WorldWideScience

Sample records for ai based prediction

  1. Pre-processing in AI based Prediction of QSARs

    CERN Document Server

    Patri, Om Prasad

    2009-01-01

    Machine learning, data mining and artificial intelligence (AI) based methods have been used to determine the relations between chemical structure and biological activity, called quantitative structure activity relationships (QSARs) for the compounds. Pre-processing of the dataset, which includes the mapping from a large number of molecular descriptors in the original high dimensional space to a small number of components in the lower dimensional space while retaining the features of the original data, is the first step in this process. A common practice is to use a mapping method for a dataset without prior analysis. This pre-analysis has been stressed in our work by applying it to two important classes of QSAR prediction problems: drug design (predicting anti-HIV-1 activity) and predictive toxicology (estimating hepatocarcinogenicity of chemicals). We apply one linear and two nonlinear mapping methods on each of the datasets. Based on this analysis, we conclude the nature of the inherent relationships betwee...

  2. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

    Science.gov (United States)

    Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

    2015-11-01

    Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques. PMID:26433903

  3. An SDR based AIS receiver for satellites

    DEFF Research Database (Denmark)

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter; Nielsen, Jens Frederik Dalsgaard

    2011-01-01

    For a few years now, there has been a high interest in monitoring the global ship traffic from space. A few satellite, capable of listening for ship borne AIS transponders have already been launched, and soon the AAUSAT3, carrying two different types of AIS receivers will also be launched. One...

  4. Maritime traffic monitoring using a space-based AIS receiver

    Science.gov (United States)

    Eriksen, Torkild; Høye, Gudrun; Narheim, Bjørn; Meland, Bente Jensløkken

    2006-05-01

    The Automatic Identification System (AIS) is a maritime safety and vessel traffic system imposed by the International Maritime Organization (IMO). The system broadcasts position reports and short messages with information about the ship and the voyage. Using frequencies in the maritime VHF band, the coverage is similar to other VHF applications, and is essentially dependent on the altitude of the antenna. For ship-to-ship communications the range is typically 20 nautical miles and for ship-to-shore up to 40 nm. A space-based AIS receiver in low earth orbit will have a range to the horizon of more than 1000 nm, giving an excellent opportunity for large-area ocean surveillance. The Norwegian Defence Research Establishment (FFI) has performed a feasibility study on reception of AIS messages from space. The results show that a ship detection probability of near 100% can be obtained for up to 1000 ships within the coverage area, and that for a standard AIS receiver a signal power margin of 10-20 dB can be achieved. On this background, swath-width analyses for European scenarios are done. It is argued that space-based reception of AIS messages is a promising way of achieving long-range identification and tracking services at marginal cost.

  5. AI AND SAR APPROACHES FOR PREDICTING CHEMICAL CARCINOGENICITY: SURVEY AND STATUS REPORT

    Science.gov (United States)

    A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoin...

  6. Prediction of ThAI TH-13 Experiment Using CFX User Defined Function

    Energy Technology Data Exchange (ETDEWEB)

    Ha, Jeong Hee; Hong, Soon Joon [FNC Technology, Yongin (Korea, Republic of); Park, Chun Tae [KAERI, Daejeon (Korea, Republic of)

    2015-05-15

    Steam with high energy and non-condensable gas induce very complex behaviors of the atmosphere inside the containment which include condensation and evaporation phenomena when an accident happens such as LOCA (Loss-Of-Coolant-Accident). Prediction of these complicated behaviors of the atmosphere inside the containment is highly important when designing a device to prevent rapid pressure increase like a passive heat exchanger in the containment. In this paper, ThAI-TH13 experiment was simulated using the ANSYS-CFX15.0. The wall condensation was modeled using Uchida correlation in a single-phase calculation by User-Defined Functions included in the CFX. A similar study was performed using CFX as described in Ref. 3. The purpose of this study is to obtain detail methods and analytical technology of the previous study throughout reproduction of the simulation. Also this study is a preliminary calculation to simulate behaviors of atmosphere in the containment based on the recreation of the previous study. ThAI-TH13 experiment was simulated using CFX based on the previous study. The wall condensation has been modeled in the single-phase simulation using Uchida correlation by user-fortran routines in CFX code. The results of this simulation predicted TH-13 experiment properly and physically in pressure distribution of the atmosphere in a THAI vessel.

  7. Prediction of ThAI TH-13 Experiment Using CFX User Defined Function

    International Nuclear Information System (INIS)

    Steam with high energy and non-condensable gas induce very complex behaviors of the atmosphere inside the containment which include condensation and evaporation phenomena when an accident happens such as LOCA (Loss-Of-Coolant-Accident). Prediction of these complicated behaviors of the atmosphere inside the containment is highly important when designing a device to prevent rapid pressure increase like a passive heat exchanger in the containment. In this paper, ThAI-TH13 experiment was simulated using the ANSYS-CFX15.0. The wall condensation was modeled using Uchida correlation in a single-phase calculation by User-Defined Functions included in the CFX. A similar study was performed using CFX as described in Ref. 3. The purpose of this study is to obtain detail methods and analytical technology of the previous study throughout reproduction of the simulation. Also this study is a preliminary calculation to simulate behaviors of atmosphere in the containment based on the recreation of the previous study. ThAI-TH13 experiment was simulated using CFX based on the previous study. The wall condensation has been modeled in the single-phase simulation using Uchida correlation by user-fortran routines in CFX code. The results of this simulation predicted TH-13 experiment properly and physically in pressure distribution of the atmosphere in a THAI vessel

  8. Prediction of shipboard electromagnetic interference (EMI) problems using artificial intelligence (AI) technology

    Science.gov (United States)

    Swanson, David J.

    1990-01-01

    The electromagnetic interference prediction problem is characteristically ill-defined and complicated. Severe EMI problems are prevalent throughout the U.S. Navy, causing both expected and unexpected impacts on the operational performance of electronic combat systems onboard ships. This paper focuses on applying artificial intelligence (AI) technology to the prediction of ship related electromagnetic interference (EMI) problems.

  9. Artificial Intelligence (AI) Based Tactical Guidance for Fighter Aircraft

    Science.gov (United States)

    McManus, John W.; Goodrich, Kenneth H.

    1990-01-01

    A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range (WVR) air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The Knowledge-Based Systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real-time in the Langley Differential Maneuvering Simulator (DMS), are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs. Alternate computing environments and programming approaches, including the use of parallel algorithms and heterogeneous computer networks are discussed, and the design and performance of a prototype concurrent TDG system are presented.

  10. The AI Bus architecture for distributed knowledge-based systems

    Science.gov (United States)

    Schultz, Roger D.; Stobie, Iain

    1991-01-01

    The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

  11. AI Based Personal Learning Environments: Directions for Long Term Research. AI Memo 384.

    Science.gov (United States)

    Goldstein, Ira P.; Miller, Mark L.

    The application of artificial intelligence (AI) techniques to the design of personal learning environments is an enterprise of both theoretical and practical interest. In the short term, the process of developing and testing intelligent tutoring programs serves as a new experimental vehicle for exploring alternative cognitive and pedagogical…

  12. An AI-Based Approach to Destination Control in Elevators

    OpenAIRE

    Koehler, Jana; Ottiger, Daniel

    2002-01-01

    Not widely known by the AI community, elevator control has become a major field of application for AI technologies. Techniques such as neural networks, genetic algorithms, fuzzy rules and, recently, multiagent systems and AI planning have been adopted by leading elevator companies not only to improve the transportation capacity of conventional elevator systems but also to revolutionize the way in which elevators interact with and serve passengers. In this article, we begin with an overview of...

  13. In Orbit Validation of the AAUSAT3 SDR based AIS receiver

    DEFF Research Database (Denmark)

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter

    2013-01-01

    During the past years, there has been a high interest in monitoring the global ship traffic from space. The recently launched AAUSAT3 satellite carries an SDR based AIS receiver, which during the past months have been transmitting space based AIS data down to the Aalborg University Ground Station...

  14. AIS基站短消息特性%Short message characteristics of AIS base stations

    Institute of Scientific and Technical Information of China (English)

    马枫; 初秀民; 严新平

    2012-01-01

    The characteristics of short messages from automatic identification system(AIS) base stations to ships in the inland river and port were studied. The principles of waveform distortion and package error were analyzed when AIS field strength was declined. In the hardware in-the- loop simulation platform, the relationship between message length and package error rate was analyzed, and the prediction model of package error rate was proposed. Combined with Hata- Okumura model, the maximum capacity for short messages sent from AIS base stations to ships was proposed in crowded channel. At last, taking the Shanghai Channel of Huangpu River as an example, using the proposed model, the maximum capacity was calculated and compared with the measured result. Comparison result indicates that the reliability of short message decreases with the increase of short message length and the decrease of fixed field strength. The number of AIS target ships communicating with AIS base stations meanwhile is limited. For the Shanghai Channel of Huangpu River, the calculated number of AIS target ship communicating with an AIS base station is 26 when the total AIS target ships is 625, and the measured value is 24, so the calculated value basically matches with the reality value. 2 tabs, 6 figs, 15 refs.%研究了在内河与港口应用环境下,由船载自动识别系统(AIS)基站发送到船舶的短消息特征,分析了AIS报文在场强下降后的波形失真与误包原理。在半实物仿真平台下,分析了消息长度与误包率的关系,并给出相应的误包率预测模型。结合Hata—Okumura模型,提出了拥挤航道下AIs基站向船舶发送短消息的极限容量。最后以黄浦江上海航道为例,运用极限计算模型对其极限容量进行了预测,并与实测结果进行了对比。对比结果表明:短消息可靠性随长度增加与场强降低而逐步下降,基站能同时进行短消息管理的AIS船舶数量有限,

  15. FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID EC-ONOMETRIC-AI ENSEMBLE LEARNING APPROACH

    Institute of Scientific and Technical Information of China (English)

    Lean YU; Shouyang WANG; Kin Keung LAI

    2008-01-01

    Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal-ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en-semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic-tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

  16. An AIS-based approach to calculate atmospheric emissions from the UK fishing fleet

    Science.gov (United States)

    Coello, Jonathan; Williams, Ian; Hudson, Dominic A.; Kemp, Simon

    2015-08-01

    The fishing industry is heavily reliant on the use of fossil fuel and emits large quantities of greenhouse gases and other atmospheric pollutants. Methods used to calculate fishing vessel emissions inventories have traditionally utilised estimates of fuel efficiency per unit of catch. These methods have weaknesses because they do not easily allow temporal and geographical allocation of emissions. A large proportion of fishing and other small commercial vessels are also omitted from global shipping emissions inventories such as the International Maritime Organisation's Greenhouse Gas Studies. This paper demonstrates an activity-based methodology for the production of temporally- and spatially-resolved emissions inventories using data produced by Automatic Identification Systems (AIS). The methodology addresses the issue of how to use AIS data for fleets where not all vessels use AIS technology and how to assign engine load when vessels are towing trawling or dredging gear. The results of this are compared to a fuel-based methodology using publicly available European Commission fisheries data on fuel efficiency and annual catch. The results show relatively good agreement between the two methodologies, with an estimate of 295.7 kilotons of fuel used and 914.4 kilotons of carbon dioxide emitted between May 2012 and May 2013 using the activity-based methodology. Different methods of calculating speed using AIS data are also compared. The results indicate that using the speed data contained directly in the AIS data is preferable to calculating speed from the distance and time interval between consecutive AIS data points.

  17. AI Topics

    OpenAIRE

    Buchanan, Bruce G; Glick, Jonathan

    2002-01-01

    The debut of the AI in the News column elsewhere in this issue of AI Magazine created a good opportunity to introduce the professional community to the AI Topics web site, home of the AI in the news virtual page. Although AI Topics is designed for the lay public, it serves a much larger audience.

  18. Autonomously generating operations sequences for a Mars Rover using AI-based planning

    Science.gov (United States)

    Sherwood, Rob; Mishkin, Andrew; Estlin, Tara; Chien, Steve; Backes, Paul; Cooper, Brian; Maxwell, Scott; Rabideau, Gregg

    2001-01-01

    This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from highlevel science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules.

  19. An AI-based layout design system for nuclear power plants

    International Nuclear Information System (INIS)

    An AI-based layout design system for nuclear power plants has been developed. The design of the layout of nuclear power plants is a time-consuming task requiring expertise, in which a lot of machinery and equipment must be arranged in a plant building considering various kinds of design constraints, i.e. spatial, functional, economical etc. Computer aided layout design systems have been widely expected and the application of AI technology is expected as a promising approach for the synthesis phase of this task. In this paper, we present an approach to the layout design of nuclear power plants based on a constraint-directed search; one of the AI techniques. In addition, we show how it was implemented with an object-oriented programming technique and give an example of its application. (author)

  20. Ai ai ai (4/4 F)

    OpenAIRE

    2011-01-01

    Laulun sanat: Ai, ai ai, kun sydämen' on pois! Kuka sen jälleen takaisin tois? Joudu, joudu, ota häntä kii, Joka minun sydämeni varasti! Joudu, joudu, ota häntä kii, Joka minun sydämeni varasti!

  1. AI based Digital Companding Scheme for OFDM system using custom constellation Mapping and selection

    Directory of Open Access Journals (Sweden)

    K.Seshadri Sastry

    2010-07-01

    Full Text Available Data rate is important in telecommunication because it is directlyproportional to the cost of transmitting the signal. Saving bits is the same as saving money . In this paper we propose new digital companding scheme for OFDM system based on using different constellation orderings (QAM modulator , depending on the data to be transmitted . Depending on the data to be transmitted AI (Artificial Intelligent block compresses and expands the signal, 8 to 3 bit compression is proposed(32:1 compression. The proposed schemewas simulated in Matlab7.4 and it was shown that the proposed companding scheme effective with low compression error.

  2. Effects of AI Addition on the Thermoelectric Properties of Zn-Sb Based Alloys

    Institute of Scientific and Technical Information of China (English)

    CUI Jiaolin; LIU Xianglian; YANG Wei; CHEN Dongyong; MAO Liding; QIAN Xin

    2009-01-01

    The β-Zn4Sb3, emerged as a compelling p-type thermoelectric material, is widely used in heat-electricity conversion in the 400-650 K range. In order to probe the effects of slight doping on the crystal structure and physical properties, we prepared the samples of Al-added Zn-Sb based alloys by spark plasma sintering and evaluated their microstructures and thermoelectric properties. After a limited Al addition into the Zn-Sb based alloys we observed many phases in the alloys, which include a major phase β-Zn4Sb3,intermetallic phases ZnSb and AISb. The major β-Zn4Sb3 phase plays a fundamental role in controlling the thermoelectric performance, the precipitated phases ZnSb and AISb are of great importance to tailor the transport properties, such as the gradual enhancement of lattice thermal conductivity, in spite of an increased phonon scattering in additional grain boundaries. The highest thermoelectric figure of merit of 0.55 is obtained for the alloy with a limited AI addition at 653 K, which is 0.08 higher than that of un-doped β-Zn4Sb3 at the corresponding temperature. Physical property experiments indicate that there is a potentiality for the improvement of thermoelectric properties if a proper elemental doping is carried out into the Zn-Sb based alloys, which was confirmed by AI addition in the present work.

  3. AIS authentication

    CERN Multimedia

    2006-01-01

    Users are invited to use the NICE password for AIS authentication. As announced in CNL June-August 2006 (see http://www.cerncourier.com/articles/cnl/3/6/14/1) it is possible to use the NICE username and password to log on to AIS. The procedure is now fully operational and users can themselves reset the AIS password such that the NICE password will be used for authentication required by AIS applications. We strongly recommend CERN users who have a NICE account (this is the case of most users) to do this, with the objective to reduce the number of passwords they need to remember. This can be achieved very easily, directly from the Change Password option on the AIS login (https://aislogin.cern.ch/). Users should just select the '[Change Password]' option displayed at the bottom of the page, provide the 'Old Password' and then click on the button 'Use Nice password' followed by 'Submit'. Change Password option on the AIS login windowSetting the AIS password - Use Nice Password It should be noted that the proce...

  4. AI-based adaptive control and design of autopilot system for nonlinear UAV

    Indian Academy of Sciences (India)

    Anil Kumar Yadav; Prerna Gaur

    2014-08-01

    The objective of this paper is to design an autopilot system for unmanned aerial vehicle (UAV) to control the speed and altitude using electronic throttle control system (ETCS) and elevator, respectively. A DC servo motor is used for designing of ETCS to control the throttle position for appropriate amount of air mass flow. Artificial Intelligence (AI)-based controllers such as fuzzy logic PD, fuzzy logic PD + I, self-tuning fuzzy logic PID (STF-PID) controller and fuzzy logic-based sliding mode adaptive controller (FLSMAC) are designed for stable autopilot system and are compared with conventional PI controller. The target of throttle, speed and altitude controls are to achieve a wide range of air speed, improved energy efficiency and fuel economy with reduced pollutant emission. The energy efficiency using specific energy rate per velocity of UAV is also presented in this paper.

  5. Hierarchical Pathfinding and AI-Based Learning Approach in Strategy Game Design

    Directory of Open Access Journals (Sweden)

    Le Minh Duc

    2008-01-01

    Full Text Available Strategy game and simulation application are an exciting area with many opportunities for study and research. Currently most of the existing games and simulations apply hard coded rules so the intelligence of the computer generated forces is limited. After some time, player gets used to the simulation making it less attractive and challenging. It is also costly and tedious to incorporate new rules for an existing game. The main motivation behind this research project is to improve the quality of artificial intelligence- (AI- based on various techniques such as qualitative spatial reasoning (Forbus et al., 2002, near-optimal hierarchical pathfinding (HPA* (Botea et al., 2004, and reinforcement learning (RL (Sutton and Barto, 1998.

  6. Combining FDI and AI approaches within causal-model-based diagnosis.

    Science.gov (United States)

    Gentil, Sylviane; Montmain, Jacky; Combastel, Christophe

    2004-10-01

    This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.

  7. AI 3D Cybug Gaming

    CERN Document Server

    Ahmed, Zeeshan

    2010-01-01

    In this short paper I briefly discuss 3D war Game based on artificial intelligence concepts called AI WAR. Going in to the details, I present the importance of CAICL language and how this language is used in AI WAR. Moreover I also present a designed and implemented 3D War Cybug for AI WAR using CAICL and discus the implemented strategy to defeat its enemies during the game life.

  8. Integration of AI-2 Based Cell-Cell Signaling with Metabolic Cues in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Arindam Mitra

    Full Text Available The quorum sensing molecule Autoinducer-2 (AI-2 is generated as a byproduct of activated methyl cycle by the action of LuxS in Escherichia coli. AI-2 is synthesized, released and later internalized in a cell-density dependent manner. Here, by mutational analysis of the genes, uvrY and csrA, we describe a regulatory circuit of accumulation and uptake of AI-2. We constructed a single-copy chromosomal luxS-lacZ fusion in a luxS + merodiploid strain and evaluated its relative expression in uvrY and csrA mutants. At the entry of stationary phase, the expression of the fusion and AI-2 accumulation was positively regulated by uvrY and negatively regulated by csrA respectively. A deletion of csrA altered message stability of the luxS transcript and CsrA protein exhibited weak binding to 5' luxS regulatory region. DNA protein interaction and chromatin immunoprecipitation analysis confirmed direct interaction of UvrY with the luxS promoter. Additionally, reduced expression of the fusion in hfq deletion mutant suggested involvement of small RNA interactions in luxS regulation. In contrast, the expression of lsrA operon involved in AI-2 uptake, is negatively regulated by uvrY and positively by csrA in a cell-density dependent manner. The dual role of csrA in AI-2 synthesis and uptake suggested a regulatory crosstalk of cell signaling with carbon regulation in Escherichia coli. We found that the cAMP-CRP mediated catabolite repression of luxS expression was uvrY dependent. This study suggests that luxS expression is complex and regulated at the level of transcription and translation. The multifactorial regulation supports the notion that cell-cell communication requires interaction and integration of multiple metabolic signals.

  9. Integration of AI-2 Based Cell-Cell Signaling with Metabolic Cues in Escherichia coli

    Science.gov (United States)

    Mitra, Arindam; Herren, Christopher D.; Patel, Isha R.; Coleman, Adam; Mukhopadhyay, Suman

    2016-01-01

    The quorum sensing molecule Autoinducer-2 (AI-2) is generated as a byproduct of activated methyl cycle by the action of LuxS in Escherichia coli. AI-2 is synthesized, released and later internalized in a cell-density dependent manner. Here, by mutational analysis of the genes, uvrY and csrA, we describe a regulatory circuit of accumulation and uptake of AI-2. We constructed a single-copy chromosomal luxS-lacZ fusion in a luxS + merodiploid strain and evaluated its relative expression in uvrY and csrA mutants. At the entry of stationary phase, the expression of the fusion and AI-2 accumulation was positively regulated by uvrY and negatively regulated by csrA respectively. A deletion of csrA altered message stability of the luxS transcript and CsrA protein exhibited weak binding to 5’ luxS regulatory region. DNA protein interaction and chromatin immunoprecipitation analysis confirmed direct interaction of UvrY with the luxS promoter. Additionally, reduced expression of the fusion in hfq deletion mutant suggested involvement of small RNA interactions in luxS regulation. In contrast, the expression of lsrA operon involved in AI-2 uptake, is negatively regulated by uvrY and positively by csrA in a cell-density dependent manner. The dual role of csrA in AI-2 synthesis and uptake suggested a regulatory crosstalk of cell signaling with carbon regulation in Escherichia coli. We found that the cAMP-CRP mediated catabolite repression of luxS expression was uvrY dependent. This study suggests that luxS expression is complex and regulated at the level of transcription and translation. The multifactorial regulation supports the notion that cell-cell communication requires interaction and integration of multiple metabolic signals. PMID:27362507

  10. AI Magazine Poster: The AI Landscape

    OpenAIRE

    Leake, David B.; Indiana University; Gary, James; Giacomo Marchesi Design

    2008-01-01

    AI's first half-century produced great accomplishments, but many of the field's successes have remained unsung beyond the AI community. AI's integration into the fabric of everyday life has had tremendous impact, but the public may not recognize its many roles or understand its fundamental goals. In response, AI Magazine has developed a poster to help educate students, faculty, and the public about AI and to spur them to find out more about the field.

  11. An AI Planning-based Tool for Scheduling Satellite Nominal Operations

    OpenAIRE

    Rodriguez-Moreno, Maria Dolores; Borrajo, Daniel; Meziat, Daniel

    2004-01-01

    Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be d...

  12. Dicty_cDB: FC-AI05 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI05 (Link to dictyBase) - - - Contig-U15516-1 FC-AI05E (Li...nk to Original site) - - - - - - FC-AI05E 1189 Show FC-AI05 Library FC (Link to library) Clone ID FC-AI05 (L...//dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI05Q.Seq.d/ Representative seq. ID FC-AI...05E (Link to Original site) Representative DNA sequence >FC-AI05 (FC-AI05Q) /CSM/FC/FC-AI/FC-AI05Q.Seq...KIVGEASLKNKGKMSRVLAAKAALSARFD ALCEVSDTSYGIAYKGAVDRRAAAIEGREVRKSLNAVKPEKSGNVAKYDHTKSATTNTTR DVATKSSKESSIKQEKQ

  13. Dicty_cDB: FC-AI23 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI23 (Link to dictyBase) - - - Contig-U15308-1 FC-AI23Z (Li...nk to Original site) - - FC-AI23Z 603 - - - - Show FC-AI23 Library FC (Link to library) Clone ID FC-AI23 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI23Q.Seq.d/ Representative seq. ID FC-AI...23Z (Link to Original site) Representative DNA sequence >FC-AI23 (FC-AI23Q) /CSM/FC/FC-AI/FC-AI23Q.Seq....LNTLAKKNEQVVEGEILAKQLTGVTAEELSEFKACFSHFDKDN DNKLNRLEFSSCLKSIGDELTEEQLNQVISKIDTDGNGTISFEEFIDYMVSSRKGTDSVE STKAAFKVMAEDKDFITEAQIRAAI

  14. Dicty_cDB: FC-AI10 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI10 (Link to dictyBase) - - - Contig-U16270-1 FC-AI10F (Li...nk to Original site) FC-AI10F 405 - - - - - - Show FC-AI10 Library FC (Link to library) Clone ID FC-AI10 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI10Q.Seq.d/ Representative seq. ID FC-AI...10F (Link to Original site) Representative DNA sequence >FC-AI10 (FC-AI10Q) /CSM/FC/FC-AI/FC-AI10Q.Seq.... sequence RKKRKSDYTSFSTYIHKLLKQITPPTNAKSNEKGDRKFTISSKAMSVMNSFVHDIFDRIA TEASGLAKKKKRQTLHSRDIQVAVRIILTGELAXHAI

  15. Dicty_cDB: FC-AI11 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI11 (Link to dictyBase) - - - Contig-U15122-1 FC-AI11E (Li...nk to Original site) - - - - - - FC-AI11E 1040 Show FC-AI11 Library FC (Link to library) Clone ID FC-AI11 (L...//dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI11Q.Seq.d/ Representative seq. ID FC-AI...11E (Link to Original site) Representative DNA sequence >FC-AI11 (FC-AI11Q) /CSM/FC/FC-AI/FC-AI11Q.Seq...VLSPEIKKGSWDEAEEELLFQLVDKHGQSWKNVAIEIKTRTDIQCRYQYFKAI MSRQTEWNQLEDDILTKKIKLMTQNNEKISFQQVSKHLARAKTTKIPRTALECK

  16. Dicty_cDB: FC-AI04 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI04 (Link to dictyBase) - - - Contig-U15121-1 FC-AI04E (Li...nk to Original site) - - - - - - FC-AI04E 772 Show FC-AI04 Library FC (Link to library) Clone ID FC-AI04 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI04Q.Seq.d/ Representative seq. ID FC-AI...04E (Link to Original site) Representative DNA sequence >FC-AI04 (FC-AI04Q) /CSM/FC/FC-AI/FC-AI04Q.Seq....qvnkhqqvvtktvsd vlvphqvhnqvfphipqqmtlvnkhqpvvtktvsdvlvphqvhnqvfphtpqlkiqvylq vfqvvvvtiisai

  17. Dicty_cDB: FC-AI21 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI21 (Link to dictyBase) - - - Contig-U16254-1 FC-AI21Z (Li...nk to Original site) - - FC-AI21Z 696 - - - - Show FC-AI21 Library FC (Link to library) Clone ID FC-AI21 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI21Q.Seq.d/ Representative seq. ID FC-AI...21Z (Link to Original site) Representative DNA sequence >FC-AI21 (FC-AI21Q) /CSM/FC/FC-AI/FC-AI21Q.Seq....YPGYMYTDLSTIYERAGRIQGRNGSITQI PILTMPNDDITHPIPDLTGYITEGQIFIDRQINNRQIYPPINVLPSLSRLMKSAI

  18. A gel-based method for purification of apolipoprotein A-I from small volumes of plasma

    OpenAIRE

    Brace, Rachel J.; Sorrenson, Brie; Sviridov, Dmitri; McCormick, Sally P. A.

    2010-01-01

    We present here a gel-based method for rapid purification of apolipoprotein A-I (apoA-I) from small volumes of human plasma. After isolation of high density lipoprotein from plasma, the apoA-I protein was separated by electrophoresis and the apoA-I band excised from the gel. The apoA-I was then eluted from the gel strip, concentrated, and delipidated ready for use. The structure and function of the gel-purified apoA-I protein was compared against apoA-I purified by the traditional size-exclus...

  19. Dicty_cDB: FC-AI01 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI01 (Link to dictyBase) - - - Contig-U15592-1 FC-AI01E (Li...nk to Original site) - - - - - - FC-AI01E 307 Show FC-AI01 Library FC (Link to library) Clone ID FC-AI01 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI01Q.Seq.d/ Representative seq. ID FC-AI...01E (Link to Original site) Representative DNA sequence >FC-AI01 (FC-AI01Q) /CSM/FC/FC-AI/FC-AI01Q.Seq....uences producing significant alignments: (bits) Value FC-AI01 (FC-AI01Q) /CSM/FC/FC-AI/FC-AI01Q.Seq.d/ 68 8e

  20. Probability of Ship on Collision Courses Based on the New PAW Using MMG Model and AIS Data

    Directory of Open Access Journals (Sweden)

    I Putu Sindhu Asmara

    2015-03-01

    Full Text Available This paper proposes an estimation method for ships on collision courses taking crash astern maneuvers based on a new potential area of water (PAW for maneuvering. A crash astern maneuver is an emergency option a ship can take when exposed to the risk of a collision with other ships that have lost control. However, lateral forces and yaw moments exerted by the reversing propeller, as well as the uncertainty of the initial speed and initial yaw rate, will move the ship out of the intended stopping position landing it in a dangerous area. A new PAW for crash astern maneuvers is thus introduced. The PAW is developed based on a probability density function of the initial yaw rate. Distributions of the yaw rates and speeds are analyzed from automatic identification system (AIS data in Madura Strait, and estimated paths of the maneuvers are simulated using a mathematical maneuvering group model.

  1. Typical and atypical AIS. Pathogenesis.

    Science.gov (United States)

    Dudin, M; Pinchuk, D

    2012-01-01

    AIS hypothesis has the right to recognition, if it explains the transition of "healthy" vertebra column into status of "scoliotic" one. AIS is the most investigated disease in the history of orthopedics, but up the present time there is no clear explanation of some its phenomena: vertebra column mono-form deformation along with its poly etiology character, interrelation of its origin and development and child's growth process etc. The key for authors' view at AIS was scoliosis with non-standard (concave side) rotation. On the bases of its' multifunctional instrumental investigation results (Rtg, EMG, EEG, optical topography, hormonal and neuropeptides trials, thermo-vision methods and other) in comparison with typical AIS was worked out the new hypothesis, part of it is suggested for discussion. In the work under observation is the sequence of appearance of typical and atypical scoliosis symptomatology beginning from the preclinical stage. PMID:22744477

  2. Design of a Satellite-based AIS Signal processor based on FPGA%基于FPGA的星载AIS信号处理器的设计

    Institute of Scientific and Technical Information of China (English)

    张喆

    2012-01-01

    According to the characters of the space-based AIS(Automatic Identification System) receiver;a system design scheme is proposed to realize the efficient receiver of signal.It is introduced the project of the hardware realization of the satellite-based AIS signal processor parts in detail and emphasized on how to realize the signal processing based on FPGA.Some simulations and experiment based on AIS receiver are presented to verifythe validity and feasibility of the proposed scheme.It is proved that this signal processor performance can meet the requirements of the space-base AIS receiver system by performing some testing.%针对星载AIS(船舶自动识别系统)接收系统提出了实现信号有效接收的总体方案。重点提供星载AIS信号处理器的硬件电路设计和基于FPGA信号处理软件设计。以AIS接收机整机为测试平台,通过仿真和试验验证了星载AIS接收机整机设计的有效性和可行性,此信号处理器可以满足星载AIS接收机的需求。

  3. Use of AI technician scores for body condition, uterine tone and uterine discharge in a model with disease and milk production parameters to predict pregnancy risk at first AI in holstein dairy cows

    NARCIS (Netherlands)

    Loeffler, S.H.; Vries, de M.J.; Schukken, Y.H.; Zeeuw, de A.C.; Dijkhuizen, A.A.; Graaf, de F.M.; Brand, A.

    1999-01-01

    Technicians recorded body condition score (BCS) and several parameters related to estrus and/or metritis for 1694 first insemination cows on 23 farms. Additional variables for modeling the adjusted odds ratios (OR) for pregnancy were data on disease prior to or within 21 days of AI and test day milk

  4. 基于HBase的AIS数据分布式存储%A Distributed Storage Method of AIS Data Based on HBase

    Institute of Scientific and Technical Information of China (English)

    孟凡君; 曹伟; 管志强

    2016-01-01

    自动识别系统(AIS)给船舶提供了电子身份标示。目前AIS数据主要通过关系数据库存储,由于AIS数据不断增加,该存储方式面临着扩展困难,存取效率低的问题。提出一种基于分布式并行处理框架Hadoop利用列数据库HBase存储AIS数据的方法。实验结果表明,Hbase实现了对AIS数据的高效存取。%The automatic identification system (AIS) provides an electronic ID for the ship. At present, the AIS data is mainly stored in relational database, which is faced with the problem of extending difficulty and low access efficiency because of the increasing of AIS data. A distributed parallel processing framework based on Hadoop is proposed to store AIS data in HBase. The experimental results show that Hbase achieves efficient access to AIS data.

  5. Proto-Examples of Data Access and Visualization Components of a Potential Cloud-Based GEOSS-AI System

    Science.gov (United States)

    Teng, William; Lynnes, Christopher

    2014-01-01

    Once a research or application problem has been identified, one logical next step is to search for available relevant data products. Thus, an early component of a potential GEOSS-AI system, in the continuum between observations and end point research, applications, and decision making, would be one that enables transparent data discovery and access by users. Such a component might be effected via the systems data agents. Presumably, some kind of data cataloging has already been implemented, e.g., in the GEOSS Common Infrastructure (GCI). Both the agents and cataloging could also leverage existing resources external to the system. The system would have some means to accept and integrate user-contributed agents. The need or desirability for some data format internal to the system should be evaluated. Another early component would be one that facilitates browsing visualization of the data, as well as some basic analyses.Three ongoing projects at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provide possible proto-examples of potential data access and visualization components of a cloud-based GEOSS-AI system. 1. Reorganizing data archived as time-step arrays to point-time series (data rods), as well as leveraging the NASA Simple Subset Wizard (SSW), to significantly increase the number of data products available, at multiple NASA data centers, for production as on-the-fly (virtual) data rods. SSWs data discovery is based on OpenSearch. Both pre-generated and virtual data rods are accessible via Web services. 2. Developing Web Feature Services to publish the metadata, and expose the locations, of pre-generated and virtual data rods in the GEOSS Portal and enable direct access of the data via Web services. SSW is also leveraged to increase the availability of both NASA and non-NASA data.3.Federating NASA Giovanni (Geospatial Interactive Online Visualization and Analysis Interface), for multi-sensor data exploration, that would allow each

  6. Dicty_cDB: FC-AI02 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI02 (Link to dictyBase) - - - Contig-U16513-1 FC-AI02E (Li...nk to Original site) - - - - - - FC-AI02E 535 Show FC-AI02 Library FC (Link to library) Clone ID FC-AI02 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI02Q.Seq.d/ Representative seq. ID FC-AI...02E (Link to Original site) Representative DNA sequence >FC-AI02 (FC-AI02Q) /CSM/FC/FC-AI/FC-AI02Q.Seq....ogy vs CSM-cDNA Score E Sequences producing significant alignments: (bits) Value FC-AI02 (FC-AI

  7. Dicty_cDB: FC-AI17 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI17 (Link to dictyBase) - - - Contig-U15690-1 FC-AI17P (Li...nk to Original site) FC-AI17F 587 FC-AI17Z 626 FC-AI17P 1212 - - Show FC-AI17 Library FC (Link to library) Clone ID FC-AI...nal site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI17Q.Seq.d/ Representative seq. ID FC-AI...17P (Link to Original site) Representative DNA sequence >FC-AI17 (FC-AI17Q) /CSM/FC/FC-AI/FC-AI...slated Amino Acid sequence ANIATVGDFLKADTVVPKMIITYNKRKQGTDYLKAVIGPILSNVIKQELNLELKPNLVYA AIISEQEIRTGEKSTLDRNV

  8. Dicty_cDB: FC-AI18 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI18 (Link to dictyBase) - - - Contig-U15590-1 FC-AI18P (Li...nk to Original site) FC-AI18F 621 FC-AI18Z 703 FC-AI18P 1324 - - Show FC-AI18 Library FC (Link to library) Clone ID FC-AI...nal site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI18Q.Seq.d/ Representative seq. ID FC-AI...18P (Link to Original site) Representative DNA sequence >FC-AI18 (FC-AI18Q) /CSM/FC/FC-AI/FC-AI...AVWPLIPGYERA DGEKQYPVAAMLCNFTKPTPTTPSLLTHDEVVTFFHEFGHVMHNMSTKVHYSMFSGTSVE RDFVECPSQLFEFWCWNKDVLVNKLSGHXKDHSKKLPTDLVERMIAAKNLNVAI

  9. Melt-Dilute Form of AI-Based Spent Nuclear Fuel Disposal Criticality Summary Report

    International Nuclear Information System (INIS)

    Criticality analysis of the proposed melt-dilute (MD) form of aluminum-based spent nuclear fuel (SNF), under geologic repository conditions, was performed [1] following the methodology documented in the Disposal Criticality Analysis Methodology Topical Report [2]. This methodology evaluates the potential for nuclear criticality for a waste form in a waste package. Criticality calculations show that even with waste package failure, followed by degradation of material within the waste package and potential loss of neutron absorber materials, sub-critical conditions can be readily demonstrated for the MD form of aluminum-based SNF

  10. Assessment of in vitro sperm characteristics and their importance in the prediction of conception rate in a bovine timed-AI program.

    Science.gov (United States)

    Oliveira, Letícia Zoccolaro; de Arruda, Rubens Paes; de Andrade, André Furugen Cesar; Celeghini, Eneiva Carla Carvalho; Reeb, Pablo Daniel; Martins, João Paulo Nascimento; dos Santos, Ricarda Maria; Beletti, Marcelo Emílio; Peres, Rogério Fonseca Guimarães; Monteiro, Fabio Morato; Hossepian de Lima, Vera Fernanda Martins

    2013-03-01

    The aims of this study were to assess in vivo fertility and in vitro sperm characteristics of different sires and to identify sperm variables important for the prediction of conception rate. Multiparous Nelore cows (n = 191) from a commercial farm underwent the same timed artificial insemination (timed-AI) protocol. Three batches of frozen semen from three Angus bulls were used (n = 9). A routine semen thawing protocol was performed in the laboratory to mimic field conditions. The following in vitro sperm analyses were performed: Computer Assisted Semen Analysis (CASA), Thermal Resistance Test (TRT), Hyposmotic Swelling Test (HOST), assessment of plasma and acrosomal membrane integrity, assessment of sperm plasma membrane stability and of lipid peroxidation by flow cytometry and assessment of sperm morphometry and chromatin structure by Toluidine Blue staining. For statistical analyses, Partial Least Squares (PLS) regression was used to explore the importance of various sperm variables in the prediction of conception rate. The following in vitro sperm variables were determined to be important predictors of conception rate: total motility (TM), progressive motility (PM), TM after 2 h of thermal incubation (TM_2 h), PM after 2 h of thermal incubation (PM_2 h), Beat Cross Frequency after 2 h of thermal incubation (BCF_2 h), percentage of rapidly moving cells after 2 h of thermal incubation (RAP_2 h), intact plasma membrane evaluated by HOST, intact plasma and acrosomal membranes evaluated by flow cytometry, intact plasma membrane suffering lipid peroxidation, major defects, total defects, morphometric width/length ratio, Fourier_0 and Fourier_2 and Chromatin Heterogeneity. We concluded that PLS regression is a suitable statistical method to identify in vitro sperm characteristics that have an important relationship with in vivo bull fertility. PMID:23428291

  11. aiNet背景抑制的单帧红外弱小目标检测%Infrared dim target detection in single image based on background suppression by aiNet

    Institute of Scientific and Technical Information of China (English)

    陈炳文; 王文伟; 秦前清

    2012-01-01

    In order to solve the problem that the current approaches cannot suppress the background clutters effectively, which results in a poor detection performance, a new infrared dim target detection approach is presented, which is based on background suppression by artificial immune network ( aiNet) and threshold segmentation by k-means cluster of rows and columns. First, the aiNet is combined with Robinson guard to build the adaptive local spatial background models as fuzzy topological memory antibody bank. In the process of antibody bank modeling, a series of antibody evolution strategies are designed based on self-organizing maps ( SOM). With these models, background clutters are suppressed according to the degree of fuzzy match between pixels and models. Then, the proposed adaptive segmentation algorithm based on k-means cluster of rows and columns is used to detect the true targets. Experimental results show that the Fl measurement of the proposed approach is up to 99% . The proposed approach is able to build the spatial background models adaptively according to the local change of image, and suppress the background clutters and highlight the targets effectively. It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.%针对现有背景抑制算法未能有效抑制背景而导致目标检测率低的问题,提出了一种基于人工免疫网络(aiNet)进行背景抑制、基于行列k均值聚类实现阈值分割的单帧红外弱小目标检测算法.首先采用aiNet结合Robinson警戒环技术,融入自组织特征映射(SOM)拓扑思想,设计一系列抗体进化策略,建立自适应局部空间背景模型—模糊拓扑记忆抗体库,并以此分析各像素点的背景模糊隶属度来抑制背景杂波;接着提出基于行列k均值聚类的阈值分割算法来检测真实目标.实验结果表明,该算法的F1指标高达99%,其能随背景的局部变化来自适应建立空间背景模型,从而

  12. An Efficient AI Based Approach for Multimedia Traffic Management in Wireless Network

    Directory of Open Access Journals (Sweden)

    Abdul Hanan Abdullah

    2013-06-01

    Full Text Available This study presents Artificial Intelligence based channel estimation and monitoring technique call AIMonitoring System (AIMS, for integration of Multimedia traffic in wireless network. Through AIMS technique every node in the network has the capability to monitor the neighbour node transmission and also before sending the traffic. The sending node evaluates the SNR ratio as primary parameter and queue limit of the receiving node as secondary. With the help of AIMS every node has the pre-determined information about the selected channel as well as node. Based on conditional and distribution probability model, the proposed Bay Estimator model analyses the SNR ratio before forwarding the multimedia traffic on selected path. We determine the performance of our proposed technique by obtaining the recursive analysis matrix methodology. Amendment of pre-distinct parameters make us capable to maintain the quality of service, multipath adaptability for attack prevention, as well as minimize the packet loss ratio.

