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. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  3. AI-Based Diagnostic Shell

    Directory of Open Access Journals (Sweden)

    R. L. Verma

    1989-01-01

    Full Text Available This paper datails the design and implementation of an AI-based diagnostic shell. The shell has a user-interface which takes in the complaint and aids the user throughout the consultation. The 'expert knowledge' is acquired and encoded in the form of 'IF-THEN' rules, The control mechanism routes through the rules chaining first backwards to identify a fault and then forwards to confirm it.Explanation facilities have been provided to enable the user query the reason for any question asked, a facility to go back and re-answer any previous question, and a trace and explanation of the path of reasoning.This shell was developed and first used for the diagnosis of a digital exchange. It was then applied for the fault-finding of the moving target indicator used in the radar.

  4. 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...... of the AIS receivers onboard AAUSAT3 is an SDR based AIS receiver. This paper serves to describe the background of the AIS system, and how the SDR based receiver has been integrated into the AAUSAT3 satellite. Amongst some of the benefits of using an SDR based receiver is, that due to its versatility, new...... detection algorithms are easily deployed, and it is easily adapted the new proposed AIS transmission channels....

  5. The AI Interdisciplinary Context: Single or Multiple Research Bases?

    Science.gov (United States)

    Khawam, Yves J.

    1992-01-01

    This study used citation analysis to determine whether the disciplines contributing to the journal literature of artificial intelligence (AI)--philosophy, psychology, linguistics, computer science, and engineering--share a common AI research base. The idea that AI consists of a completely interdisciplinary endeavor was refuted. (MES)

  6. 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...

  7. 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.

  8. 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.

  9. 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...

  10. 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.

  11. A novel position estimation method based on displacement correction in AIS.

    Science.gov (United States)

    Jiang, Yi; Zhang, Shufang; Yang, Dongkai

    2014-09-17

    A new position estimation method by using the signals from two automatic identification system (AIS) stations is proposed in this paper. The time of arrival (TOA) method is enhanced with the displacement correction, so that the vessel's position can be determined even for the situation where it can receive the signals from only two AIS base stations. Its implementation scheme based on the mathematical model is presented. Furthermore, performance analysis is carried out to illustrate the relation between the positioning errors and the displacement vector provided by auxiliary sensors. Finally, the positioning method is verified and its performance is evaluated by simulation. The results show that the positioning accuracy is acceptable.

  12. AIS, ADMINISTRATIVE INFORMATION SUPPORT

    CERN Multimedia

    AS-DB/AS-SU

    1999-01-01

    The AS-DB and AS-SU groups within the Administrative Support division now offer a central entry point to the computer based Information Services under their responsibilities: the AIS Web site at http://ais.cern.ch.It features comprehensive search and navigation facilities as well as an activity based business map to guide AIS users to the information they want. Users will be able to launch any AIS WEB application from its desktop.Enjoy a visit of the site, we value your feedback at ais.webmaster@cern.ch!AS-DB/AS-SU

  13. A Novel Position Estimation Method Based on Displacement Correction in AIS

    Directory of Open Access Journals (Sweden)

    Yi Jiang

    2014-09-01

    Full Text Available A new position estimation method by using the signals from two automatic identification system (AIS stations is proposed in this paper. The time of arrival (TOA method is enhanced with the displacement correction, so that the vessel’s position can be determined even for the situation where it can receive the signals from only two AIS base stations. Its implementation scheme based on the mathematical model is presented. Furthermore, performance analysis is carried out to illustrate the relation between the positioning errors and the displacement vector provided by auxiliary sensors. Finally, the positioning method is verified and its performance is evaluated by simulation. The results show that the positioning accuracy is acceptable.

  14. Android-based mobile AIS data display system%基于 Android的移动 AIS 数据显示系统

    Institute of Scientific and Technical Information of China (English)

    李超; 潘明阳; 王德强; 郝江凌; 李邵喜; 胡景峰

    2014-01-01

    设计并实现了一种基于Android的移动AIS数据显示系统。该系统可通过蓝牙无线连接船载AIS设备,从而在智能移动终端(手机或平台)上接收、解析AIS数据,并将其传递的船舶动态与电子海图进行叠加显示,实现对AIS船舶信息的实时监控和查询。利用操船者的移动设备运行该系统,即可形成一个简易的移动导航系统,满足内河众多小型船舶的航行需求。%The paper designed and implemented a mobile AIS data display system based on Android platform .The system can connect Shipborne AIS equipment by Bluetooth , and ena-ble an intelligent mobile device ( phone or tablet ) to receive and decode AIS data to obtain realtime information of vessels around and transmit them onto electronic nautical charts . With the functions of vessel dynamic monitoring and vessel in-formation querying , the system can act as a simple portable navigator for the ship handlers of small inland vessels by run-ning it in their mobile devices .

  15. 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.

  16. 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.

  17. AIS authentication

    CERN Document Server

    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...

  18. 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.

