). To include human beings and their real intelligence within artificial societies is now the big challenge. We present an hybrid (human-artificial) platform where experiments can be performed by simulated artificial worlds in the following manner: 1) agents' behaviors are regulated by the behaviors shown in Virtual Reality involving real human beings exposed to specific situations to simulate, and 2) technology transfers these rules into the artificial world. These form a closed-loop of real behaviors inserted into artificial agents, which can be used to study real society.

  7. Artificial sweeteners

    DEFF Research Database (Denmark)

    Raben, Anne Birgitte; Richelsen, Bjørn

    2012-01-01

    Artificial sweeteners can be a helpful tool to reduce energy intake and body weight and thereby risk for diabetes and cardiovascular diseases (CVD). Considering the prevailing diabesity (obesity and diabetes) epidemic, this can, therefore, be an important alternative to natural, calorie-containin......Artificial sweeteners can be a helpful tool to reduce energy intake and body weight and thereby risk for diabetes and cardiovascular diseases (CVD). Considering the prevailing diabesity (obesity and diabetes) epidemic, this can, therefore, be an important alternative to natural, calorie......-containing sweeteners. The purpose of this review is to summarize the current evidence on the effect of artificial sweeteners on body weight, appetite, and risk markers for diabetes and CVD in humans....

  8. Predicting Developmental Disorder in Infants Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Farin Soleimani

    2013-06-01

    Full Text Available Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother–child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants. The validated Infant Neurological International Battery test was employed to assess the infant’s development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

  9. Artificial cognition architectures

    CERN Document Server

    Crowder, James A; Friess, Shelli A

    2013-01-01

    The goal of this book is to establish the foundation, principles, theory, and concepts that are the backbone of real, autonomous Artificial Intelligence. Presented here are some basic human intelligence concepts framed for Artificial Intelligence systems. These include concepts like Metacognition and Metamemory, along with architectural constructs for Artificial Intelligence versions of human brain functions like the prefrontal cortex. Also presented are possible hardware and software architectures that lend themselves to learning, reasoning, and self-evolution

  10. Optimal approximation of linear systems by artificial immune response

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

  11. Evaluation of the Application of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems for Rainfall-Runoff Modelling in Zayandeh_rood Dam Basin

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Dastorani

    2012-01-01

    Full Text Available During recent few decades, due to the importance of the availability of water, and therefore the necesity of predicting run off resulted from rain fall there has been an increase in developing and implementation of new suitable method for prediction of run off using precipitation data. One of these approaches that have been developed in several areas of sciences including water related fields, is soft computing techniques such as artificial neural networks and fuzzy logic systems. This research was designed to evaluate the applicability of artificial neural network and adaptive neuro –fuzzy inference system to model rainfall-runoff process in Zayandeh_rood dam basin. It must be mentioned that, data have been analysed using Wingamma software, to select appropriate type and number of training input data before they can be used in the models. Then, it has been tried to evaluated applicability of artificial neural networks and neuro-fuzzy techniques to predict runoff generated from daily rainfall. Finally, the accuracy of the results produced by these methods has been compared using statistical criterion. Results taken from this research show that artificial neural networks and neuro-fuzzy technique presented different outputs in different conditions in terms of type and number of inputs variables, but both method have been able to produce acceptable results when suitable input variables and network structures are used.

  12. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  13. Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

    Science.gov (United States)

    Chen, W.-B.; Liu, W.-C.; Hsu, M.-H.

    2012-12-01

    Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

  14. Artificial Disc Replacement

    Science.gov (United States)

    ... Spondylolisthesis BLOG FIND A SPECIALIST Treatments Artificial Disc Replacement (ADR) Patient Education Committee Jamie Baisden The disc ... Disc An artificial disc (also called a disc replacement, disc prosthesis or spine arthroplasty device) is a ...

  15. An evaluation of tannery industry wastewater treatment sludge gasification by artificial neural network modeling

    International Nuclear Information System (INIS)

    Ongen, Atakan; Kurtulus Ozcan, H.; Arayıcı, Semiha

    2013-01-01

    Highlights: • We model calorific value of syn-gas from tannery industry treatment sludge. • We monitor variation of gas composition in produced gas. • Heating value of produced gas is around 1500 kcal/m 3 . • Model predictions are in close accordance with real values. -- Abstract: This paper reports on the calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater. Proximate and ultimate analyses of samples were performed. Thermochemical conversion alters the chemical structure of the waste. Dried air was used as a gasification agent at varying flow rates, which allowed the feedstock to be quickly converted into gas by means of different heterogeneous reactions. A lab-scale updraft fixed-bed steel reactor was used for thermochemical conversion of sludge samples. Artificial neural network (ANN) modeling techniques were used to observe variations in the syngas related to operational conditions. Modeled outputs showed that temporal changes of model predictions were in close accordance with real values. Correlation coefficients (r) showed that the ANN used in this study gave results with high sensitivity

  16. An evaluation of tannery industry wastewater treatment sludge gasification by artificial neural network modeling

    Energy Technology Data Exchange (ETDEWEB)

    Ongen, Atakan, E-mail: aongen@istanbul.edu.tr; Kurtulus Ozcan, H.; Arayıcı, Semiha

    2013-12-15

    Highlights: • We model calorific value of syn-gas from tannery industry treatment sludge. • We monitor variation of gas composition in produced gas. • Heating value of produced gas is around 1500 kcal/m{sup 3}. • Model predictions are in close accordance with real values. -- Abstract: This paper reports on the calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater. Proximate and ultimate analyses of samples were performed. Thermochemical conversion alters the chemical structure of the waste. Dried air was used as a gasification agent at varying flow rates, which allowed the feedstock to be quickly converted into gas by means of different heterogeneous reactions. A lab-scale updraft fixed-bed steel reactor was used for thermochemical conversion of sludge samples. Artificial neural network (ANN) modeling techniques were used to observe variations in the syngas related to operational conditions. Modeled outputs showed that temporal changes of model predictions were in close accordance with real values. Correlation coefficients (r) showed that the ANN used in this study gave results with high sensitivity.

  17. Thermally induced magnetic relaxation in square artificial spin ice

    Science.gov (United States)

    Andersson, M. S.; Pappas, S. D.; Stopfel, H.; Östman, E.; Stein, A.; Nordblad, P.; Mathieu, R.; Hjörvarsson, B.; Kapaklis, V.

    2016-11-01

    The properties of natural and artificial assemblies of interacting elements, ranging from Quarks to Galaxies, are at the heart of Physics. The collective response and dynamics of such assemblies are dictated by the intrinsic dynamical properties of the building blocks, the nature of their interactions and topological constraints. Here we report on the relaxation dynamics of the magnetization of artificial assemblies of mesoscopic spins. In our model nano-magnetic system - square artificial spin ice - we are able to control the geometrical arrangement and interaction strength between the magnetically interacting building blocks by means of nano-lithography. Using time resolved magnetometry we show that the relaxation process can be described using the Kohlrausch law and that the extracted temperature dependent relaxation times of the assemblies follow the Vogel-Fulcher law. The results provide insight into the relaxation dynamics of mesoscopic nano-magnetic model systems, with adjustable energy and time scales, and demonstrates that these can serve as an ideal playground for the studies of collective dynamics and relaxations.

  18. Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions

    Energy Technology Data Exchange (ETDEWEB)

    Duchesne, Marc A. [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada); Macchi, Arturo [Chemical and Biological Engineering Department, University of Ottawa, 161 Louis Pasteur, Ottawa, Ont. (Canada); Lu, Dennis Y.; Hughes, Robin W.; McCalden, David; Anthony, Edward J. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ontario (Canada)

    2010-08-15

    Threshold slag viscosity heuristics are often used for the initial assessment of coal gasification projects. Slag viscosity predictions are also required for advanced combustion and gasification models. Due to unsatisfactory performance of theoretical equations, an artificial neural network model was developed to predict slag viscosity over a broad range of temperatures and slag compositions. This model outperforms other slag viscosity models, resulting in an average error factor of 5.05 which is lower than the best obtained with other available models. Genesee coal ash viscosity predictions were made to investigate the effect of adding Canadian limestone and dolomite. The results indicate that magnesium in the fluxing agent provides a greater viscosity reduction than calcium for the threshold slag tapping temperature range. (author)

  19. Artificial neural networks for decision-making in urologic oncology.

    Science.gov (United States)

    Anagnostou, Theodore; Remzi, Mesut; Lykourinas, Michael; Djavan, Bob

    2003-06-01

    The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

  20. Estimating the Acquisition Price of Enshi Yulu Young Tea Shoots Using Near-Infrared Spectroscopy by the Back Propagation Artificial Neural Network Model in Conjunction with Backward Interval Partial Least Squares Algorithm

    Science.gov (United States)

    Wang, Sh.-P.; Gong, Z.-M.; Su, X.-Zh.; Liao, J.-Zh.