  13. Behaviour based Mobile Robot Navigation Technique using AI System: Experimental Investigation on Active Media Pioneer Robot

    Directory of Open Access Journals (Sweden)

    S. Parasuraman, V.Ganapathy

    2012-10-01

    Full Text Available A key issue in the research of an autonomous robot is the design and development of the navigation technique that enables the robot to navigate in a real world environment. In this research, the issues investigated and methodologies established include (a Designing of the individual behavior and behavior rule selection using Alpha level fuzzy logic system  (b Designing of the controller, which maps the sensors input to the motor output through model based Fuzzy Logic Inference System and (c Formulation of the decision-making process by using Alpha-level fuzzy logic system. The proposed method is applied to Active Media Pioneer Robot and the results are discussed and compared with most accepted methods. This approach provides a formal methodology for representing and implementing the human expert heuristic knowledge and perception-based action in mobile robot navigation. In this approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables.Keywards: Mobile robot, behavior based control, fuzzy logic, alpha level fuzzy logic, obstacle avoidance behavior and goal seek behavior

  14. UFC advisor: An AI-based system for the automatic test environment

    Science.gov (United States)

    Lincoln, David T.; Fink, Pamela K.

    1990-01-01

    The Air Logistics Command within the Air Force is responsible for maintaining a wide variety of aircraft fleets and weapon systems. To maintain these fleets and systems requires specialized test equipment that provides data concerning the behavior of a particular device. The test equipment is used to 'poke and prod' the device to determine its functionality. The data represent voltages, pressures, torques, temperatures, etc. and are called testpoints. These testpoints can be defined numerically as being in or out of limits/tolerance. Some test equipment is termed 'automatic' because it is computer-controlled. Due to the fact that effective maintenance in the test arena requires a significant amount of expertise, it is an ideal area for the application of knowledge-based system technology. Such a system would take testpoint data, identify values out-of-limits, and determine potential underlying problems based on what is out-of-limits and how far. This paper discusses the application of this technology to a device called the Unified Fuel Control (UFC) which is maintained in this manner.

  15. Information, Meaning and Eigenforms: In the Light of Sociology, Agent-Based Modeling and AI

    Directory of Open Access Journals (Sweden)

    Manfred Füllsack

    2012-08-01

    Full Text Available The paper considers the relation of Shannon-type information to those semantic and hermeneutic aspects of communication, which are often referred to as meaning. It builds on considerations of Talcott Parsons, Niklas Luhmann and Robert K. Logan and relates them to an agent-based model that reproduces key aspects of the Talking Head experiment by Luc Steels. The resulting insights seem to give reason to regard information and meaning not as qualitatively different entities, but as interrelated forms of order that emerge in the interaction of autonomous (self-referentially closed agents. Although on first sight, this way of putting information and meaning into a constructivist framework seems to open possibilities to conceive meaning in terms of Shannon-information, it also suggests a re-conceptualization of information in terms of what cybernetics calls Eigenform in order to do justice to its dynamic interrelation with meaning.

  16. Editorial: Ontogeny Recapitulates Ontegeny: AI and AI Magazine

    OpenAIRE

    Rich, Elaine

    1992-01-01

    As the AI community has matured, the role of AI Magazine has continued to evolve. Rich outlines several ways that this community-wide publication can address the current needs of AI researchers, and encourages broad participation from community members.

  17. Dicty_cDB: FC-AI22 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI22 (Link to dictyBase) - - - Contig-U15369-1 | Contig-U15732-1 FC-AI...22P (Link to Original site) FC-AI22F 583 FC-AI22Z 683 FC-AI22P 1266 - - Show FC-AI22 Library FC (...Link to library) Clone ID FC-AI22 (Link to dictyBase) Atlas ID - NBRP ID - dictyBase ID - Link to Contig Con...tig-U15369-1 | Contig-U15732-1 Original site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI...22Q.Seq.d/ Representative seq. ID FC-AI22P (Link to Original site) Representative DNA sequence >FC-AI22 (FC-AI

  18. Dicty_cDB: FC-AI08 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI08 (Link to dictyBase) - G01729 DDB0233148 Contig-U14939-1 FC-AI...08P (Link to Original site) FC-AI08F 654 FC-AI08Z 563 FC-AI08P 1217 - - Show FC-AI08 Library FC (Link... to library) Clone ID FC-AI08 (Link to dictyBase) Atlas ID - NBRP ID G01729 dictyBase ID DDB0233148 Link to ...Contig Contig-U14939-1 Original site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI...08Q.Seq.d/ Representative seq. ID FC-AI08P (Link to Original site) Representative DNA sequence >FC-AI08 (FC-AI

  19. Dicty_cDB: FC-AI07 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI07 (Link to dictyBase) - - - Contig-U15296-1 | Contig-U15756-1 FC-AI...07P (Link to Original site) FC-AI07F 580 FC-AI07Z 723 FC-AI07P 1303 - - Show FC-AI07 Library FC (...Link to library) Clone ID FC-AI07 (Link to dictyBase) Atlas ID - NBRP ID - dictyBase ID - Link to Contig Con...tig-U15296-1 | Contig-U15756-1 Original site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI...07Q.Seq.d/ Representative seq. ID FC-AI07P (Link to Original site) Representative DNA sequence >FC-AI07 (FC-AI

  20. Dicty_cDB: FC-AI24 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI24 (Link to dictyBase) - - - - FC-AI24Z (Link to Original site) - - FC-AI...24Z 693 - - - - Show FC-AI24 Library FC (Link to library) Clone ID FC-AI24 (Link to dictyBas...e) Atlas ID - NBRP ID - dictyBase ID - Link to Contig - Original site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI...24Q.Seq.d/ Representative seq. ID FC-AI24Z (Link to Original s...ite) Representative DNA sequence >FC-AI24 (FC-AI24Q) /CSM/FC/FC-AI/FC-AI24Q.Seq.d/ XXXXXXXXXXAAATTAGAAAACAAA

  1. Maritime Safety in Terms of the Availability for the AIS class B Binary Data Transmission, Based on Static Measurements, Performed on the VTS Zatoka Gdańska

    Directory of Open Access Journals (Sweden)

    Jaskólski Krzysztof

    2015-12-01

    Full Text Available The problem of the safety navigation considered only in terms of position error measurement, seems to be solved on a global scale. Thus, the operational characteristics of radio navigation systems such as availability are equally important. The integrated navigation system operate in a multi-sensor environment and it is important to determinate a temporal validity of data to make it usable in data fusion process. In the age of digital data processing, the requirements for continuity, availability, reliability and integrity information are already grown. This article analyses the problem of time stamp discrepancies of dynamic AIS class B position reports. For this purpose, the statistical summary of Latency Position Reports, derived from class B units has been presented. The navigation data recordings were conducted during 82 days of August, September and November 2014 from 20 vessels located in area of VTS ‘Zatoka Gdańska’. On the base of Latency Position Reports class B it is possible to designate the availability of AIS information system. For this purpose, the model of availability of AIS binary data transmission and research outcomes have been presented.

  2. Action-based Character AI in Video-games with CogBots Architecture: A Preliminary Report

    OpenAIRE

    Aversa, Davide; Vassos, Stavros

    2013-01-01

    In this paper we propose an architecture for specifying the interaction of non-player characters (NPCs) in the game-world in a way that abstracts common tasks in four main conceptual components, namely perception, deliberation, control, action. We argue that this architecture, inspired by AI research on autonomous agents and robots, can offer a number of benefits in the form of abstraction, modularity, re-usability and higher degrees of personalization for the behavior of each NPC. We also sh...

  3. Welcome to AI Magazine

    OpenAIRE

    Thompson, Alan M.

    1980-01-01

    As a major scientific society, the AAAI has a responsibility for promoting its field as well as informing its members of the latest technical developments. Since the latter function is adequately performed by the several journals and conference proceedings already mentioned, the editorial committee chose to assign to AI Magazine the task of providing AAAI members and the public as well with a broader perspective on the research activities within AI. The approach we intend to take includes pub...

  4. Dicty_cDB: FC-AI12 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI12 (Link to dictyBase) - - - Contig-U15484-1 FC-AI12Z (Li...nk to Original site) - - FC-AI12Z 614 - - - - Show FC-AI12 Library FC (Link to library) Clone ID FC-AI12 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI12Q.Seq.d/ Representative seq. ID FC-AI...12Z (Link to Original site) Representative DNA sequence >FC-AI12 (FC-AI12Q) /CSM/FC/FC-AI/FC-AI12Q.Seq....EKIVRRI ELLDGITCYRNEKAKDEIVLTGNSLELLSQSCATIQLRSAIKYKDVRKFLDGIYVSERNV LESN*in*riys

  5. Dicty_cDB: FC-AI06 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI06 (Link to dictyBase) - - - Contig-U16465-1 FC-AI06E (Li...nk to Original site) - - - - - - FC-AI06E 1138 Show FC-AI06 Library FC (Link to library) Clone ID FC-AI06 (L...//dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI06Q.Seq.d/ Representative seq. ID FC-AI...06E (Link to Original site) Representative DNA sequence >FC-AI06 (FC-AI06Q) /CSM/FC/FC-AI/FC-AI06Q.Seq...FGRGIDIERVNVVINYDMAESADTYLHRVGRAGRFGTK GLAISFVPSKEDPVLEQVQSKFVVSIKELVATPDPSTYMSG*kkkkkkkknlfvlksikk k*kkk*in

  6. Dicty_cDB: FC-AI09 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI09 (Link to dictyBase) - - - Contig-U16149-1 FC-AI09Z (Li...nk to Original site) - - FC-AI09Z 591 - - - - Show FC-AI09 Library FC (Link to library) Clone ID FC-AI09 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI09Q.Seq.d/ Representative seq. ID FC-AI...09Z (Link to Original site) Representative DNA sequence >FC-AI09 (FC-AI09Q) /CSM/FC/FC-AI/FC-AI09Q.Seq....*tkl ik*ilifykiknnkkkkkk Frame B: ---gt*kvpeflailfkrmasrsvlwy*rcltkakkglkapqtltik

  7. Dicty_cDB: FC-AI19 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI19 (Link to dictyBase) - - - Contig-U15115-1 FC-AI19Z (Li...nk to Original site) - - FC-AI19Z 661 - - - - Show FC-AI19 Library FC (Link to library) Clone ID FC-AI19 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI19Q.Seq.d/ Representative seq. ID FC-AI...19Z (Link to Original site) Representative DNA sequence >FC-AI19 (FC-AI19Q) /CSM/FC/FC-AI/FC-AI19Q.Seq....lmrqswvkkiesi*lvl krrkkkknnkkkkkkkkkkklfn*lvnkkn*ik*kkllcnqkk Frame B: ---*ekaieilsklfsin*kfn**ysiiigkkstkyq

  8. Dicty_cDB: FC-AI15 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI15 (Link to dictyBase) - G01730 DDB0214993 Contig-U15123-1 FC-AI...15E (Link to Original site) - - - - - - FC-AI15E 856 Show FC-AI15 Library FC (Link to library) Clone ID FC-AI...3-1 Original site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI15Q.Se...q.d/ Representative seq. ID FC-AI15E (Link to Original site) Representative DNA sequence >FC-AI15 (FC-AI15Q) /CSM/FC/FC-AI/FC-AI...AAAAAAAATA sequence update 1996.12.24 Translated Amino Acid sequence kt*riyi*KMMIKYITIAILFIASLVKADLQFSLCPTCV

  9. Dicty_cDB: FC-AI14 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI14 (Link to dictyBase) - - - Contig-U16280-1 FC-AI14Z (Li...nk to Original site) - - FC-AI14Z 671 - - - - Show FC-AI14 Library FC (Link to library) Clone ID FC-AI14 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI14Q.Seq.d/ Representative seq. ID FC-AI...14Z (Link to Original site) Representative DNA sequence >FC-AI14 (FC-AI14Q) /CSM/FC/FC-AI/FC-AI14Q.Seq....nqrllv*lvvlskklqllnsnqsfkfkkvq rmkknsvkntkn*rfvllt*nlkslkrmpksknsptkliifilkly Frame B: ---lkdl*krtphl*stcfhhptlcssrrfclwslnai

  10. AIS Ship Traffic: Hawaii: 2011-2012

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Ship position data from a satellite-based Automatic Identification System (AIS) were obtained jointly by PacIOOS (J. Potemra), SOEST/ORE of the University of Hawaii...

  11. Prediction based on mean subset

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Brown, P. J.; Madsen, Henrik;

    2002-01-01

    Shrinkage methods have traditionally been applied in prediction problems. In this article we develop a shrinkage method (mean subset) that forms an average of regression coefficients from individual subsets of the explanatory variables. A Bayesian approach is taken to derive an expression of how...... the coefficient vectors from each subset should be weighted. It is not computationally feasible to calculate the mean subset coefficient vector for larger problems, and thus we suggest an algorithm to find an approximation to the mean subset coefficient vector. In a comprehensive Monte Carlo simulation study......, it is found that the proposed mean subset method has superior prediction performance than prediction based on the best subset method, and in some settings also better than the ridge regression and lasso methods. The conclusions drawn from the Monte Carlo study is corroborated in an example in which prediction...

  12. Breast calcifications. A standardized mammographic reporting and data system to improve positive predictive value; Calcificazioni mammarie. Utilita' di un sistema standardizzato di descrizione e valutazione dei reperti mammografici ai fini del miglioramento del valore predittivo positivo

    Energy Technology Data Exchange (ETDEWEB)

    Perugini, G.; Bonzanini, B.; Valentino, C. [Azienda Ospedali Riuniti, Bergamo (Italy). Unita' operativa di radiodiagnostica

    1999-11-01

    The purpose of this work is to investigate the usefulness of a standardized reporting and data system in improving the positive predictive value of mammography in breast calcifications. Using the Breast Imaging Reporting and Data System lexicon developed by the American College of Radiology, it is defined 5 descriptive categories of breast calcifications and classified diagnostic suspicion of malignancy on a 3-grade scale (low, intermediate and high). Two radiologists reviewed 117 mammographic studies selected from those of the patients submitted to surgical biopsy for mammographically detected calcifications from January 1993 to December 1997, and classified them according to the above criteria. The positive predictive value was calculated for all examinations and for the stratified groups. Defining a standardized system for assessing and describing breast calcifications helps improve the diagnostic accuracy of mammography in clinical practice. [Italian] Scopo di questo documento e' verificare l'utilita di un protocollo standardizzato di descrizione e valutazione delle calcificazioni mammarie ai fini di migliorare il valore predittivo positivo dell'indagine mammografica presso il nostro centro. Utilizzando la terminologia proposta dall'American Collge of Radiology, denominata breast imaging reporting and data system, e' stato elaborato un protocollo che individua 5 modalita' di presentazione della calcificazioni mammarie e definisce 3 gradi di sospetto di malignita' (basso, medio, alto). Il protocollo e' stato applicato alla revisione da parte di due radiologi di 117 casi di calcificazione mammarie inviate a biopsia chirurgica presso il nostro centro nel periodo gennaio 1993-dicembre 1997; sono stati quindi calcolati il valore predittivo positivo complessivo della casistica e quello dei gruppi stratificati per grado di sospetto. La formulazione sulla base della propria esperienza di un sistema standardizzato di descrizione e

  13. Prediction of U3SI2-Al burn-up and SiC/p-AI composition effects on its thermal conductivity using metal matrix composite (MMC) model containing progressive sub-dispersion

    International Nuclear Information System (INIS)

    The model takes into account the evolution of constituent volume fraction. Sub-dispersion of disperse contains fission gas bubbles that increase with bum-up. The metal matrix could contain pore and void, a different type of disperse that vary wth time. The model is previously aimed to dispersion-nuclear fuel element. The model consists of a combination of different conductance constituent of both matrix and sub-matrix. Application is carried out to predict the fuel swelling effect on thermal conductivity of U3SI2-Al dispersion, and to volume fraction effect on conductivity of SiC-particulate reinforced AI matrix. The model shows that both fuel fraction and fission gas swelling decrease the thermal conductivity. During the start-up period of swelling the conductivity increases as aluminum pore close. then decreases most linearly. SiC/p-AI conductivity decreases most linearly with particulate volume fraction, attains 57.6% of pure AI at 50 % v/v. The author conclude that the model developed is applicable for more general MMC. (author)

  14. VLSI Architecture for 8-Point AI-based Arai DCT having Low Area-Time Complexity and Power at Improved Accuracy

    Directory of Open Access Journals (Sweden)

    Jithra Adikari

    2012-03-01

    Full Text Available A low complexity digital VLSI architecture for the computation of an algebraic integer (AI based 8-point Arai DCT algorithm is proposed. AI encoding schemes for exact representation of the Arai DCT transform based on a particularly sparse 2-D AI representation is reviewed, leading to the proposed novel architecture based on a new final reconstruction step (FRS having lower complexity and higher accuracy compared to the state-of-the-art. This FRS is based on an optimization derived from expansion factors that leads to small integer constant-coefficient multiplications, which are realized with common sub-expression elimination (CSE and Booth encoding. The reference circuit [1] as well as the proposed architectures for two expansion factors α† = 4.5958 and α′ = 167.2309 are implemented. The proposed circuits show 150% and 300% improvements in the number of DCT coefficients having error ≤ 0:1% compared to [1]. The three designs were realized using both 40 nm CMOS Xilinx Virtex-6 FPGAs and synthesized using 65 nm CMOS general purpose standard cells from TSMC. Post synthesis timing analysis of 65 nm CMOS realizations at 900 mV for all three designs of the 8-point DCT core for 8-bit inputs show potential real-time operation at 2.083 GHz clock frequency leading to a combined throughput of 2.083 billion 8-point Arai DCTs per second. The expansion-factor designs show a 43% reduction in area (A and 29% reduction in dynamic power (PD for FPGA realizations. An 11% reduction in area is observed for the ASIC design for α† = 4.5958 for an 8% reduction in total power (PT . Our second ASIC design having α′ = 167.2309 shows marginal improvements in area and power compared to our reference design but at significantly better accuracy.

  15. Proposition de hiérarchisation des 45 graphèmes de base de l’orthographe du français

    Directory of Open Access Journals (Sweden)

    Pérez Manuel

    2014-07-01

    L’association de ces trois variables nous a conduit à établir une hiérarchisation des 45 graphèmes de base du français tels que délimités par Catach et al. (1995. Cette hiérarchisation pourrait nous permettre de mieux comprendre les difficultés empiriques rencontrées par des élèves aux connaissances orthographiques non encore automatisées, et constituer ainsi une aide pour les enseignants dans leur réflexion sur la nécessaire progression des apprentissages orthographiques lexicaux.

  16. MassAI

    DEFF Research Database (Denmark)

    2011-01-01

    A software tool for general analysis and data-mining of mass-spectrometric datasets. The program features a strong emphasis on scan-by-scan identification and results-transparency. MassAI also accommodates residue level analysis of labelled runs, e.g. HDX.......A software tool for general analysis and data-mining of mass-spectrometric datasets. The program features a strong emphasis on scan-by-scan identification and results-transparency. MassAI also accommodates residue level analysis of labelled runs, e.g. HDX....

  17. Areal Informations Systemet - AIS

    DEFF Research Database (Denmark)

    Nielsen, K.; Stjernholm, M.; Olsen, B. Ø.;

    Forord: Denne rapport giver en kort beskrivelse af Areal Informations Systemet (AIS), som er et databasesystem med natur- og miljødata, som kan stedfæstes geografisk. Projektet er gennemført i perioden 1996-2000 som et samarbejdsprojekt mellem Danmarks Miljøundersøgelser (DMU), Danmarks og Grønla...

  18. AI and Mathematical Education

    OpenAIRE

    Angel Garrido

    2012-01-01

    From ancient times, the history of human beings has developed by a succession of steps and sometimes jumps, until reaching the relative sophistication of the modern brain and culture. Researchers are attempting to create systems that mimic human thinking, understand speech, or beat the best human chess player. Understanding the mechanisms of intelligence, and creating intelligent artifacts are the twin goals of Artificial Intelligence (AI). Great mathematical minds have played a key role in A...

  19. AI and Mathematical Education

    Directory of Open Access Journals (Sweden)

    Angel Garrido

    2012-01-01

    Full Text Available From ancient times, the history of human beings has developed by a succession of steps and sometimes jumps, until reaching the relative sophistication of the modern brain and culture. Researchers are attempting to create systems that mimic human thinking, understand speech, or beat the best human chess player. Understanding the mechanisms of intelligence, and creating intelligent artifacts are the twin goals of Artificial Intelligence (AI. Great mathematical minds have played a key role in AI in recent years; to name only a few: Janos Neumann (also known as John von Neumann, Konrad Zuse, Norbert Wiener, Claude E. Shannon, Alan M. Turing, Grigore Moisil, Lofti A. Zadeh, Ronald R. Yager, Michio Sugeno, Solomon Marcus, or Lászlo A. Barabási. Introducing the study of AI is not merely useful because of its capability for solving difficult problems, but also because of its mathematical nature. It prepares us to understand the current world, enabling us to act on the challenges of the future.

  20. Development of AI (Artificial Intelligence)-based simulation system for man-machine system behavior in accidental situations of nuclear power plant

    International Nuclear Information System (INIS)

    A prototype version of a computer simulation system named JACOS (JAeri COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of an NPP (nuclear power plant). The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also modeled. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. Some simulations of incidents were performed to verify the human model. It was found that AI-techniques used in the human model are suitable to simulate the operator's cognitive behavior in an NPP accident. The models of cognitive characteristics were investigated in the effects on simulated results of cognitive behaviors. (author)

  1. Evolution de l'adhérence du réseau routier français : La base de données nationale Carat

    OpenAIRE

    Hamlat, Smail; LE TURDU, Valéry; Marsac, Paul; Cerezo, Véronique

    2010-01-01

    Depuis sa création dans les années 70, la base de données nationale d'adhérence Carat (Caractéristiques des revêtements en adhérence et texture) permet de suivre l'évolution du coefficient de frottement des revêtements utilisés sur le réseau routier français en particulier à travers l'édition de fuseaux de référence. Le fuseau actualisé, présenté dans cet article, montre que les revêtements mis en oeuvre ces dernières années présentent des performances plus homogènes et moins sensibles à la v...

  2. Beyond AI: Interdisciplinary Aspects of Artificial Intelligence

    CERN Document Server

    Romportl, Jan; Zackova, Eva; Beyond Artificial Intelligence : Contemplations, Expectations, Applications

    2013-01-01

    Products of modern artificial intelligence (AI) have mostly been formed by the views, opinions and goals of the “insiders”, i.e. people usually with engineering background who are driven by the force that can be metaphorically described as the pursuit of the craft of Hephaestus. However, since the present-day technology allows for tighter and tighter mergence of the “natural” everyday human life with machines of immense complexity, the responsible reaction of the scientific community should be based on cautious reflection of what really lies beyond AI, i.e. on the frontiers where the tumultuous ever-growing and ever-changing cloud of AI touches the rest of the world.   The chapters of this boo are based on the selected subset of the presentations that were delivered by their respective authors at the conference “Beyond AI: Interdisciplinary Aspects of Artificial Intelligence” held in Pilsen in December 2011.   From its very definition, the reflection of the phenomena that lie beyond AI must be i...

  3. First results of the ThAI thermal-hydraulic containment tests

    International Nuclear Information System (INIS)

    The new-built ThAI test facility is designed to fulfill those validation requirements in the areas of thermal hydraulics and fission product behaviour, in particular iodine. More precise data from thermal-hydraulic experiments are needed for validation of lumped-parameter codes simulating severe accident sequences, e.g. for the containment code system COCOSYS presently under development at GRS. Furthermore, advanced measurement techniques are applied in ThAI to comply with the requirements of CFD codes for detailed data of, e.g., convection flow fields. Concerning fission products, ThAI aims at investigating mass transport phenomena of volatile iodine at a technical scale. This is necessary because iodine mass transport modelling is so far based on small-scale experiments, which cannot reproduce effects of real thermal-hydraulic conditions in severe accidents such as free natural convection flows or stratification of sump and atmosphere. For ThAI, radioactive iodine 123I is used as a tracer to allow accurate measurements of iodine at low, accident-relevant concentrations. The ThAI test programme consists of a thermal-hydraulic part starting end 2000, and an iodine part to be performed in 2001/2002. Code calculations for the first block of thermal-hydraulic experiments have been made well in advance to use the unique opportunity of predicting the thermal hydraulics of a still unknown facility and thus demonstrate the state of the art of the codes and their application. (author)

  4. Is Computer Vision Still AI?

    OpenAIRE

    Fisher, Robert B.

    1994-01-01

    Recent general AI conferences show a decline in both the number and the quality of vision papers, but there is tremendous growth in, and specialization of, computer vision conferences. Hence, one might conclude that computer vision is parting or has parted company with AI. This article proposes that the divorce of computer vision and AI suggested here is actually an open marriage: Although computer vision is developing through its own research agenda, there are many shared areas of interest, ...

  5. Monitoring severe accidents using AI techniques

    International Nuclear Information System (INIS)

    After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

  6. AIS data based vessel speed, course and path analysis in the Botlek area in the Port of Rotterdam

    NARCIS (Netherlands)

    Shu, Y.; Daamen, W.; Ligteringen, H.; Hoogendoorn, S.P.

    2012-01-01

    Maritime traffic safety and port capacity is increasingly important nowadays. Due to the fast development of vessel traffic in ports and waterways, a lot of attention has been paid to maritime traffic safety and port capacity. Many simulation models have been used to predict traffic safety and port

  7. 云会计环境下基于ANP的AIS可信性评估%AIS Trustworthiness Evaluation Based on ANP in Cloud Accounting Environment

    Institute of Scientific and Technical Information of China (English)

    程平; 李宁

    2014-01-01

    随着云会计的发展,其服务的可信性受到更多的关注。针对现有评估方法难以合理有效地对其进行评估的问题,提出云会计环境下基于网络层次分析法( ANP)的会计信息系统( AIS)可信性评估方法。在综合考虑用户行业类别、评估指标之间存在依赖和反馈关系、可信性需求演化等因素对可信性评估的影响后,建立包含服务、维护、声誉3个维度的可信评估指标体系,给出可信性评估模型,在此基础上通过基于ANP的可信性评估算法得出考虑指标之间相互影响的可信评估结果。仿真结果表明,该方法能够对总体水平相近的服务得到差异性评估结论,为用户选择AIS服务提供支撑。%With the development of cloud accounting,the trustworthiness of its service draws more attention. Aiming at solving the problem that most existing trustworthiness evaluation methods are hard to be reasonable and effective enough to evaluate it, this paper proposes Accounting Information System ( AIS ) trustworthiness evaluation method based on Analytic Network Process( ANP) in cloud accounting. Considering influence factors such as user’ s industry categories, dependence and feedback relationships between evaluation indexes and evolution of trustworthiness demand,it builds up trustworthiness evaluation index system which contains three dimensions:service, maintenance, reputation, establishes trustworthiness evaluation model, and based on this, through trustworthiness evaluation algorithm based on analytic network process obtained evaluation results considering the influence between indexes. Simulation experimental results show the effectiveness of this method,for it draws difference evaluation conclusion on similar service of global level, providing a strong support to users on selecting accounting information system service.

  8. 基于Europa2的智能规划动态仿真与建模%Dynamic simulation and modeling for AI planning based on Europa

    Institute of Scientific and Technical Information of China (English)

    刘越畅; 钟秀玉; 房宜汕; 陈剑彪

    2012-01-01

    Simulation and modeling are key and difficult problems for the application of AI planning (especially, temporal planning). The start-of-the-art planning visualization tools can only support domain-independent visualization and are disabled to simulate practical scenario. There are some natural relations between chronicle planning and WPF( Windows Presentation Foundation) concepts. Based on such observation, this paper investigates the design of dynamic simulation for Europa2-the open source temporal planning platform of NASA based on the technique of WPF. The paper reveals the fact that, the chronicle planner based on timelines (e. G. Europa2) can be convieniently visually modeled and dynamically simulated using WPF technique. That provides a solution for the problem of designing a practical scenario simulation and modeling for AI planning systems.%仿真与建模是智能规划(特别是时态规划)走向应用的重要而困难的研究主题.目前已有的规划可视化建模工具只能支持领域无关的仿真和建模,无法模拟现实场景.基于时间线的Chronicle规划与Microsoft最新的图形显示技术WPF在某些概念上存在自然的对应关系.基于这样的思想,针对NASA开发的开源时态规划平台——Europa2,使用WPF基础类库和相关工具,研究智能规划能够模拟现实场景的动态仿真和建模的设计方法并给出了实例演示.研究表明,对诸如Europa2一类基于时间线的Chronicle规划系统,可以应用WPF一类通用图形技术进行方便的建模和动态仿真,从而初步解决了规划系统模拟现实场景的仿真和建模难以实现的问题.

  9. KamiWaAi - Interactive 3D Sketching with Java Based on Cl(4,1) Conformal Model of Euclidean Space

    OpenAIRE

    Hitzer, Eckhard

    2013-01-01

    This paper introduces the new interactive Java sketching software KamiWaAi, recently developed at the University of Fukui. Its graphical user interface enables the user without any knowledge of both mathematics or computer science, to do full three dimensional "drawings" on the screen. The resulting constructions can be reshaped interactively by dragging its points over the screen. The programming approach is new. KamiWaAi implements geometric objects like points, lines, circles, spheres, etc...

  10. Beyond AI: Artificial Dreams Conference

    CERN Document Server

    Zackova, Eva; Kelemen, Jozef; Beyond Artificial Intelligence : The Disappearing Human-Machine Divide

    2015-01-01

    This book is an edited collection of chapters based on the papers presented at the conference “Beyond AI: Artificial Dreams” held in Pilsen in November 2012. The aim of the conference was to question deep-rooted ideas of artificial intelligence and cast critical reflection on methods standing at its foundations.  Artificial Dreams epitomize our controversial quest for non-biological intelligence, and therefore the contributors of this book tried to fully exploit such a controversy in their respective chapters, which resulted in an interdisciplinary dialogue between experts from engineering, natural sciences and humanities.   While pursuing the Artificial Dreams, it has become clear that it is still more and more difficult to draw a clear divide between human and machine. And therefore this book tries to portrait such an image of what lies beyond artificial intelligence: we can see the disappearing human-machine divide, a very important phenomenon of nowadays technological society, the phenomenon which i...

  11. Intelligent behavior generator for autonomous mobile robots using planning-based AI decision making and supervisory control logic

    Science.gov (United States)

    Shah, Hitesh K.; Bahl, Vikas; Martin, Jason; Flann, Nicholas S.; Moore, Kevin L.

    2002-07-01

    In earlier research the Center for Self-Organizing and Intelligent Systems (CSOIS) at Utah State University (USU) have been funded by the US Army Tank-Automotive and Armaments Command's (TACOM) Intelligent Mobility Program to develop and demonstrate enhanced mobility concepts for unmanned ground vehicles (UGVs). One among the several out growths of this work has been the development of a grammar-based approach to intelligent behavior generation for commanding autonomous robotic vehicles. In this paper we describe the use of this grammar for enabling autonomous behaviors. A supervisory task controller (STC) sequences high-level action commands (taken from the grammar) to be executed by the robot. It takes as input a set of goals and a partial (static) map of the environment and produces, from the grammar, a flexible script (or sequence) of the high-level commands that are to be executed by the robot. The sequence is derived by a planning function that uses a graph-based heuristic search (A* -algorithm). Each action command has specific exit conditions that are evaluated by the STC following each task completion or interruption (in the case of disturbances or new operator requests). Depending on the system's state at task completion or interruption (including updated environmental and robot sensor information), the STC invokes a reactive response. This can include sequencing the pending tasks or initiating a re-planning event, if necessary. Though applicable to a wide variety of autonomous robots, an application of this approach is demonstrated via simulations of ODIS, an omni-directional inspection system developed for security applications.

  12. Code AI Personal Web Pages

    Science.gov (United States)

    Garcia, Joseph A.; Smith, Charles A. (Technical Monitor)

    1998-01-01

    The document consists of a publicly available web site (george.arc.nasa.gov) for Joseph A. Garcia's personal web pages in the AI division. Only general information will be posted and no technical material. All the information is unclassified.

  13. Selected Works of AI Shirshov

    CERN Document Server

    Kotchetov, Michail V

    2009-01-01

    Russian mathematician AI Shirshov (1921-1981) was a pioneer in several directions of associative, Lie, Jordan, and alternative algebras, as well as groups and projective planes. This book presents translations of his selected works.

  14. Monte Carlo simulation and transmission electron microscopy studies on Ni-Mo-based alloys and Al-rich TiAI intermetallics

    International Nuclear Information System (INIS)

    The two alloy systems: namely, Ni-Mo-based alloys and Al-Ti alloys, share some common features in that the ordered structures and the ordering processes in these two systems can be described in terms of three types of superlattice tiles: squares and fat or lean rhombi. In Ni- Mo-based alloys these represent one-molecule clusters of three fcc superlattice structures: Ni4Mo (D1a), Ni3Mo (D022) and Ni2Mo (Pt2Mo-type), while in Al-Ti these represent two dimensional Ti4AI, Ti3Al and Ti2Al derivatives on Ti-rich (002) planes of the off stoichiometric TiAl (L10) phase. Evolution of short range order (SRO): 11/20 special point SRO in the case of Ni-Mo and the incommensurate SRO in the case of the Al-rich TiAl intermetallic alloys and evolution of LRO phases from these have been followed using both conventional and high resolution TEM. Corroborative evidence from Monte Carlo simulations will also be presented in order to explain the observed experimental results. Occurrence of antiphase boundaries (APBs) and their energies, as we will see, play an important role in these transformations. Predominantly two types of APBs occur in the Al5Ti3 phase in Al-rich TiAl. Monte Carlo Simulations and the experimental observations reveal both of these. These play a synergistic role in the formation of Al5Ti3 antiphase domains

  15. Trust-based collective view prediction

    CERN Document Server

    Luo, Tiejian; Xu, Guandong; Zhou, Jia

    2013-01-01

    Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View

  16. ITRAQ-based quantitative proteomics reveals apolipoprotein A-I and transferrin as potential serum markers in CA19-9 negative pancreatic ductal adenocarcinoma

    Science.gov (United States)

    Lin, Chao; Wu, Wen-Chuan; Zhao, Guo-Chao; Wang, Dan-Song; Lou, Wen-Hui; Jin, Da-Yong

    2016-01-01

    Abstract Currently the diagnosis of pancreatic ductal adenocarcinoma (PDAC) relies on CA19-9 and radiological means, whereas some patients do not have elevated levels of CA19-9 secondary to pancreatic cancer. The purpose of this study was to identify potential serum biomarkers for CA19-9 negative PDAC. A total of 114 serum samples were collected from 3 groups: CA19-9 negative PDAC patients (n = 34), CA19-9 positive PDAC patients (n = 44), and healthy volunteers (n = 36), whereas the first 12 samples from each group were used for isobaric tags for relative and absolute quantitation (iTRAQ) analysis. Thereafter, candidate biomarkers were selected for validation by enzyme-linked immunosorbent assay (ELISA) with the rest specimens. Using the iTRAQ approach, a total of 5 proteins were identified as significantly different between CA19-9 negative PDAC patients and healthy subjects according to our defined criteria. Apolipoprotein A-I (APOA-I) and transferrin (TF) were selected to validate the proteomic results by ELISA in a further 78 serum specimens. It revealed that TF significantly correlated with the degree of histological differentiation (P = 0.042), and univariate and multivariate analyses indicated that TF is an independent prognostic factor for survival (hazard ratio, 0.302; 95% confidence interval, 0.118–0.774; P = 0.013) of patients with PDAC after curative surgery. ITRAQ-based quantitative proteomics revealed that APOA-I and TF may be potential CA19-9 negative PDAC serum markers. PMID:27495108

  17. AI Sport Forecast Software

    Directory of Open Access Journals (Sweden)

    Kiyomi Cerezo Takahash

    2008-12-01

    Full Text Available This article aims to explain the development of an application whose function is to predict the results of different sporting encounters. To do this an analysis of the influential factors, algorithms and technology implemented, will be carried out.

  18. Speech Intelligibility Prediction Based on Mutual Information

    DEFF Research Database (Denmark)

    Jensen, Jesper; Taal, Cees H.

    2014-01-01

    This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related...... to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using...... a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting...

  19. Calorimeter prediction based on multiple exponentials

    Energy Technology Data Exchange (ETDEWEB)

    Smith, M.K. E-mail: mks@lanl.gov; Bracken, D.S

    2002-05-21

    Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented.

  20. Calorimeter prediction based on multiple exponentials

    International Nuclear Information System (INIS)

    Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented

  1. Calorimeter prediction based on multiple exponentials

    CERN Document Server

    Smith, M K

    2002-01-01

    Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented.

  2. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  3. Statistic Approach versus Artificial Intelligence for Rainfall Prediction Based on Data Series

    Directory of Open Access Journals (Sweden)

    Indrabayu

    2013-04-01

    Full Text Available This paper proposed a new idea in comparing two common predictors i.e. the statistic method and artificial intelligence (AI for rainfall prediction using empirical data series. The statistic method uses Auto- Regressive Integrated Moving (ARIMA and Adaptive Splines Threshold Autoregressive (ASTAR, most favorable statistic tools, while in the AI, combination of Genetic Algorithm-Neural Network (GA-NN is chosen. The results show that ASTAR gives best prediction compare to others, in term of root mean square (RMSE and following trend between prediction and actual.

  4. Time Series Prediction Based on Chaotic Attractor

    Institute of Scientific and Technical Information of China (English)

    LIKe-Ping; CHENTian-Lun; GAOZi-You

    2003-01-01

    A new prediction technique is proposed for chaotic time series. The usefulness of the technique is that it can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. A time-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the time evolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate the local dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is tested for the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this prediction technique can improve the prediction of chaotic time series.

  5. Graph Transformation and AI Planning

    NARCIS (Netherlands)

    Edelkamp, S.; Rensink, A.; Edelkamp, S.; Frank, J.

    2007-01-01

    This document provides insight to the similarities and differences of Graph Transformation and AI Planning, two rising research fields with different publication organs and tools. While graph transformation systems can be used as a graphical knowledge engineering front-end for designing planning pr

  6. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

    Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

  7. AIS-MOB(AIS便携式应急示位标)技术与应用%AIS-MOB(AIS portable emergency position indicator) Technology and Application

    Institute of Scientific and Technical Information of China (English)

    郑华

    2012-01-01

    AIS-MOB(AIS个人便携应急示位标)是一种可以安装于救生衣或随身携带的便携式应急示位报警装备,该设备可通过人工或落水自动启动,应用AIS自组织时分多址技术在AIS信道或指配的专用信道上,定时发送个人位置和报警信息,该信息可被所有AIS终端设备和基站设备接收显示,提高搜救效率。设备由AIS发射电路、GNSS定位、天线、电池等部分组成,可在紧急情况下连续工作48小时以上,可广泛应用于各类搜救系统。%AIS-MOB(AIS portable emergency position indicator) is a kind of can be installed on the lifejacket or portable emergency position indicating and alarm equipment, the equipment can be manual or automatic start falling, the application of AIS self-organized time division multiple access technology in AIS channel or assigned a dedicated channel, regularly send personal position and the alarm information, the information can be all AIS terminal equipment and base station equipment receiving display, improve search efficiency. Equipment from the AIS emission circuit, GNSS positioning, antenna, battery components, in an emergency situation more than 48 hours of continuous work, can be widely used in various types of search and rescue system.

  8. Highlights on artificial insemination (AI technology in the pig

    Directory of Open Access Journals (Sweden)

    Tarek Khalifa

    2014-03-01

    Full Text Available Over the past decade, there has been a tremendous increase in the development of field AI services in the majority of countries concerned with pig production. The objective of this paper is to review: (a the current status of swine AI in the world, (b significance and limitation of AI with liquid and frozen semen, (c the biological traits of porcine semen in relation to in-vitro sperm storage, (d the criteria used for selection of a boar stud as a semen supplier, (e how to process boar semen for liquid and frozen storage in the commercial settings and (f how to improve fertility and prolificacy of boar semen. More than 99% of the inseminations conducted worldwide are made with liquid-stored semen. AI with frozen semen is used only for upgrading the genetic base in a particular country or herd. Determining the initial quality of semen ejaculates along with the selection of the optimum storage extender has a profound effect on the quality and fertility of AI doses. Different procedures have been used for improving the fertility of preserved spermatozoa including colloidal centrifugation of the semen, intrauterine insemination and modulation of the uterine defense mechanism after AI. Development of an efficient protocol for synchronizing the time of ovulation in sows and gilts coupled with improving uterine horn insemination technique will make a breakthrough in the commercial use of frozen boar semen.

  9. AI-2 of Aggregatibacter actinomycetemcomitans Inhibits Candida albicans Biofilm Formation

    Directory of Open Access Journals (Sweden)

    Endang W. Bachtiar

    2014-07-01

    Full Text Available Aggregatibacter actinomycetemcomitans, a Gram-negative bacterium, and Candida albicans, a polymorphic fungus, are both commensals of the oral cavity but both are opportunistic pathogens that can cause oral diseases. A. actinomycetemcomitans produces a quorum-sensing molecule called autoinducer-2 (AI-2, synthesized by LuxS, that plays an important role in expression of virulence factors, in intra- but also in interspecies communication. The aim of this study was to investigate the role of AI-2 based signaling in the interactions between C. albicans and A. actinomycetemcomitans. A. actinomycetemcomitans adhered to C. albicans and inhibited biofilm formation by means of a molecule that was secreted during growth. C. albicans biofilm formation increased significantly when co-cultured with A. actinomycetemcomitans luxS, lacking AI-2 production. Addition of wild-type-derived spent medium or synthetic AI-2 to spent medium of the luxS strain, restored inhibition of C. albicans biofilm formation to wild-type levels. Addition of synthetic AI-2 significantly inhibited hypha formation of C. albicans possibly explaining the inhibition of biofilm formation. AI-2 of A. actinomycetemcomitans is synthesized by LuxS, accumulates during growth and inhibits C. albicans hypha- and biofilm formation. Identifying the molecular mechanisms underlying the interaction between bacteria and fungi may provide important insight into the balance within complex oral microbial communities.

  10. The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning

    CERN Document Server

    Caron, Sascha; Rolbiecki, Krzysztof; de Austri, Roberto Ruiz; Stienen, Bob

    2016-01-01

    A key research question at the Large Hadron Collider (LHC) is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: It requires the time consuming generation of scattering events, the simulation of the detector response, the event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiment. In the BSM-AI project we attack this challenge with a new approach. Machine learning tools are thought to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least 93 percent. It ...

  11. Monitoring Severe Accidents Using AI Techniques

    International Nuclear Information System (INIS)

    It is very difficult for nuclear power plant operators to monitor and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. The objective of this study is to develop and verify the monitoring for severe accidents using artificial intelligence (AI) techniques such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH) and fuzzy neural network (FNN). The SVC and PNN are used for event classification among the severe accidents. Also, GMDH and FNN are used to monitor for severe accidents. The inputs to AI techniques are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. In this study, 3 types of initiating events such as the hot-leg LOCA, the cold-leg LOCA and SGTR are considered and it is verified how well the proposed scenario identification algorithm using the GMDH and FNN models identifies the timings when the reactor core will be uncovered, when CET will exceed 1200 .deg. F and when the reactor vessel will fail. In cases that an initiating event develops into a severe accident, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful for operators to perform severe accident management

  12. Organisational intelligence and distributed AI

    OpenAIRE

    Kirn, Stefan

    1995-01-01

    The analysis of this chapter starts from organisational theory, and from this it draws conclusions for the design, and possible organisational applications, of Distributed AI systems. We first review how the concept of organisations has emerged from non-organised black-box entities to so-called computerised organisations. Within this context, organisational researchers have started to redesign their models of intelligent organisations with respect to the availability of advanced computing tec...

  13. SEKILAS TENTANG AVIAN INFLUENZA (AI)

    OpenAIRE

    Fauziah Elytha

    2011-01-01

    Fluburung atau Avian Influenza (AI) adalah penyakit zoonosis fatal dan menular serta dapat menginfeksi semua jenis burung, manusia, babi, kuda dan anjing, Virus Avian Influenza tipe A (hewan) dari keluarga Drthomyxoviridae telah menyerang manusia dan menyebabkan banyak korban meninggal dunia. Saat ini avian Influenza telah menjadi masalah kesehatan global yang sangat serius, termasuk di Indonesia. Sejak Juli 2005 Sampai 12 April 2006 telah ditemukan 479 kasus kumulatif dan dicurigai flu burun...

  14. Wavelet-based prediction of oil prices

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, Shahriar [Econometric Group, Department of Economics, University of Southern Denmark, DK-5230 Odense M (Denmark); Weinreich, Ilona [Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhr Campus, D-53424 Remagen (Germany)]. E-mail: weinreich@rheinahrcampus.de; Reinarz, Dominik [Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhr Campus, D-53424 Remagen (Germany)

    2005-07-01

    This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced.

  15. FACTORS INFLUENCING CUSTOMER BEHAVIOR-BASED CRM PERFORMANCE OF AIS IN BANGKOK%影响曼谷AIS公司基于客户行为的CRM效能的几个因子

    Institute of Scientific and Technical Information of China (English)

    林海

    2012-01-01

    关系营销对很多公司来说都是新理念。在很多国家,关系型的交换已取代交易型的交换成为规范。本文主要是测试一个客户关系管理模型,通过研究下列影响曼谷AIS公司基于客户行为的CRM效能的几个因子及它们之间的相互关系来进行,这几个因子分别是:服务质量,客户价值,客户满意度,以及品牌忠诚度。研究表明AIS公司应该致力于改进其服务质量中的移情性维度。同时,AIS如欲在将来市场成功,则应该增加其功能价值,并减少客户付出感知。%Relationship marketing has emerged as a big new idea for many companies. Relational, as opposed to transactional exchange is the norm in many countries. The primary objective of this study is to test an integrated model for the customer relationship management performance by studying the following factors' effects on customer behavior-based CRM performance of AIS in Bangkok and their interrelationships: service quality, customer value, customer satisfaction, and brand loyalty. The finding sug- gests that AIS should focus on improving its service quality dimension of empathy. Meanwhile, AIS should also increase functional value and reduce customer perceived sacrifice if it's going to succeed in the future market.

  16. 基于船舶AIS信息的可疑船只监测研究%Monitoring of Intrusive Vessels Based on an Automatic Identification System (AIS)

    Institute of Scientific and Technical Information of China (English)

    郭浩; 张晰; 安居白; 李冠宇

    2013-01-01

    中国海洋资源丰富,邻国船只时常非法航入中国领海或经济专属区.为了有效地保护和开发海洋资源,利用船舶自动识别系统(AIS)提供的船位、船速及航向等动态信息与船名、呼号、吃水及危险货物等静态信息,对某邻国船只于2012年4月在其专属经济区以及中国海域航行特征和船只特征进行分析.%Vessels from the neighboring countries often enter into the territorial waters and exclusive economic zone of China illegally.In order to protect our marine resources,this paper analyzes the characteristics and sailing features of ships from one neighboring country of China that entered the exclusive economic zone and the sea of China in April 2012.In particular,an automatic identification system (AIS) is used to collect the related information regarding ships,such as position,speed,heading,name,call sign,draft and dangerous goods carried,etc.Then,the geographic distribution,velocity and regular route pattern of vessels are used to develop a ship traffic information database.This paper provides an effective way for monitoring intrusive vessels,in order to protect China's marine rights.

  17. 基于阵列赋形的去卫星AIS消息碰撞方法%A New SAT-AIS Message Collision Concellation Method Based on Array Beamforming

    Institute of Scientific and Technical Information of China (English)

    李杰; 张舸; 郑蕊; 孟庆达

    2016-01-01

    岸基AIS系统覆盖范围在40海里左右,卫星AIS系统有效的解决了岸基AIS系统覆盖范围小问题,但是存在着不同SOTDMA单元的消息碰撞难题。文章提出了一种基于阵列赋形的卫星AIS系统去消息碰撞方法,仿真实验结果表明,在保持相同接收技术情况下,该方法碰撞概率仅为传统方案的25%。%Satellite AIS is a new efficient method which can enlarge the service area from a 40nm range to a global view,while it encountered a serious problem in concealing message collision of different SOTDMA cells. A new SAT-AIS message collision concellation method based on array beamforming is proposed to overcome this problem. Simulation result shows, with the same receiving scheme,the collision probability of the proposed method is only 25% of the conventional one.

  18. Adsorption of apolipoprotein A-I to biological membranes. A statistical mechanical model

    Science.gov (United States)

    Gross, Eitan

    2012-07-01

    Apolipoprotein A-I (apo A-I), the main protein component of high-density lipoprotein (HDL), reduces the risk for atherosclerosis by removing cholesterol from the membrane of foam cells. Experiments with model membrane systems have indicated, however, that membrane cholesterol reduces apo A-I binding to the membrane. Foam cells resolve this discrepancy electrostatically by co-inserting negatively charged phospholipids in their membrane. Here we present a statistical mechanical model to account for the effect of cholesterol. Our model is based on the Haugen and May model which takes into account the dipolar nature of the zwitterionic phospholipid head group in the membrane, in which the positive end of the zwitterionic dipole moment can move randomly on a hemispherical surface with a radius equal to the arm of the dipole moment and with the negative end fixed at the hydrocarbon layer. Adsorption of a positively charged apo A-I macroion to the surface of the membrane modifies the electric field within the head group region and induces lateral demixing of phospholipid molecules in the membrane. Results from numerical integration of model equations show that i) as a result of the strong charge-dipole electrostatic coupling, the positive end of the dipoles tilts away from the adsorbed macroion in a cooperative manner; and ii) cholesterol reduces macroion adsorption to the membrane by reducing the surface area of the membrane and restricting the dipoles range of rotation. Model predictions for the change in free energy of adsorption to zwitterionic membrane are in good agreement with previously reported experimental data with liposomes. The model can assist in designing new mimetic peptides.

  19. Colored Noise Prediction Based on Neural Network

    Institute of Scientific and Technical Information of China (English)

    Gao Fei; Zhang Xiaohui

    2003-01-01

    A method for predicting colored noise by introducing prediction of nonhnear time series is presented By adopting three kinds of neural networks prediction models, the colored noise prediction is studied through changing the filter bandwidth for stochastic noise and the sampling rate for colored noise The results show that colored noise can be predicted The prediction error decreases with the increasing of the sampling rate or the narrowing of the filter bandwidth. If the parameters are selected properly, the prediction precision can meet the requirement of engineering implementation. The results offer a new reference way for increasing the ability for detecting weak signal in signal processing system

  20. Tools and techniques for AIS Strategic Planning

    OpenAIRE

    Monod, Emmanuel; Watson, Richard

    2003-01-01

    AIS went through and will continue to undergo evolution and revolution as it grows. This article analyzes the current state of AIS and concludes it is in or approaching a crisis of priorities. Planning is the recommended path for solving this crisis. Four planning methods are proposed: stakeholder analysis, service matrix analysis, missions matrix analysis, and a four-year budget cycle. Keywords: AIS, planning, planning methods, priority setting, stakeholder analysis, service matrix analysis,...

  1. Predicting Scientific Success Based on Coauthorship Networks

    CERN Document Server

    Sarigöl, Emre; Scholtes, Ingo; Garas, Antonios; Schweitzer, Frank

    2014-01-01

    We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.

  2. DMC modified algorithm based on time series prediction principle

    Institute of Scientific and Technical Information of China (English)

    齐维贵; 朱学莉

    2002-01-01

    The application of heating load prediction and predictive control to the heat supply system for energysaving and high quality heat supply is discussed by first introducing the time series prediction principle, and thesequence model, parameter identification and least variance prediction principle, and then giving the heatingload and model error prediction based on this principle. As an improvement of DMC algorithm, the load predic-tion is used as a set point of DMC, and the prediction error is used as a corrected value of predictive control.Finally, the simulation results of two prediction methods to heat supply system are given.

  3. Prospecting the future with AI

    Directory of Open Access Journals (Sweden)

    Julian Gonzalez

    2009-12-01

    Full Text Available If we were able to foresee the future, we could be prepared to reduce the impact of bad situations as well as getting the most of profiting periods. Our world is a dynamic system that evolves as time goes by. The number of variables that can influence in future situations outnumbers our capacity of prediction at a first glance. This article will show an alternative way to foresee potential future scenarios based on human experts’ opinion, what can be considered as aknowledge modeling tool.

  4. JGOMAS: New Approach to AI Teaching

    Science.gov (United States)

    Barella, A.; Valero, S.; Carrascosa, C.

    2009-01-01

    This paper presents a new environment for teaching practical work in AI subjects. The main purpose of this environment is to make AI techniques more appealing to students and to facilitate the use of the toolkits which are currently widely used in research and development. This new environment has a toolkit for developing and executing agents,…

  5. The Relevance of AI Research to CAI.

    Science.gov (United States)

    Kearsley, Greg P.

    This article provides a tutorial introduction to Artificial Intelligence (AI) research for those involved in Computer Assisted Instruction (CAI). The general theme is that much of the current work in AI, particularly in the areas of natural language understanding systems, rule induction, programming languages, and socratic systems, has important…

  6. LuxS-independent formation of AI-2 from ribulose-5-phosphate

    Directory of Open Access Journals (Sweden)

    Hardie Kim R

    2008-06-01

    Full Text Available Abstract Background In many bacteria, the signal molecule AI-2 is generated from its precursor S-ribosyl-L-homocysteine in a reaction catalysed by the enzyme LuxS. However, generation of AI-2-like activity has also been reported for organisms lacking the luxS gene and the existence of alternative pathways for AI-2 formation in Escherichia coli has recently been predicted by stochastic modelling. Here, we investigate the possibility that spontaneous conversion of ribulose-5-phosphate could be responsible for AI-2 generation in the absence of luxS. Results Buffered solutions of ribulose-5-phosphate, but not ribose-5-phosphate, were found to contain high levels of AI-2 activity following incubation at concentrations similar to those reported in vivo. To test whether this process contributes to AI-2 formation by bacterial cells in vivo, an improved Vibrio harveyi bioassay was used. In agreement with previous studies, culture supernatants of E. coli and Staphylococcus aureus luxS mutants were found not to contain detectable levels of AI-2 activity. However, low activities were detected in an E. coli pgi-eda-edd-luxS mutant, a strain which degrades glucose entirely via the oxidative pentose phosphate pathway, with ribulose-5-phosphate as an obligatory intermediate. Conclusion Our results suggest that LuxS-independent formation of AI-2, via spontaneous conversion of ribulose-5-phosphate, may indeed occur in vivo. It does not contribute to AI-2 formation in wildtype E. coli and S. aureus under the conditions tested, but may be responsible for the AI-2-like activities reported for other organisms lacking the luxS gene.

  7. Community Detection Based on Link Prediction Methods

    CERN Document Server

    Cheng, Hui-Min

    2016-01-01

    Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from diffrent perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improve the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

  8. Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

    Science.gov (United States)

    Parnell, Gregory S.; Rowell, William F.; Valusek, John R.

    1987-01-01

    In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education.

  9. Enabling technologies and methods for the retro-fitting of AI software into NPPS

    International Nuclear Information System (INIS)

    Scottish Nuclear have a number of plant monitoring and training applications at their operational nuclear power plant which could benefit from the introduction of artificial intelligence (AI) software. An outline of early work on two current AI developments in the areas of advanced operator aids and intelligent training is given. A generic workstation based engineering simulator (WES) which provides a prototyping environment for AI product application development and evaluation and development of human-computer interface (HCI) designs for plant installation is described. It is concluded that the WES architecture facilitates both migration of the prototype AI application to the plant and collaboration in the AI field between Scottish Nuclear and other organizations. (author). 1 ref., 6 figs

  10. Artificial intelligence. Fears of an AI pioneer.

    Science.gov (United States)

    Russell, Stuart; Bohannon, John

    2015-07-17

    From the enraged robots in the 1920 play R.U.R. to the homicidal computer H.A.L. in 2001: A Space Odyssey, science fiction writers have embraced the dark side of artificial intelligence (AI) ever since the concept entered our collective imagination. Sluggish progress in AI research, especially during the “AI winter” of the 1970s and 1980s, made such worries seem far-fetched. But recent breakthroughs in machine learning and vast improvements in computational power have brought a flood of research funding— and fresh concerns about where AI may lead us. One researcher now speaking up is Stuart Russell, a computer scientist at the University of California, Berkeley, who with Peter Norvig, director of research at Google, wrote the premier AI textbook, Artificial Intelligence: A Modern Approach, now in its third edition. Last year, Russell joined the Centre for the Study of Existential Risk at Cambridge University in the United Kingdom as an AI expert focusing on “risks that could lead to human extinction.” Among his chief concerns, which he aired at an April meeting in Geneva, Switzerland, run by the United Nations, is the danger of putting military drones and weaponry under the full control of AI systems. This interview has been edited for clarity and brevity.

  11. A Distributed AI Aided 3D Domino Game

    CERN Document Server

    Amrahov, Şahin Emrah

    2010-01-01

    In the article a turn-based game played on four computers connected via network is investigated. There are three computers with natural intelligence and one with artificial intelligence. Game table is seen by each player's own view point in all players' monitors. Domino pieces are three dimensional. For distributed systems TCP/IP protocol is used. In order to get 3D image, Microsoft XNA technology is applied. Domino 101 game is nondeterministic game that is result of the game depends on the initial random distribution of the pieces. Number of the distributions is equal to the multiplication of following combinations: . Moreover, in this game that is played by four people, players are divided into 2 pairs. Accordingly, we cannot predict how the player uses the dominoes that is according to the dominoes of his/her partner or according to his/her own dominoes. The fact that the natural intelligence can be a player in any level affects the outcome. These reasons make it difficult to develop an AI. In the article ...

  12. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  13. Predictive visual tracking based on least absolute deviation estimation

    Institute of Scientific and Technical Information of China (English)

    Rongtai Cai; Yanjie Wang

    2008-01-01

    To cope with the occlusion and intersection between targets and the environment, location prediction is employed in the visual tracking system. Target trace is fitted by sliding subsection polynomials based on least absolute deviation (LAD) estimation, and the future location of target is predicted with the fitted trace. Experiment results show that the proposed location prediction algorithm based on LAD estimation has significant robustness advantages over least square (LS) estimation, and it is more effective than LS-based methods in visual tracking.

  14. Activity Prediction: A Twitter-based Exploration

    OpenAIRE

    Weerkamp, W.; Rijke, de, M.

    2012-01-01

    Social media platforms allow users to share their messages with everyone else. In microblogs, e.g., Twitter, people mostly report on what they did, they talk about current activities, and mention things they plan to do in the near future. In this paper, we propose the task of activity prediction, that is, trying to establish a set of activities that are likely to become popular at a later time. We perform a small-scale initial experiment, in which we try to predict popular activities for the ...

  15. National AIS at 1 Minute Intervals

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — OCM plans to contract for AIS data per the following description. The United States Automatic Identification System Database contains vessel traffic data for...

  16. Theoretical bases analysis of scientific prediction on marketing principles

    OpenAIRE

    A.S. Rosohata

    2012-01-01

    The article presents an overview categorical apparatus of scientific predictions and theoretical foundations results of scientific forecasting. They are integral part of effective management of economic activities. The approaches to the prediction of scientists in different fields of Social science and the categories modification of scientific prediction, based on principles of marketing are proposed.

  17. Theoretical bases analysis of scientific prediction on marketing principles

    Directory of Open Access Journals (Sweden)

    A.S. Rosohata

    2012-06-01

    Full Text Available The article presents an overview categorical apparatus of scientific predictions and theoretical foundations results of scientific forecasting. They are integral part of effective management of economic activities. The approaches to the prediction of scientists in different fields of Social science and the categories modification of scientific prediction, based on principles of marketing are proposed.

  18. Positioning Error Analysis of Ranging-Mode Using AIS Signals in China

    Directory of Open Access Journals (Sweden)

    Kai Zheng

    2016-01-01

    Full Text Available In order to provide resilient position, navigation, and time (PNT information for E-Navigation, the ranging-mode (R-Mode positioning using automatic identification system (AIS signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP for time of arrival (TOA and time difference of arrival (TDOA used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

  19. From Constructionist to Constructivist A.I.

    OpenAIRE

    Thórisson, Kristinn R.

    2009-01-01

    The development of artificial intelligence systems has to date been largely one of manual labor. This Constructionist approach to A.I. has resulted in a diverse set of isolated solutions to relatively small problems. Small success stories of putting these pieces together in robotics, for example, has made people optimistic that continuing on this path would lead to artificial general intelligence. This is unlikely. “The A.I. problem” has been divided up without much guidance from science or t...

  20. Learning game AI programming with Lua

    CERN Document Server

    Young, David

    2014-01-01

    If you are a game developer or a general programmer who wishes to focus on programming systems and techniques to build your game AI without creating low-level interfaces in a game engine, then this book is for you. Knowledge of C++ will come in handy to debug the entirety of the AI sandbox and expand on the features present within the book, but it is not required.

  1. A BRB Based Fault Prediction Method of Complex Electromechanical Systems

    Directory of Open Access Journals (Sweden)

    Bangcheng Zhang

    2015-01-01

    Full Text Available Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB. Moreover, an evidential reasoning (ER based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.

  2. Accretion in Radiative Equipartition (AiRE) Disks

    CERN Document Server

    Yazdi, Yasaman K

    2016-01-01

    Standard accretion disk theory (Shakura & Sunyaev 1973) predicts that the total pressure in disks at typical (sub-)Eddington accretion rates becomes radiation pressure dominated. However, radiation pressure dominated disks are thermally unstable. Since these disks are observed in approximate steady state over the instability time-scale, our accretion models in the radiation pressure dominated regime (i.e. inner disk) need to be modified. Here, we present a modification to the SS model, where radiation pressure is in equipartition with gas pressure in the inner region. We call these flows Accretion in Radiative Equipartition (AiRE) Disks. We introduce the basic features of AiRE disks and show how they modify disk properties such as the Toomre parameter and central temperature. We then show that the accretion rate of AiRE disks is limited from above and below, by Toomre and nodal sonic point instabilities, respectively. The former leads to a strict upper limit on the mass of supermassive black holes as a fu...

  3. Feature Selection for Neural Network Based Stock Prediction

    Science.gov (United States)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  4. Data Storage Management Using Ai Methods

    Directory of Open Access Journals (Sweden)

    Wlodzimierz Funika

    2013-01-01

    Full Text Available Data management and monitoring is an important issue in scientific computa-tion. Scientists want to access their data as quickly as possible. Some experi-ments need to store a lot of data which have to be secure. By saying this wemean that this data can not disappear or be damaged also the data storageshould be as cheap as possible. In this paper we present an approach to theautomation of monitoring and management of data storage. We introduce aknowledge based system which is able to manage data, i.e., make decisions onmigrating data, replicating or removing it. We discuss some of the existingsolutions which are popular on the market. In this paper we aim to present oursystem which uses such AI techniques like fuzzy logic or a rule-based expertsystem to deal with data storage management. We exploit in this system acost model to analyze the proposed solutions. The operations performed byour system are aimed to optimize the usage of the monitored infrastructure.

  5. Link prediction based on path entropy

    CERN Document Server

    Xu, Zhongqi; Yang, Jian

    2015-01-01

    Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream link predictors.

  6. Robustness of Prediction Based Delay Compensation for Nonlinear Systems

    CERN Document Server

    Findeisen, Rolf; Pannek, Jürgen; Varutti, Paolo

    2011-01-01

    Control of systems where the information between the controller, actuator, and sensor can be lost or delayed can be challenging with respect to stability and performance. One way to overcome the resulting problems is the use of prediction based compensation schemes. Instead of a single input, a sequence of (predicted) future controls is submitted and implemented at the actuator. If suitable, so-called prediction consistent compensation and control schemes, such as certain predictive control approaches, are used, stability of the closed loop in the presence of delays and packet losses can be guaranteed. In this paper, we show that control schemes employing prediction based delay compensation approaches do posses inherent robustness properties. Specifically, if the nominal closed loop system without delay compensation is ISS with respect to perturbation and measurement errors, then the closed loop system employing prediction based delay compensation techniques is robustly stable. We analyze the influence of the...

  7. LUT observations of the mass-transferring binary AI Dra

    Science.gov (United States)

    Liao, Wenping; Qian, Shengbang; Li, Linjia; Zhou, Xiao; Zhao, Ergang; Liu, Nianping

    2016-06-01

    Complete UV band light curve of the eclipsing binary AI Dra was observed with the Lunar-based Ultraviolet Telescope (LUT) in October 2014. It is very useful to adopt this continuous and uninterrupted light curve to determine physical and orbital parameters of the binary system. Photometric solutions of the spot model are obtained by using the W-D (Wilson and Devinney) method. It is confirmed that AI Dra is a semi-detached binary with secondary component filling its critical Roche lobe, which indicates that a mass transfer from the secondary component to the primary one should happen. Orbital period analysis based on all available eclipse times suggests a secular period increase and two cyclic variations. The secular period increase was interpreted by mass transfer from the secondary component to the primary one at a rate of 4.12 ×10^{-8}M_{⊙}/yr, which is in agreement with the photometric solutions. Two cyclic oscillations were due to light travel-time effect (LTTE) via the presence of two cool stellar companions in a near 2:1 mean-motion resonance. Both photometric solutions and orbital period analysis confirm that AI Dra is a mass-transferring binary, the massive primary is filling 69 % of its critical Roche lobe. After the primary evolves to fill the critical Roche lobe, the mass transfer will be reversed and the binary will evolve into a contact configuration.

  8. Model Based Predictive Control of a Fully Parallel Robot

    OpenAIRE

    Vivas, Oscar Andrès; Poignet, Philippe

    2003-01-01

    This paper deals with an efficient application of a model based predictive control in parallel machines. A receding horizon control strategy based on a simplified dynamic model is implemented. Experimental results are shown for the H4 robot, a fully parallel structure providing 3 degrees of freedom (dof) in translation and 1 dof in rotation. The model based predictive control and the commonly used computed torque control strategies are compared. The tracking performances and the robustness wi...

  9. Copula-based prediction of economic movements

    Science.gov (United States)

    García, J. E.; González-López, V. A.; Hirsh, I. D.

    2016-06-01

    In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

  10. Reproductive outcome with GnRH inclusion at 24 or 36h following a prostaglandin F2α-based protocol for timed AI in ewes.

    Science.gov (United States)

    Olivera-Muzante, J; Gil, J; Viñoles, C; Fierro, S

    2013-05-01

    The objective of this experiment was to study the reproductive performance obtained after a short-interval prostaglandin (PG) F2α-based protocol for timed artificial insemination (TAI) in sheep (Synchrovine®: two injections of PG 7 d apart), including a GnRH analogue at 24 or 36h after the second PG injection. The experiment involved 296 Corriedale ewes (206 multiparous and 90 nulliparous) grazing natural pastures during the breeding season (March-April; UTU "La Carolina", Flores Uruguay, 33° S-57° W). Ewes were assigned to three treatment groups: a) Synchrovine® (Control, n=101): two injections of D-Cloprostenol 75μg, 7 d apart, b) Synchrovine®+GnRH24 (n=98): Synchrovine® plus GnRH (busereline acetate 8.4μg) 24h after the second PG injection, and c) Synchrovine®+GnRH36 (n=97): Synchrovine® plus GnRH 36h after the second PG injection. All ewes were subjected to cervical TAI (Day 0), 44 to 47h after second PG injection, with fresh extended semen pool from six rams. Reproductive performance of ewes having ovulations and ovulation rate on Day 10, estrous cycle length in ewes that returned to estrus and non-return rate to estrus up to Day 22, fertility, prolificacy and fecundity on Day 70 were analyzed. Ewes having ovulations, ovulation rate, estrous cycle length and prolificacy did not differ between groups (P>0.05). However, non-return to estrus, fertility and fecundity was decreased in Synchrovine®+GnRH24 (P0.05). It was concluded that the reproductive performance obtained by Synchovine® TAI protocol was impaired by GnRH at 24h and not improved by GnRH administered at 36h after the second PG injection. PMID:23537480

  11. Size-based predictions of food web patterns

    DEFF Research Database (Denmark)

    Zhang, Lai; Hartvig, Martin; Knudsen, Kim;

    2014-01-01

    simulations with varying species richness. To this end, we develop a new size- and trait-based food web model that can be simplified into an analytically solvable size-based model. We confirm existing solutions for the size distribution and derive novel predictions for maximum trophic level and invasion...... of species are continuously distributed on a size-trait axis. It is, however, an open question whether such predictions are valid for a food web with a finite number of species embedded in a network structure. We address this question by comparing the size-based predictions to results from dynamic food web...... resistance. Our results show that the predicted size-spectrum exponent is borne out in the simulated food webs even with few species, albeit with a systematic bias. The predicted maximum trophic level turns out to be an upper limit since simulated food webs may have a lower number of trophic levels...

  12. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...

  13. Exact Amplitude--Based Resummation QCD Predictions and LHC Data

    CERN Document Server

    Ward, B F L; Yost, S A

    2014-01-01

    We present the current status of the comparisons with the respective data of the predictions of our approach of exact amplitude-based resummation in quantum field theory as applied to precision QCD calculations as needed for LHC physics, using the MC Herwiri1.031. The agreement between the theoretical predictions and the data exhibited continues to be encouraging.

  14. AIS 2005与AIS 1998在评价创伤救治结局中的应用比较%Comparative study between AIS 1998 and AIS 2005 for evaluation of injury severity and major trauma outcome

    Institute of Scientific and Technical Information of China (English)

    赵兴吉; 都定元; 孔令文; 张为民; 谭远康; 苏泓洁; 马丁

    2006-01-01

    目的比较简明损伤定级(AIS)2005与AIS 1998在评价创伤严重度及创伤救治结局的差异,探讨最新版AIS 2005应用的可行性及实用价值.方法采用AIS 2005和AIS 1998,对我院2003年1月~2005年5月救治的3110例创伤病例资料进行回顾性分析.结果 (1)随ISS值递增,两组病死率、并发症发生率均呈上升趋势;ISS>20,AIS 2005组病死率上升趋势更为明显,在ISS>15~≤20,AIS 2005组病死率较AIS 1998组有显著降低(P=0.001).除AIS 2005组ISS≤15并发症发生率较AIS 1998组下降外(P=0.035),其余各ISS分值段AIS 2005组并发症发生率较AIS 1998组上升趋势更为明显.(2)随ISS值升高,修正创伤评分(RTS)、创伤与损伤严重度评分(TRISS)、创伤严重度特征评分(ASCOT)、ASCOT-CHINA值逐渐降低,且AIS 2005生存概率预测值分布较AIS 1998大.AIS 2005预测性评分指标的区别度和敏感性均高于AIS 1998,除ASCOT-CHINA准确性、ASCOT特异性低于AIS 1998,ASCOT存活误判率高于AIS 1998外,ASCOT、TRISS准确性,ASCOT-CHINA、TRISS特异性均高于AIS 1998,ASCOT-CHINA、TRISS存活误判率较AIS 1998低,AIS 2005对生存组、死亡组生存概率预测优于AIS 1998.结论以AIS 2005为基础的ISS、TRISS、ASCOT等方法评价创伤及其结局预测总体上优于AIS 1998;建议使用AIS 2005评价多发伤时,以ISS>20界定为严重多发伤可能更为合理.

  15. Combining AI Methods for Learning Bots in a Real-Time Strategy Game

    Directory of Open Access Journals (Sweden)

    Robin Baumgarten

    2009-01-01

    Full Text Available We describe an approach for simulating human game-play in strategy games using a variety of AI techniques, including simulated annealing, decision tree learning, and case-based reasoning. We have implemented an AI-bot that uses these techniques to form a novel approach for planning fleet movements and attacks in DEFCON, a nuclear war simulation strategy game released in 2006 by Introversion Software Ltd. The AI-bot retrieves plans from a case-base of recorded games, then uses these to generate a new plan using a method based on decision tree learning. In addition, we have implemented more sophisticated control over low-level actions that enable the AI-bot to synchronize bombing runs, and used a simulated annealing approach for assigning bombing targets to planes and opponent cities to missiles. We describe how our AI-bot operates, and the experimentation we have performed in order to determine an optimal configuration for it. With this configuration, our AI-bot beats Introversion's finite state machine automated player in 76.7% of 150 matches played. We briefly introduce the notion of ability versus enjoyability and discuss initial results of a survey we conducted with human players.

  16. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  17. Entropy-based link prediction in weighted networks

    CERN Document Server

    Xu, Zhongqi; Sharafat, Rajput Ramiz; Li, Lunbo; Yang, Jian

    2016-01-01

    Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \\cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.

  18. A Case Based Reasoning aluminium thermal analysis platform for the prediction of W319 Al cast component characteristics

    Directory of Open Access Journals (Sweden)

    R. Lashkari

    2009-09-01

    Full Text Available Purpose: This paper presents the research on the development of the Aluminum Thermal Analysis Technology Platform (AlTAP utilizing a Case Based Reasoning (CBR Caspian shell for interpretation of industrial cooling curves and predicting alloy and cast component characteristics.Design/methodology/approach: CBR being a branch of Artificial Intelligence (AI that solves problems based on understanding and adaptation of previous experiences is suitable for interpretation of the AlTAP results since this is a knowledge intensive activity which requires a fair amount of experience.Findings: The integrated AlTAP and CBR system was found to be useful for the prediction of melt thermal characteristics, cast component mechanical and structural properties.Practical implications: Industrial trials confirmed the technical capabilities of the AlTAP/CBR Platform for the on-line quality control and prediction of 319 melt characteristics and the aluminum engine block’s (Cosworth casting process engineering specifications.Originality/value: An automated AlTAP Platform integrated with a CBR system is a new Quality Control concept in the area of the aluminum automotive casting.

  19. AI-2 biosynthesis module in a magnetic nanofactory alters bacterial response via localized synthesis and delivery.

    Science.gov (United States)

    Fernandes, Rohan; Bentley, William E

    2009-02-01

    Nanofactories are nano-dimensioned and comprised of modules serving various functions that alter the response of targeted cells when deployed by locally synthesizing and delivering cargo to the surfaces of the targeted cells. In its basic form, a nanofactory consists of a minimum of two functional modules: a cell capture module and a synthesis module. In this work, magnetic nanofactories that alter the response of targeted bacteria by the localized synthesis and delivery of the "universal" bacterial quorum sensing signal molecule autoinducer AI-2 are demonstrated. The magnetic nanofactories consist of a cell capture module (chitosan-mag nanoparticles) and an AI-2 biosynthesis module that contains both AI-2 biosynthetic enzymes Pfs and LuxS on a fusion protein (His-LuxS-Pfs-Tyr, HLPT) assembled together. HLPT is hypothesized to be more efficient than its constituent enzymes (used separately) at conversion of the substrate SAH to product AI-2 on account of the proximity of the two enzymes within the fusion protein. HLPT is demonstrated to be more active than the constituent enzymes, Pfs and LuxS, over a wide range of experimental conditions. The magnetic nanofactories (containing bound HLPT) are also demonstrated to be more active than free, unbound HLPT. They are also shown to elicit an increased response in targeted Escherichia coli cells, due to the localized synthesis and delivery of AI-2, when compared to the response produced by the addition of AI-2 directly to the cells. Studies investigating the universality of AI-2 and unraveling AI-2 based quorum sensing in bacteria using magnetic nanofactories are envisioned. The prospects of using such multi-modular nanofactories in developing the next generation of antimicrobials based on intercepting and interrupting quorum sensing based signaling are discussed.

  20. Protein-Based Urine Test Predicts Kidney Transplant Outcomes

    Science.gov (United States)

    ... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...

  1. Prediction-based estimating functions: Review and new developments

    DEFF Research Database (Denmark)

    Sørensen, Michael

    2011-01-01

    The general theory of prediction-based estimating functions for stochastic process models is reviewed and extended. Particular attention is given to optimal estimation, asymptotic theory and Gaussian processes. Several examples of applications are presented. In particular, partial observation...

  2. CLINICAL DATABASE ANALYSIS USING DMDT BASED PREDICTIVE MODELLING

    Directory of Open Access Journals (Sweden)

    Srilakshmi Indrasenan

    2013-04-01

    Full Text Available In recent years, predictive data mining techniques play a vital role in the field of medical informatics. These techniques help the medical practitioners in predicting various classes which is useful in prediction treatment. One of such major difficulty is prediction of survival rate in breast cancer patients. Breast cancer is a common disease these days and fighting against it is a tough battle for both the surgeons and the patients. To predict the survivability rate in breast cancer patients which helps the medical practitioner to select the type of treatment a predictive data mining technique called Diversified Multiple Decision Tree (DMDT classification is used. Additionally, to avoid difficulties from the outlier and skewed data, it is also proposed to perform the improvement of training space by outlier filtering and over sampling. As a result, this novel approach gives the survivability rate of the cancer patients based on which the medical practitioners can choose the type of treatment.

  3. Local-prediction-based difference expansion reversible watermarking.

    Science.gov (United States)

    Dragoi, Ioan-Catalin; Coltuc, Dinu

    2014-04-01

    This paper investigates the use of local prediction in difference expansion reversible watermarking. For each pixel, a least square predictor is computed on a square block centered on the pixel and the corresponding prediction error is expanded. The same predictor is recovered at detection without any additional information. The proposed local prediction is general and it applies regardless of the predictor order or the prediction context. For the particular cases of least square predictors with the same context as the median edge detector, gradient-adjusted predictor or the simple rhombus neighborhood, the local prediction-based reversible watermarking clearly outperforms the state-of-the-art schemes based on the classical counterparts. Experimental results are provided.