    2017-09-01

    Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5-5935.8, 7613.6-7848.9, 8091.8-8327.1, 8331-8566.2, 9287.5-9522.5, and 9526.6-9761.9 cm-1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares-back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

  1. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  2. Artificial life and life artificialization in Tron

    Directory of Open Access Journals (Sweden)

    Carolina Dantas Figueiredo

    2012-12-01

    Full Text Available Cinema constantly shows the struggle between the men and artificial intelligences. Fiction, and more specifically fiction films, lends itself to explore possibilities asking “what if?”. “What if”, in this case, is related to the eventual rebellion of artificial intelligences, theme explored in the movies Tron (1982 and Tron Legacy (2010 trat portray the conflict between programs and users. The present paper examines these films, observing particularly the possibility programs empowering. Finally, is briefly mentioned the concept of cyborg as a possibility of response to human concerns.

  3. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing and Artificial Intelligence Models (ANN, SVM: The Case of Greek Electricity Market

    Directory of Open Access Journals (Sweden)

    George P. Papaioannou

    2016-08-01

    Full Text Available In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC technique and the traditional multiple regression (PC-regression, for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN and Support Vector Machines (SVM. Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

  4. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2010-01-01

    Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

  5. Magnetotransport in Artificial Kagome Spin Ice

    Science.gov (United States)

    Chern, Gia-Wei

    2017-12-01

    Magnetic nanoarrays with special geometries exhibit nontrivial collective behaviors similar to those observed in spin-ice materials. Here, we present a circuit model to describe the complex magnetotransport phenomena in artificial kagome spin ice. In this picture, the system can be viewed as a resistor network driven by voltage sources that are located at vertices of the honeycomb array. The differential voltages across different terminals of these sources are related to the ice rules that govern the local magnetization ordering. The circuit model relates the transverse Hall voltage of kagome ice to the underlying spin correlations. Treating the magnetic nanoarray as metamaterials, we present a mesoscopic constitutive equation relating the Hall resistance to magnetization components of the system. We further show that the Hall signal is significantly enhanced when the kagome ice undergoes a magnetic-charge-ordering transition. Our analysis can be readily generalized to other lattice geometries, providing a quantitative method for the design of magnetoresistance devices based on artificial spin ice.

  6. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    Science.gov (United States)

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  7. Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram; Dabrowska, Dominika; Sadikoglu, Fahreddin

    2017-05-01

    As an innovation, both black box and physical-based models were incorporated into simulating groundwater flow and contaminant transport. Time series of groundwater level (GL) and chloride concentration (CC) observed at different piezometers of study plain were firstly de-noised by the wavelet-based de-noising approach. The effect of de-noised data on the performance of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated. Wavelet transform coherence was employed for spatial clustering of piezometers. Then for each cluster, ANN and ANFIS models were trained to predict GL and CC values. Finally, considering the predicted water heads of piezometers as interior conditions, the radial basis function as a meshless method which solves partial differential equations of GFCT, was used to estimate GL and CC values at any point within the plain where there is not any piezometer. Results indicated that efficiency of ANFIS based spatiotemporal model was more than ANN based model up to 13%.

  8. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer ... N-hexane (HPLC grade) was purchased from. Fisher Scientific. ..... Simultaneous Quantification of Seven Flavonoids in.

  9. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

  10. Design and optimization of multi-class series-parallel linear electromagnetic array artificial muscle.

    Science.gov (United States)

    Li, Jing; Ji, Zhenyu; Shi, Xuetao; You, Fusheng; Fu, Feng; Liu, Ruigang; Xia, Junying; Wang, Nan; Bai, Jing; Wang, Zhanxi; Qin, Xiansheng; Dong, Xiuzhen

    2014-01-01

    Skeletal muscle exhibiting complex and excellent precision has evolved for millions of years. Skeletal muscle has better performance and simpler structure compared with existing driving modes. Artificial muscle may be designed by analyzing and imitating properties and structure of skeletal muscle based on bionics, which has been focused on by bionic researchers, and a structure mode of linear electromagnetic array artificial muscle has been designed in this paper. Half sarcomere is the minimum unit of artificial muscle and electromagnetic model has been built. The structural parameters of artificial half sarcomere actuator were optimized to achieve better movement performance. Experimental results show that artificial half sarcomere actuator possesses great motion performance such as high response speed, great acceleration, small weight and size, robustness, etc., which presents a promising application prospect of artificial half sarcomere actuator.

  11. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  12. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

  13. Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

    Science.gov (United States)

    Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

    2016-04-01

    The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

  14. Aspects of artificial neural networks and experimental noise

    NARCIS (Netherlands)

    Derks, E.P.P.A.

    1997-01-01

    About a decade ago, artificial neural networks (ANN) have been introduced to chemometrics for solving problems in analytical chemistry. ANN are based on the functioning of the brain and can be used for modeling complex relationships within chemical data. An ANN-model can be obtained by earning or

  15. Artificial muscles for a novel simulator in minimally invasive spine surgery.

    Science.gov (United States)

    Hollensteiner, Marianne; Fuerst, David; Schrempf, Andreas

    2014-01-01

    Vertebroplasty and kyphoplasty are commonly used minimally invasive methods to treat vertebral compression fractures. Novice surgeons gather surgical skills in different ways, mainly by "learning by doing" or training on models, specimens or simulators. Currently, a new training modality, an augmented reality simulator for minimally invasive spine surgeries, is going to be developed. An important step in investigating this simulator is the accurate establishment of artificial tissues. Especially vertebrae and muscles, reproducing a comparable haptical feedback during tool insertion, are necessary. Two artificial tissues were developed to imitate natural muscle tissue. The axial insertion force was used as validation parameter. It appropriates the mechanical properties of artificial and natural muscles. Validation was performed on insertion measurement data from fifteen artificial muscle tissues compared to human muscles measurement data. Based on the resulting forces during needle insertion into human muscles, a suitable material composition for manufacturing artificial muscles was found.

  16. Fate and behaviour of phenanthrene in the natural and artificial soils

    International Nuclear Information System (INIS)

    Hofman, Jakub; Rhodes, Angela; Semple, Kirk T.

    2008-01-01

    OECD artificial soil has been used routinely as a standardized substrate for soil toxicity tests. However, can be the fate, behaviour and effects of contaminants in artificial soil extrapolated to natural soils? The aim of our study was to verify this hypothesis by comparing the loss, extraction, and bioavailability of phenanthrene in three artificial and three natural soils of comparable organic carbon content. Soils were spiked with 14 C-phenanthrene and total 14 C-activity change, the fractions extracted by dichloromethane, 70% ethanol, and hydroxypropyl-β-cyclodextrin, the fraction mineralized by Pseudomonas sp., and taken up by Enchytraeus albidus were measured after 1, 14, 42, and 84 d aging. The loss, extraction, biodegradation and uptake were several times lower in the artificial than natural soils and these differences increased with increasing soil-phenanthrene contact time. These results imply that artificial soil should be used cautiously for the prediction of fate and behaviour in natural soils. - Artificial soils show substantially different fate and behaviour of phenanthrene than natural soils, which cannot be easily extrapolated or modelled

  17. Degradation of ticarcillin by subcritial water oxidation method: Application of response surface methodology and artificial neural network modeling.