  4. Substituted Benzamides Containing Azaspiro Rings as Upregulators of Apolipoprotein A-I Transcription

    Directory of Open Access Journals (Sweden)

    Bin Hong

    2012-06-01

    Full Text Available Apolipoprotein A-I (Apo A-I is the principal protein component of high density lipoprotein (HDL, which is generally considered as a potential therapeutic target against atherosclerosis. The understanding of the Apo A-I regulation mechanism has fuelled the development of novel HDL targeted therapeutic approaches. To identify novel agents that can upregulate Apo A-I expression, we performed a cell-based reporter assay to screen 25,600 small molecules. Based on the dataset obtained from screening, a series of novel analogs of substituted benzamides containing azaspiro rings were assessed for their ability to induce the transcription of the Apo A-I gene, and the structure-activity relationship (SAR around these analogs was also proposed. The results indicated that the trifluoromethyl substituted benzamide containing an azaspiro ring is a promising backbone for designing Apo A-I transcriptional upregulator and could be viable leads for development of new drugs to prevent and treat atherosclerosis in the future.

  5. Applying AI to the Writer's Learning Environment.

    Science.gov (United States)

    Houlette, Forrest

    1991-01-01

    Discussion of current applications of artificial intelligence (AI) to writing focuses on how to represent knowledge of the writing process in a way that links procedural knowledge to other types of knowledge. A model is proposed that integrates the subtasks of writing into the process of writing itself. (15 references) (LRW)

  6. AI & Law, logic and argument schemes

    NARCIS (Netherlands)

    Prakken, Henry

    2006-01-01

    This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification

  7. Network Traffic Prediction based on Particle Swarm BP Neural Network

    Directory of Open Access Journals (Sweden)

    Yan Zhu

    2013-11-01

    Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.

  8. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  9. Intracellular and extracellular processing of human apolipoprotein A-I: secreted apolipoprotein A-I isoprotein 2 is a propeptide.

    OpenAIRE

    Zannis, V I; Karathanasis, S K; Keutmann, H. T.; Goldberger, G; Breslow, J L

    1983-01-01

    We have recently proposed that the major secreted isoprotein form of human apolipoprotein A-I (designated apo A-I2) is modified extracellularly to become the predominant apo A-I form seen in plasma (designated apo A-I4). In the current report we demonstrate that the primary translation product of human apo A-I (designated apo A-I2p) has a 24-amino-acid NH2-terminal extension with a sequence of Met-Lys-Ala-Ala-Val-Leu-Thr-Leu-Ala-Val-Leu-Phe- Leu-Thr-Gly-Ser-Gln-Ala-Arg-His-Phe-Trp-Gln-Gln. Th...

  10. Cross-Sectional Comparison of Executive Attention Function in Normally Aging Long-Term T'ai Chi, Meditation, and Aerobic Fitness Practitioners Versus Sedentary Adults

    OpenAIRE

    Hawkes, Teresa D; Manselle, Wayne; Woollacott, Marjorie H

    2014-01-01

    This cross-sectional field study documented the effect of long-term t'ai chi, meditation, or aerobic exercise training versus a sedentary lifestyle on executive function. It was predicted that long-term training in t'ai chi and meditation plus exercise would produce greater benefits to executive function than aerobic exercise. T'ai chi and meditation plus exercise include mental and physical training. Fifty-four volunteers were tested: t'ai chi (n=10); meditation+exercise (n=16); aerobic exer...

  11. Traffic Prediction Scheme based on Chaotic Models in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Xiangrong Feng

    2013-09-01

    Full Text Available Based on the local support vector algorithm of chaotic time series analysis, the Hannan-Quinn information criterion and SAX symbolization are introduced. Then a novel prediction algorithm is proposed, which is successfully applied to the prediction of wireless network traffic. For the correct prediction problems of short-term flow with smaller data set size, the weakness of the algorithms during model construction is analyzed by study and comparison to LDK prediction algorithm. It is verified the Hannan-Quinn information principle can be used to calculate the number of neighbor points to replace pervious empirical method, which uses the number of neighbor points to acquire more accurate prediction model. Finally, actual flow data is applied to confirm the accuracy rate of the proposed algorithm LSDHQ. It is testified by our experiments that it also has higher performance in adaptability than that of LSDHQ algorithm.

  12. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non-linear...... systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  13. The soluble pyocins S2 and S4 from Pseudomonas aeruginosa bind to the same FpvAI receptor.

    Science.gov (United States)

    Elfarash, Ameer; Wei, Qing; Cornelis, Pierre

    2012-09-01

    Soluble (S-type) pyocins are Pseudomonas aeruginosa bacteriocins that kill nonimmune P. aeruginosa cells by gaining entry via a specific receptor, which, in the case of pyocin S2, is the siderophore pyoverdine receptor FpvAI, and in the case of pyocin S3, FpvAII. The nucleic acid sequence at the positions 4327697-4327359 of P. aeruginosa PAO1 genome was not annotated, but it was predicted to encode the immunity gene of the flanking pyocin S4 gene (PA3866) based on our analysis of the genome sequence. Using RT-PCR, the expression of the immunity gene was detected, confirming the existence of an immunity gene overlapping the S4 pyocin gene. The PA3866 coding for pyocin S4 and the downstream gene coding for the immunity protein were cloned and expressed in Escherichia coli and the His-tagged S4 pyocin was obtained in pure form. Forty-three P. aeruginosa strains were typed via PCR to identify their ferripyoverdine receptor gene (fpvAI-III) and were tested for their sensitivity to pyocin S4. All S4-sensitive strains had the type I ferripyoverdine receptor fpvA gene. Some S4-resistant type I fpvA-positive strains were detected, but all of them had the S4 immunity gene, and, following the deletion of the immunity gene, became S4-sensitive. The fpvAI receptor gene was deleted in a S4-sensitive strain, and, as expected, the mutant became resistant to S4. The N-terminal receptor binding domain (RBD) of pyocin S2, which also uses the FpvAI receptor to enter the cell, was cloned in the pET-15b vector, and expressed in E. coli. When the purified RBD was mixed with pyocin S4 at different ratios, an inhibition of killing was observed, indicating that S2 RBD competes with the pyocin S4 for the binding to the FpvAI receptor. The S2 RBD was also shown to enhance the expression of the pvdA pyoverdine gene, suggesting that it, like pyoverdine, works via the known siderophore-mediated signalization pathway.

  14. Noncausal spatial prediction filtering based on an ARMA model

    Institute of Scientific and Technical Information of China (English)

    Liu Zhipeng; Chen Xiaohong; Li Jingye

    2009-01-01

    Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

  15. LS-SVR and AGO Based Time Series Prediction Method

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shou-peng; LIU Shan; CHAI Wang-xu; ZHANG Jia-qi; GUO Yang-ming

    2016-01-01

    Recently , fault or health condition prediction of complex systems becomes an interesting research topic.However, it is difficult to establish precise physical model for complex systems , and the time series properties are often necessary to be incorporated for the prediction in practice .Currently ,the LS -SVR is widely adopted for prediction of systems with time series data .In this paper , in order to improve the prediction accuracy, accumulated generating operation (AGO) is carried out to improve the data quality and regularity of raw time series data based on grey system theory;then, the inverse accumulated generating operation ( IAGO) is performed to obtain the prediction results .In addition , due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR, a modified Gaussian radial basis function (RBF) is proposed.The requirements of distance functions-based kernel functions are satisfied , which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity .The presented model is applied to the analysis of benchmarks .As indicated by the results , the proposed method is an effective prediction one with good precision .

  16. Disaster prediction of coal mine gas based on data mining

    Institute of Scientific and Technical Information of China (English)

    SHAO Liang-shan; FU Gui-xiang

    2008-01-01

    The technique of data mining was provided to predict gas disaster in view of thecharacteristics of coal mine gas disaster and feature knowledge based on gas disaster.The rough set theory was used to establish data mining model of gas disaster prediction,and rough set attributes relations was discussed in prediction model of gas disaster tosupplement the shortages of rough intensive reduction method by using information en-tropy criteria. The effectiveness and practicality of data mining technology in the predictionof gas disaster is confirmed through practical application.

  17. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    OpenAIRE

    2014-01-01

    Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies fo...

  18. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    Directory of Open Access Journals (Sweden)

    Milan Vukićević

    2014-01-01

    Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

  19. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  20. Characterization of the apolipoprotein AI and CIII genes in the domestic pig

    Energy Technology Data Exchange (ETDEWEB)

    Birchbauer, A.; Knipping, G.; Juritsch, B.; Zechner, R. (Univ. of Graz (Austria)); Aschauer, H. (Sandoz-Forschungs Institut Ges.m.b.H., Vienna (Austria))

    1993-03-01

    The apolipoproteins (apo) AI and CIII are important constituents of triglyceride-rich lipoproteins and high-density lipoproteins. In humans, apo AI is believed to play an important protective role in the pathogenesis of arteriosclerosis, whereas apo CIII might be involved in the development of hypertriglyceridemia. Both human genes are located within a gene cluster on chromosome 11. Although the domestic pig has been widely used as an animal model in arteriosclerosis and lipid research, the porcine apolipoproteins genes are poorly characterized. In this report, the complete nucleotide sequences of the porcine apo AI and CIII genes are presented and the authors demonstrate, for the first time, apo CIII expression in the pig. Both genes are composed of four exons and three introns and resemble closely their human counterparts with regard to the transcriptional start sites, exon sizes, intron sizes, exon-intron borders, and the size of the intergenic region. The predicted pig apo AI is a protein of 241 amino acids, which is 2 amino acids shorter than human apo AI. The protein sequence was found to be very homologous to apo AI sequences in other mammalian species. Apo AI expression was detected on the mRNA level in porcine liver and intestine. The apo CIII gene encodes a protein with 73 amino acids, which is 6 amino acids shorter than human apo CIII. In contrast to the three isoforms of apo CIII found in humans, only one major isoform was detected in the pig. Presumably this isoform is unglycosylated. In addition to apo CIII expression in the liver and the intestine, a truncated form of apo CIII mRNA was also found in porcine kidney. The studies demonstrate the presence of an apo CIII gene, an apo CIII mRNA, and an apo CIII protein in the pig and, therefore, exclude a hypothesized apo CIII deficiency in these animals. 53 refs., 5 figs.

  1. Why Don't Accounting Students like AIS?

    Science.gov (United States)

    Vatanasakdakul, Savanid; Aoun, Chadi

    2011-01-01

    Purpose: The demand for Accounting Information Systems (AIS) knowledge has increased exponentially over the past two decades, but studying AIS has not proved easy for many accounting students. The aim of the study is to understand the challenges accounting students face in studying AIS through investigation of the factors which may be contributing…

  2. A regulator's viewpoint on the use of AI in the nuclear industry

    International Nuclear Information System (INIS)

    The regulatory position within the UK places the responsibility for safety on a nuclear site with the licensee. The licensee must produce a safety case that demonstrates that the plant is safe. Assessment of this safety case is performed by the regulator in the Health and Safety Executive (HSE) using HSE's published Safety Assessment Principles. This paper presents the personal views of a regulator on the use of artificial intelligence (AI) in the nuclear industry. The AI-based system considered are restricted to expert systems, neural networks and fuzzy logic. Some of the safety issues are discussed. The particular concerns associated with software-based systems are compounded by some of the inherent problems of AI-based systems. Such is the nature of these concerns that an acceptable safety case will be difficult to mount for safety systems and post-accident monitoring systems. However, given the present maturity of the technology, acceptable safety cases might be formulated in the areas of operator advisory systems designed to reduce accident frequencies, and robotics where dose reduction is important. There are also arguments that suggests that suitable safety cases could be formulated for AI-based plant control systems. Nevertheless, AI-based systems should be treated with caution - further research is required into suitable designs and safety demonstration techniques. (author)

  3. A vertical handoff decision algorithm based on ARMA prediction model

    Science.gov (United States)

    Li, Ru; Shen, Jiao; Chen, Jun; Liu, Qiuhuan

    2012-01-01

    With the development of computer technology and the increasing demand for mobile communications, the next generation wireless networks will be composed of various wireless networks (e.g., WiMAX and WiFi). Vertical handoff is a key technology of next generation wireless networks. During the vertical handoff procedure, handoff decision is a crucial issue for an efficient mobility. Based on auto regression moving average (ARMA) prediction model, we propose a vertical handoff decision algorithm, which aims to improve the performance of vertical handoff and avoid unnecessary handoff. Based on the current received signal strength (RSS) and the previous RSS, the proposed approach adopt ARMA model to predict the next RSS. And then according to the predicted RSS to determine whether trigger the link layer triggering event and complete vertical handoff. The simulation results indicate that the proposed algorithm outperforms the RSS-based scheme with a threshold in the performance of handoff and the number of handoff.

  4. Synthetic information prediction system for crisis mine based on GIS

    Institute of Scientific and Technical Information of China (English)

    Yuxin Ye; Ping Yu; Shi Wang; Shuisheng Ye

    2006-01-01

    Reserves of some kinds of the crisis mines will be lack now or from now on, because of lacking seriously reserves of mineral resources and the crisis of exploring bases in support. So that it is urgent to predict, appraise, development and utilize the replaceable resources of the crisis mines. The mineral resources prediction software system of synthetic information is intelligent GIS which is used to quantitative prediction of large-scale synthetic information mineral target. It takes the geological body and the mineral resource body as a unit. And it analyzes the ore deposit genesis and metallotect, knows the spatial distribution laws of the ore deposit and ore body, and establish the prospecting model based on the concept of establishing the three-dimensional space of a mine. This paper will primarily discuss some important problems as follows: the secondary development of various kinds of data(including geology, geophysical prospecting, geochemical prospecting and remote sensing, etc); process synthetically and establish the synthetic information interpretative map base; correspond prospecting model with synthetic information of ore deposit; divided into statistical units of metallogenic information synthetic anomalies based on the synthetic information anomalies of ore control, then research the metallogenic information variable of unit synthetically and make quantitative prediction according to choose the quantitative prediction math model which is suitable to the demands of large-scale precision; at last, finish the target area optimization of ore deposit (body).

  5. Radar Image Processing and AIS Target Fusion

    OpenAIRE

    Heymann, F.; P. Banys; Saez, C.

    2015-01-01

    Collision avoidance is one of the high-level safety objectives and requires a complete and reliable description of maritime traffic situation. A combined use of data provided by independent data sources is an approach to improve the accuracy and integrity of traffic situation related information. In this paper we study the usage of radar images for automatic identification system (AIS) and radar fusion. Therefore we simulate synthetic radar images and evaluate the tracking performance of t...

  6. AI & Law, logic and argument schemes

    OpenAIRE

    Prakken, Henry

    2006-01-01

    This paper reviews the history of AI & Law research from the perspective of argument schemes. It starts with the observation that logic, although very well applicable to legal reasoning when there is uncertainty, vagueness and disagreement, is too abstract to give a fully satisfactory classification of legal argument types. It therefore needs to be supplemented with an argument-scheme approach, which classifies arguments not according to their logical form but according to their content, in p...

  7. Application of chaotic prediction model based on wavelet transform on water quality prediction

    Science.gov (United States)

    Zhang, L.; Zou, Z. H.; Zhao, Y. F.

    2016-08-01

    Dissolved oxygen (DO) is closely related to water self-purification capacity. In order to better forecast its concentration, the chaotic prediction model, based on the wavelet transform, is proposed and applied to a certain monitoring section of the Mentougou area of the Haihe River Basin. The result is compared with the simple application of the chaotic prediction model. The study indicates that the new model aligns better with the real data and has a higher accuracy. Therefore, it will provide significant decision support for water protection and water environment treatment.

  8. [Predicting suicide or predicting the unpredictable in an uncertain world: Reinforcement Learning Model-Based analysis].

    Science.gov (United States)

    Desseilles, Martin

    2012-01-01

    In general, it appears that the suicidal act is highly unpredictable with the current scientific means available. In this article, the author submits the hypothesis that predicting suicide is complex because it results in predicting a choice, in itself unpredictable. The article proposes a Reinforcement learning model-based analysis. In this model, we integrate on the one hand, four ascending modulatory neurotransmitter systems (acetylcholine, noradrenalin, serotonin, and dopamine) with their regions of respective projections and afferences, and on the other hand, various observations of brain imaging identified until now in the suicidal process.

  9. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  10. Prediction on carbon dioxide emissions based on fuzzy rules

    Science.gov (United States)

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  11. Predicting cycle 24 using various dynamo-based tools

    Directory of Open Access Journals (Sweden)

    M. Dikpati

    2008-02-01

    Full Text Available Various dynamo-based techniques have been used to predict the mean solar cycle features, namely the amplitude and the timings of onset and peak. All methods use information from previous cycles, including particularly polar fields, drift-speed of the sunspot zone to the equator, and remnant magnetic flux from the decay of active regions. Polar fields predict a low cycle 24, while spot zone migration and remnant flux both lead to predictions of a high cycle 24. These methods both predict delayed onset for cycle 24. We will describe how each of these methods relates to dynamo processes. We will present the latest results from our flux-transport dynamo, including some sensitivity tests and how our model relates to polar fields and spot zone drift methods.

  12. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel;

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...... and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present...

  13. Online Adaptation of Game AI with Evolutionary Learning

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Since the beginning of computer games era,artificial intelligence (AI) has been a standard feature of games. The current emphasis in computer game AI is improving the quality of opponent AI. Our research question reads: How can unsupervised online learning be incorporated in Computer Role Playing Game (CRPG) to improve the strategy of the opponent AI? Our goal is to use online evolutionary learning to design strategies that can defeat the opponent. So we apply a novel technique called dynamic scripting that realizes online adaptation of scripted opponent AI and report on experiments performed in a simulated CRPG to assess the adaptive performance obtained with the technique.

  14. Fast prediction unit selection method for HEVC intra prediction based on salient regions

    Science.gov (United States)

    Feng, Lei; Dai, Ming; Zhao, Chun-lei; Xiong, Jing-ying

    2016-07-01

    In order to reduce the computational complexity of the high efficiency video coding (HEVC) standard, a new algorithm for HEVC intra prediction, namely, fast prediction unit (PU) size selection method for HEVC based on salient regions is proposed in this paper. We first build a saliency map for each largest coding unit (LCU) to reduce its texture complexity. Secondly, the optimal PU size is determined via a scheme that implements an information entropy comparison among sub-blocks of saliency maps. Finally, we apply the partitioning result of saliency map on the original LCUs, obtaining the optimal partitioning result. Our algorithm can determine the PU size in advance to the angular prediction in intra coding, reducing computational complexity of HEVC. The experimental results show that our algorithm achieves a 37.9% reduction in encoding time, while producing a negligible loss in Bjontegaard delta bit rate ( BDBR) of 0.62%.

  15. A NEW ADMISSION CONTROL APPROACH BASED ON PREDICTION

    Institute of Scientific and Technical Information of China (English)

    Lu Kaining; Jin Zhigang; Zou Jun

    2002-01-01

    Admission control plays an important role in providing QoS to network users. Motivated by the measurement-based admission control algorithm, this letter proposed a new admission control approach for integrated service packet network based on traffic prediction. In the letter, FARIMA(p, d, q) models in the admission control algorithm is deployed. A method to simplify the FARIMA model fitting procedure and hence to reduce the time of traffic modeling and prediction is suggested. The feasibility-study experiments show that FARIMA models which have less number of parameters can be used to model and predict actual traffic on quite a large time scale. Simulation results validate the promising approach.

  16. Endpoint Prediction of EAF Based on Multiple Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    YUAN Ping; MAO Zhi-zhong; WANG Fu-li

    2007-01-01

    The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines (MSVM). In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub-models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

  17. Snippet-based relevance predictions for federated web search

    NARCIS (Netherlands)

    Demeester, Thomas; Nguyen, Dong; Trieschnigg, Dolf; Develder, Chris; Hiemstra, Djoerd

    2013-01-01

    How well can the relevance of a page be predicted, purely based on snippets? This would be highly useful in a Federated Web Search setting where caching large amounts of result snippets is more feasible than caching entire pages. The experiments reported in this paper make use of result snippets and

  18. Multi-Agent Reinforcement Learning Algorithm Based on Action Prediction

    Institute of Scientific and Technical Information of China (English)

    TONG Liang; LU Ji-lian

    2006-01-01

    Multi-agent reinforcement learning algorithms are studied. A prediction-based multi-agent reinforcement learning algorithm is presented for multi-robot cooperation task. The multi-robot cooperation experiment based on multi-agent inverted pendulum is made to test the efficency of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation strategy much faster than the primitive multiagent reinforcement learning algorithm.

  19. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

    2005-07-01

    Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

  20. Lifetime prediction based on Gamma processes from accelerated degradation data

    Institute of Scientific and Technical Information of China (English)

    Wang Haowei; Xu Tingxue; Mi Qiaoli

    2015-01-01

    Accelerated degradation test is a useful approach to predict the product lifetime at the normal use stress level, especially for highly reliable products. Two kinds of the lifetime prediction based on Gamma processes were studied. One was to predict the lifetime of the population from accelerated degradation data, and the other was to predict the lifetime of an individual by taking the accelerated degradation data as prior information. For an extensive application, the Gamma process with a time transformation and random effects was considered. A novel contribution is that a deducing method for determining the relationships between the shape and scale parameters of Gamma processes and accelerated stresses was presented. When predicting the lifetime of an indi-vidual, Bayesian inference methods were adopted to improve the prediction accuracy, in which the conjugate prior distribution and the non-conjugate prior distribution of random parameters were studied. The conjugate prior distribution only considers the random effect of the scale parameter while the non-conjugate prior distribution considers the random effects of both the scale and shape parameter. The application and usefulness of the proposed method was demonstrated by the accelerated degradation data of carbon-film resistors.

  1. Predicting Difficult Laparoscopic Cholecystectomy Based on Clinicoradiological Assessment

    Science.gov (United States)

    Udachan, Tejaswini V; Sasnur, Prasad; Baloorkar, Ramakanth; Sindgikar, Vikram; Narasangi, Basavaraj

    2015-01-01

    Introduction Laparoscopic cholecystectomy (LC) is the gold standard treatment for symptomatic cholelithiasis. However, of all Laparoscopic cholecystectomies, 1-13% requires conversion to an open for various reasons. Thus, for surgeons it would be helpful to establish criteria that would predict difficult laparoscopic cholecystectomy and conversion preoperatively. But there is no clear consensus among the laparoscopic surgeons regarding the parameters predicting the difficult dissection and conversion to open cholecystectomy. Aim To assess the clinical and radiological parameters for predicting the difficult laparoscopic cholecystectomy and its conversion. Materials and Methods This was a prospective study conducted from October 2010 to October 2014. Total of 180 patients meeting the inclusion criteria undergoing LC were included in the study. Four parameters were assessed to predict the difficult LC. These parameters were: 1) Gallbladder wall thickness; 2) Pericholecystic fluid collection; 3) Number of attacks; 4) Total leucocyte count. The statistical analysis was done using Z-test. Results Out of 180 patients included in this study 126 (70%) were easy, 44 (24.44%) were difficult and 3 (5.56%) patients required conversion to open cholecystectomy. The overall conversion rate was 5.6%. The TLC>11000, more than 2 previous attacks of cholecystitis, GB wall thickness of >3mm and Pericholecystic collection were all statistically significant for predicting the difficult LC and its conversion. Conclusion The difficult laparoscopic cholecystectomy and conversion to open surgery can be predicted preoperatively based on number of previous attacks of cholecystitis, WBC count, Gall bladder wall thickness and Pericholecystic collection. PMID:26816942

  2. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

    2006-05-15

    Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

  3. Acute hepatotoxicity: a predictive model based on focused illumina microarrays.

    Science.gov (United States)

    Zidek, Nadine; Hellmann, Juergen; Kramer, Peter-Juergen; Hewitt, Philip G

    2007-09-01

    Drug-induced hepatotoxicity is a major issue for drug development, and toxicogenomics has the potential to predict toxicity during early toxicity screening. A bead-based Illumina oligonucleotide microarray containing 550 liver specific genes has been developed. We have established a predictive screening system for acute hepatotoxicity by analyzing differential gene expression profiles of well-known hepatotoxic and nonhepatotoxic compounds. Low and high doses of tetracycline, carbon tetrachloride (CCL4), 1-naphthylisothiocyanate (ANIT), erythromycin estolate, acetaminophen (AAP), or chloroform as hepatotoxicants, clofibrate, theophylline, naloxone, estradiol, quinidine, or dexamethasone as nonhepatotoxic compounds, were administered as a single dose to male Sprague-Dawley rats. After 6, 24, and 72 h, livers were taken for histopathological evaluation and for analysis of gene expression alterations. All hepatotoxic compounds tested generated individual gene expression profiles. Based on leave-one-out cross-validation analysis, gene expression profiling allowed the accurate discrimination of all model compounds, 24 h after high dose treatment. Even during the regeneration phase, 72 h after treatment, CCL4, ANIT, and AAP were predicted to be hepatotoxic, and only these three compounds showed histopathological changes at this time. Furthermore, we identified 64 potential marker genes responsible for class prediction, which reflected typical hepatotoxicity responses. These genes and pathways, commonly deregulated by hepatotoxicants, may be indicative of the early characterization of hepatotoxicity and possibly predictive of later hepatotoxicity onset. Two unknown test compounds were used for prevalidating the screening test system, with both being correctly predicted. We conclude that focused gene microarrays are sufficient to classify compounds with respect to toxicity prediction. PMID:17522070

  4. Prediction of eukaryotic gene structures based on multilevel optimization

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yanhong; YANG Lei; WANG Hui; LU Feng; WAN Honghui

    2004-01-01

    Computational gene structure prediction, which is valuable for finding new genes and understanding the composition of genomes, plays a very important role in various kinds of genome projects. For eukaryotic gene structures, however, the prediction accuracy of existing methods is still limited. This paper presents a method of predicting eukaryotic gene structures based on multilevel optimization. The complicated problem of predicting gene structure in eukaryotic DNA sequence containing multiple genes can be decomposed into a series of sub-problems at several levels with decreasing complexity, including the gene level (single-exon gene, multi-exon gene), the element level (exon, intron, etc.), and the feature level (functional site signals, codon usage preference, etc.). On the basis of this decomposition, a multilevel model for the prediction of complex gene structures is created by a multilevel optimization process, in which the models dealing with sub-problems at low complexity level are first optimized respectively, and then optimally combined together to form models for those sub-problems at higher complexity level. Based on the multilevel model, a dynamic programming algorithm is designed to search for optimal gene structures from DNA sequences, and a new program GeneKey (1.0) for the prediction of eukaryotic gene structures is developed. Testing results with widely used datasets demonstrate that the prediction accuracies of GeneKey (1.0) at the nucleotide level, exon level and gene level are all higher than that of the well known program GENSCAN. A web server of GeneKey(1.0) is available at http://infosci.hust.edu.cn

  5. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LIDong-Mei; WANGZheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.

  6. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

    Jacobsen, Nis Sand; Essington, Timothy E.; Andersen, Ken Haste

    2016-01-01

    Ecosystem modeling is becoming an integral part of fisheries management, but there is a need to identify differences between predictions derived from models employed for scientific and management purposes. Here, we compared two models: a biomass-based food-web model (Ecopath with Ecosim (Ew...... of forage fisheries removal. In both cases, the differences are due to the presumed degree of trophic overlap between juveniles of large-bodied fish and adult stages of forage fish. These differences highlight how each model’s emphasis on distinct details of ecological processes affects its predictions...

  7. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LI Dong-Mei; WANG Zheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.

  8. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  9. Analysis of fault detection method based on predictive filter approach

    Institute of Scientific and Technical Information of China (English)

    LI Ji; ZHANG Hongyue

    2005-01-01

    A new detection method for component faults based on predictive filters together with the fault detectability, false alarm rate, missed alarm rate and upper bound of detection time are proposed. The efficiency of the method is illustrated by a simulation example of a second-order system. It is shown that the fault detection method using predictive filters has a small delay, a low false alarm rate and a low missed alarm rate. Furthermore the filter can give accurate estimates of states even after a fault occurs. The real-time estimation provided by this method can also be used for fault tolerant control.

  10. Prediction of potential drug targets based on simple sequence properties

    Directory of Open Access Journals (Sweden)

    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  11. Prediction Research of Red Tide Based on Improved FCM

    Directory of Open Access Journals (Sweden)

    Xiaomei Hu

    2016-01-01

    Full Text Available Red tides are caused by the combination effects of many marine elements. The complexity of the marine ecosystem makes it hard to find the relationship between marine elements and red tides. The algorithm of fuzzy c-means (FCM can get clear classification of things and expresses the fuzzy state among different things. Therefore, a prediction algorithm of red tide based on improved FCM is proposed. In order to overcome the defect of FCM which is overdependent on the initial cluster centers and the objective function, this paper gains the initial cluster centers through the principle of regional minimum data density and the minimum mean distance. The feature weighted cluster center is added to the objective function. Finally, the improved FCM algorithm is applied in the prediction research of red tide, and the results show that the improved FCM algorithm has good denoising ability and high accuracy in the prediction of red tides.

  12. Ontology-based prediction of surgical events in laparoscopic surgery

    Science.gov (United States)

    Katić, Darko; Wekerle, Anna-Laura; Gärtner, Fabian; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

    2013-03-01

    Context-aware technologies have great potential to help surgeons during laparoscopic interventions. Their underlying idea is to create systems which can adapt their assistance functions automatically to the situation in the OR, thus relieving surgeons from the burden of managing computer assisted surgery devices manually. To this purpose, a certain kind of understanding of the current situation in the OR is essential. Beyond that, anticipatory knowledge of incoming events is beneficial, e.g. for early warnings of imminent risk situations. To achieve the goal of predicting surgical events based on previously observed ones, we developed a language to describe surgeries and surgical events using Description Logics and integrated it with methods from computational linguistics. Using n-Grams to compute probabilities of followup events, we are able to make sensible predictions of upcoming events in real-time. The system was evaluated on professionally recorded and labeled surgeries and showed an average prediction rate of 80%.

  13. Reducing the Prediction Horizon in NMPC: An Algorithm Based Approach

    CERN Document Server

    Pannek, Jürgen

    2011-01-01

    In order to guarantee stability, known results for MPC without additional terminal costs or endpoint constraints often require rather large prediction horizons. Still, stable behavior of closed loop solutions can often be observed even for shorter horizons. Here, we make use of the recent observation that stability can be guaranteed for smaller prediction horizons via Lyapunov arguments if more than only the first control is implemented. Since such a procedure may be harmful in terms of robustness, we derive conditions which allow to increase the rate at which state measurements are used for feedback while maintaining stability and desired performance specifications. Our main contribution consists in developing two algorithms based on the deduced conditions and a corresponding stability theorem which ensures asymptotic stability for the MPC closed loop for significantly shorter prediction horizons.

  14. Temperature prediction control based on least squares support vector machines

    Institute of Scientific and Technical Information of China (English)

    Bin LIU; Hongye SU; Weihua HUANG; Jian CHU

    2004-01-01

    A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity.The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel.In the process of system running,the off-line model is linearized at each sampling instant,and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant.The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay.The results of the experiment verify the effectiveness and merit of the algorithm.

  15. Predictive Control-Based Optimal Nonliear Reentry Guidance Law.

    Directory of Open Access Journals (Sweden)

    S. E. Tablole

    1998-04-01

    Full Text Available This Paper discusses a new nominal riding reentry guidance low design based on a nonlinear optimal predictive control approach. In this method, the error between the actual trajectory and the nominal trajectory is predicted, and a quadratic cost function of these predicted errors is minimised, resulting in an optimal feedback guidance law design. The guidance low thus obtained does not require linearisation of the equations of motion. A nominal trajectory is selected which Satisfies the vehicle constraints and mission objectives. This nominal data is used with actual data to evaluate the guidance low. simulations have been carried out for a planar trajectory for a variety of initial condition errors and also for off nominal conditions and the results are presented.

  16. The Attribute for Hydrocarbon Prediction Based on Attenuation

    Science.gov (United States)

    Hermana, Maman; Harith, Z. Z. T.; Sum, C. W.; Ghosh, D. P.

    2014-03-01

    Hydrocarbon prediction is a crucial issue in the oil and gas industry. Currently, the prediction of pore fluid and lithology are based on amplitude interpretation which has the potential to produce pitfalls in certain conditions of reservoir. Motivated by this fact, this work is directed to find out other attributes that can be used to reduce the pitfalls in the amplitude interpretation. Some seismic attributes were examined and studies showed that the attenuation attribute is a better attribute for hydrocarbon prediction. Theoretically, the attenuation mechanism of wave propagation is associated with the movement of fluid in the pore; hence the existence of hydrocarbon in the pore will be represented by attenuation attribute directly. In this paper we evaluated the feasibility of the quality factor ratio of P-wave and S-wave (Qp/Qs) as hydrocarbon indicator using well data and also we developed a new attribute based on attenuation for hydrocarbon prediction -- Normalized Energy Reduction Stack (NERS). To achieve these goals, this work was divided into 3 main parts; estimating the Qp/Qs on well log data, testing the new attribute in the synthetic data and applying the new attribute on real data in Malay Basin data. The result show that the Qp/Qs is better than Poisson's ratio and Lamda over Mu as hydrocarbon indicator. The curve, trend analysis and contrast of Qp/Qs is more powerful at distinguishing pore fluid than Poisson ratio and Lamda over Mu. The NERS attribute was successful in distinguishing the hydrocarbon from brine on synthetic data. Applying this attribute on real data on Malay basin, the NERS attribute is qualitatively conformable with the structure and location where the gas is predicted. The quantitative interpretation of this attribute for hydrocarbon prediction needs to be investigated further.

  17. Neural Network Predictive Control Based Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Ali Mohamed Yousef

    2012-04-01

    Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.

  18. A New Particle Swarm Optimization Based Stock Market Prediction Technique

    Directory of Open Access Journals (Sweden)

    Essam El. Seidy

    2016-04-01

    Full Text Available Over the last years, the average person's interest in the stock market has grown dramatically. This demand has doubled with the advancement of technology that has opened in the International stock market, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, the analysis and prediction of future values and trends of the financial markets have got more attention, and due to large applications in different business transactions, stock market prediction has become a critical topic of research. In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM is applied to the task of training an adaptive linear combiner to form a new stock market prediction model. This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. The experimental results show that the proposed technique is superior than the other PSO based models according to the prediction accuracy.

  19. Chaos Time Series Prediction Based on Membrane Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Meng Li

    2015-01-01

    Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.

  20. Predicting online ratings based on the opinion spreading process

    Science.gov (United States)

    He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

    2015-10-01

    Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

  1. Microstructural studies of suck cast (Zr-SS)-3 and 5 AI alloys for nuclear metallic waste form

    International Nuclear Information System (INIS)

    Management of radioactive metallic waste using 'alloy melting route' is currently being investigated. For disposal of Zr and SS base nuclear metallic wastes, Zr-stainless steel (SS) hybrid alloys are being considered as baseline alloys for developing metallic-waste-form (MWF) alloys. In this context Zr-16 wt. %55 has been selected for MWF alloy in our previous study. In present study, to include amorphous phase in this alloy, 3 and 5 wt. % Al has been added in order to improve desirable properties and useful features of MWF and the two alloys have been prepared by suck casting techniques. Microstructure of these alloys have been investigated by optical and electron microscopy which shows occurrence of two different phases, e.g. dark grey and white phases, in (Zr-16 SS)-3 Al and three different phases, e.g. grey, dark grey and white phases in (Zr-16 SS)-5 AI. Electron diffraction and X-ray diffraction (XRD) analyses of these two alloy specimens revealed the occurrence of Zr (Fe, Cr, AI) (dark grey) and Zr2 (Fe, Cr, AI) (white) phases in (Zr-16 SS)-3 Al whereas, Zr (Fe, Cr, AI) (dark grey), Zr2 (Fe, Cr, AI) (grey) and Zr3(Fe, Cr, AI) (white) phases were found in (Zr-16 SS)-5 AI. In addition, presence of amorphous phase was indicated by XRD analysis that could be confirmed by transmission electron microscopy of these two alloys. (author)

  2. Support vector machine-based multi-model predictive control

    Institute of Scientific and Technical Information of China (English)

    Zhejing BA; Youxian SUN

    2008-01-01

    In this paper,a support vector machine-based multi-model predictive control is proposed,in which SVM classification combines well with SVM regression.At first,each working environment is modeled by SVM regression and the support vector machine network-based model predictive control(SVMN-MPC)algorithm corresponding to each environment is developed,and then a multi-class SVM model is established to recognize multiple operating conditions.As for control,the current environment is identified by the multi-class SVM model and then the corresponding SVMN.MPCcontroller is activated at each sampling instant.The proposed modeling,switching and controller design is demonstrated in simulation results.

  3. Stabilisation of difference equations with noisy prediction-based control

    Science.gov (United States)

    Braverman, E.; Kelly, C.; Rodkina, A.

    2016-07-01

    We consider the influence of stochastic perturbations on stability of a unique positive equilibrium of a difference equation subject to prediction-based control. These perturbations may be multiplicative We begin by relaxing the control parameter in the deterministic equation, and deriving a range of values for the parameter over which all solutions eventually enter an invariant interval. Then, by allowing the variation to be stochastic, we derive sufficient conditions (less restrictive than known ones for the unperturbed equation) under which the positive equilibrium will be globally a.s. asymptotically stable: i.e. the presence of noise improves the known effectiveness of prediction-based control. Finally, we show that systemic noise has a "blurring" effect on the positive equilibrium, which can be made arbitrarily small by controlling the noise intensity. Numerical examples illustrate our results.

  4. In silico network topology-based prediction of gene essentiality

    CERN Document Server

    da Silva, Joao Paulo Muller; Mombach, Jose Carlos Merino; Vieira, Renata; da Silva, Jose Guliherme Camargo; Lemke, Ney; Sinigaglia, Marialva

    2007-01-01

    The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision tree-based machine learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes...

  5. Predictive Potential Field-Based Collision Avoidance for Multicopters

    Science.gov (United States)

    Nieuwenhuisen, M.; Schadler, M.; Behnke, S.

    2013-08-01

    Reliable obstacle avoidance is a key to navigating with UAVs in the close vicinity of static and dynamic obstacles. Wheel-based mobile robots are often equipped with 2D or 3D laser range finders that cover the 2D workspace sufficiently accurate and at a high rate. Micro UAV platforms operate in a 3D environment, but the restricted payload prohibits the use of fast state-of-the-art 3D sensors. Thus, perception of small obstacles is often only possible in the vicinity of the UAV and a fast collision avoidance system is necessary. We propose a reactive collision avoidance system based on artificial potential fields, that takes the special dynamics of UAVs into account by predicting the influence of obstacles on the estimated trajectory in the near future using a learned motion model. Experimental evaluation shows that the prediction leads to smoother trajectories and allows to navigate collision-free through passageways.

  6. Mobility Prediction Based Neighborhood Discovery for Mobile Ad Hoc Networks

    OpenAIRE

    Li, Xu; Mitton, Nathalie; Simplot-Ryl, David

    2010-01-01

    Hello protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/aliveness by periodic `hello' messages. Central to any hello protocol is the determination of `hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based hello protocol, named ...

  7. Prediction Research of Red Tide Based on Improved FCM

    OpenAIRE

    Xiaomei Hu; Dong Wang; Hewei Qu; Xinran Shi

    2016-01-01

    Red tides are caused by the combination effects of many marine elements. The complexity of the marine ecosystem makes it hard to find the relationship between marine elements and red tides. The algorithm of fuzzy c-means (FCM) can get clear classification of things and expresses the fuzzy state among different things. Therefore, a prediction algorithm of red tide based on improved FCM is proposed. In order to overcome the defect of FCM which is overdependent on the initial cluster centers and...

  8. Seminal Quality Prediction Using Clustering-Based Decision Forests

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-08-01

    Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.

  9. Design of integrated ship monitoring system using SAR, RADAR, and AIS

    Science.gov (United States)

    Yang, Chan-Su; Kim, Tae-Ho; Hong, Danbee; Ahn, Hyung-Wook

    2013-06-01

    When we talk about for the ship detection, identification and its classification, we need to go for the wide area of monitoring and it may be possible only through satellite based monitoring approach which monitors and covers coastal as well as the oceanic zone. Synthetic aperture radar (SAR) has been widely used to detect targets of interest with the advantage of the operating capability in all weather and luminance free condition (Margarit and Tabasco, 2011). In EU waters, EMSA(European Maritime Safety Agency) is operating the SafeSeaNet and CleanSeaNet systems which provide the current positions of all ships and oil spill monitoring information in and around EU waters in a single picture to Member States using AIS, LRIT and SAR images. In many countries, a similar system has been developed and the key of the matter is to integrate all available data. This abstract describes the preliminary design concept for an integration system of RADAR, AIS and SAR data for vessel traffic monitoring. SAR sensors are used to acquire image data over large coverage area either through the space borne or airborne platforms in UTC. AIS reports should be also obtained on the same date as of the SAR acquisition for the purpose to perform integration test. Land-based RADAR can provide ships positions detected and tracked in near real time. In general, SAR are used to acquire image data over large coverage area, AIS reports are obtained from ship based transmitter, and RADAR can monitor continuously ships for a limited area. In this study, we developed individual ship monitoring algorithms using RADAR(FMCW and Pulse X-band), AIS and SAR(RADARSAT-2 Full-pol Mode). We conducted field experiments two times for displaying the RADAR, AIS and SAR integration over the Pyeongtaek Port, South Korea.

  10. Radar Image Processing and AIS Target Fusion

    Directory of Open Access Journals (Sweden)

    F. Heymann

    2015-09-01

    Full Text Available Collision avoidance is one of the high-level safety objectives and requires a complete and reliable description of maritime traffic situation. A combined use of data provided by independent data sources is an approach to improve the accuracy and integrity of traffic situation related information. In this paper we study the usage of radar images for automatic identification system (AIS and radar fusion. Therefore we simulate synthetic radar images and evaluate the tracking performance of the particle filter algorithm as the most promising filter approach. During the filter process the algorithm estimates the target position and velocity which we finally compare with the known position of the simulation. This approach allows the performance analysis of the particle filter for vessel tracking on radar images. In a second extended simulation we add the respective AIS information of the target vessel and study the gained level of improvement for the particle filter approach. The work of this paper is integrated in the research and development activities of DLR Institute of Communications and Navigation dealing with the introduction of data and system integrity into the maritime traffic system. One of the aimed objectives is the automatic assessment of the traffic situation aboard a vessel including integrity information.

  11. AIS spectra of desert shrub canopies

    Science.gov (United States)

    Murray, R.; Isaacson, D. L.; Schrumpf, B. J.; Ripple, W. J.; Lewis, A. J.

    1986-01-01

    Airborne Imaging Spectrometer (AIS) data were collected 30 August 1985 from a desert shrub community in central Oregon. Spectra from artificial targets placed on the test site and from bare soil, big sagebrush (Artemesia tridentata wyomingensis), silver sagebrush (Artemesia cana bolander), and exposed volcanic rocks were studied. Spectral data from grating position 3 (tree mode) were selected from 25 ground positions for analysis by Principal Factor Analysis (PFA). In this grating position, as many as six factors were identified as significant in contributing to spectral structure. Channels 74 through 84 (tree mode) best characterized between-class differences. Other channels were identified as nondiscriminating and as associated with such errors as excessive atmospheric absorption and grating positin changes. The test site was relatively simple with the two species (A. tridentata and A. cana) representing nearly 95% of biomass and with only two mineral backgrounds, a montmorillonitic soil and volcanic rocks. If, as in this study, six factors of spectral structure can be extracted from a single grating position from data acquired over a simple vegetation community, then AIS data must be considered rich in information-gathering potential.

  12. Humans and Machines in the Evolution of AI in Korea

    OpenAIRE

    Zhang, Byoung-Tak; Seoul National University

    2016-01-01

    Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted e...

  13. Neural Network Based Model for Predicting Housing Market Performance

    Institute of Scientific and Technical Information of China (English)

    Ahmed Khalafallah

    2008-01-01

    The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.

  14. Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

    Science.gov (United States)

    Xiao, Rui; Gao, Junbin; Bossomaier, Terry

    2016-01-01

    A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression. PMID:27695102

  15. Predicting the effective thermal conductivity of carbon nanotube based nanofluids

    Energy Technology Data Exchange (ETDEWEB)

    Sastry, N N Venkata; Bhunia, Avijit; Sundararajan, T; Das, Sarit K [Department of Mechanical Engineering, Indian Institute of Technology, Madras, Chennai 600 036 (India)

    2008-02-06

    Adding a small volume fraction of carbon nanotubes (CNTs) to a liquid enhances the thermal conductivity significantly. Recent experimental findings report an anomalously wide range of enhancement values that continue to perplex the research community and remain unexplained. In this paper we present a theoretical model based on three-dimensional CNT chain formation (percolation) in the base liquid and the corresponding thermal resistance network. The model considers random CNT orientation and CNT-CNT interaction forming the percolating chain. Predictions are in good agreement with almost all available experimental data. Results show that the enhancement critically depends on the CNT geometry (length), volume fraction, thermal conductivity of the base liquid and the nanofluid (CNT-liquid suspension) preparation technique. Based on the physical mechanism of heat conduction in the nanofluid, we introduce a new dimensionless parameter that alone characterizes the nanofluid thermal conductivity with reasonable accuracy ({approx} {+-} 5%)

  16. Partitioning of minimotifs based on function with improved prediction accuracy.

    Directory of Open Access Journals (Sweden)

    Sanguthevar Rajasekaran

    Full Text Available BACKGROUND: Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions. METHODOLOGY/PRINCIPAL FINDINGS: Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components. CONCLUSIONS/SIGNIFICANCE: Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is approximately 4.6 times that of random minimotifs. For the molecular function filter this ratio is approximately 2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters.

  17. Link prediction based on local information considering preferential attachment

    Science.gov (United States)

    Zeng, Shan

    2016-02-01

    Link prediction in complex networks has attracted much attention in many fields. In this paper, a common neighbors plus preferential attachment index is presented to estimate the likelihood of the existence of a link between two nodes based on local information of the nearest neighbors. Numerical experiments on six real networks demonstrated the high effectiveness and efficiency of the new index compared with five well-known and widely accepted indices: the common neighbors, resource allocation index, preferential attachment index, local path index and Katz index. The new index provides competitively accurate prediction with local path index and Katz index while has less computational complexity and is more accurate than the other two indices.

  18. Human Posture and Movement Prediction based on Musculoskeletal Modeling

    DEFF Research Database (Denmark)

    Farahani, Saeed Davoudabadi

    2014-01-01

    Abstract This thesis explores an optimization-based formulation, so-called inverse-inverse dynamics, for the prediction of human posture and motion dynamics performing various tasks. It is explained how this technique enables us to predict natural kinematic and kinetic patterns for human posture...... and motion using AnyBody Modeling System (AMS). AMS uses inverse dynamics to analyze musculoskeletal systems and is, therefore, limited by its dependency on input kinematics. We propose to alleviate this dependency by assuming that voluntary postures and movement strategies in humans are guided by a desire...... investigated, a scaling to the mean height and body mass may be sufficient, while other questions require subject-specific models. The movement is parameterized by means of time functions controlling selected degrees-of-freedom (DOF). Subsequently, the parameters of these functions, usually referred...

  19. Yarn Properties Prediction Based on Machine Learning Method

    Institute of Scientific and Technical Information of China (English)

    YANG Jian-guo; L(U) Zhi-jun; LI Bei-zhi

    2007-01-01

    Although many works have been done to constructprediction models on yarn processing quality, the relationbetween spinning variables and yam properties has not beenestablished conclusively so far. Support vector machines(SVMs), based on statistical learning theory, are gainingapplications in the areas of machine learning and patternrecognition because of the high accuracy and goodgeneralization capability. This study briefly introduces theSVM regression algorithms, and presents the SVM basedsystem architecture for predicting yam properties. Model.selection which amounts to search in hyper-parameter spaceis performed for study of suitable parameters with grid-research method. Experimental results have been comparedwith those of artificial neural network(ANN) models. Theinvestigation indicates that in the small data sets and real-life production, SVM models are capable of remaining thestability of predictive accuracy, and more suitable for noisyand dynamic spinning process.

  20. Adaptive quality prediction of batch processes based on PLS model

    Institute of Scientific and Technical Information of China (English)

    LI Chun-fu; ZHANG Jie; WANG Gui-zeng

    2006-01-01

    There are usually no on-line product quality measurements in batch and semi-batch processes,which make the process control task very difficult.In this paper,a model for predicting the end-product quality from the available on-line process variables at the early stage of a batch is developed using partial least squares (PLS)method.Furthermore,some available mid-course quality measurements are used to rectify the final prediction results.To deal with the problem that the process may change with time,recursive PLS (RPLS) algorithm is used to update the model based on the new batch data and the old model parameters after each batch.An application to a simulated batch MMA polymerization process demonstrates the effectiveness of the proposed method.

  1. Preliminary geological investigation of AIS data at Mary Kathleen, Queensland, Australia

    Science.gov (United States)

    Huntington, J. F.; Green, A. A.; Craig, M. D.; Cocks, T. D.

    1986-01-01

    The Airborne Imaging Spectrometer (AIS) was flown over granitic, volcanic, and calc-silicate terrain around the Mary Kathleen Uranium Mine in Queensland, in a test of its mineralocial mapping capabilities. An analysis strategy and restoration and enhancement techniques were developed to process the 128 band AIS data. A preliminary analysis of one of three AIS flight lines shows that the data contains considerable spectral variation but that it is also contaminated by second-order leakage of radiation from the near-infrared region. This makes the recognition of expected spectral absorption shapes very difficult. The effect appears worst in terrains containing considerable vegetation. Techniques that try to predict this supplementary radiation coupled with the log residual analytical technique show that expected mineral absorption spectra can be derived. The techniques suggest that with additional refinement correction procedures, the Australian AIS data may be revised. Application of the log residual analysis method has proved very successful on the cuprite, Nevada data set, and for highlighting the alunite, linite, and SiOH mineralogy.

  2. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

    DEFF Research Database (Denmark)

    Lunde, Asger; Brix, Anne Floor

    In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared to the...... performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from...... the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability to...

  3. The (Dis)appearance of Ai Weiwei: Translations and (In)visibilities

    DEFF Research Database (Denmark)

    Glud, Louise Nørgaard; Stenbøg, Anne Sofie Christensen; Albrechtslund, Anders

    This paper offers a study of surveillance themes relating to Ai Weiwei’s highly discussed disappearance and later reappearance in 2011. Our study is based on an Actor-Network Theory (ANT) approach and we focus on the manifold negotiations and (in)visibilities relating to the dramatic events as well...

  4. The (Dis)appearance of Ai Weiwei: Translations and (In)visibilities

    DEFF Research Database (Denmark)

    Stenbøg, Sofie; Nørgaard Glud, Louise; Albrechtslund, Anders

    2013-01-01

    This paper offers a study of surveillance themes relating to Ai Weiwei’s highly discussed disappearance and later reappearance in 2011. Our study is based on an Actor-Network Theory (ANT) approach and we focus on the manifold negotiations and (in)visibilities relating to the dramatic events as well...

  5. Computer-aided and predictive models for design of controlled release of pesticides

    DEFF Research Database (Denmark)

    Suné, Nuria Muro; Gani, Rafiqul

    2004-01-01

    In the field of pesticide controlled release technology, a computer based model that can predict the delivery of the Active Ingredient (AI) from fabricated units is important for purposes of product design and marketing. A model for the release of an M from a microcapsule device is presented...

  6. AI And Early Vision - Part II

    Science.gov (United States)

    Julesz, Bela

    1989-08-01

    A quarter of a century ago I introduced two paradigms into psychology which in the intervening years have had a direct impact on the psychobiology of early vision and an indirect one on artificial intelligence (AI or machine vision). The first, the computer-generated random-dot stereogram (RDS) paradigm (Julesz, 1960) at its very inception posed a strategic question both for AI and neurophysiology. The finding that stereoscopic depth perception (stereopsis) is possible without the many enigmatic cues of monocular form recognition - as assumed previously - demonstrated that stereopsis with its basic problem of finding matches between corresponding random aggregates of dots in the left and right visual fields became ripe for modeling. Indeed, the binocular matching problem of stereopsis opened up an entire field of study, eventually leading to the computational models of David Marr (1982) and his coworkers. The fusion of RDS had an even greater impact on neurophysiologists - including Hubel and Wiesel (1962) - who realized that stereopsis must occur at an early stage, and can be studied easier than form perception. This insight recently culminated in the studies by Gian Poggio (1984) who found binocular-disparity - tuned neurons in the input stage to the visual cortex (layer IVB in V1) in the monkey that were selectively triggered by dynamic RDS. Thus the first paradigm led to a strategic insight: that with stereoscopic vision there is no camouflage, and as such was advantageous for our primate ancestors to evolve the cortical machinery of stereoscopic vision to capture camouflaged prey (insects) at a standstill. Amazingly, although stereopsis evolved relatively late in primates, it captured the very input stages of the visual cortex. (For a detailed review, see Julesz, 1986a)

  7. The Ai project: historical and ecological contexts.

    Science.gov (United States)

    Matsuzawa, Tetsuro

    2003-12-01

    This paper aims to review a long-term research project exploring the chimpanzee mind within historical and ecological contexts. The Ai project began in 1978 and was directly inspired by preceding ape-language studies conducted in Western countries. However, in contrast with the latter, it has focused on the perceptual and cognitive capabilities of chimpanzees rather than communicative skills between humans and chimpanzees. In the original setting, a single chimpanzee faced a computer-controlled apparatus and performed various kinds of matching-to-sample discrimination tasks. Questions regarding the chimpanzee mind can be traced back to Wolfgang Koehler's work in the early part of the 20th century. Yet, Japan has its unique natural and cultural background: it is home to an indigenous primate species, the Japanese snow monkey. This fact has contributed to the emergence of two previous projects in the wild led by the late Kinji Imanishi and his students. First, the Koshima monkey project began in 1948 and became famous for its discovery of the cultural propagation of sweet-potato washing behavior. Second, pioneering work in Africa, starting in 1958, aimed to study great apes in their natural habitat. Thanks to the influence of these intellectual ancestors, the present author also undertook the field study of chimpanzees in the wild, focusing on tool manufacture and use. This work has demonstrated the importance of social and ecological perspectives even for the study of the mind. Combining experimental approaches with a field setting, the Ai project continues to explore cognition and behavior in chimpanzees, while its focus has shifted from the study of a single subject toward that of the community as a whole. PMID:14566577

  8. FACE: the barefaced facts of AI potency

    International Nuclear Information System (INIS)

    The use of third-generation aromatase inhibitors (AIs), such as anastrozole and letrozole, as initial adjuvant hormonal therapy in postmenopausal women (PMW) with hormone receptor-positive (HR+) breast cancer offers a significant benefit over tamoxifen for reducing recurrence risk. Clinical studies, including the Arimidex Tamoxifen Alone or in Combination (ATAC) and the Breast International Group (BIG) 1–98 trials, have proven that both anastrozole and letrozole are, respectively, superior to tamoxifen in improving disease-free survival. Although differing in design, objectives, and follow-up time, these trials offer some insight into the comparative clinical efficacy of these two nonsteroidal AIs. In particular, results from BIG 1–98 show that letrozole significantly reduces early distant metastatic (DM) events, which constitute the majority of early recurrence events. Subsequently, there is a beneficial overall survival effect emerging in the trial, whereas survival is unchanged with anastrozole after 100 months of follow-up in ATAC. Significant differences in the potency of these two drugs, vis-à-vis their degree of aromatase inhibition, have been observed in comparative trials and show that letrozole causes a more complete suppression of estrogen levels than does anastrozole. Whether this difference in potency is relevant to reductions in DM events during adjuvant therapy remains unclear. The Femara Anastrozole Clinical Evaluation trial is addressing this issue in a more unequivocal manner by comparing initial adjuvant treatment with anastrozole or letrozole in a population of breast cancer patients at high risk of recurrence: PMW with HR+ disease and axillary lymph node involvement

  9. Model Predictive Control-Based Fast Charging for Vehicular Batteries

    Directory of Open Access Journals (Sweden)

    Zhibin Song

    2011-08-01

    Full Text Available Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs. In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC. A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV charge method.

  10. Time series prediction of mining subsidence based on a SVM

    Institute of Scientific and Technical Information of China (English)

    Li Peixian; Tan Zhixiang; Yah Lili; Deng Kazhong

    2011-01-01

    In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines (SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used asindicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%.the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.

  11. Predicting activity approach based on new atoms similarity kernel function.

    Science.gov (United States)

    Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

    2015-07-01

    Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods.

  12. Future of AI application to electric power field

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, Hideo; Sakaguchi, Toshiaki (The Tokyo Electric Power Co., Inc., Tokyo, Japan Mitsubishi Electric Corp., Tokyo (Japan))

    1989-08-20

    This report forecasts the future of applying ES (Expert System) to the power system based on the trend of an information treating technology and a social economy. The future power system will definitely find more advanced systems of the automatization and the business mechanization, and highly advanced communication and information system. Forecast tells that the 21st. Century will see the more highly informationalized system which may be called a computer-integrated power system. Then, the expert system will play an essential role. Up to this time, the front scene has been the application of ES to the power system, but the application of the robotics is also an important problem among the AI technologies. Application to the operation of monitoring, patrolling and operation is a problem of the future. Technical items to be researched and developed in the power system are a means of know-how expression and a large scale software system development. 8 refs., 1 fig.

  13. Integrating the Wall Street Journal into AIS Courses

    Science.gov (United States)

    Kohlmeyer, James M., III

    2008-01-01

    While it is important for accounting information systems (AIS) students to understand computer technology, internal controls and business processes, such knowledge is of little use without reference to appropriate contexts. Integrating Wall Street Journal (WSJ) readings and discussions into AIS classes can enrich learning by stimulating…

  14. Analysis of received AIS data from a LEO Cubesat

    DEFF Research Database (Denmark)

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter; Tausen, Mathias;

    2014-01-01

    /Q signal is sampled with a 750 kSPS A/D converter and further processed by an Analog De- vices DSP. The algorithm first analyses the stream to identify possible AIS transmissions on the two different AIS channels. If likely transmissions are identified, the center frequency of the transmission is estimated...

  15. AI-2-dependent gene regulation in Staphylococcus epidermidis

    Directory of Open Access Journals (Sweden)

    Sturdevant Daniel E

    2008-01-01

    Full Text Available Abstract Background Autoinducer 2 (AI-2, a widespread by-product of the LuxS-catalyzed S-ribosylhomocysteine cleavage reaction in the activated methyl cycle, has been suggested to serve as an intra- and interspecies signaling molecule, but in many bacteria AI-2 control of gene expression is not completely understood. Particularly, we have a lack of knowledge about AI-2 signaling in the important human pathogens Staphylococcus aureus and S. epidermidis. Results To determine the role of LuxS and AI-2 in S. epidermidis, we analyzed genome-wide changes in gene expression in an S. epidermidis luxS mutant and after addition of AI-2 synthesized by over-expressed S. epidermidis Pfs and LuxS enzymes. Genes under AI-2 control included mostly genes involved in sugar, nucleotide, amino acid, and nitrogen metabolism, but also virulence-associated genes coding for lipase and bacterial apoptosis proteins. In addition, we demonstrate by liquid chromatography/mass-spectrometry of culture filtrates that the pro-inflammatory phenol-soluble modulin (PSM peptides, key virulence factors of S. epidermidis, are under luxS/AI-2 control. Conclusion Our results provide a detailed molecular basis for the role of LuxS in S. epidermidis virulence and suggest a signaling function for AI-2 in this bacterium.

  16. Pedagogy and the PC: Trends in the AIS Curriculum

    Science.gov (United States)

    Badua, Frank

    2008-01-01

    The author investigated the array of course topics in accounting information systems (AIS), as course syllabi embody. The author (a) used exploratory data analysis to determine the topics that AIS courses most frequently offered and (b) used descriptive statistics and econometric analysis to trace the diversity of course topics through time,…

  17. Ai Weiwei - 21. sajandi märter? / Tanel Veenre

    Index Scriptorium Estoniae

    Veenre, Tanel, 1977-

    2011-01-01

    Hiina kunstniku, inimõiguslase ja teisitimõtleja Ai Weiwei elust, õpingutest, loomingust ja tegevusest. Hiinas kunstniku tagakiusamisest, arreteerimisest, maksupettuses süüdistamisest jm. Kunstniku toetajatest. Inglise kunstileht Art Review valis Ai Weiwei 2011. a. kõige mõjuvõimsamaks kunstitegelaseks

  18. Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

    Directory of Open Access Journals (Sweden)

    Isabel Pôças

    2015-12-01

    Full Text Available Several vegetation indices (VI derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd. The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r2 smaller than 0.67. However, the results of r2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs. The optimal Visible Atmospherically Resistant Index (VARI and Normalized Difference Greenness Vegetation Index (NDGI showed the higher r2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R2 = 0.79 and a regression coefficient b = 0.96. Similar R2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93. Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

  19. Phosphate-based glasses: Prediction of acoustical properties

    Science.gov (United States)

    El-Moneim, Amin Abd

    2016-04-01

    In this work, a comprehensive study has been carried out to predict the composition dependence of bulk modulus and ultrasonic attenuation coefficient in the phosphate-based glass systems PbO-P2O5, Li2O-TeO2-B2O3-P2O5, TiO2-Na2O-CaO-P2O5 and Cr2O3-doped Na2O-ZnO-P2O5 at room temperature. The prediction is based on (i) Makishima-Mackenzie theory, which correlates the bulk modulus with packing density and dissociation energy per unit volume, and (ii) Our recently presented semi-empirical formulas, which correlate the ultrasonic attenuation coefficient with the oxygen density, mean atomic ring size, first-order stretching force constant and experimental bulk modulus. Results revealed that our recently presented semi-empirical formulas can be applied successfully to predict changes of ultrasonic attenuation coefficient in binary PbO-P2O5 glasses at 10 MHz frequency and in quaternary Li2O-TeO2-B2O3-P2O5, TiO2-Na2O-CaO-P2O5 and Cr2O3-Na2O-ZnO-P2O5 glasses at 5 MHz frequency. Also, Makishima-Mackenzie theory appears to be valid for the studied glasses if the effect of the basic structural units that present in the glass network is taken into account.

  20. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    Smaili, Fatima Z.

    2016-05-25

    The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.

  1. 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.

  2. A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

    Science.gov (United States)

    Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

    2016-08-01

    Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources

  3. The Residual-based Predictiveness Curve - A Visual Tool to Assess the Performance of Prediction Models

    OpenAIRE

    Casalicchio, Giuseppe; Bischl, Bernd; Boulesteix, Anne-Laure; Schmid, Matthias

    2015-01-01

    It is agreed among biostatisticians that prediction models for binary outcomes should satisfy two essential criteria: First, a prediction model should have a high discriminatory power, implying that it is able to clearly separate cases from controls. Second, the model should be well calibrated, meaning that the predicted risks should closely agree with the relative frequencies observed in the data. The focus of this work is on the predictiveness curve, which has been proposed by Huang et ...

  4. A prediction method based on grey system theory in equipment condition based maintenance

    International Nuclear Information System (INIS)

    Grey prediction is a modeling method based on historical or present, known or indefinite information, which can be used for forecasting the development of the eigenvalues of the targeted equipment system and setting up the model by using less information. In this paper, the postulate of grey system theory, which includes the grey generating, the sorts of grey generating and the grey forecasting model, is introduced first. The concrete application process, which includes the grey prediction modeling, grey prediction, error calculation, equal dimension and new information approach, is introduced secondly. Application of a so-called 'Equal Dimension and New Information' (EDNI) technology in grey system theory is adopted in an application case, aiming at improving the accuracy of prediction without increasing the amount of calculation by replacing old data with new ones. The proposed method can provide a new way for solving the problem of eigenvalue data exploding in equal distance effectively, short time interval and real time prediction. The proposed method, which was based on historical or present, known or indefinite information, was verified by the vibration prediction of induced draft fan of a boiler of the Yantai Power Station in China, and the results show that the proposed method based on grey system theory is simple and provides a high accuracy in prediction. So, it is very useful and significant to the controlling and controllable management in safety production. (authors)

  5. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    Science.gov (United States)

    Kovalenko, Andriy

    2014-08-01

    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  6. Biomass prediction model in maize based on satellite images

    Science.gov (United States)

    Mihai, Herbei; Florin, Sala

    2016-06-01

    Monitoring of crops by satellite techniques is very useful in the context of precision agriculture, regarding crops management and agricultural production. The present study has evaluated the interrelationship between maize biomass production and satellite indices (NDVI and NDBR) during five development stages (BBCH code), highlighting different levels of correlation. Biomass production recorded was between 2.39±0.005 t ha-1 (12-13 BBCH code) and 51.92±0.028 t ha-1 (83-85 BBCH code), in relation to vegetation stages studied. Values of chlorophyll content ranged from 24.1±0.25 SPAD unit (12-13 BBCH code) to 58.63±0.47 SPAD unit (71-73 BBCH code), and the obtained satellite indices ranged from 0.035641±0.002 and 0.320839±0.002 for NDVI indices respectively 0.035095±0.034 and 0.491038±0.018 in the case of NDBR indices. By regression analysis it was possible to obtain predictive models of biomass in maize based on the satellite indices, in statistical accurate conditions. The most accurate prediction was possible based on NDBR index (R2 = 0.986, F = 144.23, p<0.001, RMSE = 1.446), then based on chlorophyll content (R2 = 0.834, F = 16.14, p = 0.012, RMSE = 6.927) and NDVI index (R2 = 0.682, F = 3.869, p = 0.116, RMSE = 12.178).

  7. Cognitive control predicts use of model-based reinforcement learning.

    Science.gov (United States)

    Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D

    2015-02-01

    Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791

  8. Method of predicting Splice Sites based on signal interactions

    Directory of Open Access Journals (Sweden)

    Deogun Jitender S

    2006-04-01

    Full Text Available Abstract Background Predicting and proper ranking of canonical splice sites (SSs is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. Results According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE and Intronic (ISE Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. Conclusion Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. Reviewers This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand.

  9. Sentiment Prediction Based on Dempster-Shafer Theory of Evidence

    Directory of Open Access Journals (Sweden)

    Mohammad Ehsan Basiri

    2014-01-01

    Full Text Available Sentiment prediction techniques are often used to assign numerical scores to free-text format reviews written by people in online review websites. In order to exploit the fine-grained structural information of textual content, a review may be considered as a collection of sentences, each with its own sentiment orientation and score. In this manner, a score aggregation method is needed to combine sentence-level scores into an overall review rating. While recent work has concentrated on designing effective sentence-level prediction methods, there remains the problem of finding efficient algorithms for score aggregation. In this study, we investigate different aggregation methods, as well as the cases in which they perform poorly. According to the analysis of existing methods, we propose a new score aggregation method based on the Dempster-Shafer theory of evidence. In the proposed method, we first detect the polarity of reviews using a machine learning approach and then, consider sentence scores as evidence for the overall review rating. The results from two public social web datasets show the higher performance of our method in comparison with existing score aggregation methods and state-of-the-art machine learning approaches.

  10. Bayesian predictive modeling for genomic based personalized treatment selection.

    Science.gov (United States)

    Ma, Junsheng; Stingo, Francesco C; Hobbs, Brian P

    2016-06-01

    Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches. PMID:26575856

  11. Chemotaxis to the Quorum-Sensing Signal AI-2 Requires the Tsr Chemoreceptor and the Periplasmic LsrB AI-2-Binding Protein▿

    OpenAIRE

    Hegde, Manjunath; Englert, Derek L.; Schrock, Shanna; Cohn, William B.; Vogt, Christian; Wood, Thomas K.; Manson, Michael D.; Jayaraman, Arul

    2010-01-01

    AI-2 is an autoinducer made by many bacteria. LsrB binds AI-2 in the periplasm, and Tsr is the l-serine chemoreceptor. We show that AI-2 strongly attracts Escherichia coli. Both LsrB and Tsr are necessary for sensing AI-2, but AI-2 uptake is not, suggesting that LsrB and Tsr interact directly in the periplasm.

  12. Demand Management Based on Model Predictive Control Techniques

    Directory of Open Access Journals (Sweden)

    Yasser A. Davizón

    2014-01-01

    Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.

  13. Traffic Prediction Based on Correlation of Road Sections

    Directory of Open Access Journals (Sweden)

    Xiaodan Huang

    2013-10-01

    Full Text Available Road section data packet is very necessary for the estimation and prediction in short-time traffic condition. However, previous researches on this problem are lack of quantitative analysis. A section correlation analyzing method with traffic flow microwave data is proposed for this problem. It is based on the metric multidimensional scaling theory. With a dissimilarity matrix, scalar product matrix can be calculated. Subsequently, a reconstructing matrix of section traffic flow could be got with principal components factor analysis, which could display section groups in low dimension. It is verified that the new method is reliable and effective. After that, Auto Regressive Moving Average (A RMA model is used for forecasting traffic flow and lane occupancy. Finally, a simulated example has shown that the technique is effective and exact. The theoretical analysis indicates that the forecasting model and algorithms have a broad prospect for practical application.  

  14. Mining Behavior Based Safety Data to Predict Safety Performance

    Energy Technology Data Exchange (ETDEWEB)

    Jeffrey C. Joe

    2010-06-01

    The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.

  15. Provably Safe and Robust Learning-Based Model Predictive Control

    CERN Document Server

    Aswani, Anil; Sastry, S Shankar; Tomlin, Claire

    2011-01-01

    Controller design for systems typically faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many control practitioners to focus on the former. However, there is a renewed interest in improving system performance to deal with growing energy and pollution constraints. This paper describes a learning-based model predictive control (MPC) scheme. The MPC provides deterministic guarantees on robustness and safety, and the learning is used to identify richer models of the system to improve controller performance. Our scheme uses a linear model with bounds on its uncertainty to construct invariant sets which help to provide the guarantees, and it can be generalized to other classes of models and to pseudo-spectral methods. This framework allows us to handle state and input constraints and optimize system performance with respect to a cost function. The learning occurs through the use of an oracle which returns the value and gradient of unmodeled dynamics at discr...

  16. A Prediction-based Smart Meter Data Generator

    DEFF Research Database (Denmark)

    Iftikhar, Nadeem; Liu, Xiufeng; Nordbjerg, Finn Ebertsen;

    2016-01-01

    With the prevalence of cloud computing and In-ternet of Things (IoT), smart meters have become one of the main components of smart city strategy. Smart meters generate large amounts of fine-grained data that is used to provide useful information to consumers and utility companies for decision-making....... Now-a-days, smart meter analytics systems consist of analytical algorithms that process massive amounts of data. These analytics algorithms require ample amounts of realistic data for testing and verification purposes. However, it is usually difficult to obtain adequate amounts of realistic data......, mainly due to privacy issues. This paper proposes a smart meter data generator that can generate realistic energy consumption data by making use of a small real-world dataset as seed. The generator generates data using a prediction-based method that depends on historical energy consumption patterns along...

  17. Containment code comparison exercise on experiment ThAI TH7

    Energy Technology Data Exchange (ETDEWEB)

    Fischer, K.; Rastogi, A. K. [Becker Technologies GmbH, Eschborn (Germany); Braun, T.; Drath, T. [Ruhr-Universitaet Bochum, Bochum (Germany); Lyubar, A. [TU Muenchen, Garching (Germany); Schwarz, S. [Gesellschaft fuer Anlagen- und Reaktorsicherheit mbH, Garching (Germany)

    2003-07-01

    The paper summarises the results of a code comparison exercise based on the containment thermal- hydraulic experiment TH7 in the ThAI test facility. Phenomena addressed in the experiment are atmospheric thermal stratification and mixing, steam injection in form of a free jet and against an impingement plate, heat transfer and steam condensation at walls, condensate collection in pools, fog formation in the atmosphere, complex geometric configuration. The test was simulated by three types of codes: the advanced lumped-parameter containment code COCOSYS, the GOTHIC code (developed for nuclear reactor applications) operating under CFD model option, and three industrial CFD codes STAR-CD, CFX and FLUENT for general simulation purposes. The comparison of blind predictions with test data indicates that a necessary requirement for realistic simulation is the availability of a reliable model for steam condensation on walls. While such model was available in the nuclear codes, it was missing in the industrial CFD codes, and efforts to implement such model via user coding were only partly successful under the given time restrictions of the exercise. Phenomena of second order importance like fog formation and transport or condensate runoff are simulated to various degrees of detail and realism, or completely omitted. For the simulation of the thermal stratification, no advantage of CFD over lumped parameter models was found. Experimental code validation data on flow velocity distributions in the foggy atmosphere are lacking due to limitations of the optical measurement systems.

  18. Containment code comparison exercise on experiment ThAI TH7

    International Nuclear Information System (INIS)

    The paper summarises the results of a code comparison exercise based on the containment thermal- hydraulic experiment TH7 in the ThAI test facility. Phenomena addressed in the experiment are atmospheric thermal stratification and mixing, steam injection in form of a free jet and against an impingement plate, heat transfer and steam condensation at walls, condensate collection in pools, fog formation in the atmosphere, complex geometric configuration. The test was simulated by three types of codes: the advanced lumped-parameter containment code COCOSYS, the GOTHIC code (developed for nuclear reactor applications) operating under CFD model option, and three industrial CFD codes STAR-CD, CFX and FLUENT for general simulation purposes. The comparison of blind predictions with test data indicates that a necessary requirement for realistic simulation is the availability of a reliable model for steam condensation on walls. While such model was available in the nuclear codes, it was missing in the industrial CFD codes, and efforts to implement such model via user coding were only partly successful under the given time restrictions of the exercise. Phenomena of second order importance like fog formation and transport or condensate runoff are simulated to various degrees of detail and realism, or completely omitted. For the simulation of the thermal stratification, no advantage of CFD over lumped parameter models was found. Experimental code validation data on flow velocity distributions in the foggy atmosphere are lacking due to limitations of the optical measurement systems

  19. Cyberwar XXI: quantifying the unquantifiable: adaptive AI for next-generation conflict simulations

    Science.gov (United States)

    Miranda, Joseph; von Kleinsmid, Peter; Zalewski, Tony

    2004-08-01

    The era of the "Revolution in Military Affairs," "4th Generation Warfare" and "Asymmetric War" requires novel approaches to modeling warfare at the operational and strategic level of modern conflict. For example, "What if, in response to our planned actions, the adversary reacts in such-and-such a manner? What will our response be? What are the possible unintended consequences?" Next generation conflict simulation tools are required to help create and test novel courses of action (COA's) in support of real-world operations. Conflict simulations allow non-lethal and cost-effective exploration of the "what-if" of COA development. The challenge has been to develop an automated decision-support software tool which allows competing COA"s to be compared in simulated dynamic environments. Principal Investigator Joseph Miranda's research is based on modeling an integrated military, economic, social, infrastructure and information (PMESII) environment. The main effort was to develop an adaptive AI engine which models agents operating within an operational-strategic conflict environment. This was implemented in Cyberwar XXI - a simulation which models COA selection in a PMESII environment. Within this framework, agents simulate decision-making processes and provide predictive capability of the potential behavior of Command Entities. The 2003 Iraq is the first scenario ready for V&V testing.

  20. Hereditary nephropathic systemic amyloidosis caused by a novel variant apolipoprotein A-I.

    Science.gov (United States)

    Persey, M R; Booth, D R; Booth, S E; van Zyl-Smit, R; Adams, B K; Fattaar, A B; Tennent, G A; Hawkins, P N; Pepys, M B

    1998-02-01

    We report a family with autosomal-dominant hereditary systemic amyloidosis in three generations, presenting with renal involvement. Two members of the current generation received renal transplants for end-stage renal failure 16 and 18 years ago, and remain very well clinically despite massive visceral amyloidosis. Two other members of this generation, aged 32 and 47 years, have massive systemic amyloid but no clinical disability. Individuals known to be affected in previous generations died of renal failure in early adult life. Amyloid deposits in the proband, one of the transplanted individuals, were composed of apolipoprotein A-I (apoA-I), and among living family members there was complete concordance between amyloidosis and the presence of a novel 9 base pair in-frame deletion mutation in exon 4 of the apoA-I gene, causing a loss of residues Glu70Phe71Trp72. This predicts the acquisition of a single extra positive charge by mature apoA-I, and this variant was detected in the plasma of all carriers. All the previously reported amyloidogenic variants of apoA-I also carry an extra positive charge, indicating that this electrostatic change is likely to be relevant to the amyloidogenicity of apoA-I. PMID:9461086

  1. SNP annotation-based whole genomic prediction and selection

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, Luc; Jensen, Just;

    2015-01-01

    into a training (968 pigs) and a validation dataset (304 pigs) by assigning records as before and after January 1, 2012, respectively. SNP were annotated by 14 different classes using Ensembl variant effect prediction. Predictive accuracy and prediction bias were calculated using Bayesian Power LASSO...... SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP groups. Genomic prediction has accuracy comparable to observed phenotype, and use of genomic prediction can be cost...... effective by replacing feed intake measurement. Genomic annotation had less impact on predictive accuracy traits considered here but may be different for other traits. It is the first study to provide useful insights into biological classes of SNP driving the whole genomic prediction for complex traits in...

  2. Highlights on artificial insemination (AI) technology in the pig

    OpenAIRE

    Tarek Khalifa; Constantinos Rekkas; Foteini Samartzi; Aristotelis Lymberopoulos; Kostas Kousenidis; Toni Dovenski

    2014-01-01

    Over the past decade, there has been a tremendous increase in the development of field AI services in the majority of countries concerned with pig production. The objective of this paper is to review: (a) the current status of swine AI in the world, (b) significance and limitation of AI with liquid and frozen semen, (c) the biological traits of porcine semen in relation to in-vitro sperm storage, (d) the criteria used for selection of a boar stud as a semen supplier, (e) how to process boar s...

  3. Dictionnaire igbo-français - suivi d'un index français-igbo

    OpenAIRE

    Ugochukwu, Francoise; Okafor, Peter

    2004-01-01

    The first bilingual dictionary of standard Igbo ever published, sponsored by the French Institute in Africa, Nigeria. L'igbo est l'une des trois principales langues nigérianes de par le nombre de ses locuteurs. Ce volume, qui compte près de 4000 entrées igbo, est le premier véritable dictionnaire igbo-français. On y trouve le vocabulaire courant, le vocabulaire technique et spécialisé approuvé par le comité de standardisation de l'igbo, ainsi qu'un bon nombre de mots du milieu naturel et d...

  4. BAYESIAN PREDICTION FOR THE TWO-PARAMETER EXPONENTIAL DISTRIBUTION BASED ON TYPE Ⅱ DOUBLY CENSORING

    Institute of Scientific and Technical Information of China (English)

    LiYanling; ZhaoXuanmin; XieWenxian

    2005-01-01

    The two-parameter exponential distribution is proposed to be an underlying model, and prediction bounds for future observations are obtained by using Bayesian approach. Prediction intervals are derived for unobserved lifetimes in one-sample prediction and twosample prediction based on type Ⅱ doubly censored samples. A numerical example is given to illustrate the procedures,prediction intervals are investigated via Monte Carlo method,and the accuracy of prediction intervals is presented.

  5. Grey Theory Based Vibration Prediction for Steam Turbines

    Institute of Scientific and Technical Information of China (English)

    LIANG Lei; SU Leitao; FENG Yongxin; JIANG Dongxiang

    2012-01-01

    The Mechanism of grey prediction method and its application advantages in safety prediction were described.This method was used to predict the vibration amplitude within the coming 6 hours for a steam turbine.Moreover,the prediction results were examined using correlation degree and posterior error.Results indicated that,this grey prediction method had small posterior error and got a grade Ⅰ accuracy.We can foreknow the variation trend of vibration amplitude of the steam turbine through this method,so as to take timely countermeasures.Therefore,the operation safety of the power unit was improved.