    Science.gov (United States)

    Yabalak, Erdal

    2018-05-18

    This study was performed to investigate the mineralization of ticarcillin in the artificially prepared aqueous solution presenting ticarcillin contaminated waters, which constitute a serious problem for human health. 81.99% of total organic carbon removal, 79.65% of chemical oxygen demand removal, and 94.35% of ticarcillin removal were achieved by using eco-friendly, time-saving, powerful and easy-applying, subcritical water oxidation method in the presence of a safe-to-use oxidizing agent, hydrogen peroxide. Central composite design, which belongs to the response surface methodology, was applied to design the degradation experiments, to optimize the methods, to evaluate the effects of the system variables, namely, temperature, hydrogen peroxide concentration, and treatment time, on the responses. In addition, theoretical equations were proposed in each removal processes. ANOVA tests were utilized to evaluate the reliability of the performed models. F values of 245.79, 88.74, and 48.22 were found for total organic carbon removal, chemical oxygen demand removal, and ticarcillin removal, respectively. Moreover, artificial neural network modeling was applied to estimate the response in each case and its prediction and optimizing performance was statistically examined and compared to the performance of central composite design.

  18. Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Anson Chui Yan Tang

    2012-01-01

    Full Text Available In view of lacking a quantifiable traditional Chinese medicine (TCM pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group. A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

  19. Development of artificial neural network models for supercritical fluid solvency in presence of co-solvents

    Energy Technology Data Exchange (ETDEWEB)

    Shokir, Eissa Mohamed El-Moghawry; El-Midany, Ayman Abdel-Hamid [Cairo University, Giza (Egypt); Al-Homadhi, Emad Souliman; Al-Mahdy, Osama [King Saud University, Riyadh (Saudi Arabia)

    2014-08-15

    This paper presents the application of artificial neural networks (ANN) to develop new models of liquid solvent dissolution of supercritical fluids with solutes in the presence of cosolvents. The neural network model of the liquid solvent dissolution of CO{sub 2} was built as a function of pressure, temperature, and concentrations of the solutes and cosolvents. Different experimental measurements of liquid solvent dissolution of supercritical fluids (CO{sub 2}) with solutes in the presence of cosolvents were collected. The collected data are divided into two parts. The first part was used in building the models, and the second part was used to test and validate the developed models against the Peng- Robinson equation of state. The developed ANN models showed high accuracy, within the studied variables range, in predicting the solubility of the 2-naphthol, anthracene, and aspirin in the supercritical fluid in the presence and absence of co-solvents compared to (EoS). Therefore, the developed ANN models could be considered as a good tool in predicting the solubility of tested solutes in supercritical fluid.

  20. [Total artificial heart].

    Science.gov (United States)

    Antretter, H; Dumfarth, J; Höfer, D

    2015-09-01

    To date the CardioWest™ total artificial heart is the only clinically available implantable biventricular mechanical replacement for irreversible cardiac failure. This article presents the indications, contraindications, implantation procedere and postoperative treatment. In addition to a overview of the applications of the total artificial heart this article gives a brief presentation of the two patients treated in our department with the CardioWest™. The clinical course, postoperative rehabilitation, device-related complications and control mechanisms are presented. The total artificial heart is a reliable implant for treating critically ill patients with irreversible cardiogenic shock. A bridge to transplantation is feasible with excellent results.

  1. Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

    International Nuclear Information System (INIS)

    Zare, Mansour; Vahdati Khaki, Jalil

    2012-01-01

    Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

  2. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  3. An artificial intelligence approach to accelerator control systems

    International Nuclear Information System (INIS)

    Schultz, D.E.; Hurd, J.W.; Brown, S.K.

    1987-01-01

    An experiment was recently started at LAMPF to evaluate the power and limitations of using artificial intelligence techniques to solve problems in accelerator control and operation. A knowledge base was developed to describe the characteristics and the relationships of the first 30 devices in the LAMPF H+ beam line. Each device was categorized and pertinent attributes for each category defined. Specific values were assigned in the knowledge base to represent each actual device. Relationships between devices are modeled using the artificial intelligence techniques of rules, active values, and object-oriented methods. This symbolic model, built using the Knowledge Engineering Environment (KEE) system, provides a framework for analyzing faults, tutoring trainee operators, and offering suggestions to assist in beam tuning. Based on information provided by the domain expert responsible for tuning this portion of the beam line, additional rules were written to describe how he tunes, how he analyzes what is actually happening, and how he deals with failures. Initial results have shown that artificial intelligence techniques can be a useful adjunct to traditional methods of numerical simulation. Successful and efficient operation of future accelerators may depend on the proper merging of symbolic reasoning and conventional numerical control algorithms

  4. Artificial Intelligence in Astronomy

    Science.gov (United States)

    Devinney, E. J.; Prša, A.; Guinan, E. F.; Degeorge, M.

    2010-12-01

    From the perspective (and bias) as Eclipsing Binary researchers, we give a brief overview of the development of Artificial Intelligence (AI) applications, describe major application areas of AI in astronomy, and illustrate the power of an AI approach in an application developed under the EBAI (Eclipsing Binaries via Artificial Intelligence) project, which employs Artificial Neural Network technology for estimating light curve solution parameters of eclipsing binary systems.

  5. Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

    International Nuclear Information System (INIS)

    Susmikanti, Mike; Sulistyo, Jos

    2014-01-01

    Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to develop code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix

  6. Exploring the Role of Genetic Algorithms and Artificial Neural Networks for Interpolation of Elevation in Geoinformation Models

    Science.gov (United States)

    Bagheri, H.; Sadjadi, S. Y.; Sadeghian, S.

    2013-09-01

    One of the most significant tools to study many engineering projects is three-dimensional modelling of the Earth that has many applications in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling (DTM). DTM has numerous applications in the fields of sciences, engineering, design and various project administrations. One of the most significant events in DTM technique is the interpolation of elevation to create a continuous surface. There are several methods for interpolation, which have shown many results due to the environmental conditions and input data. The usual methods of interpolation used in this study along with Genetic Algorithms (GA) have been optimised and consisting of polynomials and the Inverse Distance Weighting (IDW) method. In this paper, the Artificial Intelligent (AI) techniques such as GA and Neural Networks (NN) are used on the samples to optimise the interpolation methods and production of Digital Elevation Model (DEM). The aim of entire interpolation methods is to evaluate the accuracy of interpolation methods. Universal interpolation occurs in the entire neighbouring regions can be suggested for larger regions, which can be divided into smaller regions. The results obtained from applying GA and ANN individually, will be compared with the typical method of interpolation for creation of elevations. The resulting had performed that AI methods have a high potential in the interpolation of elevations. Using artificial networks algorithms for the interpolation and optimisation based on the IDW method with GA could be estimated the high precise elevations.

  7. [Preparation of nano-nacre artificial bone].

    Science.gov (United States)

    Chen, Jian-ting; Tang, Yong-zhi; Zhang, Jian-gang; Wang, Jian-jun; Xiao, Ying

    2008-12-01

    To assess the improvements in the properties of nano-nacre artificial bone prepared on the basis of nacre/polylactide acid composite artificial bone and its potential for clinical use. The compound of nano-scale nacre powder and poly-D, L-lactide acid (PDLLA) was used to prepare the cylindrical hollow artificial bone, whose properties including raw material powder scale, pore size, porosity and biomechanical characteristics were compared with another artificial bone made of micron-scale nacre powder and PDLLA. Scanning electron microscope showed that the average particle size of the nano-nacre powder was 50.4-/+12.4 nm, and the average pore size of the artificial bone prepared using nano-nacre powder was 215.7-/+77.5 microm, as compared with the particle size of the micron-scale nacre powder of 5.0-/+3.0 microm and the pore size of the resultant artificial bone of 205.1-/+72.0 microm. The porosities of nano-nacre artificial bone and the micron-nacre artificial bone were (65.4-/+2.9)% and (53.4-/+2.2)%, respectively, and the two artificial bones had comparable compressive strength and Young's modulus, but the flexural strength of the nano-nacre artificial bone was lower than that of the micro-nacre artificial bone. The nano-nacre artificial bone allows better biodegradability and possesses appropriate pore size, porosity and biomechanical properties for use as a promising material in bone tissue engineering.