  6. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  7. Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

    Science.gov (United States)

    Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

    2015-12-01

    The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

  8. Risk Reducing Effect of AIS Implementation on Collision Risk

    DEFF Research Database (Denmark)

    Lützen, Marie; Friis-Hansen, Peter

    2003-01-01

    , as a result, the community will undoubtedly observe an increase in navigational safety. However, to the authors? knowledge, no study has so far rigorously quantified the risk reducing effect of using AIS as an integrated part of the navigational system. The objective of this study is to fill this gap....... The risk reducing effect of AIS is quantified by building a Bayesian network facilitating an evaluation of the effect of AIS on the navigational officer?s reaction ability in a potential, critical collision situation. The time-dependent change in the risk reducing effect on ship collisions is analysed...... that the risk reducing effect on the collision risk of implementing AIS on a vessel will be approximately 55 % and independent of the bridge type....

  9. 4th June: AIS and NICE/MAIL unique authentication

    CERN Multimedia

    The AIS and NICE teams

    2007-01-01

    Over the past few years, the IT department has been in the process of streamlining CERN users' access to all central computing services. The long term goal is to converge on a unique computer account, which will increase computer security and simplify account maintenance. The next step of this process will occur on the 4th June 2007, as of when authenticating on the AIS applications (EDH, HRT, CET, APT, ERT, CRA, Foundation, ...) and on NICE (Windows) and MAIL will be done using the same username and password. As a reminder, this account can also be used on EDMS, INDICO, CDS and SIMBA. Thus starting on the 4th June 2007, authentication on the AIS applications must be done using your AIS username and your MAIL/NICE password. Thanks for your understanding, The AIS and NICE teams

  10. 4th June: AIS and NICE/MAIL unique authentication

    CERN Multimedia

    AIS and NICE teams

    2007-01-01

    Over the past few years, the IT department has been in the process of streamlining CERN users' access to all central computing services. The long term goal is to converge on a unique computer account, which will increase computer security and simplify account maintenance. The next step of this process will occur on 4th June 2007, as of when authenticating on the AIS applications (EDH, HRT, CET, APT, ERT, CRA, Foundation, ...) and on NICE (Windows) and MAIL will be done using the same username and password. As a reminder, this account can also be used on EDMS, INDICO, CDS and SIMBA. So starting on 4th June 2007, authentication on the AIS applications must be done using your AIS username and your MAIL/NICE password. Thanks for your understanding, The AIS and NICE teams

  11. Liquid Robotics Wave Glider, Honey Badger (G3), 2015, AIS

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Liquid Robotics Wave Glider, Honey Badger (G3), 2015, AIS. The MAGI mission is to use the Wave Glider to sample the late summer chlorophyll bloom that develops near...

  12. Using AI Planning Techniques for Army Small Unit Operations

    OpenAIRE

    Tate, Austin; Levine, John; Jarvis, Peter; Dalton, Jeffrey

    1999-01-01

    In this paper, we outline the requirements of a planning and decision aid to support US Army small unit operations in urban terrain and show how AI planning technologies can be exploited in that context. The work is a rare example of a comprehensive use of AI technologies across the whole planning lifecycle, set in a realistic application in which the actual user community set the requirements. The phases involved include: * Domain knowledge elicitation * Rich plan representation and use ...

  13. Bayesian Predictive Densities Based on Latent Information Priors

    OpenAIRE

    Komaki, Fumiyasu

    2010-01-01

    Construction methods for prior densities are investigated from a predictive viewpoint. Predictive densities for future observables are constructed by using observed data. The simultaneous distribution of future observables and observed data is assumed to belong to a parametric submodel of a multinomial model. Future observables and data are possibly dependent. The discrepancy of a predictive density to the true conditional density of future observables given observed data is evaluated by the ...

  14. Star-sensor-based predictive Kalman filter for satelliteattitude estimation

    Institute of Scientific and Technical Information of China (English)

    林玉荣; 邓正隆

    2002-01-01

    A real-time attitude estimation algorithm, namely the predictive Kalman filter, is presented. This algorithm can accurately estimate the three-axis attitude of a satellite using only star sensor measurements. The implementation of the filter includes two steps: first, predicting the torque modeling error, and then estimating the attitude. Simulation results indicate that the predictive Kalman filter provides robust performance in the presence of both significant errors in the assumed model and in the initial conditions.

  15. Protein Structure Prediction: Knowledge-based Approaches for Loop Prediction and Model Quality Assessment

    OpenAIRE

    Benkert, Pascal

    2007-01-01

    Knowledge of the three-dimensional structure of proteins is of vital importance for understanding their function and for the rational development of new drugs. Homology modelling is currently the most successful method for the prediction of the structure of a protein from its sequence. A structural model is thereby built by incorporating information from experimentally solved proteins showing an evolutionary relationship to the target protein. The accurate prediction of loop regions which fre...

  16. The temporal stability and predictive validity of affect-based and cognition-based intentions

    NARCIS (Netherlands)

    Keer, M.; Conner, M.; Putte, B. van den; Neijens, P.

    2014-01-01

    Recent research has revealed individual differences in the extent to which people base their intentions on affect and cognition. Two studies are presented that assess whether such differences predict the strength of individuals' intention-behaviour relationships. Participants completed measures of a

  17. Compatibility of pedigree-based and marker-based relationships for single-step genomic prediction

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund

    2012-01-01

    -based relationship matrix and the pedigree-based relationship matrix. The compatibility issue involves which allele frequencies to use in the marker-based relationship matrix, and also that adjustments of this matrix to the pedigree-based relationship matrix are needed. In addition, it has been overlooked......Single-step methods for genomic prediction have recently become popular because they are conceptually simple and in practice such a method can completely replace a pedigree-based method for routine genetic evaluation. An issue with single-step methods is compatibility between the marker...... in the base population. Here, two ideas are explored. The first idea is to instead adjust the pedigree-based relationship matrix to be compatible to the marker-based relationship matrix, whereas the second idea is to include the likelihood for the observed markers. A single-step method is used where...

  18. 基于AIS的船舶驾驶实时增强仿真系统%Study of Real-time Augmented Environment for Ship Handling Based on AIS Data

    Institute of Scientific and Technical Information of China (English)

    刘喜作; 宋元; 李彩霞; 邹文萌

    2013-01-01

    目前航海模拟器主要用于航海教学与技术培训,而现场使用真实的航行水域中气象环境及目标船信息进行船舶指挥决策仿真的很少.研究系统构成要素,基于AIS的实时真实信息与虚拟环境融合,经验性船舶流体动力模型,运用统一数据源进行电子海图、雷达和三维视景数据精确匹配可视化方法,采用人在回路中的半实物仿真方式,建立船舶驾驶实时增强仿真系统,形成更加逼真的航行区域环境,能够更真实地反映舰船航行时的人机互动,有助于辅助决策结果的实时性、可信性与有效性,实现了船舶航行指挥决策论证和特殊操纵方案评估,实验结果表明了系统的可行性.%Marine simulators are mainly used for marine education and training,but they are less used for navigation command and decision-making simulation on the living condition of real weather and real vessel information.By studying the key factors of this system,the real-time augmented environment visa AIS(Automatic Identification System),establishing ship dynamics model under normal and extreme maritime conditions,using the identical data to make the precision visual model for electronic chart,navigation radar and three dimension scene,the virtual testing system under the semi-physical closed loop simulation is more realistic in the navigational place and enables operator to be involved with more authentic interactivity.The system gives the results more quickly,authoritatively and availably,and offers competency assessment for navigation command and special vessel maneuvering.The feasibility of the system has been verified by the engineering experiments.

  19. Prediction of Silicon-Based Layered Structures for Optoelectronic Applications

    Science.gov (United States)

    Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun; CCMG Team

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV and 1.59 eV, respectively. Their potential applications for light emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

  20. Forbush Decrease Prediction Based on Remote Solar Observations

    Science.gov (United States)

    Dumbović, M.; Vršnak, B.; Čalogović, J.

    2016-01-01

    We employ remote observations of coronal mass ejections (CMEs) and the associated solar flares to forecast the CME-related Forbush decreases, i.e. short-term depressions in the galactic cosmic-ray flux. The relation between the Forbush effect at Earth and remote observations of CMEs and associated solar flares is studied via a statistical analysis. Relations between Forbush decrease magnitude and several CME/flare parameters were found: the initial CME speed, apparent width, source position, associated solar-flare class, and the effect of successive-CME occurrence. Based on the statistical analysis, remote solar observations are employed to forecast a Forbush-decrease. For this purpose, an empirical probabilistic model is constructed that uses selected remote solar observations of the CME and associated solar flare as input and gives the expected Forbush-decrease magnitude range as output. The forecast method is evaluated using several verification measures, indicating that as the forecast tends to be more specific, it is less reliable, which is its main drawback. However, the advantages of the method are that it provides an early prediction and that the input does not necessarily depend on using a spacecraft.

  1. Coal Calorific Value Prediction Based on Projection Pursuit Principle

    Directory of Open Access Journals (Sweden)

    QI Minfang

    2012-10-01

    Full Text Available The calorific value of coal is an important factor for the economic operation of coal-fired power plant. However, calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the prediction of calorific value of coal have a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of coal calorific value, establish a soft measurement model for calorific value of coal based on projection pursuit principle combined with genetic algorithm to optimize parameters, and support vector machine algorithm. It is shown by an example that the model has a stronger objectivity, effective and feasible for avoiding the disadvantage of the artificially decided weights of feature indexes. The model could provide a good guidance for the calculation of the coal calorific value and optimization operation of coal-fired power plants.  

  2. Multidimensional Analysis of Magnetic Resonance Imaging Predicts Early Impairment in Thoracic and Thoracolumbar Spinal Cord Injury.

    Science.gov (United States)

    Mabray, Marc C; Talbott, Jason F; Whetstone, William D; Dhall, Sanjay S; Phillips, David B; Pan, Jonathan Z; Manley, Geoffrey T; Bresnahan, Jacqueline C; Beattie, Michael S; Haefeli, Jenny; Ferguson, Adam R

    2016-05-15

    Literature examining magnetic resonance imaging (MRI) in acute spinal cord injury (SCI) has focused on cervical SCI. Reproducible systems have been developed for MRI-based grading; however, it is unclear how they apply to thoracic SCI. Our hypothesis is that MRI measures will group as coherent multivariate principal component (PC) ensembles, and that distinct PCs and individual variables will show discriminant validity for predicting early impairment in thoracic SCI. We undertook a retrospective cohort study of 25 patients with acute thoracic SCI who underwent MRI on admission and had American Spinal Injury Association Impairment Scale (AIS) assessment at hospital discharge. Imaging variables of axial grade, sagittal grade, length of injury, thoracolumbar injury classification system (TLICS), maximum canal compromise (MCC), and maximum spinal cord compression (MSCC) were collected. We performed an analytical workflow to detect multivariate PC patterns followed by explicit hypothesis testing to predict AIS at discharge. All imaging variables loaded positively on PC1 (64.3% of variance), which was highly related to AIS at discharge. MCC, MSCC, and TLICS also loaded positively on PC2 (22.7% of variance), while variables concerning cord signal abnormality loaded negatively on PC2. PC2 was highly related to the patient undergoing surgical decompression. Variables of signal abnormality were all negatively correlated with AIS at discharge with the highest level of correlation for axial grade as assessed with the Brain and Spinal Injury Center (BASIC) score. A multiple variable model identified BASIC as the only statistically significant predictor of AIS at discharge, signifying that BASIC best captured the variance in AIS within our study population. Our study provides evidence of convergent validity, construct validity, and clinical predictive validity for the sampled MRI measures of SCI when applied in acute thoracic and thoracolumbar SCI.

  3. Predictive spectroscopy and chemical imaging based on novel optical systems

    Science.gov (United States)

    Nelson, Matthew Paul

    1998-10-01

    This thesis describes two futuristic optical systems designed to surpass contemporary spectroscopic methods for predictive spectroscopy and chemical imaging. These systems are advantageous to current techniques in a number of ways including lower cost, enhanced portability, shorter analysis time, and improved S/N. First, a novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated. A regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal directly proportional to the chemical/physical property for which the regression vector was designed. Second, a novel optical system is described which takes a single-shot approach to chemical imaging with high spectroscopic resolution using a dimension-reduction fiber-optic array. Images are focused onto a two- dimensional matrix of optical fibers which are drawn into a linear distal array with specific ordering. The distal end is imaged with a spectrograph equipped with an ICCD camera for spectral analysis. Software is used to extract the spatial/spectral information contained in the ICCD images and deconvolute them into wave length-specific reconstructed images or position-specific spectra which span a multi-wavelength space. This thesis includes a description of the fabrication of two dimension-reduction arrays as well as an evaluation of the system for spatial and spectral resolution, throughput, image brightness, resolving power, depth of focus, and channel cross-talk. PCA is performed on the images by treating rows of the ICCD images as spectra and plotting the scores of each PC as a function of reconstruction position. In addition, iterative target transformation factor analysis (ITTFA) is performed on the spectroscopic images to generate ``true'' chemical maps of samples. Univariate zero-order images, univariate first

  4. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

    Directory of Open Access Journals (Sweden)

    Kurgan Lukasz

    2008-10-01

    Full Text Available Abstract Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM values serve as an input to the support vector machine (SVM predictor. Results We show that (1 all four predicted secondary structures are useful; (2 the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3 the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an

  5. Model-based Purchase Predictions for Large Assortments

    NARCIS (Netherlands)

    B.J.D. Jacobs (Bruno); A.C.D. Donkers (Bas); D. Fok (Dennis)

    2016-01-01

    textabstractBeing able to accurately predict what a customer will purchase next is of paramount importance to successful online retailing. In practice, customer purchase history data is readily available to make such predictions, sometimes complemented with customer characteristics. Given the large

  6. Annotation-Based Whole Genomic Prediction and Selection

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc;

    in their contribution to estimated genomic variances and in prediction of genomic breeding values by applying SNP annotation approaches to feed efficiency. Ensembl Variant Predictor (EVP) and Pig QTL database were used as the source of genomic annotation for 60K chip. Genomic prediction was performed using the Bayes...... prove useful for less heritable traits such as diseases and fertility...

  7. Viscosity Prediction of Hydrocarbon Mixtures Based on the Friction Theory

    DEFF Research Database (Denmark)

    Zeberg-Mikkelsen, Claus Kjær; Cisneros, Sergio; Stenby, Erling Halfdan

    2001-01-01

    The application and capability of the friction theory (f-theory) for viscosity predictions of hydrocarbon fluids is further illustrated by predicting the viscosity of binary and ternary liquid mixtures composed of n-alkanes ranging from n-pentane to n-decane for wide ranges of temperature and from...

  8. Prediction of epitopes using neural network based methods

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2011-01-01

    In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, hav...

  9. SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating.

    Science.gov (United States)

    Lee, Young-Joo; Cho, Soojin

    2016-03-02

    Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed.

  10. Prediction

    CERN Document Server

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  11. Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

  12. 76 FR 44045 - Establishment of the SANE/SART AI/AN Initiative Committee

    Science.gov (United States)

    2011-07-22

    ... of Justice Programs Establishment of the SANE/SART AI/AN Initiative Committee AGENCY: Office for... (SART) American Indian/Alaskan Native (AI/AN) Initiative (``SANE/SART AI/AN Initiative Committee'' or... (FACA), as amended, 5 U.S.C., App. 2. The SANE/SART AI/AN Initiative Committee will provide the...

  13. STUDY ON GIS BASED PREDICTION SYSTEM FOR RADIO WAVE PROPAGATION IN URBAN REGION

    Institute of Scientific and Technical Information of China (English)

    Lai Jin; Huang Chang; Zhu Shouzheng

    2002-01-01

    In this letter, an integrated application of the prediction for radio wave propagation with the Geographic Information System (GIS) is presented and a real prediction system based on GIS is implemented.

  14. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening.

    Science.gov (United States)

    Kalliokoski, Tuomo; Salo, Heikki S; Lahtela-Kakkonen, Maija; Poso, Antti

    2009-12-01

    As tautomerism and ionization may significantly change the interaction possibilities between a ligand and a target protein, these phenomena could have an effect on structure-based virtual screening. Tautomeric- and protonation-state enumeration ensures that the state with optimal interaction possibilities is included in the screening process, as the predicted state may not always be the optimal binder. However, there is very little information published if tautomer and protomer enumeration actually improves the enrichment of active molecules compared to the alternative of using a predicted form of each molecule. In this study, a retrospective virtual screening was performed using AutoDock on 19 drug targets with a publicly available data set. It is proposed that tautomer and protomer prediction can significantly save computing resources and can yield similar results to enumeration. PMID:19928753

  15. Wavelet Neural Network Based Traffic Prediction for Next Generation Network

    Institute of Scientific and Technical Information of China (English)

    Zhao Qigang; Li Qunzhan; He Zhengyou

    2005-01-01

    By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

  16. On-time clinical phenotype prediction based on narrative reports

    Science.gov (United States)

    Bejan, Cosmin A.; Vanderwende, Lucy; Evans, Heather L.; Wurfel, Mark M.; Yetisgen-Yildiz, Meliha

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an important factor in achieving better results for this problem is to determine how much information to extract from the patient reports in the time interval between the patient admission time and the current prediction time. PMID:24551325

  17. Action prediction based on anticipatory brain potentials during simulated driving

    Science.gov (United States)

    Khaliliardali, Zahra; Chavarriaga, Ricardo; Gheorghe, Lucian Andrei; Millán, José del R.

    2015-12-01

    Objective. The ability of an automobile to infer the driver’s upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver’s intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating. Approach. We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by ‘Start’/‘Stop’ cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features. Main results. (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 μV at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a ‘Red’ traffic light) versus events that do not require such action (No-go condition; e.g. a ‘Yellow’ light); with an average single trial classification performance of 0.83 ± 0.13 for braking and 0.79 ± 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 ± 200 ms before the action with a detection rate of 0.77 ± 0.12 in offline analysis. Significance. We show for the first time the feasibility of predicting the driver’s intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.

  18. High Precision Prediction of Rolling Force Based on Fuzzy and Nerve Method for Cold Tandem Mill

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; SHAN Xiu-ying; NIU Zhao-ping

    2008-01-01

    The rolling force model for cold tandem mill was put forward by using the Elman dynamic recursive network method, based on the actual measured data. Furthermore, a good assumption is put forward, which brings a full universe of discourse self-adjusting factor fuzzy control, closed-loop adjusting, based on error feedback and expertise into a rolling force prediction model, to modify prediction outputs and improve prediction precision and robustness. The simulated results indicate that the method is highly effective and the prediction precision is better than that of the traditional method. Predicted relative error is less than ±4%, so the prediction is high precise for the cold tandem mill.

  19. Predictability of Shanghai Stock Market by Agent-based Mix-game Model

    CERN Document Server

    Gou, C

    2005-01-01

    This paper reports the effort of using agent-based mix-game model to predict financial time series. It introduces the prediction methodology by means of mix-game model and gives an example of its application to forecasting Shanghai Index. The results show that this prediction methodology is effective and agent-based mix-game model is a potential good model to predict time series of financial markets.

  20. An Efficient Deterministic Approach to Model-based Prediction Uncertainty

    Data.gov (United States)

    National Aeronautics and Space Administration — Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the...

  1. Recurrent neural networks-based multivariable system PID predictive control

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi

    2007-01-01

    A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.

  2. Grammar-based feature generation for time-series prediction

    CERN Document Server

    De Silva, Anthony Mihirana

    2015-01-01

    This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

  3. Sequence- and structure-based prediction of eukaryotic proteinphosphorylation sites

    DEFF Research Database (Denmark)

    Blom, Nikolaj; Gammeltoft, Steen; Brunak, Søren

    1999-01-01

    Protein phosphorylation at serine, threonine or tyrosine residues affects a multitude of cellular signaling processes. Howis specificity in substrate recognition and phosphorylation by protein kinases achieved? Here, we present an artificialneural network method that predicts phosphorylation site...

  4. Accurate Multisteps Traffic Flow Prediction Based on SVM

    OpenAIRE

    Zhang Mingheng; Zhen Yaobao; Hui Ganglong; Chen Gang

    2013-01-01

    Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM) are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the mul...

  5. SVM-based prediction of caspase substrate cleavage sites

    OpenAIRE

    Wee, Lawrence JK; Tan, Tin Wee; Ranganathan, Shoba

    2006-01-01

    Background Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector...

  6. SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

    Institute of Scientific and Technical Information of China (English)

    钟伟民; 何国龙; 皮道映; 孙优贤

    2005-01-01

    A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

  7. Celebrating AI's Fiftieth Anniversary and Continuing Innovation at the AAAI/IAAI-06 Conferences

    OpenAIRE

    Hedberg, Sara R.

    2006-01-01

    The seeds of AI were sewn at the Dartmouth Conference in the summer of 1956. John McCarthy, then an assistant mathematics professor at Dartmouth, organized the conference and coined the name "artificial intelligence" in his conference proposal. This summer AAAI celebrates the first 50 years of AI; and continues to foster the fertile fields of AI at the National AI conference (AAAI-06) and Innovative Applications of AI conference (IAAI-06) in Boston.

  8. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-07-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  9. Prediction of solar cycle based on the invariant

    Institute of Scientific and Technical Information of China (English)

    LIU Shijun; YU Xiaoding; CHEN Yongyi

    2003-01-01

    A new method of predicting solar activities has been introduced in this paper. The method can predict both the occurrence time and the maximum number of sunspot at the same time. By studying the variation of sunspot, we find that the combination of the several variables was nearly invariable during the entire solar cycles, as called invariant. And just only by determining the start time of a cycle, we can predict the occurrence time of cycle's peak value accurately. Furthermore, according to observational data of the sunspot cycles, it showed that the sunspot maximum number has correlation not only with the prophase variety of the number in the cycle but also with the anaphase of the previous period. So we can introduce an equivalent regression coefficient, which can dynamically self-adapt to different cycle lengths, and effectively solve the inconsistency between the accuracy and the lead-time of the forecast. It can guarantee the satisfied accuracy and effectively increases the lead-time of the forecast. This method can predict the maximum sunspot number for solar cycle at the approximate half rise of the period. This method predicts that the occurrence time of the maximum sunspot number for cycle 24 will be in January 2011.

  10. A CHAID Based Performance Prediction Model in Educational Data Mining

    Directory of Open Access Journals (Sweden)

    R. Bhaskaran

    2010-01-01

    Full Text Available The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO. A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.

  11. AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

    CERN Document Server

    Cheng, J; 10.1613/jair.764

    2011-01-01

    Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang...

  12. Numerical simulation of electromagnetic and flow fields of TiAI melt under electric field

    Institute of Scientific and Technical Information of China (English)

    Zhang Yong; Ding Hongsheng; Jiang Sanyong; Chen Ruirun; Guo Jingjie

    2010-01-01

    This article aims at building an electromagnetic and fluid model, based on the Maxwell equations and Navier-Stokes equations, in TiAI melt under two electric fields. FEM (Finite Element Method) and APDL (ANSYS Parametric Design Language) were employed to perform the simulation, model setup, loading and problem solving. The melt in molds of same cross section area with different flakiness ratio (i.e. width/depth) under the load of sinusoidal current or pulse current was analyzed to obtain the distribution of electromagnetic field and flow field. The results show that the induced magnetic field occupies sufficiently the domain of the melt in the mold with a flakiness ratio of 5:1. The melt is driven bipolarly from the center in each electric field. It is also found that the pulse electric field actuates the TiAI melt to flow stronger than what the sinusoidal electric field does.

  13. Discovering Knowledge from AIS Database for Application in VTS

    Science.gov (United States)

    Tsou, Ming-Cheng

    The widespread use of the Automatic Identification System (AIS) has had a significant impact on maritime technology. AIS enables the Vessel Traffic Service (VTS) not only to offer commonly known functions such as identification, tracking and monitoring of vessels, but also to provide rich real-time information that is useful for marine traffic investigation, statistical analysis and theoretical research. However, due to the rapid accumulation of AIS observation data, the VTS platform is often unable quickly and effectively to absorb and analyze it. Traditional observation and analysis methods are becoming less suitable for the modern AIS generation of VTS. In view of this, we applied the same data mining technique used for business intelligence discovery (in Customer Relation Management (CRM) business marketing) to the analysis of AIS observation data. This recasts the marine traffic problem as a business-marketing problem and integrates technologies such as Geographic Information Systems (GIS), database management systems, data warehousing and data mining to facilitate the discovery of hidden and valuable information in a huge amount of observation data. Consequently, this provides the marine traffic managers with a useful strategic planning resource.

  14. Genomic prediction in families of perennial ryegrass based on genotyping-by-sequencing

    DEFF Research Database (Denmark)

    Ashraf, Bilal

    In this thesis we investigate the potential for genomic prediction in perennial ryegrass using genotyping-by-sequencing (GBS) data. Association method based on family-based breeding systems was developed, genomic heritabilities, genomic prediction accurancies and effects of some key factors wer e...... prediction. Overall, GBS allows for genomic prediction in breeding families of perennial ryegrass and holds good potential to expedite genetic gain and encourage the application of genomic prediction......In this thesis we investigate the potential for genomic prediction in perennial ryegrass using genotyping-by-sequencing (GBS) data. Association method based on family-based breeding systems was developed, genomic heritabilities, genomic prediction accurancies and effects of some key factors wer...

  15. Prediction of Geological Subsurfaces Based on Gaussian Random Field Models

    Energy Technology Data Exchange (ETDEWEB)

    Abrahamsen, Petter

    1997-12-31

    During the sixties, random functions became practical tools for predicting ore reserves with associated precision measures in the mining industry. This was the start of the geostatistical methods called kriging. These methods are used, for example, in petroleum exploration. This thesis reviews the possibilities for using Gaussian random functions in modelling of geological subsurfaces. It develops methods for including many sources of information and observations for precise prediction of the depth of geological subsurfaces. The simple properties of Gaussian distributions make it possible to calculate optimal predictors in the mean square sense. This is done in a discussion of kriging predictors. These predictors are then extended to deal with several subsurfaces simultaneously. It is shown how additional velocity observations can be used to improve predictions. The use of gradient data and even higher order derivatives are also considered and gradient data are used in an example. 130 refs., 44 figs., 12 tabs.

  16. How AI localisation in plant tissues determines the targeted pest spectrum of different chemistries

    DEFF Research Database (Denmark)

    Buchholz, Anke; Trapp, Stefan

    Many pests suck on the vascular system and/or cells of different plant tissues. The sucking target in the cell differs between pests such as Hemiptera (e.g. aphids and whiteflies) or Acari (mites). The agronomic control of sucking pests is most effective with pesticides taken up orally. The cuticle....... The predictions were compared to the measured biological effects against three different arthropods. Test compounds differed in log P (-0.1 to 4.3) and pKa (4.1 to 10.7). Efficacies in different bioassays are discussed with the postulated cellular AI localisation and the individual feeding behaviour...... of the targeted pest....

  17. Investigation of graphite pile radiation features of uranium-graphite reactor AI

    International Nuclear Information System (INIS)

    Paper presents the results of the examination of the Mayak PA uranium-graphite AI reactor stack. On the basis of the mentioned results one drew up the report on the stack nuclear safety and the radiation certificate. The radiation examination enabled to determine the level, the composition and the distribution of the stack contamination and the distribution of the neutron and γ-radiation, to predict variation of radionuclide activity within the graphite depending on the cooling time. The mentioned data are necessary to ensure safety analysis and to make decisions on further stages of the reactor decommissioning

  18. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    OpenAIRE

    Lei Yang; Xianglong Tang

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based m...

  19. Well-log based prediction of thermal conductivity

    DEFF Research Database (Denmark)

    Fuchs, Sven; Förster, Andrea

    Rock thermal conductivity (TC) is paramount for the determination of heat flow and the calculation of temperature profiles. Due to the scarcity of drill cores compared to the availability of petrophysical well logs, methods are desired to indirectly predict TC in sedimentary basins. Most of the w......Rock thermal conductivity (TC) is paramount for the determination of heat flow and the calculation of temperature profiles. Due to the scarcity of drill cores compared to the availability of petrophysical well logs, methods are desired to indirectly predict TC in sedimentary basins. Most...

  20. Annotation-Based Whole Genomic Prediction and Selection

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc;

    using the BayesCπ method and applied to 1,272 Duroc pigs with both genotypic and phenotypic records including residual (RFI) and daily feed intake (DFI), average daily gain (ADG) and back fat (BF)). Records were split into a training (968 pigs) and a validation dataset (304 pigs). SNPs were annotated by...... 14 different classes. Predictive accuracy was 0.531, 0.532, 0.302, and 0.344 for DFI, RFI, ADG and BF, respectively. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from...

  1. Distributed estimation based on observations prediction in wireless sensor networks

    KAUST Repository

    Bouchoucha, Taha

    2015-03-19

    We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.

  2. Traffic Prediction Based on SVM Training Sample Divided by Time

    Directory of Open Access Journals (Sweden)

    Lingli Li

    2013-07-01

    Full Text Available In recent years, the volume of traffic is rapidly increasing. When vehicles running through the tunnel are more intensive or move slowly, the tunnel environment occurs deteriorated sharply, which affects the normal operation of the vehicle in the tunnel. This paper uses the result of previous mining association rules to select feature items and to establish four training samples divided by time. Then the training samples are utilized to create the SVM classification model. Finally the trained SVM model is used to prediction the tunnel traffic situation. Through traffic situation prediction, effective decisions can be made before traffic jams, and ensure that the tunnel traffic is normal.  

  3. Predictive functional control of integrating process based on impulse response

    Institute of Scientific and Technical Information of China (English)

    Bin ZHANG; Ping LI; Weidong ZHANG

    2004-01-01

    The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of the load disturbance, a novel error compensation method is proposed. Then predictive functional control of integrating process is designed. The method given generates a simple control structure, which can significantly reduce online computation. Furthermore, the tuning of the controller is fairly straightforward. Simulation results indicate that the designed control system is relatively robust to the parameters variation of the process.

  4. Targeted Expression and Secretion of Human Apoprotein AI,Apoprotein E and Lecithin-choles-terol Acyltransferase from Myogenic Cell

    Institute of Scientific and Technical Information of China (English)

    范乐明; 张慧; 于书真; 陈琪; 魏恩会; 王南; 蔡海江

    2001-01-01

    Objective To investigate the possibility of heterologous expression for apoAI, apoE and LCAT by skeletal muscle cells and secretion into blood and to develop a safe and convenient gene therapy method for atherosclerosis. Methods Viral and nonviral vectors containing apoAI, apoE or LCAT genes were constructed and transfected into myogenic cells in vitro or injected directed into mouse skeletal muscle. The expression efficiencies of these vectors were investigated by ELISA assay for human apoAI and apoE3 and by the proteoliposome method for human LCAT. Genomic DNA was extracted from stable transduced myoblasts and analyzed for the presence of vector sequence by PCR amplifications. Immunocytochemistry assay was also performed to make an intuitionistic detection for the expression of transgene in myoblasts. Results All viral or nonviral vectors constructed in present study expressed the transgenes efficiently in mice skeletal muscles in vivo or cultured myoblasts in vitro. The transgene expression level of cells transfected with AAV-based plasmid vectors were 2-4 times higher then that of cells transfected with conventional plasmid vectors. Additionally, cells transfected with AAV-based bicistronic vector or tricistronic retroviral vector expressed both human apoAI and LCAT simultaneously. The sequences of retroviral or AAV-based plasmid vectors were found to be retained in host cells after transfection when that of conventional plasmid vectors were lost. Furthermore, transduced myoblasts maintained the ability for heterologous expression of human apoAI and LCAT even after differentiation into myotubes. For cells transfected with retroviral vectors, stable transduced clones can be selected by G418 and continued to efficiently express human apoAI and LCAT for 3 months. Conclusion These finds indicated that mice skeletal muscles or cultured myoblasts transduced with viral or non-viral vectors could efficiently express and secret human apoAI, apoE and LCAT. It suggested

  5. Condition-based prediction of time-dependent reliability in composites

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a reliability-based prediction methodology to obtain the remaining useful life of composite materials subjected to fatigue degradation....

  6. Behavior-Based Budget Management Using Predictive Analytics

    Energy Technology Data Exchange (ETDEWEB)

    Troy Hiltbrand

    2013-03-01

    Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently under similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.

  7. Example-Based Human Pose Recovery under Predicted Partial Occlusions

    NARCIS (Netherlands)

    Poppe, Ronald; Babuska, Robert; Groen, Frans C A.

    2010-01-01

    For human pose recovery, the presence of occlusions due to objects or other persons in the scene remains a difficult problem to cope with. However, recent advances in the area of human detection allow for simultaneous segmentation of humans and the prediction of occluded regions. In this chapter, we

  8. Predicting active users' personality based on micro-blogging behaviors.

    Science.gov (United States)

    Li, Lin; Li, Ang; Hao, Bibo; Guan, Zengda; Zhu, Tingshao

    2014-01-01

    Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 839 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory [corrected]. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.

  9. UAV Formation Flight Based on Nonlinear Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Zhou Chao

    2012-01-01

    Full Text Available We designed a distributed collision-free formation flight control law in the framework of nonlinear model predictive control. Formation configuration is determined in the virtual reference point coordinate system. Obstacle avoidance is guaranteed by cost penalty, and intervehicle collision avoidance is guaranteed by cost penalty combined with a new priority strategy.

  10. Increasing the Target Prediction Accuracy of MicroRNA Based on Combination of Prediction Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammed Q. Shatnawi

    2016-06-01

    Full Text Available MicroRNA is an oligonucleotide that plays a role in the pathogenesis of several diseases (mentioning Cancer. It is a non-coding RNA that is involved in the control of gene expression through the binding and inhibition of mRNA. In this study, three algorithms were implemented in WEKA software using two testing modes to analyze five datasets of miRNA families. The data mining techniques are used to compare the interactions of miRNA-mRNA that it either belongs to the same gene-family or to different families, and to establish a biological scheme that explains how the biological parameters are involved or less involved in miRNA-mRNA prediction. The factors that were involved in the prediction process includs match, mismatch, bulge, loop, and score to represent the binding characteristics, while the position, 3’UTR length, and chromosomal location and chromosomal categorizations represent the characteristics of the target mRNA. These attributes can provide an empirical guidance for study of specific miRNA family to scan the whole human genome for novel targets. This research provides promising results that can be utilized for current and future research in this field.

  11. A strip thickness prediction method of hot rolling based on D_S information reconstruction

    Institute of Scientific and Technical Information of China (English)

    孙丽杰; 邵诚; 张利

    2015-01-01

    To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method (DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, ibaAnalyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment (BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

  12. Position Prediction Based Frequency Control of Beacons in Vehicular Ad Hoc Networks

    OpenAIRE

    Jizhao Liu; Quan Wang

    2015-01-01

    In VANETs, frequent beacon broadcasting can lead to high bandwidth consumption and channel congestion. In this paper, a position prediction based beacon approach is proposed to reduce beacon frequency and decrease bandwidth consumption. Vehicles track their neighbors using the predicted position instead of using periodic beacon broadcasting. Only when the prediction error is higher than a predefined tolerance will a beacon broadcasting be triggered. For improving the prediction accuracy, we c...

  13. Haplotype Based Genome-Enabled Prediction of Traits Across Nordic Red Cattle Breeds

    OpenAIRE

    Castro Dias Cuyabano, Beatriz; Lund, Mogens Sandø; Rosa, G. J. M.; Gianola, Daniel; Su, Guosheng

    2014-01-01

    SNP markers have been widely explored in genome based prediction. This study explored the use of haplotype blocks (haploblocks) to predict five milk production traits (fertility, mastitis, protein, fat and milk yield), using a mix of Nordic Red cattle as reference population for training. Predictions were performed under a Bayesian approach comparing a GBLUP and a mixture model. In general, predictions were more reliable when using haploblocks instead of individual SNPs as predictors. The Dan...

  14. AIS reception from a CubeSat in LEO

    DEFF Research Database (Denmark)

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter; Nielsen, Jens Frederik Dalsgaard

    2013-01-01

    into a low-Earth orbit with a semi-major axis of 7156 km, i.e. 800 km altitude, near circular, dusk-dawn Sun-synchronous orbit. From this orbit the AIS antenna system, which consists of a dipole antenna, has a foot print diameter of approximately 6000 km. During the first pass over the primary ground station...... downlinked to the AAU ground station for further processing. In this paper we will explain how the two different AIS receivers are working, provide an analysis of the capabilities of the receivers in orbit, and will present some of the preliminary performance metrics which have been found for the two...

  15. 山区航道 AIS 信号场强分布特性%Distribution characteristic of AIS signal field intensity along mountainous waterway

    Institute of Scientific and Technical Information of China (English)

    初秀民; 刘潼; 马枫; 刘兴龙; 钟鸣

    2014-01-01

    Due to the shadowing effect of AIS mountains signals,there were many blind areas along mountainous waterways limiting the application of AIS.Okumura-Hata model was used to study the reliability of AIS communication system in those areas.29 test points,which were primarily served by three base stations at Bahekou,Shipai,and Xiba located along the Three Gorges Dam segment,were set.Among the 29 test points,13 test points were in mountainous areas and 16 test points were in open areas.The actual field intensities of the 29 test points were measured and compared with theoretical field intensities.A linear regression model was used to optimize the corrected parameter of Okumura-Hata model.The correcting field intensities at the 13 test points in mountainous areas and at 9 out of 16 test points in open areas,having a distance greater than 2 .9 km from the base stations,were calculated.In order to verify the correctness of modified model,verification test was carried out for 6 test points along Chongqing—Yongchuansegment.Analysis result indicates that a distance of 3 km is a critical threshold for AIS signaltransmission.When the propagation distance is less than 3 km,the AIS signal is good and theAIS field intensity curve is smooth.However,when the propagation distance is more than 3 km,the AIS signal quality reduces sharply and the curve is steep.The distribution trend of theoreticalfield intensity calculated by Okumura-Hata model is consistent with that of actual field intensity,but there are still gaps between the theoretical values and the actual values.In verification test,the average values of actual field intensity, theoretical field intensity, and correcting fieldintensity at 6 test points are -1 06.6 3 6 ,-1 00.9 82 ,-1 07.7 1 0 dBm,respectively.The averageerror and precision rate of calculated result of Okumura-Hata model are 5.6 54 dBm and 94.6 1 5 %respectively,and the values of correcting model are 1.07 1 dBm and 9 8.3 2 9 % respectively.4 tabs,1 4 figs,20

  16. Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)

    International Nuclear Information System (INIS)

    To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h−1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles. (paper)

  17. Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)

    Science.gov (United States)

    Zhang, Enlai; Hou, Liang; Shen, Chao; Shi, Yingliang; Zhang, Yaxiang

    2016-01-01

    To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h-1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles.

  18. Structure-based prediction of protein-folding transition paths

    CERN Document Server

    Jacobs, William M

    2016-01-01

    We propose a general theory to describe the distribution of protein-folding transition paths. We show that transition paths follow a predictable sequence of high-free-energy transient states that are separated by free-energy barriers. Each transient state corresponds to the assembly of one or more discrete, cooperative units, which are determined directly from the native structure. We show that the transition state on a folding pathway is reached when a small number of critical contacts are formed between a specific set of substructures, after which folding proceeds downhill in free energy. This approach suggests a natural resolution for distinguishing parallel folding pathways and provides a simple means to predict the rate-limiting step in a folding reaction. Our theory identifies a common folding mechanism for proteins with diverse native structures and establishes general principles for the self-assembly of polymers with specific interactions.

  19. Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks.

    OpenAIRE

    Li, Xu; Mitton, Nathalie; Simplot-Ryl, David

    2011-01-01

    International audience Hello protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/ aliveness by periodic 'hello' messages. Central to a hello protocol is the determination of 'hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction bas...

  20. Online Forums Hotspot Prediction Based on Sentiment Analysis

    OpenAIRE

    V. Murali Bhaskarn; K. Nirmala Devi

    2012-01-01

    Problem statement: Online forums hotspot prediction is one of the significant research areas in web mining, which can help people make proper decision in daily life. Online forums, news reports and blogs, are containing large volume of public opinion information. Rapid growth of network arouses much attention on public opinion, it is important to analyse the public opinion in time and understands the trends of their opinion correctly. Approach: The sentiment analysis and text mining are impor...