  8. Determination of Pole and Rotation Period of not Stabilized Artificial Satellite by Use of Model "diffuse Cylinder"

    Science.gov (United States)

    Kolesnik, S. Ya.; Dobrovolsky, A. V.; Paltsev, N. G.

    The algorithm of determination of orientation of rotation axis (pole) and rotation period of satellite, simulated by a cylinder, which is precessing around of vector of angular moment of pulse with constant nutation angle is offered. The Lambert's law of light reflection is accepted. Simultaneously, dependence of light reflection coefficient versus phase angle is determined. The model's simulation confirm applicability of this method. Results of the calculations for artificial satellite No 28506 are carried out.

  9. Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

    International Nuclear Information System (INIS)

    Ekonomou, L; Karampelas, P; Vita, V; Chatzarakis, G E

    2011-01-01

    One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service

  10. Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

    Science.gov (United States)

    Ekonomou, L.; Karampelas, P.; Vita, V.; Chatzarakis, G. E.

    2011-04-01

    One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service.

  11. Optimality in Microwave-Assisted Drying of Aloe Vera ( Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling

    Science.gov (United States)

    Das, Chandan; Das, Arijit; Kumar Golder, Animes

    2016-10-01

    The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. `Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.

  12. Pneumatic Artificial Muscles Based on Biomechanical Characteristics of Human Muscles

    Directory of Open Access Journals (Sweden)

    N. Saga

    2006-01-01

    Full Text Available This article reports the pneumatic artificial muscles based on biomechanical characteristics of human muscles. A wearable device and a rehabilitation robot that assist a human muscle should have characteristics similar to those of human muscle. In addition, since the wearable device and the rehabilitation robot should be light, an actuator with a high power to weight ratio is needed. At present, the McKibben type is widely used as an artificial muscle, but in fact its physical model is highly nonlinear. Therefore, an artificial muscle actuator has been developed in which high-strength carbon fibres have been built into the silicone tube. However, its contraction rate is smaller than the actual biological muscles. On the other hand, if an artificial muscle that contracts axially is installed in a robot as compactly as the robot hand, big installing space is required. Therefore, an artificial muscle with a high contraction rate and a tendon-driven system as a compact actuator were developed, respectively. In this study, we report on the basic structure and basic characteristics of two types of actuators.

  13. FPGA controlled artificial vascular system

    Directory of Open Access Journals (Sweden)

    Laqua D.

    2015-09-01

    Full Text Available Monitoring the oxygen saturation of an unborn child is an invasive procedure, so far. Transabdominal fetal pulse oximetry is a promising method under research, used to estimate the oxygen saturation of a fetus noninvasively. Due to the nature of the method, the fetal information needs to be extracted from a mixed signal. To properly evaluate signal processing algorithms, a phantom modeling fetal and maternal blood circuits and tissue layers is necessary. This paper presents an improved hardware concept for an artificial vascular system, utilizing an FPGA based CompactRIO System from National Instruments. The experimental model to simulate the maternal and fetal blood pressure curve consists of two identical hydraulic circuits. Each of these circuits consists of a pre-pressure system and an artificial vascular system. Pulse curves are generated by proportional valves, separating these two systems. The dilation of the fetal and maternal artificial vessels in tissue substitutes is measured by transmissive and reflective photoplethysmography. The measurement results from the pressure sensors and the transmissive optical sensors are visualized to show the functionality of the pulse generating systems. The trigger frequency for the maternal valve was set to 1 per second, the fetal valve was actuated at 0.7 per second for validation. The reflective curve, capturing pulsations of the fetal and maternal circuit, was obtained with a high power LED (905 nm as light source. The results show that the system generates pulse curves, similar to its physiological equivalent. Further, the acquired reflective optical signal is modulated by the alternating diameter of the tubes of both circuits, allowing for tests of signal processing algorithms.

  14. Evaluating portland cement concrete degradation by sulphate exposure through artificial neural networks modeling

    International Nuclear Information System (INIS)

    Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro; Costa, Rodrigo Moyses; Tello, Cledola Cassia Oliveira de

    2015-01-01

    A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)

  15. Evaluating portland cement concrete degradation by sulphate exposure through artificial neural networks modeling

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Douglas Nunes de; Bourguignon, Lucas Gabriel Garcia; Tolentino, Evandro, E-mail: tolentino@timoteo.cefetmg.br [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET-MG), Timoteo, MG (Brazil); Costa, Rodrigo Moyses, E-mail: rodrigo@moyses.com.br [Universidade de Itauna, Itauna, MG (Brazil); Tello, Cledola Cassia Oliveira de, E-mail: tellocc@cdtn.br [Centro de Desenvolvimento da Tecnologia Nucelar (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

    2015-07-01

    A concrete is durable if it has accomplished the desired service life in the environment in which it is exposed. The durability of concrete materials can be limited as a result of adverse performance of its cement-paste matrix or aggregate constituents under either chemical or physical attack. Among other aggressive chemical exposures, the sulphate attack is an important concern. Water, soils and gases, which contain sulphate, represent a potential threat to the durability of concrete structures. Sulphate attack in concrete leads to the conversion of the hydration products of cement to ettringite, gypsum, and other phases, and also it leads to the destabilization of the primary strength generating calcium silicate hydrate (C-S-H) gel. The formation of ettringite and gypsum is common in cementitious systems exposed to most types of sulphate solutions. The present work presents the application of the neural networks for estimating deterioration of various concrete mixtures due to exposure to sulphate solutions. A neural networks model was constructed, trained and tested using the available database. In general, artificial neural networks could be successfully used in function approximation problems in order to approach the data generation function. Once data generation function is known, artificial neural network structure is tested using data not presented to the network during training. This paper is intent to provide the technical requirements related to the production of a durable concrete to be used in the structures of the Brazilian near-surface repository of radioactive wastes. (author)

  16. Application of artificial neural network model for groundwater level forecasting in a river island with artificial influencing factors

    Science.gov (United States)

    Lee, Sanghoon; Yoon, Heesung; Park, Byeong-Hak; Lee, Kang-Kun

    2017-04-01

    Groundwater use has been increased for various purposes like agriculture, industry or drinking water in recent years, the issue related to sustainability on the groundwater use also has been raised. Accordingly, forecasting the groundwater level is of great importance for planning sustainable use of groundwater. In a small island surrounded by the Han River, South Korea, seasonal fluctuation of the groundwater level is characterized by multiple factors such as recharge/discharge event of the Paldang dam, Water Curtain Cultivation (WCC) during the winter season, operation of Groundwater Heat Pump System (GWHP). For a period when the dam operation is only occurred in the study area, a prediction of the groundwater level can be easily achieved by a simple cross-correlation model. However, for a period when the WCC and the GWHP systems are working together, the groundwater level prediction is challenging due to its unpredictable operation of the two systems. This study performed Artificial Neural Network (ANN) model to forecast the groundwater level in the river area reflecting the various predictable/unpredictable factors. For constructing the ANN models, two monitoring wells, YSN1 and YSO8, which are located near the injection and abstraction wells for the GWHP system were selected, respectively. By training with the groundwater level data measured in January 2015 to August 2015, response of groundwater level by each of the surface water level, the WCC and the GWHP system were evaluated. Consequentially, groundwater levels in December 2015 to March 2016 were predicted by ANN models, providing optimal fits in comparison to the observed water levels. This study suggests that the ANN model is a useful tool to forecast the groundwater level in terms of the management of groundwater. Acknowledgement : Financial support was provided by the "R&D Project on Environmental Management of Geologic CO2 Storage" from the KEITI (Project Number: 2014001810003) This research was

  17. Techniques of artificial intelligence applied to the electric power expansion distribution system planning problem; Tecnicas de inteligencia artificial aplicadas ao problema de planejamento da expansao do sistema de distribuicao de energia eletrica

    Energy Technology Data Exchange (ETDEWEB)

    Froes, Salete Maria

    1996-07-01

    A tool named Constrained Decision Problem (CDP), which is based on Artificial Intelligence and a specific application to Distribution System Planning is described. The CDP allows multiple objective optimization that does not, necessarily, result in a single optimal solution. First, a literature review covers published works related to Artificial Intelligence applications to Electric Power Distribution Systems, emphasizing feeder restoration and reconfiguration. Some concepts related to Artificial Intelligence are described, with particular attention to Planning and to Constrained Decision Problems. Following, an Electric Power System planning model is addressed by using the CDP tool. Some case studies illustrate the Distribution Planning model, which are compared with standard optimization models. Concluding, some comments establishing the possibilities of CDP applications are followed by a view on future developments. (author)

  18. Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment.