  1. Lasso based feature selection for malaria risk exposure prediction

    OpenAIRE

    Kouwayè, Bienvenue; Fonton, Noël; Rossi, Fabrice

    2015-01-01

    In life sciences, the experts generally use empirical knowledge to recode variables, choose interactions and perform selection by classical approach. The aim of this work is to perform automatic learning algorithm for variables selection which can lead to know if experts can be help in they decision or simply replaced by the machine and improve they knowledge and results. The Lasso method can detect the optimal subset of variables for estimation and prediction under some conditions. In this p...

  2. Production, Purification, and Characterization of Thermostable α-Amylase Produced by Bacillus licheniformis Isolate AI20

    Directory of Open Access Journals (Sweden)

    Yasser R. Abdel-Fattah

    2013-01-01

    Full Text Available An optimization strategy, based on statistical experimental design, is employed to enhance the production of thermostable α-amylase by a thermotolerant B. licheniformis AI20 isolate. Using one variant at time (OVAT method, starch, yeast extract, and CaCl2 were observed to influence the enzyme production significantly. Thereafter, the response surface methodology (RSM was adopted to acquire the best process conditions among the selected variables, where a three-level Box-Behnken design was employed to create a polynomial quadratic model correlating the relationship between the three variables and α-amylase activity. The optimal combination of the major constituents of media for α-amylase production was 1.0% starch, 0.75% yeast extract, and 0.02% CaCl2. The predicted optimum α-amylase activity was 384 U/mL/min, which is two folds more than the basal medium conditions. The produced α-amylase was purified through various chromatographic techniques. The estimated enzyme molecular mass was 55 kDa and the α-amylase had an optimal temperature and pH of 60–80°C and 6–7.5, respectively. Values of Vmax and Km for the purified enzyme were 454 mU/mg and 0.709 mg/mL. The α-amylase enzyme showed great stability against different solvents. Additionally, the enzyme activity was slightly inhibited by detergents, sodium dodecyl sulphate (SDS, or chelating agents such as EDTA and EGTA. On the other hand, great enzyme stability against different divalent metal ions was observed at 0.1 mM concentration, but 10 mM of Cu2+ or Zn2+ reduced the enzyme activity by 25 and 55%, respectively.

  3. Tensor-Based Link Prediction in Intermittently Connected Wireless Networks

    CERN Document Server

    Zayani, Mohamed-Haykel; Slama, Ines; Zeghlache, Djamal

    2011-01-01

    Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, we propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. Our prediction method keeps track of the spatio-temporal aspects of nodes behaviors organized as a third-order tensor that aims to records the evolution of the network topology. After collapsing the tensor information, we compute the degree of similarity for each pair of nodes using the Katz measure. This metric gives us an indication on the link occurrence between two nodes relying on their closene...

  4. Time Series Outlier Detection Based on Sliding Window Prediction

    Directory of Open Access Journals (Sweden)

    Yufeng Yu

    2014-01-01

    Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

  5. AIS TLS-ESPRIT feature selection for prostate tissue characterization

    Science.gov (United States)

    Mohamed, S. S.; Youssef, A. M.; El-Saadany, E. F.; Salama, M. M. A.

    2006-03-01

    The work in this paper aims for analyzing spectral features of the prostate using Trans-Rectal Ultra-Sound images (TRUS) for tissue classification. This research is expected to augment beginner radiologists' decision with the experience of more experienced radiologists. Moreover, Since, in some situations the biopsy results in false negatives due to inaccurate biopsy locations, therefore this research also aims to assist in determining the biopsy locations to decrease the false negative results. In this paper, a new technique for prostate tissue characterization is developed. The proposed system is composed of four stages. The first stage is automatically identifying Regions Of Interest (ROIs). This is achieved using the Gabor multiresolution analysis method, where preliminary regions are identified using the frequency response of the pixels, pixels that have the same response to the same filter are assigned to the same cluster. Next, the radiologist knowledge is integrated to the system to select the most suspicious ROIs among the prelimianry identified regions. The second stage is constructing the spectral features from the identified ROIs. The proposed technique is based on a novel spectral feature set for the TRUS images using the Total Least Square Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT). Classifier based feature selection is then performed to select the most salient features using the recently proposed Artificial Immune System (AIS) optimization technique. Finally, Support Vector Machine (SVM) classifier is used as an accuracy measure, our proposed system obtains a classification accuracy of 94.4%, with 100% sensitivity and 83.3% sensetivity.

  6. A CBR-based and MAHP-based customer value prediction model for new product development.

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. PMID:25162050

  7. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

    Directory of Open Access Journals (Sweden)

    Yu-Jie Zhao

    2014-01-01

    Full Text Available In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM. CBR (case based reasoning can reduce experts’ workload and evaluation time, while MAHP (multiplicative analytic hierarchy process can use actual but average influencing factor’s effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers’ transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM’s three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment.

  8. Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform

    Data.gov (United States)

    National Aeronautics and Space Administration — Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of...

  9. Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

    Science.gov (United States)

    Lee, Young-Jin

    2015-01-01

    This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

  10. Predicting magnetostructural trends in FeRh-based ternary systems

    Science.gov (United States)

    Barua, Radhika; Jiménez-Villacorta, Félix; Lewis, L. H.

    2013-09-01

    Correlations between magnetic transition temperatures and the average weighted valence band electron concentration ((s + d) electrons/atom) have led to the development of a phenomenological model that predicts the influence of elemental substitution on the magnetostructural response of bulk B2-ordered Fe(Rh1-xMx) or (Fe1-xMx)Rh alloys (M = transition elements; x FeRh with Cu and Au additions. The data and associated trends indicate that the lattice and electronic free energies are both equally important in driving the magnetostructural transition in the bulk FeRh system.

  11. Design of Over Center Valves Based on Predictable Performance

    DEFF Research Database (Denmark)

    Hansen, M.R.; Andersen, T.O.; Pedersen, P.;

    2004-01-01

    A typical oil hydraulic over center valve system and a time domain simulation model is introduced together with a hypothesis that flow force compensation should reduce the inherent oscillatory behavior of such hydraulic systems. A few results are shown from a parameter study that confirms...... this assumption and an approach to design over center valve geometries that have negative flow forces is presented with emphasis on predictability. In conclusion it is made clear that negative flow forces in the over center valve cannot solve the instability problem in general, however, it might very well...

  12. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten

    2010-01-01

    preprocessing [Dau et al., 1997. J. Acoust. Soc. Am. 102, 2892-2905] with a simple central stage that describes the similarity of the test signal with the corresponding reference signal at a level of the internal representation of the signals. The model was compared with previous approaches, whereby a speech...... in noise experiment was used for training and an ideal binary mask experiment was used for evaluation. All three models were able to capture the trends in the speech in noise training data well, but the proposed model provides a better prediction of the binary mask test data, particularly when the binary...

  13. Predicting tissue-specific expressions based on sequence characteristics

    KAUST Repository

    Paik, Hyojung

    2011-04-30

    In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.

  14. Short-Term Wind Power Prediction and Comprehensive Evaluation based on Multiple Methods

    Directory of Open Access Journals (Sweden)

    Zhaowei Wang

    2013-12-01

    Full Text Available Firstly, this study used prediction methods, including Kalman filter method, the GARCH (Generalized Autoregressive Conditional Heteroskedasticity model and the BP neural network model based on time sequence, to predict real-timely the wind power. And then, we construct indexes such as mean absolute error, root-mean-square error, accuracy rate and percent of pass to have error analysis on the predictive effect and get the best results of prediction effect that based on time sequence of the BP neural network model. Finally, we concluded the universal rule between the relative prediction error of single typhoon electric unit power of and the prediction relative error of total machine power by the analysis into lateral error indicators. And we analyze the influence on the error of the prediction result that resulting from the converge of wind generator power.

  15. Predicting Siltation in Entrance Channel Based on Wind Conditions

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The siltation induced by wind waves in an entrance channel is one of the prime factors influencing the operation efficiency of a port. It is necessary to predict the siltation accurately for dredging and ship operation passing through the entrance of the port. However, it is difficult to apply the traditional method to predicting entrance siltation because of its complex computational procedure and lacking the data of ocean dynamic elements in the specified sea area. From the view of energy conservation, a direct relationship between wind conditions and sediment deposition can be founded. On the basis of the above methodology, an empirical formula expressed by wind conditions for forecasting the siltation in the entrance channel is set up. The wind conditions are easily obtained from the local meteorological stations or weather maps, so the formula established in this paper is more convenient and practical than the traditional method. A case study is provided, in which the emopirical formula is calibrated and verified utilizing the measured wind and siltation conditions in the entrance channel of the port. Comparisons between computed values and measured data show satisfactory agreement.

  16. Online Forums Hotspot Prediction Based on Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    V. Murali Bhaskarn

    2012-01-01

    Full Text Available Problem statement: Online forums hotspot prediction is one of the significant research areas in web mining, which can help people make proper decision in daily life. Online forums, news reports and blogs, are containing large volume of public opinion information. Rapid growth of network arouses much attention on public opinion, it is important to analyse the public opinion in time and understands the trends of their opinion correctly. Approach: The sentiment analysis and text mining are important key elements for forecasting the hotspots in online forums. Most of the traditional text mining work on static data sets, while the online hotspot forecasts works on the web information dynamically and timely. The earlier work on text information processing focuses in the factual domain rather than opinion domain. Due to the semi structured or unstructured characteristics of online public opinion, we introduce traditional Vector Space Model (VSM to express them and then use K-means to perform hotspot detection, then we use J48 classifier to perform hotspot forecast. Results: The experimentation is conducted by Rapid Miner tool and performance of proposed method J48 is compared with other method, such as Naive Bayes. The consistency between K-means and J48 is validated using three metrics. They are accuracy, sensitivity and specificity. Conclusion: The experiment helps to identify that K-means and J48 together to predict forums hotspot. The results that have been obtained using J48 present a noticeable consistency with the results achieved by K-means clustering.

  17. Structure-Based Predictive model for Coal Char Combustion.

    Energy Technology Data Exchange (ETDEWEB)

    Hurt, R.; Colo, J [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

    1997-09-24

    During the third quarter of this project, progress was made on both major technical tasks. Progress was made in the chemistry department at OSU on the calculation of thermodynamic properties for a number of model organic compounds. Modelling work was carried out at Brown to adapt a thermodynamic model of carbonaceous mesophase formation, originally applied to pitch carbonization, to the prediction of coke texture in coal combustion. This latter work makes use of the FG-DVC model of coal pyrolysis developed by Advanced Fuel Research to specify the pool of aromatic clusters that participate in the order/disorder transition. This modelling approach shows promise for the mechanistic prediction of the rank dependence of char structure and will therefore be pursued further. Crystalline ordering phenomena were also observed in a model char prepared from phenol-formaldehyde carbonized at 900{degrees}C and 1300{degrees}C using high-resolution TEM fringe imaging. Dramatic changes occur in the structure between 900 and 1300{degrees}C, making this char a suitable candidate for upcoming in situ work on the hot stage TEM. Work also proceeded on molecular dynamics simulations at Boston University and on equipment modification and testing for the combustion experiments with widely varying flame types at Ohio State.

  18. Block-Based Parallel Intra Prediction Scheme for HEVC

    Directory of Open Access Journals (Sweden)

    Jie Jiang

    2012-08-01

    Full Text Available Advanced video coding standards have become widely deployed in numerous products, such as multimedia service, broadcasting, mobile television, video conferences, surveillance systems and so on. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every ten years. However, dramatically increased computational complexity is one of the many problems brought by the trend. With recent advancement of VLSI (the Very Large Scale Integration semiconductor technology contributing to the emerging digital multimedia word, this paper intends to investigate efficient parallel architecture for the emerging high efficiency video coding (HEVC standard to speed up the intra coding process, without any prediction modes ignored. Parallelism is achieved by limiting the reference pixels of the 4 × 4 subblocks, allowing the subblocks to use different direction modes to predict the residuals. Experimental implementations of the proposed algorithm are demonstrated by using a set of video test sequences that are widely used and freely available. The results show that the proposed algorithm can achieve a satisfying intra parallelism without any significant performance lose.

  19. Artificial Intelligence: Is the Future Now for A.I.?

    Science.gov (United States)

    Ramaswami, Rama

    2009-01-01

    In education, artificial intelligence (AI) has not made much headway. In the one area where it would seem poised to lend the most benefit--assessment--the reliance on standardized tests, intensified by the demands of the No Child Left Behind Act of 2001, which holds schools accountable for whether students pass statewide exams, precludes its use.…

  20. The 2010 Mario AI Championship: Level Generation Track

    DEFF Research Database (Denmark)

    Shaker, Noor; Togelius, Julian; Yannakakis, Georgios N.;

    2011-01-01

    The Level Generation Competition, part of the IEEE CIS-sponsored 2010 Mario AI Championship, was to our knowledge the world’s first procedural content generation competition. Competitors participated by submitting level generators — software that generates new levels for a version of Super Mario...

  1. A Systems Development Life Cycle Project for the AIS Class

    Science.gov (United States)

    Wang, Ting J.; Saemann, Georgia; Du, Hui

    2007-01-01

    The Systems Development Life Cycle (SDLC) project was designed for use by an accounting information systems (AIS) class. Along the tasks in the SDLC, this project integrates students' knowledge of transaction and business processes, systems documentation techniques, relational database concepts, and hands-on skills in relational database use.…

  2. The hidden dangers of experimenting in distributed AI

    NARCIS (Netherlands)

    Meyer, A.P.; Smit, A.; Kempen, M.; Wijngaards, N.

    2006-01-01

    Research on multi-agent systems often involves experiments, also in situations where humans interact with agents. Consequently, the field of experimental (human) sciences becomes more and more relevant. This paper clarifies how things can and often do go wrong in distributed AI experiments. We show

  3. AI/Simulation Fusion Project at Lawrence Livermore National Laboratory

    International Nuclear Information System (INIS)

    This presentation first discusses the motivation for the AI Simulation Fusion project. After discussing very briefly what expert systems are in general, what object oriented languages are in general, and some observed features of typical combat simulations, it discusses why putting together artificial intelligence and combat simulation makes sense. We then talk about the first demonstration goal for this fusion project

  4. Analysis of raw AIS spectrum recordings from a LEO satellite

    DEFF Research Database (Denmark)

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter

    2014-01-01

    The AAUSAT3 satellite is a 1U cubesat, which has been developed by students at Aalborg University, Denmark in collaboration with the Danish Maritime Authority. The satellite was launched in February 2013 on a mission to monitor ships from space using their AIS broadcast signals as an indication...

  5. Link prediction based on temporal similarity metrics using continuous action set learning automata

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2016-10-01

    Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for each non-connected link and outputs the links with higher similarity scores as the prediction result. In this paper, we propose a new link prediction method based on temporal similarity metrics and Continuous Action set Learning Automata (CALA). The proposed method takes advantage of using different similarity metrics as well as different time periods. In the proposed algorithm, we try to model the link prediction problem as a noisy optimization problem and use a team of CALAs to solve the noisy optimization problem. CALA is a reinforcement based optimization tool which tries to learn the optimal behavior from the environment feedbacks. To determine the importance of different periods and similarity metrics on the prediction result, we define a coefficient for each of different periods and similarity metrics and use a CALA for each coefficient. Each CALA tries to learn the true value of the corresponding coefficient. Final link prediction is obtained from a combination of different similarity metrics in different times based on the obtained coefficients. The link prediction results reported here show satisfactory of the proposed method for some social network data sets.

  6. Evidence-based gene predictions in plant genomes

    Science.gov (United States)

    Automated evidence-based gene building is a rapid and cost-effective way to provide reliable gene annotations on newly sequenced genomes. One of the limitations of evidence-based gene builders, however, is their requirement for gene expression evidence—known proteins, full-length cDNAs, or expressed...

  7. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole;

    1999-01-01

    of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  8. An optimized item-based collaborative filtering recommendation algorithm based on item genre prediction

    Science.gov (United States)

    Zhang, De-Jia

    2009-07-01

    With the fast development of Internet, many systems have emerged in e-commerce applications to support the product recommendation. Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, with the adding of user and item scales, user-item ratings are becoming extremely sparsity and recommender systems utilizing traditional collaborative filtering are facing serious challenges. To address the issue, this paper presents an approach to compute item genre similarity, through mapping each item with a corresponding descriptive genre, and computing similarity between genres as similarity, then make basic predictions according to those similarities to lower sparsity of the user-item ratings. After that, item-based collaborative filtering steps are taken to generate predictions. Compared with previous methods, the presented collaborative filtering employs the item genre similarity can alleviate the sparsity issue in the recommender systems, and can improve accuracy of recommendation.

  9. Nonlinear model predictive control based on collective neurodynamic optimization.

    Science.gov (United States)

    Yan, Zheng; Wang, Jun

    2015-04-01

    In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach. PMID:25608315

  10. Business Bankruptcy Prediction Based on Survival Analysis Approach

    Directory of Open Access Journals (Sweden)

    Ming - Chang Lee

    2014-04-01

    Full Text Available This study sampled companies listed on Taiwan Stock Exchange that examined financial distress between 200 3 and 20 09 . It uses the survival analysis to find the main indicators which can explain the business bankruptcy in Taiwan. This paper use s the Cox P roportional H azard M odel to assess the usefulness of traditional financial ratios and market variables as predictors of the probability of business failure to a given time. This paper presents empirical results of a study regarding 12 financial ratios as predictors of business failure in Taiwan. It showed that it does not need many ratios to be able to anticipate potential business bankruptcy. T he financial distress probability model is constructed using Profitability, Leverage, Efficiency and Valuation ratio variables. In the proposed steps of business failure prediction model , it used detail SAS procedure. The study proves that t he accuracies of classification of the mode in o verall accuracy of classification are 87.93%

  11. Prediction of liquefaction potential based on CPT up-sampling

    Science.gov (United States)

    Sadoghi Yazdi, Javad; Kalantary, Farzin; Sadoghi Yazdi, Hadi

    2012-07-01

    Cone penetration test data has been widely used for determination of the threshold of seismically induced soil liquefaction. However, possible inaccuracies in the collected data from case histories as well as natural variability of parameters and other uncertainties associated with natural phenomenon have yet prohibited a conclusive definition for this threshold. Various classification techniques have been used to define the most reliable correlations. However, available liquefied to non-liquefied data imbalance has caused learning bias to the majority class in the learning model of the pattern recognition systems. This has adversely affected the outcome of such approaches and in order to overcome this problem Support Vector Data Description (SVDD) strategy is employed to "up sample" the minority data. In other words SVDD, which is robust against noisy samples, is used to generate virtual data points for the minority class, bearing the same characteristics as the non-virtual samples. In order to specify the most appropriate data range a sphere boundary around the main body of the data are sought through an optimization process. The data inside the obtained boundary are the target data and the ones outside it are the outliers or so-called "noise", to be neglected. This procedure reduces the issue of class intermixture in the fringe zone and produces relatively well defined class that then is fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier for determination of liquefaction potential. The predictions are then examined to evaluate the reliability and validation of the overall technique and compared with other prediction methods using confusion matrix. It is shown that the overall accuracy of the proposed technique is higher than all previously proposed methods and only equal to the Support Vector Machine (SVM) technique. Furthermore an improvement in the F-score of the non-liquefied data recognition has been achieved in relation to all previously

  12. A New SVM-Based Modeling Method of Cabin Path Loss Prediction

    OpenAIRE

    Xiaonan Zhao; Chunping Hou; Qing Wang

    2013-01-01

    A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers perf...

  13. Research of prediction for mine earthquake basing on underground rock's movement and deformation mechanism

    Institute of Scientific and Technical Information of China (English)

    LI Yong-jing

    2008-01-01

    Movement and deformation of underground rock include vertical dislocation and horizontal deformation, and the energy released by mine earthquake can be calculated basing on deformation energy. So put forwards the prediction for degree and spread of mine earthquake according to the underground rock's movement and deformation. The actual number of times and spread of mine earthquake on site were greatly identical to the prediction. The practice proves the possibility of prediction for mine earthquake basing on the analysis of underground rock's movement and deformation, and sets up new approach of mine earthquake prediction.

  14. Prediction of adsorption of small molecules in porous materials based on ab initio force field method

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Computational prediction of adsorption of small molecules in porous materials has great impact on the basic and applied research in chemical engineering and material sciences. In this work,we report an approach based on grand canonical ensemble Monte Carlo(GCMC) simulations and ab initio force fields. We calculated the adsorption curves of ammonia in ZSM-5 zeolite and hydrogen in MOF-5(a metal-organic-framework material). The predictions agree well with experimental data. Because the predictions are based on the first principle force fields,this approach can be used for the adsorption prediction of new molecules or materials without experimental data as guidance.

  15. Network Traffic Prediction based on Particle Swarm BP Neural Network

    OpenAIRE

    Yan Zhu; Guanghua Zhang; Jing Qiu

    2013-01-01

    The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and part...

  16. GIS Based Landslide Hazard Mapping Prediction in Ulu Klang, Malaysia

    OpenAIRE

    Mukhlisin Muhammad; Ilyias Idris; Al Sharif Salazar; Khairul Nizam; Mohd Raihan Taha

    2010-01-01

    Since 1993, a number of landslides have been reported in Ulu Klang, Malaysia. These landslides caused fatalities and economic losses. Most of these landslides occurred in man-made slopes. Geographical Information System (GIS) is proposed to be used as the based machine for the production of landslide hazard map. This study highlights the area based landslide hazard assessment at Ulu Klang area using GIS application in order to help the engineer or the town planner to identify the most suitabl...

  17. Route Prediction Based Vehicular Mobility Management Scheme for VANET

    OpenAIRE

    DaeWon Lee; Yoon-Ho Kim; HwaMin Lee

    2014-01-01

    Since improvement of wireless communication, IP based mobility management protocols have been studied to provide seamless communication and mobility management. The vehicular ad hoc network (VANET) is one of mobility management protocols, especially providing seamless connection with inter/intra/inner vehicle communication. However, each vehicle moves fast that causes short-lived connections with Access Router (AR). Based on vehicles’ characteristic, it is hard to provide the availability of ...

  18. Applications of AI, machine vision and robotics

    CERN Document Server

    Boyer, Kim; Bunke, H

    1995-01-01

    This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book pr

  19. Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM

    Directory of Open Access Journals (Sweden)

    Xunyuan Yin

    2013-01-01

    Full Text Available The strapdown inertial navigation systems (SINS have been widely used for many vehicles, such as commercial airplanes, Unmanned Aerial Vehicles (UAVs, and other types of aircrafts. In order to evaluate the navigation errors precisely and efficiently, a prediction method based on support vector machine (SVM is proposed for positioning error assessment. Firstly, SINS error models that are used for error calculation are established considering several error resources with respect to inertial units. Secondly, flight paths for simulation are designed. Thirdly, the -SVR based prediction method is proposed to predict the positioning errors of navigation systems, and particle swarm optimization (PSO is used for the SVM parameters optimization. Finally, 600 sets of error parameters of SINS are utilized to train the SVM model, which is used for the performance prediction of new navigation systems. By comparing the predicting results with the real errors, the latitudinal predicting accuracy is 92.73%, while the longitudinal predicting accuracy is 91.64%, and PSO is effective to increase the prediction accuracy compared with traditional SVM with fixed parameters. This method is also demonstrated to be effective for error prediction for an entire flight process. Moreover, the prediction method can save 75% of calculation time compared with analyses based on error models.

  20. AIS - Milestone of Marine Communication and Navigation%AIS-海事通信和导航的里程碑

    Institute of Scientific and Technical Information of China (English)

    陆林生

    2003-01-01

    主要介绍海事通信中自动识别系统(AIS)的概念、意义,以及AIS的发展、组成和技术特点.并简要介绍了AIS的工作过程及IMO等国际组织对该设备装船的相关要求.

  1. Anti-inflammatory activity of polyphenolics from açai (Euterpe oleracea Martius) in intestinal myofibroblasts CCD-18Co cells.

    Science.gov (United States)

    Dias, Manoela Maciel dos Santos; Martino, Hércia Stampini Duarte; Noratto, Giuliana; Roque-Andrade, Andrea; Stringheta, Paulo César; Talcott, Stephen; Ramos, Afonso Mota; Mertens-Talcott, Susanne U

    2015-10-01

    The demand for tropical fruits high in polyphenolics including açai (Euterpe oleracea Mart.) has been increasing based on ascribed health benefits and antioxidant properties. This study evaluated the anti-inflammatory activities of açai polyphenolics in human colon myofibroblastic CCD-18Co cells to investigate the suppression of reactive oxygen species (ROS), and mRNA and protein expression of inflammatory proteins. Non-cytotoxic concentrations of açai extract, 1-5 mg gallic acid equivalent L(-1), were selected. The generation of ROS was induced by lipopolysaccharide (LPS) and açai extract partially reversed this effect to 0.53-fold of the LPS-control. Açai extract (5 mg GAE L(-1)) down-regulated LPS-induced mRNA-expression of tumor necrosis factor alpha, TNF-α (to 0.42-fold), cyclooxygenase 2, COX-2 (to 0.61-fold), toll-like receptor-4, TLR-4 (to 0.52-fold), TNF receptor-associated factor 6, TRAF-6 (to 0.64-fold), nuclear factor kappa-B, NF-κB (to 0.76-fold), vascular cell adhesion molecule 1, VCAM-1 (to 0.71-fold) and intercellular adhesion molecule 1, ICAM-1 (to 0.68-fold). The protein levels of COX-2, TLR-4, p-NF-κB and ICAM-1 were induced by LPS and the açai extract partially reversed this effect in a dose-dependent manner. These results suggest the anti-inflammatory effect of açai polyphenolic extract in intestinal cells are at least in part mediated through the inhibition of ROS and the expression of TLR-4 and NF-κB. Results indicate the potential for açai polyphenolics in the prevention of intestinal inflammation. PMID:26243669

  2. A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures.

    Science.gov (United States)

    Tsang, Herbert H; Wiese, Kay C

    2015-01-01

    Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures. PMID:26558299

  3. Predictive Software Measures based on Z Specifications - A Case Study

    Directory of Open Access Journals (Sweden)

    Andreas Bollin

    2012-07-01

    Full Text Available Estimating the effort and quality of a system is a critical step at the beginning of every software project. It is necessary to have reliable ways of calculating these measures, and, it is even better when the calculation can be done as early as possible in the development life-cycle. Having this in mind, metrics for formal specifications are examined with a view to correlations to complexity and quality-based code measures. A case study, based on a Z specification and its implementation in ADA, analyzes the practicability of these metrics as predictors.

  4. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    sparsity, making it difficult for gaze based n-gram models to handle real test scenarios. It has already been seen that some important eye-movement phenomena are captured better by scanpaths than considering individual fixations, saccades and pauses. In this talk, we propose and validate an n-gram based...... gaze model for reading. The units contributing to each n-gram will be scanpaths (in a temporal order). We describe different scanpath extraction techniques and chose the one which minimizes the entropy/perplexity of the system. To handle data sparsity, we cluster the scanpaths into several groups...

  5. Construct Method of Predicting Satisfaction Model Based on Technical Characteristics

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-an; DENG Qian; SUN Guan-long; ZHANG Wei-she

    2011-01-01

    In order to construct objective relatively mapping relationship model between customer requirements and product technical characteristics, a novel approach based on customer satisfactions information digging from case products and satisfaction information of expert technical characteristics was put forward in this paper. Technical characteristics evaluation values were expressed by rough number, and technical characteristics target sequence was determined on the basis of efficiency, cost type and middle type in this method. Use each calculated satisfactions of customers and technical characteristics as input and output elements to construct BP network model. And we use MATLAB software to simulate this BP network model based on the case of electric bicycles.

  6. A CHAID Based Performance Prediction Model in Educational Data Mining

    CERN Document Server

    Ramaswami, M

    2010-01-01

    The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO). A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student r...

  7. Predicting dispersal distance in mammals: a trait-based approach.

    Science.gov (United States)

    Whitmee, Sarah; Orme, C David L

    2013-01-01

    Dispersal is one of the principal mechanisms influencing ecological and evolutionary processes but quantitative empirical data are unfortunately scarce. As dispersal is likely to influence population responses to climate change, whether by adaptation or by migration, there is an urgent need to obtain estimates of dispersal distance. Cross-species correlative approaches identifying predictors of dispersal distance can provide much-needed insights into this data-scarce area. Here, we describe the compilation of a new data set of natal dispersal distances and use it to test life-history predictors of dispersal distance in mammals and examine the strength of the phylogenetic signal in dispersal distance. We find that both maximum and median dispersal distances have strong phylogenetic signals. No single model performs best in describing either maximum or median dispersal distances when phylogeny is taken into account but many models show high explanatory power, suggesting that dispersal distance per generation can be estimated for mammals with comparatively little data availability. Home range area, geographic range size and body mass are identified as the most important terms across models. Cross-validation of models supports the ability of these variables to predict dispersal distances, suggesting that models may be extended to species where dispersal distance is unknown.

  8. Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results

    Directory of Open Access Journals (Sweden)

    Corneliu Lazăr

    2016-09-01

    Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.

  9. A Romanian Prosody Prediction Module Based on a Functional Intonational Model

    Directory of Open Access Journals (Sweden)

    Doina Jitca

    2012-10-01

    Full Text Available This paper presents a prosodic prediction module used by the Romanian Text-to-Speech (TtS system in intonation synthesis. The prosody prediction refers to the surface generation of the F0 contour, based on the F0 patterns assigned to the functional categories of the prosodic units. Prior to the prediction module presentation, the paper includes a summary of these functional categories and the partial melodic contour descriptions based on functional labels. The block diagram of the prediction module outlines two main processing steps: the phrasing prediction for building the utterance tree and the selection of the melodic contours of its groups. Both processing steps are exemplified within a case study of Romanian text speech synthesis. The prosody prediction results are discussed and compared with natural F0 contours of different speakers.

  10. Predicting soil water repellency by hydrophobic organic compounds and their vegetation origin

    Science.gov (United States)

    Mao, J.; Nierop, K. G. J.; Rietkerk, M.; Dekker, S. C.

    2015-02-01

    It is widely accepted that soil water repellency (SWR) is mainly caused by plant-derived hydrophobic organic compounds in soils; such hydrophobic compounds are defined as SWR-markers. However, the detailed influence of SWR-markers on SWR is yet unclear and the knowledge of their original sources is still limited. The aims of this study are to select important SWR-markers to predict SWR based on their correlation with SWR and to determine their origin. In our study, sandy soils with different SWR were collected, along with their covering vegetation, i.e. plant leaves/needles and roots. A sequential extraction procedure was applied to the soils to obtain three organic fractions: DCM / MeOH soluble fraction (D), DCM / MeOH insoluble fraction of IPA / NH3 extract (AI) and DCM / MeOH soluble fraction of IPA / NH3 extract (AS), which were subdivided into ten dominant SWR-marker groups: (D) fatty acid, (D) alcohol, (D) alkane, (AI) fatty acid, (AI) alcohol, (AI) ω-hydroxy fatty acid, (AI) α, ω-dicarboxylic acid, (AS) fatty acid, (AS) alcohol and (AS) ω-hydroxy fatty acid. Waxes and biopolyesters of the vegetation were also sequentially extracted from plants. In short, the soils with higher SWR have significantly higher relative concentrations of (AS) alcohols. A number of indications suggest that (AS) alcohols are mainly derived from roots and most likely produced by microbial hydrolysis of biopolyesters/suberins. In addition, the strong correlation between the biomarkers of plant tissues and SWR-markers in soils suggests that it is more accurate to predict SWR of topsoils using ester-bound alcohols from roots, and to predict SWR of subsoils using root-derived ω-hydroxy fatty acids and α, ω-dicarboxylic acids. Our analysis indicates that plant roots have a primary role influencing SWR relative to plant leaves.

  11. Haplotype Based Genome-Enabled Prediction of Traits Across Nordic Red Cattle Breeds

    DEFF Research Database (Denmark)

    Castro Dias Cuyabano, Beatriz; Lund, Mogens Sandø; Rosa, G J M;

    SNP markers have been widely explored in genome based prediction. This study explored the use of haplotype blocks (haploblocks) to predict five milk production traits (fertility, mastitis, protein, fat and milk yield), using a mix of Nordic Red cattle as reference population for training.......1% higher reliability than with the individual SNP approach in mastitis. This work gives evidence that predictions using haploblocks along with a combined training population of dairy cattle, may improve prediction accuracy of important traits in the individual populations........ Predictions were performed under a Bayesian approach comparing a GBLUP and a mixture model. In general, predictions were more reliable when using haploblocks instead of individual SNPs as predictors. The Danish Red cattle presented the largest benefit in predictive ability from haploblocks, achieving 5...

  12. Predicting Garden Path Sentences Based on Natural Language Understanding System

    Directory of Open Access Journals (Sweden)

    DU Jia-li

    2012-12-01

    Full Text Available Natural language understanding (NLU focusing onmachine reading comprehension is a branch of natural language processing (NLP. The domain of the developing NLU system covers from sentence decoding to text understanding and the automatic decoding of GP sentence belongs to the domain of NLU system. GP sentence is a special linguistic phenomenon in which processing breakdown and backtracking are two key features. Ifthe syntax-based system can present the special features of GP sentence and decode GP sentence completely and perfectly, NLU system can improve the effectiveness and develop the understanding skill greatly. On the one hand, by means of showing Octav Popescu’s model of NLU system, we argue that the emphasis on the integration of syntactic, semantic and cognitive backgrounds in system is necessary. On the other hand, we focus on the programming skill of IF-THEN-ELSE statement used in N-S flowchart and highlight the function of context free grammar (CFG created to decode GP sentence. On the basis of example-based analysis, we reach the conclusion that syntaxbased machine comprehension is technically feasible and semantically acceptable, and that N-S flowchart and CFG can help NLU system present the decoding procedure of GP sentence successfully. In short, syntax-based NLU system can bring a deeper understanding of GP sentence and thus paves the way for further development of syntax-based natural language processing and artificial intelligence.

  13. Prediction of Seismic Damage-Based Degradation in RC Structures

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Gupta, Vinay K.; Nielsen, Søren R.K.

    useful correlations of strength and stiffness degradation with damage wherein a simple damage parameter based on maximum and yield displacements and ductility supply ratio has been considered. The proposed model has also been used to demonstrate that ignoring aftershocks in case of impulsive ground...

  14. AI in medical education--another grand challenge for medical informatics.

    Science.gov (United States)

    Lillehaug, S I; Lajoie, S P

    1998-03-01

    The potential benefits of artificial intelligence in medicine (AIM) were never realized as anticipated. This paper addresses ways in which such potential can be achieved. Recent discussions of this topic have proposed a stronger integration between AIM applications and health information systems, and emphasize computer guidelines to support the new health care paradigms of evidence-based medicine and cost-effectiveness. These proposals, however, promote the initial definition of AIM applications as being AI systems that can perform or aid in diagnoses. We challenge this traditional philosophy of AIM and propose a new approach aiming at empowering health care workers to become independent self-sufficient problem solvers and decision makers. Our philosophy is based on findings from a review of empirical research that examines the relationship between the health care personnel's level of knowledge and skills, their job satisfaction, and the quality of the health care they provide. This review supports addressing the quality of health care by empowering health care workers to reach their full potential. As an aid in this empowerment process we argue for reviving a long forgotten AIM research area, namely, AI based applications for medical education and training. There is a growing body of research in artificial intelligence in education that demonstrates that the use of artificial intelligence can enhance learning in numerous domains. By examining the strengths of these educational applications and the results from previous AIM research we derive a framework for empowering medical personnel and consequently raising the quality of health care through the use of advanced AI based technology.

  15. Energy-efficiency and future knowledge tradeoff in small cells prediction-based strategies

    OpenAIRE

    De Mari, Matthieu; Calvanese Strinati, Emilio; Debbah, Merouane

    2014-01-01

    International audience —Predictive small cells networks and proactive re-source allocation are considered as one of the key mechanisms for increasing the long-term energy-efficiency of communication networks. Learning techniques exploit repetitive patterns in human behavior to predict some future transmission contexts of the network. In this paper, we target to improve the energy efficiency of delay-tolerant transmissions by enabling flexibility in resource allocation with prediction-based...

  16. Estimation of boundary parameters and prediction of transmission loss based upon ray acoustics

    Institute of Scientific and Technical Information of China (English)

    GUO Yuhong; FAN Minyi; HUI Junying

    2000-01-01

    Estimation of boundary parameters and prediction of transmission loss using a coherent channel model based upon ray acoustics and sound propagation data collected in field experiments are presented. Comparison between the prediction results and the experiment data indicates that the adopted sound propagation model is valuable, both selection and estimation methods on boundary parameters are reasonable, and the prediction performance of transmission loss is favorable.

  17. Based on Multi-Factors Grey Prediction Control for Elevator Velocity Modulation

    OpenAIRE

    Jiming Wang; Jin Ning; Ye Fei

    2012-01-01

    This paper uses the double-factors grey prediction and the fuzzy controller for the elevator car speed control. We introduce double-factors grey control to predict car vibration for elevator speed during the operation. Simulation results show that based on multi-factors gray prediction fuzzy PI control for elevator velocity modulation system closer than simple gray fuzzy PI control elevator speed control system to the actual operation. The control effect of double factors grey fuzzy PI contro...

  18. Parallel kd-Tree Based Approach for Computing the Prediction Horizon Using Wolf’s Method

    OpenAIRE

    Águila, J. J.; Arias, E.; Artigao, M. M.; Miralles, J.J.

    2015-01-01

    In different fields of science and engineering, a model of a given underlying dynamical system can be obtained by means of measurement data records called time series. This model becomes very important to understand the original system behaviour and to predict the future values of that system. From the model, parameters such as the prediction horizon can be computed to obtain the point where the prediction becomes useless. In this work, a new parallel kd-tree based approach for computing the ...

  19. Prediction of nucleosome positioning based on transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    Xianfu Yi

    Full Text Available BACKGROUND: The DNA of all eukaryotic organisms is packaged into nucleosomes, the basic repeating units of chromatin. The nucleosome consists of a histone octamer around which a DNA core is wrapped and the linker histone H1, which is associated with linker DNA. By altering the accessibility of DNA sequences, the nucleosome has profound effects on all DNA-dependent processes. Understanding the factors that influence nucleosome positioning is of great importance for the study of genomic control mechanisms. Transcription factors (TFs have been suggested to play a role in nucleosome positioning in vivo. PRINCIPAL FINDINGS: Here, the minimum redundancy maximum relevance (mRMR feature selection algorithm, the nearest neighbor algorithm (NNA, and the incremental feature selection (IFS method were used to identify the most important TFs that either favor or inhibit nucleosome positioning by analyzing the numbers of transcription factor binding sites (TFBSs in 53,021 nucleosomal DNA sequences and 50,299 linker DNA sequences. A total of nine important families of TFs were extracted from 35 families, and the overall prediction accuracy was 87.4% as evaluated by the jackknife cross-validation test. CONCLUSIONS: Our results are consistent with the notion that TFs are more likely to bind linker DNA sequences than the sequences in the nucleosomes. In addition, our results imply that there may be some TFs that are important for nucleosome positioning but that play an insignificant role in discriminating nucleosome-forming DNA sequences from nucleosome-inhibiting DNA sequences. The hypothesis that TFs play a role in nucleosome positioning is, thus, confirmed by the results of this study.

  20. A Teleoperation System Based on Predictive Simulation and Its Application to Spacecraft Maintenance

    Institute of Scientific and Technical Information of China (English)

    LI Ming-fu; LI Shi-qi; ZHAO Di; ZHU Wen-ge

    2008-01-01

    A teleoperation system based on predictive simulation is proposed for the sake of compensating the large time delay in the process of teleoperation to a degree and providing a friendly operating interface. The framework and function architecture of the system is discussed firstly. Then, the operator interface and a graphics simulation system is described in detail. Furthermore, a predictive algorithm aiming at position control based teleoperation is studied in our research, and the relative framework of predictive simulation is discussed. Finally, the system is applied to spacecraft breakdown maintenance; multi-agent reinforcement learning based semi-autonomous teleoperation is discussed at the same time for safe operation.