    Science.gov (United States)

    Picos-Benítez, Alain R; López-Hincapié, Juan D; Chávez-Ramírez, Abraham U; Rodríguez-García, Adrián

    2017-03-01

    The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.

  19. Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

    Science.gov (United States)

    Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

    2013-01-01

    Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

  20. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

    Science.gov (United States)

    Enshaei, A; Robson, C N; Edmondson, R J

    2015-11-01

    The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

  1. Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network

    International Nuclear Information System (INIS)

    Lin, Jing-Fung; Sheu, Jer-Jia

    2016-01-01

    Citric acid coated (citrate-stabilized) magnetite (Fe 3 O 4 ) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF. - Highlights: • The feedforward ANN is applied for modeling of retardance in CA coated FFs. • ANN can predict the retardance at excellent program with acceptable error to MR. • The proposed ANN has high ability for the prediction of retardance.

  2. Artificial Intelligence Study (AIS).

    Science.gov (United States)

    1987-02-01

    ARTIFICIAL INTELLIGNECE HARDWARE ....... 2-50 AI Architecture ................................... 2-49 AI Hardware ....................................... 2...ftf1 829 ARTIFICIAL INTELLIGENCE STUDY (RIS)(U) MAY CONCEPTS 1/3 A~NLYSIS AGENCY BETHESA RD R B NOJESKI FED 6? CM-RP-97-1 NCASIFIED /01/6 M |K 1.0...p/ - - ., e -- CAA- RP- 87-1 SAOFŔ)11 I ARTIFICIAL INTELLIGENCE STUDY (AIS) tNo DTICFEBRUARY 1987 LECT 00 I PREPARED BY RESEARCH AND ANALYSIS

  3. EPR studies of a red wine bottle deposit, and the precipitates from a 'model' wine , and a white wine, both artificially aged

    International Nuclear Information System (INIS)

    Mitri, M.

    2003-01-01

    Full text: A red wine waxy bottle deposit is known to be an anthocyanin-protein compound. The EPR signal shows the presence of a free radical signal, and a Cu(2+) signal with N superhyperfme structure. Subsequently, EPR study of a model wine, catechin being the only phenol, showed a Cu(2+) signal and a free radical signal. The precipitate thrown by the model wine after artificial aging for 3 months at 45C showed a Cu(2+) signal of different bonding, and a free radical signal. All the previously mentioned Cu(2+) signals showed (differing) hyperfine structures. The precipitate thrown by a similarly artificially aged Chardonnay showed a free radical signal, and a Cu(2+) signal without hyperfine structure: no Cu(2+) signal was detected in the mother liquor. The Cu(2+) bonding in each case will be discussed

  4. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

    Science.gov (United States)

    Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

    2015-01-01

    Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

  5. Kinematic Analysis of 3-DOF Planer Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jolly Atit Shah

    2012-07-01

    Full Text Available Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the forward and inverse kinematics of 3-DOF robotic manipulator with revolute joints. In this study the Denavit- Hartenberg (D-H model is used to model robot links and joints. Also forward and inverse kinematics solution has been achieved using Artificial Neural Networks for 3-DOF robotic manipulator. It shows that by using artificial neural network the solution we get is faster, acceptable and has zero error.

  6. Assessment of safety regulation using an artificial society

    International Nuclear Information System (INIS)

    Furuta, Kazuo; Nagase, Masaya

    2005-01-01

    This study proposes using an artificial society to assess impacts of safety regulation on the society. The artificial society used in this study is a multi-agent system, which consists of many agents representing companies. The agents cannot survive unless they get profits by producing some products. Safety regulation functions as the business environment, which the agents will evolve to fit to. We modeled this process of survival and adaptation by the genetic algorithm. Using the proposed model, case simulations were performed to compare various regulation styles, and some interesting insights were obtained how regulation style influences behavior of the agents and then productivity and safety level of the industry. In conclusion, an effective method for assessment of safety regulation has been developed, and then several insights were shown in this study

  7. Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Priya Selvanathan Shanmuga

    2016-01-01

    Full Text Available The advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation measuring instruments available at Innovation Center, MIT Manipal were integrated with a Raspberry Pi to allow remote access to the data through the university Local Area Network. The connections of the data loggers and the Raspberry Pi were enclosed in a plastic box to prevent damage from the rainfall and humidity in Manipal. The solar radiation data was used to validate an Artificial Neural Network model which was developed using various meterological data from 2011-2015.

  8. An artificial muscle model unit based on inorganic nanosheet sliding by photochemical reaction.

    Science.gov (United States)

    Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Sasamoto, Shin; Tanamura, Yoshihiko; Shimada, Tetsuya; Masui, Dai; Takagi, Shinsuke; Tachibana, Hiroshi; Tong, Zhiwei; Inoue, Haruo

    2013-04-21

    From the viewpoint of developing photoresponsive supramolecular systems in microenvironments to exhibit more sophisticated photo-functions even at the macroscopic level, inorganic/organic hybrid compounds based on clay or niobate nanosheets as the microenvironments were prepared, characterized, and examined for their photoreactions. We show here a novel type of artificial muscle model unit having much similarity with that in natural muscle fibrils. Upon photoirradiation, the organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale, and the interlayer spaces in the layered hybrid structure shrink and expand vertically. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) compared with the molecular size of ~1 nm, based on a reversible sliding mechanism.

  9. Quo Vadis, Artificial Intelligence?

    OpenAIRE

    Berrar, Daniel; Sato, Naoyuki; Schuster, Alfons

    2010-01-01

    Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervou...

  10. Inteligencia artificial en vehiculo

    OpenAIRE

    Amador Díaz, Pedro

    2012-01-01

    Desarrollo de un robot seguidor de líneas, en el que se implementan diversas soluciones de las áreas de sistemas embebidos e inteligencia artificial. Desenvolupament d'un robot seguidor de línies, en el qual s'implementen diverses solucions de les àrees de sistemes encastats i intel·ligència artificial. Follower robot development of lines, in which various solutions are implemented in the areas of artificial intelligence embedded systems.

  11. Artificial Intelligence and Moral intelligence

    Directory of Open Access Journals (Sweden)

    Laura Pana

    2008-07-01

    means for supplementing the objective decision with a subjective one. Machine ethics can/will be of the highest quality because it will be derived from the sciences, modelled by techniques and accomplished by technologies. If our theoretical hypothesis about a specific moral intelligence, necessary for the implementation of an artificial moral conduct, is correct, then some theoretical and technical issues appear, but the following working hypotheses are possible: structural, functional and behavioural. The future of human and/or artificial morality is to be anticipated.

  12. Criminal Aspects of Artificial Abortion

    OpenAIRE

    Hartmanová, Leona

    2016-01-01

    Criminal Aspects of Artificial Abortion This diploma thesis deals with the issue of artificial abortion, especially its criminal aspects. Legal aspects are not the most important aspects of artificial abortion. Social, ethical or ideological aspects are of the same importance but this diploma thesis cannot analyse all of them. The main issue with artificial abortion is whether it is possible to force a pregnant woman to carry a child and give birth to a child when she cannot or does not want ...

  13. The impact of the artificial intervertebral disc on functioning the lumbar spine

    Directory of Open Access Journals (Sweden)

    Mańsko M.

    2015-12-01

    Full Text Available In the hereby thesis the anatomy of the lumbar vertebra and intervertebral disc were presented. Functioning and kinematics of the spine and intervertebral forces were described.Full three – dimensional model of the lumbar vertebrae L2 – L4 was created. On the basis of it model of artificial intervertebral disc was constructed (between L2 and L3. The simplified model of vertebra L2 was formulated via finite elements method. Processed model has been used for biomechanical analysis.Strength calculations were made and appropriate conclusions were drawn. Presented results show behavior influenced of three – dimensional model of the lumbar vertebra with artificial intervertebral disc by operation of loads.

  14. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    International Nuclear Information System (INIS)

    Ahmadi, Rouhollah; Khamehchi, Ehsan

    2013-01-01

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data

  15. Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks

    Energy Technology Data Exchange (ETDEWEB)

    Ahmadi, Rouhollah, E-mail: rouhollahahmadi@yahoo.com [Amirkabir University of Technology, PhD Student at Reservoir Engineering, Department of Petroleum Engineering (Iran, Islamic Republic of); Khamehchi, Ehsan [Amirkabir University of Technology, Faculty of Petroleum Engineering (Iran, Islamic Republic of)

    2013-12-15

    Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.

  16. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

    Science.gov (United States)

    Jahedi Rad, Shahpour; Kaveh, Mohammad; Sharabiani, Vali Rasooli; Taghinezhad, Ebrahim

    2018-05-01

    The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R 2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R 2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

  17. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2017-01-01

    Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Improving Artificial Neural Network Forecasts with Kalman Filtering ...

    African Journals Online (AJOL)

    In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...

  19. Implementation and Validation of Artificial Intelligence Techniques for Robotic Surgery

    OpenAIRE

    Aarshay Jain; Deepansh Jagotra; Vijayant Agarwal

    2014-01-01

    The primary focus of this study is implementation of Artificial Intelligence (AI) technique for developing an inverse kinematics solution for the Raven-IITM surgical research robot [1]. First, the kinematic model of the Raven-IITM robot was analysed along with the proposed analytical solution [2] for inverse kinematics problem. Next, The Artificial Neural Network (ANN) techniques was implemented. The training data for the same was careful selected by keeping manipulability constraints in mind...

  20. ANALYSIS OF HUMAN INTUITION TOWARDS ARTIFICIAL INTUITION SYNTHESIS FOR ROBOTICS

    OpenAIRE

    Octavio Diaz-Hernandez; Victor J. Gonzalez-Villela

    2017-01-01

    Human intuition is an unconscious mental process aimed to solve problems without using a rational decision-making process. Meanwhile, the artificial intuition is a limited representation of human intuition, and it models intuitive ability of solving problems in order to be implemented in machines. In this work, we performed an analysis about analogies between human and artificial intuition using INPUTS, PROCESSING, and OUTPUTS. Mainly, we have focused on synthetize algorithms that improve rob...

  1. Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

    International Nuclear Information System (INIS)

    Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos

    2009-01-01

    Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

  2. [Trachea repair and reconstruction with new composite artificial trachea transplantation].

    Science.gov (United States)

    Liu, Wenliang; Xiao, Peng; Liang, Hengxing; An, Ran; Cheng, Gang; Yu, Fenglei

    2013-03-01

    To construct a new composite artificial trachea and to investigate the feasibility of trachea repair and reconstruction with the new composite artificial trachea transplantation in dogs. The basic skeleton of the new composite artificial trachea was polytetrafluoroethylene vascular prosthesis linked with titanium rings at both ends. Dualmesh was sutured on titanium rings. Sixteen dogs, weighing (14.9 +/- 2.0) kg, female or male, were selected. The 5 cm cervical trachea was resected to prepare the cervical trachea defect model. The trachea repair and reconstruction was performed with the new composite artificial trachea. Then fiberoptic bronchoscope examination, CT scan and three-dimensinal reconstruction were conducted at immediate, 1 month, and 6 months after operation. Gross observation and histological examination were conducted at 14 months to evaluate the repair and reconstruction efficacy. No dog died during operation of trachea reconstruction. One dog died of dyspnea at 37, 41, 55, 66, 140, and 274 days respectively because of anastomotic dehiscence and artificial trachea displacement; the other 10 dogs survived until 14 months. The fiberoptic bronchoscope examination, CT scan and three-dimensinal reconstruction showed that artificial tracheas were all in good location without twisting at immediate after operation; mild stenosis occurred and anastomoses had slight granulation in 6 dogs at 1 month; severe stenosis developed and anastomosis had more granulation in 1 dog and the other dogs were well alive without anastomotic stenosis at 6 months. At 14 months, gross observation revealed that outer surface of the artificial trachea were encapsulated by fibrous connective tissue in all of 10 dogs. Histological examination showed inflammatory infiltration and hyperplasia of fibrous tissue and no epithelium growth on the inner wall of the artificial trachea. The new composite artificial trachea can be used to repair and reconstruct defect of the trachea for a short

  3. Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

    Science.gov (United States)

    Singh, Kunwar P; Gupta, Shikha; Ojha, Priyanka; Rai, Premanjali

    2013-04-01

    The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.

  4. An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers

    International Nuclear Information System (INIS)

    Tudón-Martínez, J C; Lozoya-Santos, J J; Morales-Menendez, R; Ramirez-Mendoza, R A

    2012-01-01

    A static model for a magneto-rheological (MR) damper based on artificial neural networks (ANNs) is proposed, and an intensive and experimental study is presented for designing the ANN structure. The ANN model does not require time delays in the input vector. Besides the electric current signal, only one additional sensor is used to achieve a reliable MR damper structure. The model is experimentally validated with two commercial MR dampers of different characteristics: MR 1 damper with continuous actuation and MR 2 damper with two levels of actuation. The error to signal ratio (ESR) index is used to measure the model accuracy; for both MR dampers, an average value of 6.03% of total error is obtained from different experiments, which are designed to explore the nonlinearities of the MR phenomenon at different frequencies by including the impact of the electric current fluctuations. The proposed ANN model is compared with other well known parametric models; the qualitative and quantitative comparison among the models highlights the advantages of the ANN for representing a commercial MR damper. The ESR index was reduced by the ANN-based model by up to 29% with respect to the parametric models for the MR 1 damper and up to 40% for the MR 2 damper. The force–velocity diagram is used to compare the modeling properties of each approach: (1) the Bingham model cannot describe the hysteresis of both MR dampers and the distribution function of the modeled force varies from the experimental data, (2) the algebraic models have complications in representing the nonlinear behavior of the asymmetric damper (MR 2 ) and, (3) the ANN-based MR damper can model the nonlinearities of both MR dampers and presents good scalability; the accuracy of the results supports the use of this model for the validation of semi-active suspension control systems for a vehicle, by using nonlinear simulations. (paper)

  5. A model to predict the level of artificial radionuclides in environmental materials in the Severn Estuary and the Bristol Channel

    International Nuclear Information System (INIS)

    McColl, N.P.

    1988-01-01

    The NRPB SEVERN compartment model, of the Bristol Channel and Severn Estuary, has been developed for used in predicting environmental concentrations of artificial radionuclides present in the estuary. A comparison between predicted and measured values of salinity and environmental 137 Cs concentrations has demonstrated the overall validity of the model. SEVERN has been used to assess the radiological impact of radionuclides present in the estuary which result from low-level routine discharges from the nuclear power industry. (author)

  6. Magnetic response of brickwork artificial spin ice

    Science.gov (United States)

    Park, Jungsik; Le, Brian L.; Sklenar, Joseph; Chern, Gia-Wei; Watts, Justin D.; Schiffer, Peter

    2017-07-01

    We have investigated the response of brickwork artificial spin ice to an applied in-plane magnetic field through magnetic force microscopy, magnetotransport measurements, and micromagnetic simulations. We find that, by sweeping an in-plane applied field from saturation to zero in a narrow range of angles near one of the principal axes of the lattice, the moments of the system fall into an antiferromagnetic ground state in both connected and disconnected structures. Magnetotransport measurements of the connected lattice exhibit unique signatures of this ground state. Also, modeling of the magnetotransport demonstrates that the signal arises at vertex regions in the structure, confirming behavior that was previously seen in transport studies of kagome artificial spin ice.

  7. Control Systems for Hyper-Redundant Robots Based on Artificial Potential Method

    Directory of Open Access Journals (Sweden)

    Mihaela Florescu

    2015-06-01

    Full Text Available This paper presents the control method of hyper-redundant robots based on the artificial potential approach. The principles of this method are shown and a suggestive example is offered. Then, the artificial potential method is applied to the case of a tentacle robot starting from the dynamic model of the robot. In addition, a series of results that are obtained through simulation is presented.