  1. Immobile Robots: AI in the New Millennium

    Science.gov (United States)

    Williams, Brian C.; Nayak, P. Pandurang

    1996-01-01

    A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.

  2. Knowledge base and neural network approach for protein secondary structure prediction.

    Science.gov (United States)

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods.

  3. Predicting Garden Path Sentences Based on Natural Language Understanding System

    OpenAIRE

    DU Jia-li; YU Ping-fang

    2012-01-01

    Natural language understanding (NLU) focusing onmachine reading comprehension is a branch of natural language processing (NLP). The domain of the developing NLU system covers from sentence decoding to text understanding and the automatic decoding of GP sentence belongs to the domain of NLU system. GP sentence is a special linguistic phenomenon in which processing breakdown and backtracking are two key features. Ifthe syntax-based system can present the special features of GP sentence and de...

  4. Grey Prediction Based Software Stage-Effort Estimation

    Institute of Scientific and Technical Information of China (English)

    WANG Yong; SONG Qinbao; SHEN Junyi

    2007-01-01

    The software stage-effort estimation can be used to dynamically adjust software project schedule, further to help make the project finished on budget. This paper presents a grey model Verhulst based method for stage-effort estimation during software development process, a bias correction technology was used to improve the estimation accuracy. The proposed method was evaluated with a large-scale industrial software engineering database. The results are very encouraging and indicate the method has considerable potential.

  5. Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning.

    Science.gov (United States)

    Byrne, Kaileigh A; Davis, Tyler; Worthy, Darrell A

    2016-07-01

    Dopaminergic genes play an important role in cognitive function. DRD2 and DARPP-32 dopamine receptor gene polymorphisms affect striatal dopamine binding potential, and the Val158Met single-nucleotide polymorphism of the COMT gene moderates dopamine availability in the pFC. Our study assesses the role of these gene polymorphisms on performance in two rule-based category learning tasks. Participants completed unidimensional and conjunctive rule-based tasks. In the unidimensional task, a rule along a single stimulus dimension can be used to distinguish category members. In contrast, a conjunctive rule utilizes a combination of two dimensions to distinguish category members. DRD2 C957T TT homozygotes outperformed C allele carriers on both tasks, and DARPP-32 AA homozygotes outperformed G allele carriers on both tasks. However, we found an interaction between COMT and task type where Met allele carriers outperformed Val homozygotes in the conjunctive rule task, but both groups performed equally well in the unidimensional task. Thus, striatal dopamine binding may play a critical role in both types of rule-based tasks, whereas prefrontal dopamine binding is important for learning more complex conjunctive rule tasks. Modeling results suggest that striatal dopaminergic genes influence selective attention processes whereas cortical genes mediate the ability to update complex rule representations. PMID:26918585

  6. Small-time scale network traffic prediction based on a local support vector machine regression model

    Institute of Scientific and Technical Information of China (English)

    Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua

    2009-01-01

    In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.

  7. Fuzzy-logic based learning style prediction in e-learning using web interface information

    Indian Academy of Sciences (India)

    L Jegatha Deborah; R Sathiyaseelan; S Audithan; P Vijayakumar

    2015-04-01

    he e-learners' excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder–Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly.

  8. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  9. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning. PMID:24578640

  10. Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO

    OpenAIRE

    Morteza Ashhar; Amin Rostami Motamed

    2014-01-01

    Conventional statistical techniques for forecasting are constrained by the underlying seasonality, non-stationary and other factors. Increasingly over the past decade, Artificial intelligence (AI) methods including Artificial Neural network (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) etc. have been used successfully to perform predictions in financial markets and other areas. This study presents a hybrid inertia factor and constriction coefficient PSO-based methodolog...

  11. 10 CFR 1017.28 - Processing on Automated Information Systems (AIS).

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 4 2010-01-01 2010-01-01 false Processing on Automated Information Systems (AIS). 1017.28... UNCLASSIFIED CONTROLLED NUCLEAR INFORMATION Physical Protection Requirements § 1017.28 Processing on Automated Information Systems (AIS). UCNI may be processed or produced on any AIS that complies with the guidance in...

  12. 77 FR 68150 - Meeting of the SANE/SART AI/AN Initiative Committee

    Science.gov (United States)

    2012-11-15

    ... of Justice Programs Meeting of the SANE/SART AI/AN Initiative Committee AGENCY: Office for Victims of... Assault Nurse Examiner (SANE) Sexual Assault Response Team (SART) American Indian/Alaskan Native (AI/AN) Initiative (``SANE/SART AI/AN Initiative Committee'' or ``Committee'') will meet to carry out its mission...

  13. 78 FR 17232 - Meeting of the SANE/SART AI/AN Initiative Committee

    Science.gov (United States)

    2013-03-20

    ... of Justice Programs Meeting of the SANE/SART AI/AN Initiative Committee AGENCY: Office for Victims of... Indian/ Alaska Native (AI/AN) Sexual Assault Nurse Examiner (SANE)--Sexual Assault Response Team (SART..., victim-centered responses to sexual violence within AI/AN communities. DATES AND LOCATIONS: The...

  14. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    Energy Technology Data Exchange (ETDEWEB)

    CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

    1998-06-04

    During the past quarter of this project, significant progress continued was made on both major technical tasks. Progress was made at OSU on advancing the application of computational chemistry to oxidative attack on model polyaromatic hydrocarbons (PAHs) and graphitic structures. This work is directed at the application of quantitative ab initio molecular orbital theory to address the decomposition products and mechanisms of coal char reactivity. Previously, it was shown that the �hybrid� B3LYP method can be used to provide quantitative information concerning the stability of the corresponding radicals that arise by hydrogen atom abstraction from monocyclic aromatic rings. In the most recent quarter, these approaches have been extended to larger carbocyclic ring systems, such as coronene, in order to compare the properties of a large carbonaceous PAH to that of the smaller, monocyclic aromatic systems. It was concluded that, at least for bond dissociation energy considerations, the properties of the large PAHs can be modeled reasonably well by smaller systems. In addition to the preceding work, investigations were initiated on the interaction of selected radicals in the �radical pool� with the different types of aromatic structures. In particular, the different pathways for addition vs. abstraction to benzene and furan by H and OH radicals were examined. Thus far, the addition channel appears to be significantly favored over abstraction on both kinetic and thermochemical grounds. Experimental work at Brown University in support of the development of predictive structural models of coal char combustion was focused on elucidating the role of coal mineral matter impurities on reactivity. An �inverse� approach was used where a carbon material was doped with coal mineral matter. The carbon material was derived from a high carbon content fly ash (Fly Ash 23 from the Salem Basin Power Plant. The ash was obtained from Pittsburgh #8 coal (PSOC 1451). Doped

  15. An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids

    DEFF Research Database (Denmark)

    Wang, Yanbo; Chen, Zhe; Wang, Xiongfei;

    2015-01-01

    This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...

  16. Predicting the Attitude Flow in Dialogue Based on Multi-Modal Speech Cues

    DEFF Research Database (Denmark)

    Juel Henrichsen, Peter; Allwood, Jens

    2013-01-01

    We present our experiments on attitude detection based on annotated multi-modal dialogue data1. Our long-term goal is to establish a computational model able to predict the attitudinal patterns in humanhuman dialogue. We believe, such prediction algorithms are useful tools in the pursuit...

  17. Prediction of creamy mouthfeel based on texture attribute ratings of dairy desserts

    NARCIS (Netherlands)

    Weenen, H.; Jellema, R.H.; Wijk, de R.A.

    2006-01-01

    A quantitative predictive model for creamy mouthfeel in dairy desserts was developed, using PLS multivariate analysis of texture attributes. Based on 40 experimental custard desserts, a good correlation was obtained between measured and predicted creamy mouthfeel ratings. The model was validated by

  18. Performance prediction for Grid workflow activities based on features-ranked RBF network

    Institute of Scientific and Technical Information of China (English)

    Wang Jie; Duan Rubing; Farrukh Nadeem

    2009-01-01

    Accurate performance prediction of Grid workflow activities can help Grid schedulers map activities to appropriate Grid sites. This paper describes an approach based on features-ranked RBF neural network to predict the performance of Grid workflow activities. Experimental results for two kinds of real world Grid workflow activities are presented to show effectiveness of our approach.

  19. The Prediction of Fatigue Life Based on Four Point Bending Test

    NARCIS (Netherlands)

    Pramesti, F.P.; Molenaar, A.A.A.; Van de Ven, M.F.C.

    2013-01-01

    To be able to devise optimum strategies for maintenance and rehabilitation, it is essential to formulate an accurate prediction of pavement life and its maintenance needs. One of the pavement life prediction methods is based on the pavement's capability to sustain fatigue. If it were possible to hav

  20. Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

    Institute of Scientific and Technical Information of China (English)

    WU Xiao-guang; SHI Zhong-kun

    2006-01-01

    The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.

  1. State Prediction of Chaotic System Based on ANN Model

    Institute of Scientific and Technical Information of China (English)

    YUE Yi-hong; HAN Wen-xiu

    2002-01-01

    The choice of time delay and embedding dimension is very important to the phase space reconstruction of any chaotic time series. In this paper, we determine optimal time delay by computing autocorrelation function of time series. Optimal embedding dimension is given by means of the relation between embedding dimension and correlation dimension of chaotic time series. Based on the methods above,we choose ANN model to appoximate the given true system. At the same time, a new algorithm is applied to determine the network weights. At the end of this paper, the theory above is demonstrated through the research of time series generated by Logistic map.

  2. Risk prediction of cardiovascular death based on the QTc interval

    DEFF Research Database (Denmark)

    Nielsen, Jonas B; Graff, Claus; Rasmussen, Peter V;

    2014-01-01

    risk groups when the QTc interval was added to a conventional risk model for CVD. CONCLUSION: Important differences were observed across subgroups when the absolute long-term risk of CVD was estimated based on QTc interval duration. The accuracy of the personalized CVD prognosis can be improved when.......1 years, 6647 persons died from cardiovascular causes. Long-term risks of CVD were estimated for subgroups defined by age, gender, cardiovascular disease, and QTc interval categories. In general, we observed an increased risk of CVD for both very short and long QTc intervals. Prolongation of the QTc...

  3. Energy savings in mobile broadband network based on load predictions

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels Bundgaard;

    2012-01-01

    in wireless networks. To save energy in MBNs, one of the options is to turn off parts of the network equipment in areas where traffic falls below a specific predefined threshold. This paper looks at a methodology for identifying periods of the day when cells or sites carrying low traffic are candidates...... for being totally or partly switched off, given that the decrease in service quality can be controlled gracefully when the sites are switched off. Based on traffic data from an operational network, potential average energy savings of approximately 30% with some few low traffic cells/sites reaching up to 99...

  4. Prediction of Tumor Outcome Based on Gene Expression Data

    Institute of Scientific and Technical Information of China (English)

    Liu Juan; Hitoshi Iba

    2004-01-01

    Gene expression microarray data can be used to classify tumor types. We proposed a new procedure to classify human tumor samples based on microarray gene expressions by using a hybrid supervised learning method called MOEA+WV (Multi-Objective Evolutionary Algorithm+Weighted Voting). MOEA is used to search for a relatively few subsets of informative genes from the high-dimensional gene space, and WV is used as a classification tool. This new method has been applied to predicate the subtypes of lymphoma and outcomes of medulloblastoma. The results are relatively accurate and meaningful compared to those from other methods.

  5. The Use of AIS Data for Identifying and Mapping Calcareous Soils in Western Nebraska

    Science.gov (United States)

    Samson, S. A.

    1985-01-01

    The identification of calcareous soils, through unique spectral responses of the vegetation to the chemical nature of calcareous soils, can improve the accuracy of delineating the boundaries of soil mapping units over conventional field techniques. The objective of this experiment is to evaluate the use of the Airborne Imaging Spectrometer (AIS) in the identification and delineation of calcareous soils in the western Sandhills of Nebraska. Based upon statistical differences found in separating the spectral curves below 1.3 microns, calcareous and non-calcareous soils may be identified by differences in species of vegetation. Additional work is needed to identify biogeochemical differences between the two soils.

  6. Forbush Decrease Prediction Based on the Remote Solar Observations

    CERN Document Server

    Dumbovic, Mateja; Calogovic, Jasa

    2015-01-01

    We employ remote observations of coronal mass ejections (CMEs) and the associated solar flares to forecast the CME-related Forbush decreases, i.e., short-term depressions in the galactic cosmic-ray flux. The relationship between the Forbush effect at the Earth and remote observations of CMEs and associated solar flares is studied via a statistical analysis. Relationships between Forbush decrease magnitude and several CME/flare parameters was found, namely the initial CME speed, apparent width, source position, associated solar-flare class and the effect of successive-CME occurrence. Based on the statistical analysis, remote solar observations are employed for a Forbush-decrease forecast. For that purpose, an empirical probabilistic model is constructed that uses selected remote solar observations of CME and associated solar flare as an input, and gives expected Forbush-decrease magnitude range as an output. The forecast method is evaluated using several verification measures, indicating that as the forecast t...

  7. Theoretically predicted Fox-7 based new high energy density molecules

    Science.gov (United States)

    Ghanta, Susanta

    2016-08-01

    Computational investigation of CHNO based high energy density molecules (HEDM) are designed with FOX-7 (1, 1-dinitro 2, 2-diamino ethylene) skeleton. We report structures, stability and detonation properties of these new molecules. A systematic analysis is presented for the crystal density, activation energy for nitro to nitrite isomerisation and the C-NO2 bond dissociation energy of these molecules. The Atoms in molecules (AIM) calculations have been performed to interpret the intra-molecular weak H-bonding interactions and the stability of C-NO2 bonds. The structure optimization, frequency and bond dissociation energy calculations have been performed at B3LYP level of theory by using G03 quantum chemistry package. Some of the designed molecules are found to be more promising HEDM than FOX-7 molecule, and are proposed to be candidate for synthetic purpose.

  8. 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.

  9. Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

    Institute of Scientific and Technical Information of China (English)

    Zhu Hongqiu; Yang Chunhua; Gui Weihua

    2009-01-01

    To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning (CBR) with adaptive particle swarm optimization (PSO). The number of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

  10. Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    Yun-Fei Wang; Huan Chen; Yan-Hong Zhou

    2005-01-01

    A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.

  11. Accuracy of depolarization and delay spread predictions using advanced ray-based modeling in indoor scenarios

    Directory of Open Access Journals (Sweden)

    Mani Francesco

    2011-01-01

    Full Text Available Abstract This article investigates the prediction accuracy of an advanced deterministic propagation model in terms of channel depolarization and frequency selectivity for indoor wireless propagation. In addition to specular reflection and diffraction, the developed ray tracing tool considers penetration through dielectric blocks and/or diffuse scattering mechanisms. The sensitivity and prediction accuracy analysis is based on two measurement campaigns carried out in a warehouse and an office building. It is shown that the implementation of diffuse scattering into RT significantly increases the accuracy of the cross-polar discrimination prediction, whereas the delay-spread prediction is only marginally improved.

  12. Future role of AI/Robotics in physical security

    International Nuclear Information System (INIS)

    Manpower requirements for physical security systems place a heavy burden on operating security budgets. Technology innovations which free personnel or which make security personnel more efficient in carrying out their tasks is an important means of dealing with budget and manpower constraints. It is believed that AI/Robotics will be important technologies to alleviate these problems in the future. There are three types of applications for AI and Robotics technology that may: (l) help security personnel perform their tasks more effectively or efficiently, (2) perform tasks that security personnel would otherwise perform (free up people), and (3) perform tasks that cannot be performed by security personnel at this time. This paper discusses the various types of security applications that are presently being considered for the above areas and briefly describes a few examples of the application of this technology

  13. Le système d’identification automatique (AIS)

    OpenAIRE

    Serry, Arnaud; Lévêque, Laurent

    2015-01-01

    Le système automatique d’identification (AIS) des navires est un outil destiné à accroitre la sécurité de la navigation et l’efficacité de la gestion du trafic maritime. Son utilisation renforce à la fois la sécurité et la sureté maritime. Ses apports sont indéniables malgré quelques carences et limites techniques. Cet article présente les apports et les utilisations de la technologie AIS à l’origine d’une importante manne d’informations riches pour l’étude et la compréhension des circulation...

  14. Soil types and forest canopy structures in southern Missouri: A first look with AIS data

    Science.gov (United States)

    Green, G. M.; Arvidson, R. E.

    1986-01-01

    Spectral reflectance properties of deciduous oak-hickory forests covering the eastern half of the Rolla Quadrangle were examined using Thematic Mapper (TM) data acquired in August and December, 1982 and Airborne Imaging Spectrometer (AIS) data acquired in August, 1985. For the TM data distinctly high relative reflectance values (greater than 0.3) in the near infrared (Band 4, 0.73 to 0.94 micrometers) correspond to regions characterized by xeric (dry) forests that overlie soils with low water retention capacities. These soils are derived primarily from rhyolites. More mesic forests characterized by lower TM band 4 relative reflectances are associated with soils of higher retention capacities derived predominately from non-cherty carbonates. The major factors affecting canopy reflectance appear to be the leaf area index (LAI) and leaf optical properties. The Suits canopy reflectance model predicts the relative reflectance values for the xeric canopies. The mesic canopy reflectance is less well matched and incorporation of canopy shadowing caused by the irregular nature of the mesic canopy may be necessary. Preliminary examination of high spectral resolution AIS data acquired in August of 1985 reveals no more information than found in the broad band TM data.

  15. Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad; Ning, Xia; Agarwal, Khushbu; Purohit, Sumit; Pesantez, Paola

    2016-02-01

    Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.

  16. Physics-based Modeling Tools for Life Prediction and Durability Assessment of Advanced Materials Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The technical objectives of this program are: (1) to develop a set of physics-based modeling tools to predict the initiation of hot corrosion and to address pit and...

  17. A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

    NARCIS (Netherlands)

    Pas, van de N.C.A.; Woutersen, R.A.; Ommen, van B.; Rietjens, I.M.C.M.; Graaf, de A.A.

    2012-01-01

    Increased plasma cholesterol concentration is associated with increased risk of cardiovascular disease. This study describes the development, validation, and analysis of a physiologically based kinetic (PBK) model for the prediction of plasma cholesterol concentrations in humans. This model was dire

  18. High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a novel, high-accuracy, high-fidelity, multiresolution (MRES), wavelet-based framework for efficient prediction of airframe noise sources and...

  19. High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction Project

    Data.gov (United States)

    National Aeronautics and Space Administration — An integrated framework is proposed for efficient prediction of rotorcraft and airframe noise. A novel wavelet-based multiresolution technique and high-order...

  20. Parler français en algerie jujements et attitudes

    OpenAIRE

    Belkaid, Nihal

    2014-01-01

    Après l'accession de l’Algérie à son indépendance en 1962 l'usage de langue français en Algérie a fait l'objet de nombreuse polémique et connotation négative par les arabophone détracteurs de la langues française

  1. Oligonucleotide-mediated gene editing of Apolipoprotein A-I.

    OpenAIRE

    Disterer, P

    2008-01-01

    Apolipoprotein A-I (ApoA-I) is the major protein constituent of high density lipoprotein (HDL) and controls reverse cholesterol transport, an important process in preventing atherosclerosis. A natural point mutation, ApoA-lMiiano (ApoA-Im) enhances the atheroprotective potential of HDL. Here, I attempt to introduce this specific modification into the genome of mammalian cells using the gene therapy strategy of oligonucleotide-mediated gene editing. I showed successful APOA-I gene editing in r...

  2. Development and research on the GIS-based landslide prediction system of the Three Gorges area

    Science.gov (United States)

    Ge, Qiao; Tang, Zhongshi; Wang, Haiwei

    2008-10-01

    In this paper we discussed the development and research of the GIS-based landslide prediction system of the Three Gorges area. First of all, we systematically revisited the basic issues of the landslide prediction, including the principles of landslide prediction, the division of sliding-time and sliding-deformation stages, prediction parameters selection and monitoring sites selection. In addition to reviewing the landslide prediction models, this paper detailed discussed an improved model which makes an integration of the results of multiple prediction models. On the basis of those landslide prediction models, we developed a GIS-based landslide prediction system by using Visual C#.NET and ESRI ArcObjects components. Finally, this paper selected two typical landslide cases in the Three Gorges area: the Xintan landslide and the Lianzi Cliff dangerous rock body, and used the system to calculate and analyze. It validated the applicability and accuracy of the prediction models, made a test of the practicality of the system, and achieved good results.

  3. Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking

    OpenAIRE

    Liu, Wenxi; Chan, Antoni B.; Lau, Rynson W. H.; Manocha, Dinesh

    2014-01-01

    We present a multiple-person tracking algorithm, based on combining particle filters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion. In addition to position and velocity, our tracking algorithm can estimate the internal goals (desired destination or desired velocity) of the tracked pedestrian in an online manner, thus removing the need to specify this information beforehand. Furthermore, we leverage the longer-term predictions of...

  4. Novel Method of Predicting Network Bandwidth Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    沈伟; 冯瑞; 邵惠鹤

    2004-01-01

    In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.

  5. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    OpenAIRE

    Peng Liu; Lei Yang; Daming Shi; Xianglong Tang

    2015-01-01

    A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction net...

  6. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  7. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  8. Analysis of energy-based algorithms for RNA secondary structure prediction

    OpenAIRE

    Hajiaghayi Monir; Condon Anne; Hoos Holger H

    2012-01-01

    Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andr...

  9. Predicted Link Expiration Time Based Connected Dominating Sets for Mobile Ad hoc Networks

    OpenAIRE

    Pervis Fly; Natarajan Meghanathan

    2010-01-01

    We propose an algorithm to determine stable connecteddominating sets (CDS), based on the predicted link expiration time(LET), for mobile ad hoc networks (MANETs). The proposed LETbasedCDS algorithm is the first such algorithm that constructs a CDSbased on edge weights represented by predicted link expiration time,rather the traditional approach of using node weights like the wellknownmaximum density-based CDS (MaxD-CDS) algorithm. Theconstruction of the LET-CDS starts with the inclusion of th...

  10. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model

    Science.gov (United States)

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-01-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking. PMID:27498888

  11. Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties

    Directory of Open Access Journals (Sweden)

    Bing Niu

    2013-01-01

    Full Text Available It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test.

  12. Prediction of substrate-enzyme-product interaction based on molecular descriptors and physicochemical properties.

    Science.gov (United States)

    Niu, Bing; Huang, Guohua; Zheng, Linfeng; Wang, Xueyuan; Chen, Fuxue; Zhang, Yuhui; Huang, Tao

    2013-01-01

    It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test. PMID:24455714

  13. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model.

    Science.gov (United States)

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-01-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking. PMID:27498888

  14. Fatigue Strength Prediction of Drilling Materials Based on the Maximum Non-metallic Inclusion Size

    Science.gov (United States)

    Zeng, Dezhi; Tian, Gang; Liu, Fei; Shi, Taihe; Zhang, Zhi; Hu, Junying; Liu, Wanying; Ouyang, Zhiying

    2015-12-01

    In this paper, the statistics of the size distribution of non-metallic inclusions in five drilling materials were performed. Based on the maximum non-metallic inclusion size, the fatigue strength of the drilling material was predicted. The sizes of non-metallic inclusions in drilling materials were observed to follow the inclusion size distribution rule. Then the maximum inclusion size in the fatigue specimens was deduced. According to the prediction equation of the maximum inclusion size and fatigue strength proposed by Murakami, fatigue strength of drilling materials was obtained. Moreover, fatigue strength was also measured through rotating bending tests. The predicted fatigue strength was significantly lower than the measured one. Therefore, according to the comparison results, the coefficients in the prediction equation were revised. The revised equation allowed the satisfactory prediction results of fatigue strength of drilling materials at the fatigue life of 107 rotations and could be used in the fast prediction of fatigue strength of drilling materials.

  15. A rank-based Prediction Algorithm of Learning User's Intention

    Science.gov (United States)

    Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

    Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

  16. Hippocampal Attractor Dynamics Predict Memory-Based Decision Making.

    Science.gov (United States)

    Steemers, Ben; Vicente-Grabovetsky, Alejandro; Barry, Caswell; Smulders, Peter; Schröder, Tobias Navarro; Burgess, Neil; Doeller, Christian F

    2016-07-11

    Memories are thought to be retrieved by attractor dynamics if a given input is sufficiently similar to a stored attractor state [1-5]. The hippocampus, a region crucial for spatial navigation [6-12] and episodic memory [13-18], has been associated with attractor-based computations [5, 9], receiving support from the way rodent place cells "remap" nonlinearly between spatial representations [19-22]. In humans, nonlinear response patterns have been reported in perceptual categorization tasks [23-25]; however, it remains elusive whether human memory retrieval is driven by attractor dynamics and what neural mechanisms might underpin them. To test this, we used a virtual reality [7, 11, 26-28] task where participants learned object-location associations within two distinct virtual reality environments. Participants were subsequently exposed to four novel intermediate environments, generated by linearly morphing the background landscapes of the familiar environments, while tracking fMRI activity. We show that linear changes in environmental context cause linear changes in activity patterns in sensory cortex but cause dynamic, nonlinear changes in both hippocampal activity pattern and remembered locations. Furthermore, the sigmoidal response in the hippocampus scaled with the strength of the sigmoidal pattern in spatial memory. These results indicate that mnemonic decisions in an ambiguous novel context relate to putative attractor dynamics in the hippocampus, which support the dynamic remapping of memories. PMID:27345167

  17. Predicting Geotechnical Investigation Using the Knowledge Based System

    Directory of Open Access Journals (Sweden)

    Bojan Žlender

    2016-01-01

    Full Text Available The purpose of this paper is to evaluate the optimal number of investigation points and each field test and laboratory test for a proper description of a building site. These optimal numbers are defined based on their minimum and maximum number and with the equivalent investigation ratio. The total increments of minimum and maximum number of investigation points for different building site conditions were determined. To facilitate the decision-making process for a number of investigation points, an Adaptive Network Fuzzy Inference System (ANFIS was proposed. The obtained fuzzy inference system considers the influence of several entry parameters and computes the equivalent investigation ratio. The developed model (ANFIS-SI can be applied to characterize any building site. The ANFIS-SI model takes into account project factors which are evaluated with a rating from 1 to 10. The model ANFIS-SI, with integrated recommendations can be used as a systematic decision support tool for engineers to evaluate the number of investigation points, field tests, and laboratory tests for a proper description of a building site. The determination of the optimal number of investigative points and the optimal number of each field test and laboratory test is presented on reference case.

  18. Extracting uranium from seawater: Promising AI series adsorbents

    International Nuclear Information System (INIS)

    A series of adsorbent (AI10 through AI17) were successfully developed at ORNL by radiation induced graft polymerization (RIGP) of acrylonitrile (AN) and vinylphosphonic acid (VPA) (at different mole/mole ratios) onto high surface area polyethylene fiber, with higher degree of grafting which ranges from 110 300%. The grafted nitrile groups were converted to amidoxime groups by reaction with 10 wt% hydroxylamine at 80 C for 72 hours. The amidoximated adsorbents were then conditioned with 0.44M KOH at 80 C followed by screening at ORNL with simulated seawater spiked with 8 ppm uranium. Uranium adsorption capacity in simulated seawater screening ranged from 171-187 g-U/kg-ads irrespective of %DOG. The performance of the adsorbents for uranium adsorption in natural seawater was also carried out using flow-through-column at Pacific Northwest National Laboratory (PNNL). The three hours KOH conditioning was better for higher uranium uptake than one hour. The adsorbent AI11 containing AN and VPA at the mole ration of 3.52, emerged as the potential candidate for higher uranium adsorption (3.35 g-U/Kg-ads.) after 56 days of exposure in the seawater in the flow-through-column. The rate vanadium adsorption over uranium was linearly increased throughout the 56 days exposure. The total vanadium uptake was ~5 times over uranium after 56 days

  19. Absolute parameters for AI Phoenicis using WASP photometry

    CERN Document Server

    Kirkby-Kent, J A; Serenelli, A M; Turner, O D; Evans, D F; Anderson, D R; Hellier, C; West, R G

    2016-01-01

    AI Phe is a double-lined, detached binary, in which a K-type sub-giant star totally eclipses its main-sequence companion every 24.6 days. This configuration makes AI Phe ideal for testing stellar evolutionary models. Difficulties in obtaining a complete lightcurve mean the precision of existing radii measurements could be improved. Our aim is to improve the precision of the radius measurements for the stars in AI Phe using high-precision photometry from WASP, and use these improved radius measurements together with estimates of the masses, temperatures and composition of the stars to place constraints on the mixing length, helium abundance and age of the system. A best-fit ebop model is used to obtain lightcurve parameters, with their standard errors calculated using a prayer-bead algorithm. These were combined with previously published spectroscopic orbit results, to obtain masses and radii. A Bayesian method is used to estimate the age of the system for model grids with different mixing lengths and helium a...

  20. A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2013-01-01

    Full Text Available In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA, which was used to seek the optimal parameters of the rule-based model; and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations’ data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as “net income to stock broker’s equality,” “quick ratio,” “retained earnings to total assets,” “stockholders’ equity to total assets,” and “financial expenses to sales.” The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA, ant colony algorithm (ACA, and GACA. The average computation time of the model is 2.02 s.

  1. Link Prediction in evolving networks based on the popularity of nodes

    CERN Document Server

    Wang, Tong; Fu, Zhong-qian

    2016-01-01

    Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict the missing edges or identify the spurious edges, and attracts much attention from various fields. The key issue of link prediction is to estimate the likelihood of two nodes in networks. Most current approaches of link prediction base on static structural analysis and ignore the temporal aspects of evolving networks. Unlike previous work, in this paper, we propose a popularity based structural perturbation method (PBSPM) that characterizes the similarity of an edge not only from existing connections of networks, but also from the popularity of its two endpoints, since popular nodes have much more probability to form links between themselves. By taking popularity of nodes into account, PBSPM could suppress nodes that have high importance, but gradually become inactive. Therefore the proposed method is inclined to predict potential edges between active nodes, rather than edges between inactive nodes....

  2. State of the art and challenges in sequence based T-cell epitope prediction

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Hoof, Ilka; Lund, Ole;

    2010-01-01

    field has evolved significantly. Methods have now been developed that produce highly accurate binding predictions for many alleles and integrate both proteasomal cleavage and transport events. Moreover have so-called pan-specific methods been developed, which allow for prediction of peptide binding to......Sequence based T-cell epitope predictions have improved immensely in the last decade. From predictions of peptide binding to major histocompatibility complex molecules with moderate accuracy, limited allele coverage, and no good estimates of the other events in the antigen-processing pathway, the...... MHC alleles characterized by limited or no peptide binding data. Most of the developed methods are publicly available, and have proven to be very useful as a shortcut in epitope discovery. Here, we will go through some of the history of sequence-based predictions of helper as well as cytotoxic T cell...

  3. SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

    Science.gov (United States)

    Wang, Yao; Zhang, Zhongzhao; Ma, Lin; Chen, Jiamei

    2014-01-01

    Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements. PMID:25143975

  4. A class-based link prediction using Distance Dependent Chinese Restaurant Process

    Science.gov (United States)

    Andalib, Azam; Babamir, Seyed Morteza

    2016-08-01

    One of the important tasks in relational data analysis is link prediction which has been successfully applied on many applications such as bioinformatics, information retrieval, etc. The link prediction is defined as predicting the existence or absence of edges between nodes of a network. In this paper, we propose a novel method for link prediction based on Distance Dependent Chinese Restaurant Process (DDCRP) model which enables us to utilize the information of the topological structure of the network such as shortest path and connectivity of the nodes. We also propose a new Gibbs sampling algorithm for computing the posterior distribution of the hidden variables based on the training data. Experimental results on three real-world datasets show the superiority of the proposed method over other probabilistic models for link prediction problem.

  5. Predictor-based error correction method in short-term climate prediction

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In terms of the basic idea of combining dynamical and statistical methods in short-term climate prediction, a new prediction method of predictor-based error correction (PREC) is put forward in order to effectively use statistical experiences in dynamical prediction. Analyses show that the PREC can reasonably utilize the significant correlations between predictors and model prediction errors and correct prediction errors by establishing statistical prediction model. Besides, the PREC is further applied to the cross-validation experiments of dynamical seasonal prediction on the operational atmosphere-ocean coupled general circulation model of China Meteorological Administration/National Climate Center by selecting the sea surface temperature index in Ni(n)o3 region as the physical predictor that represents the prevailing ENSO-cycle mode of interannual variability in climate system. It is shown from the prediction results of summer mean circulation and total precipitation that the PREC can improve predictive skills to some extent. Thus the PREC provides a new approach for improving short-term climate prediction.

  6. An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

    Science.gov (United States)

    Chen, Xi; Wang, Lily; Ishwaran, Hemant

    2010-09-01

    Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

  7. Structure Based Predictive Model for Coal Char Combustion

    Energy Technology Data Exchange (ETDEWEB)

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

    This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed

  8. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  9. Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks

    Institute of Scientific and Technical Information of China (English)

    WANG Yongji; WANG Hong

    2004-01-01

    A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

  10. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  11. A New Approach for Lossless Image Compression Based on Fuzzy Adaptive Prediction

    Institute of Scientific and Technical Information of China (English)

    Wu Yingqian(吴颖谦); Fang Tao; Shi Pengfei

    2004-01-01

    This paper proposes a novel approach for image lossless compression based on fuzzy logic and adaptive prediction. By a flexible strategy, the method can acquire a set of original predictors describing the more detail characteristic. Using a neural network, the proposed method can more efficiently organize the training of original predictors and implement adaptive prediction in fuzzy style. In entropy coding phase, the context-based conditional adaptive arithmetic encoding is adopted. The experiments demonstrate the characteristics make the approach achieve good tradeoff between computational complexity and efficiency of prediction and good performance for lossless compression.

  12. Intrusion Detection for Wireless Sensor Network Based on Traffic Prediction Model

    Science.gov (United States)

    Zhijie, Han; Ruchuang, Wang

    In this paper, the authors first propose an efficient traffic prediction algorithm for sensor nodes which exploits the Markov model. Based on this algorithm, a distributed anomaly detection scheme, TPID(Traffic Prediction based Intrusion Detection), is designed to detect the attacks which make more influence on packet traffic, such as selective forwarding attacks, DOS attacks. In TPID, each node acts independently when predicting the traffic and detecting an anomaly. Neither special hardware nor nodes cooperation is needed. The scheme is evaluated and compared with other method in experiments. Results show that the proposed scheme obtain high detection ratio with less computation and communication cost.

  13. An Intelligent Man-Machine Dialogue System Based on AI

    OpenAIRE

    Kuijpers, Bart; Dockx, Kris

    1998-01-01

    Abstract We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate ...

  14. High Temperature coatings based on β-NiAI

    Energy Technology Data Exchange (ETDEWEB)

    Severs, Kevin [Iowa State Univ., Ames, IA (United States)

    2012-01-01

    High temperature alloys are reviewed, focusing on current superalloys and their coatings. The synthesis, characerization, and oxidation performance of a NiAl–TiB2 composite are explained. A novel coating process for Mo–Ni–Al alloys for improved oxidation performance is examined. The cyclic oxidation performance of coated and uncoated Mo–Ni–Al alloys is discussed.

  15. Development of an AI-based Rapid Manufacturing advice system

    OpenAIRE

    Munguía, Javier; Lloveras, Joaquim; Llorens, Sonia; Laoui, Tahar

    2010-01-01

    Abstract The purpose of this paper is to assess the possibility of using Rapid Manufacturing (RM) as a final manufacturing route through a comparison of RM capabilities vs. conventional manufacturing routes. This is done by means of a computer-aided system intended to guide the designer in the selection of optimum production parameters according to general product requirements proper of the first design stages. The proposed system makes use of a number of Artificial Intelligence (A...

  16. Colored Image Compression Using Gradient Adjustment Prediction Based Wavelet

    Directory of Open Access Journals (Sweden)

    Muna F.H. Al-Sammraie

    2011-01-01

    Full Text Available Problem statement: Uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive digital audio, image and video (multimedia based web applications, have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signals central to signal-storage and digital communication technology. Approach: The objective includes developing and applying an efficient Space-Frequency Segmentation (SFS as an image partitioning scheme, then using an appropriate entropy-coding algorithm that can be used with the developed segmentation to improve compression performance, particularly in the case of still image compression. The proposed compression system focuses on an innovative scheme for adaptive wavelet coding technique combined with spatial encoding. Result: Experiments conducted using the proposed system produced encouraging results. The entropyspatial coders used in the proposed system produced better results than those obtained by using the basic arithmetic coder. It provides more appropriate rate-distortion optimization for the spacefrequency segmentation than the basic arithmetic coder does. The proposed compression system implies some control coding parameters; the effects of these parameters were investigated to determine the suitable range for each one of them. Conclusion: We conclude that a comparison between the energy of two partitioning types (space and frequency shows that the energy of frequency partitioning is greater than the space partitioning from the point of view of quality of compressed image. And also the selection of parameter value used in SFS

  17. Absolute parameters for AI Phoenicis using WASP photometry

    Science.gov (United States)

    Kirkby-Kent, J. A.; Maxted, P. F. L.; Serenelli, A. M.; Turner, O. D.; Evans, D. F.; Anderson, D. R.; Hellier, C.; West, R. G.

    2016-06-01

    Context. AI Phe is a double-lined, detached eclipsing binary, in which a K-type sub-giant star totally eclipses its main-sequence companion every 24.6 days. This configuration makes AI Phe ideal for testing stellar evolutionary models. Difficulties in obtaining a complete lightcurve mean the precision of existing radii measurements could be improved. Aims: Our aim is to improve the precision of the radius measurements for the stars in AI Phe using high-precision photometry from the Wide Angle Search for Planets (WASP), and use these improved radius measurements together with estimates of the masses, temperatures and composition of the stars to place constraints on the mixing length, helium abundance and age of the system. Methods: A best-fit ebop model is used to obtain lightcurve parameters, with their standard errors calculated using a prayer-bead algorithm. These were combined with previously published spectroscopic orbit results, to obtain masses and radii. A Bayesian method is used to estimate the age of the system for model grids with different mixing lengths and helium abundances. Results: The radii are found to be R1 = 1.835 ± 0.014 R⊙, R2 = 2.912 ± 0.014 R⊙ and the masses M1 = 1.1973 ± 0.0037 M⊙, M2 = 1.2473 ± 0.0039 M⊙. From the best-fit stellar models we infer a mixing length of 1.78, a helium abundance of YAI = 0.26 +0.02-0.01 and an age of 4.39 ± 0.32 Gyr. Times of primary minimum show the period of AI Phe is not constant. Currently, there are insufficient data to determine the cause of this variation. Conclusions: Improved precision in the masses and radii have improved the age estimate, and allowed the mixing length and helium abundance to be constrained. The eccentricity is now the largest source of uncertainty in calculating the masses. Further work is needed to characterise the orbit of AI Phe. Obtaining more binaries with parameters measured to a similar level of precision would allow us to test for relationships between helium

  18. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.

  19. Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.

  20. A data base approach for prediction of deforestation-induced mass wasting events

    Science.gov (United States)

    Logan, T. L.

    1981-01-01

    A major topic of concern in timber management is determining the impact of clear-cutting on slope stability. Deforestation treatments on steep mountain slopes have often resulted in a high frequency of major mass wasting events. The Geographic Information System (GIS) is a potentially useful tool for predicting the location of mass wasting sites. With a raster-based GIS, digitally encoded maps of slide hazard parameters can be overlayed and modeled to produce new maps depicting high probability slide areas. The present investigation has the objective to examine the raster-based information system as a tool for predicting the location of the clear-cut mountain slopes which are most likely to experience shallow soil debris avalanches. A literature overview is conducted, taking into account vegetation, roads, precipitation, soil type, slope-angle and aspect, and models predicting mass soil movements. Attention is given to a data base approach and aspects of slide prediction.