  8. Role-based Rights in Artificial Social Systems

    NARCIS (Netherlands)

    G. Boella (Guido); L.W.N. van der Torre (Leon)

    2005-01-01

    htmlabstract In this paper we use normative systems to introduce roles and rights in the game-theoretic artificial social systems developed by Shoham and Tennenholtz. We model normative systems as socially constructed agents whose behavior is determined by a set of role playing agents. Roles are

  9. Modeling the long-term effect of winter cover crops on nitrate transport in artificially drained fields across the Midwest U.S.

    Science.gov (United States)

    A fall-planted cover crop is a management practice with multiple benefits including reducing nitrate losses from artificially drained fields. We used the Root Zone Water Quality Model (RZWQM) to simulate the impact of a cereal rye cover crop on reducing nitrate losses from drained fields across five...

  10. Linear modeling of nonlinear systems using artificial neural networks based on I/O data and its application in power plant boiler modeling

    International Nuclear Information System (INIS)

    Ghaffari, A.; Nikkhah Bahrami, M.; Mohammadzaheri, M.

    2005-01-01

    In this paper a new method for linear modeling of nonlinear systems is presented. The method is based on the design of an artificial neural network with two layers. The network is trained only according to the input-output data of the system. The weights of connections in this network, represents the coefficients of the transfer function. For systems with linear behavior the method of least square error represents the best linear model of the system. However, for nonlinear systems, such as some subsystems in power plants boilers LSE does not represent the best linear approximation of the system, necessarily. In this paper a new linear modeling method is presented and applied to some subsystems in a power plant boiler. Comparison between the transfer function obtained in this way and by least square error method,shows that the neural network method gives better linear models for these nonlinear systems

  11. Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

    Science.gov (United States)

    Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

    2015-10-01

    Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

  12. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  13. Artificial consciousness and the consciousness-attention dissociation.

    Science.gov (United States)

    Haladjian, Harry Haroutioun; Montemayor, Carlos

    2016-10-01

    Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems-these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Artificial Hydration and Nutrition

    Science.gov (United States)

    ... Crisis Situations Pets and Animals myhealthfinder Food and Nutrition Healthy Food Choices Weight Loss and Diet Plans ... Your Health Resources Healthcare Management Artificial Hydration and Nutrition Artificial Hydration and Nutrition Share Print Patients who ...

  15. Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

    DEFF Research Database (Denmark)

    Grappiolo, Corrado; Togelius, Julian; Yannakakis, Georgios N.

    2013-01-01

    This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in...

  16. Artificial intelligence

    International Nuclear Information System (INIS)

    Perret-Galix, D.

    1992-01-01

    A vivid example of the growing need for frontier physics experiments to make use of frontier technology is in the field of artificial intelligence and related themes. This was reflected in the second international workshop on 'Software Engineering, Artificial Intelligence and Expert Systems in High Energy and Nuclear Physics' which took place from 13-18 January at France Telecom's Agelonde site at La Londe des Maures, Provence. It was the second in a series, the first having been held at Lyon in 1990

  17. Artificial fish schools : Collective effects of school size, body size, and body form

    NARCIS (Netherlands)

    Kunz, H.; Hemelrijk, C.K.

    2003-01-01

    Individual-based models of schooling in fish have demonstrated that, via processes of self-organization. artificial fish may school in the absence of a leader or external stimuli, using local information only. We study for the first time how body size and body form of artificial fish affect school

  18. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    International Nuclear Information System (INIS)

    Pendashteh, Ali Reza; Fakhru'l-Razi, A.; Chaibakhsh, Naz; Abdullah, Luqman Chuah; Madaeni, Sayed Siavash; Abidin, Zurina Zainal

    2011-01-01

    Highlights: → Hypersaline oily wastewater was treated in a membrane bioreactor. → The effects of salinity and organic loading rate were evaluated. → The system was modeled by neural network and optimized by genetic algorithm. → The model prediction agrees well with experimental values. → The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m 3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m 3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  19. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Pendashteh, Ali Reza [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Environmental Research Institute, Iranian Academic Center for Education, Culture and Research (ACECR), Rasht (Iran, Islamic Republic of); Fakhru' l-Razi, A., E-mail: fakhrul@eng.upm.edu.my [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Chaibakhsh, Naz [Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Abdullah, Luqman Chuah [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia); Madaeni, Sayed Siavash [Chemical Engineering Department, Razi University, Kermanshah (Iran, Islamic Republic of); Abidin, Zurina Zainal [Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E. (Malaysia)

    2011-08-30

    Highlights: {yields} Hypersaline oily wastewater was treated in a membrane bioreactor. {yields} The effects of salinity and organic loading rate were evaluated. {yields} The system was modeled by neural network and optimized by genetic algorithm. {yields} The model prediction agrees well with experimental values. {yields} The model can be used to obtain effluent characteristics less than discharge limits. - Abstract: A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m{sup 3} day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O and G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m{sup 3} day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.

  20. Examination of mitral regurgitation with a goat heart model for the development of intelligent artificial papillary muscle.

    Science.gov (United States)

    Shiraishi, Y; Yambe, T; Yoshizawa, M; Hashimoto, H; Yamada, A; Miura, H; Hashem, M; Kitano, T; Shiga, T; Homma, D

    2012-01-01

    Annuloplasty for functional mitral or tricuspid regurgitation has been made for surgical restoration of valvular diseases. However, these major techniques may sometimes be ineffective because of chamber dilation and valve tethering. We have been developing a sophisticated intelligent artificial papillary muscle (PM) by using an anisotropic shape memory alloy fiber for an alternative surgical reconstruction of the continuity of the mitral structural apparatus and the left ventricular myocardium. This study exhibited the mitral regurgitation with regard to the reduction in the PM tension quantitatively with an originally developed ventricular simulator using isolated goat hearts for the sophisticated artificial PM. Aortic and mitral valves with left ventricular free wall portions of isolated goat hearts (n=9) were secured on the elastic plastic membrane and statically pressurized, which led to valvular leaflet-papillary muscle positional change and central mitral regurgitation. PMs were connected to the load cell, and the relationship between the tension of regurgitation and PM tension were measured. Then we connected the left ventricular specimen model to our hydraulic ventricular simulator and achieved hemodynamic simulation with the controlled tension of PMs.

  1. Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

    Directory of Open Access Journals (Sweden)

    F. J. Barbero

    2006-09-01

    Full Text Available In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain, a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA, a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%. The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%. This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

  2. Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

    OpenAIRE

    Kutyłowska Małgorzata

    2017-01-01

    The paper presents the results of artificial neural networks application to the availability indicator prediction. The forecasted results indicate that artificial networks may be used to model the reliability level of the water supply systems. The network was trained using 147 and 173 operational data from one Polish medium-sized city (distribution pipes and house connections, respectively). 50% of all data was chosen for learning, 25% for testing and 25% for validation. In prognosis phase, t...

  3. An Examination of Application of Artificial Neural Network in Cognitive Radios

    International Nuclear Information System (INIS)

    Salau, H Bello; Onwuka, E N; Aibinu, A M

    2013-01-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

  4. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  5. Fluid propulsion using magnetically-actuated artificial cilia : Experiments and simulations

    NARCIS (Netherlands)

    Khaderi, Syed; Hussong, Jeanette; Westerweel, Jerry; den Toonder, Jaap; Onck, Patrick

    2013-01-01

    We conducted a combined modelling and experimental approach to explore the underlying physical mechanisms responsible for fluid flow caused by magnetically-actuated plate-like artificial cilia. After independently calibrating the elastic and magnetic properties of the cilia, the model predictions

  6. Fluid propulsion using magnetically-actuated artificial cilia : experiments and simulations

    NARCIS (Netherlands)

    Khaderi, S.N.; Hussong, J.; Westerweel, J.; Toonder, den J.M.J.; Onck, P.R.

    2013-01-01

    We conducted a combined modelling and experimental approach to explore the underlying physical mechanisms responsible for fluid flow caused by magnetically-actuated plate-like artificial cilia. After independently calibrating the elastic and magnetic properties of the cilia, the model predictions

  7. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models.

    Science.gov (United States)

    Allawi, Mohammed Falah; Jaafar, Othman; Mohamad Hamzah, Firdaus; Abdullah, Sharifah Mastura Syed; El-Shafie, Ahmed

    2018-05-01

    Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

  8. Hydraulically actuated artificial muscles

    Science.gov (United States)

    Meller, M. A.; Tiwari, R.; Wajcs, K. B.; Moses, C.; Reveles, I.; Garcia, E.

    2012-04-01

    Hydraulic Artificial Muscles (HAMs) consisting of a polymer tube constrained by a nylon mesh are presented in this paper. Despite the actuation mechanism being similar to its popular counterpart, which are pneumatically actuated (PAM), HAMs have not been studied in depth. HAMs offer the advantage of compliance, large force to weight ratio, low maintenance, and low cost over traditional hydraulic cylinders. Muscle characterization for isometric and isobaric tests are discussed and compared to PAMs. A model incorporating the effect of mesh angle and friction have also been developed. In addition, differential swelling of the muscle on actuation has also been included in the model. An application of lab fabricated HAMs for a meso-scale robotic system is also presented.

  9. Application of a new model for groundwater age distributions: Modeling and isotopic analysis of artificial recharge in the Rialto-Colton basin, California

    Science.gov (United States)

    Ginn, T.R.; Woolfenden, L.

    2002-01-01

    A project for modeling and isotopic analysis of artificial recharge in the Rialto-Colton basin aquifer in California, is discussed. The Rialto-Colton aquifer has been divided into four primary and significant flowpaths following the general direction of groundwater flow from NW to SE. The introductory investigation include sophisticated chemical reaction modeling, with highly simplified flow path simulation. A comprehensive reactive transport model with the established set of geochemical reactions over the whole aquifer will also be developed for treating both reactions and transport realistically. This will be completed by making use of HBGC123D implemented with isotopic calculation step to compute Carbon-14 (C14) and stable Carbon-13 (C13) contents of the water. Computed carbon contents will also be calibrated with the measured carbon contents for assessment of the amount of imported recharge into the Linden pond.

  10. Evolution Engines and Artificial Intelligence

    Science.gov (United States)

    Hemker, Andreas; Becks, Karl-Heinz

    In the last years artificial intelligence has achieved great successes, mainly in the field of expert systems and neural networks. Nevertheless the road to truly intelligent systems is still obscured. Artificial intelligence systems with a broad range of cognitive abilities are not within sight. The limited competence of such systems (brittleness) is identified as a consequence of the top-down design process. The evolution principle of nature on the other hand shows an alternative and elegant way to build intelligent systems. We propose to take an evolution engine as the driving force for the bottom-up development of knowledge bases and for the optimization of the problem-solving process. A novel data analysis system for the high energy physics experiment DELPHI at CERN shows the practical relevance of this idea. The system is able to reconstruct the physical processes after the collision of particles by making use of the underlying standard model of elementary particle physics. The evolution engine acts as a global controller of a population of inference engines working on the reconstruction task. By implementing the system on the Connection Machine (Model CM-2) we use the full advantage of the inherent parallelization potential of the evolutionary approach.

  11. Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

    Directory of Open Access Journals (Sweden)

    Hyun-Joo Oh

    2017-09-01

    Full Text Available The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN and boosted tree (BT, for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

  12. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... microzonation map is produced for research area. Based on the obtained results, it can be stated that the trained neural network is capable in prediction of liquefaction potential with an acceptable level of confidence. At the end, zoning of the city is carried out based on the prediction of liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

  13. The Japanese artificial organs scene: current status.

    Science.gov (United States)

    Mitamura, Yoshinori; Murabayashi, Shun

    2005-08-01

    Artificial organs and regenerative medicine are the subjects of very active research and development (R&D) in Japan and various artificial organs are widely used in patients. Results of the R&D are presented at the annual conference of the Japanese Society for Artificial Organs (JSAO). Progress in the fields of artificial organs and regenerative medicine are reviewed annually in the Japanese Journal of Artificial Organs. The official English-language journal of JSAO, Journal of Artificial Organs, also publishes many original articles by Japanese researchers. Although the annual conference and the publications of JSAO provide the world with update information on artificial organs and regenerative medicine in Japan, the information is not always understood appropriately in the rest of the world, mainly due to language problems. This article therefore introduces the current status of artificial organs and regenerative medicine in Japan. Artificial hearts and metabolic support systems are reviewed here and other interesting areas such as regenerative medicine can be found elsewhere.

  14. 3D-modelling of the thermal circumstances of a lake under artificial aeration

    Science.gov (United States)

    Tian, Xiaoqing; Pan, Huachen; Köngäs, Petrina; Horppila, Jukka

    2017-12-01

    A 3D-model was developed to study the effects of hypolimnetic aeration on the temperature profile of a thermally stratified Lake Vesijärvi (southern Finland). Aeration was conducted by pumping epilimnetic water through the thermocline to the hypolimnion without breaking the thermal stratification. The model used time transient equation based on Navier-Stokes equation. The model was fitted to the vertical temperature distribution and environmental parameters (wind, air temperature, and solar radiation) before the onset of aeration, and the model was used to predict the vertical temperature distribution 3 and 15 days after the onset of aeration (1 August and 22 August). The difference between the modelled and observed temperature was on average 0.6 °C. The average percentage model error was 4.0% on 1 August and 3.7% on 22 August. In the epilimnion, model accuracy depended on the difference between the observed temperature and boundary conditions. In the hypolimnion, the model residual decreased with increasing depth. On 1 August, the model predicted a homogenous temperature profile in the hypolimnion, while the observed temperature decreased moderately from the thermocline to the bottom. This was because the effect of sediment was not included in the model. On 22 August, the modelled and observed temperatures near the bottom were identical demonstrating that the heat transfer by the aerator masked the effect of sediment and that exclusion of sediment heat from the model does not cause considerable error unless very short-term effects of aeration are studied. In all, the model successfully described the effects of the aerator on the lake's temperature profile. The results confirmed the validity of the applied computational fluid dynamic in artificial aeration; based on the simulated results, the effect of aeration can be predicted.

  15. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  16. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-01

    How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

  17. Pneumatic artificial muscle and its application on driving variable trailing-edge camber wing

    Science.gov (United States)

    Yin, Weilong; Liu, Libo; Chen, Yijin; Liu, Yanju; Leng, Jinsong

    2010-04-01

    As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, the experimental setup to measure the static output force of pneumatic artificial muscle was designed and the relationship between the static output force and the air pressure was investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. A variable camber wing based on the pneumatic artificial muscle was developed and the variable camber wing model was manufactured to validate the variable camber concept. Wind tunnel tests were conducted in the low speed wind tunnel. Experimental result shows that the wing camber increases with increasing air pressure.

  18. Artificial learners adopting normative conventions from human teachers

    Directory of Open Access Journals (Sweden)

    Cederborg Thomas

    2017-11-01

    Full Text Available This survey provides an overview of implemented systems, theoretical work, as well as studies of biological systems relevant to the design of artificial learners trying to figure out what a human teacher would like them to do. Implementations of artificial learners are covered, with a focus on experiments trying to find better interpretations of human behavior, as well as algorithms that autonomously improve a model of the teacher. A distinction is made between learners trying to interpret teacher behavior in order to learn what the teacher would like the learner to do on the one hand, and learners whose explicit or implicit goal is to get something from the teacher on the other hand (for example rewards, or knowledge about how the world works. The survey covers the former type of systems. Human teachers are covered, focusing on studies that say something concrete about how one should interpret the behavior of a human teacher that is interacting with an artificial learner. Certain types of biological learners are interesting as inspiration for the types of artificial systems we are concerned with. The survey focus on studies of biological learners adopting normative conventions, as well as joint intentionality team efforts.

  19. Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

    Science.gov (United States)

    Arbabi, Vahid; Pouran, Behdad; Campoli, Gianni; Weinans, Harrie; Zadpoor, Amir A

    2016-03-21

    One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

    Institute of Scientific and Technical Information of China (English)

    John ABBOT; Jennifer MAROHASY

    2012-01-01

    In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error (RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.