  19. AI applications in sheet metal forming

    CERN Document Server

    Hussein, Hussein

    2017-01-01

    This book comprises chapters on research work done around the globe in the area of artificial intelligence (AI) applications in sheet metal forming. The first chapter offers an introduction to various AI techniques and sheet metal forming, while subsequent chapters describe traditional procedures/methods used in various sheet metal forming processes, and focus on the automation of those processes by means of AI techniques, such as KBS, ANN, GA, CBR, etc. Feature recognition and the manufacturability assessment of sheet metal parts, process planning, strip-layout design, selecting the type and size of die components, die modeling, and predicting die life are some of the most important aspects of sheet metal work. Traditionally, these activities are highly experience-based, tedious and time consuming. In response, researchers in several countries have applied various AI techniques to automate these activities, which are covered in this book. This book will be useful for engineers working in sheet metal industri...

  20. 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.

  1. Artificial intelligence library for html5 based games: DignityAI

    Directory of Open Access Journals (Sweden)

    Berkan Uslu

    2017-02-01

    Full Text Available Today, acceleration of internet and common use of web pages, revealed the necessity of work with any browser smoothly for each application without of requirement of any plug-in. Generally, HTML5 is a new body of standards which is formed with the combination of CSS and JavaScript. In this context, by analysing game engines developed for HTML5, their features and advantages are investigated. Although, these game engines are close to catch up with the level of popular game engines, it is seen that none of artificial intelligence library was developed for HTML5 based games up to now. In this study, DignityAI artificial intelligence library is developed to fill this deficiency. Developed library has ability to be integrated to all HTML5 games independently from game engine and to add artificial intelligence dynamics to these games.

  2. 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.

  3. JACOS: AI-based simulation system for man-machine system behavior in NPP

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment; Kawase, Katsumi [CSK Corp., Tokyo (Japan); Komiya, Akitoshi [Computer Associated Laboratory, Inc., Hitachinaka, Ibaraki (Japan)

    2001-08-01

    A prototype 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 a 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 taken into account. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. This report is prepared as User's Manual for JACOS. The first chapter of this report describes both operator and plant models in detail. The second chapter includes instructive descriptions for program installation, building of a knowledge base for operator model, execution of simulation and analysis of simulation results. The examples of simulation with JACOS are shown in the third chapter. (author)

  4. 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.

  5. Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

    Science.gov (United States)

    Olsher, Daniel

    2014-10-01

    Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies.

  6. An Energy Dense-AI-NaBH4-PEMFC Based Power Generator for Unmanned Undersea Vehicles

    Science.gov (United States)

    2016-03-01

    combination of two hydrides (NaBH4 and KBH4) in the presence ofNaOH stabilizer resulted in the improvement in mechanical and chemical stability of...liquid phases in the NaBH4 slurry. Furthermore, KBH4 is less reactive than NaBH4 in aqueous so lutions which also adds to the improved chemical ...generation unit consisting ofNaBH4 delivery system, AI-NaBH4 hydrolysis reactor and PEMFC has been designed, assembled and performance tested. The unit ’ s

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. Estimating ship emissions based on AIS data for port of Tianjin, China

    Science.gov (United States)

    Chen, Dongsheng; Zhao, Yuehua; Nelson, Peter; Li, Yue; Wang, Xiaotong; Zhou, Ying; Lang, Jianlei; Guo, Xiurui

    2016-11-01

    A detailed exhaust emission inventory of ships by using Automatic Identification System (AIS) data was developed for Tianjin Port, one of the top 10 world container ports and the largest port in North China. It was found that in 2014, ship emissions are 2.93 × 104, 4.13 × 104, 4.03 × 103, 3.72 × 103, 1.72 × 103 and 3.57 × 103 tonnes of SO2, NOx, PM10, PM2.5, NMVOC and CO respectively, which are equivalent to 11.07%, 9.40%, 2.43%, 3.10%, 0.43% and 0.16% respectively of the non-ship anthropogenic totals in Tianjin. The total CO2 emissions is approximately 1.97 × 106 tonnes. The container ships and dry bulk cargo ships contributed about 70% of the total ship emissions of NOx, SO2 and PM10. Pollutants were mainly emitted during cruise and hotelling modes, and the highest intensities of emissions located in the vicinity of fairways, berth and anchorage areas in Tianjin Port. Distinctive difference between the lowest (February) and the highest (September) monthly emissions is due to the adjustment of freight volume during the Chinese New Year and the months before and after it.

  14. 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.

  15. 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

  16. 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。

  17. The Availability Of Automatic Identification System (AIS Based On Latency Position Reports In The Gulf Of Gdansk

    Directory of Open Access Journals (Sweden)

    Jaskólski Krzysztof

    2014-06-01

    Full Text Available The problem of determining geographic position considered only in terms of measurement error, seems to be solved on a global scale. In view of the above, from the nineties, the operational characteristics of radio-navigation systems 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 position reports. For this purpose, the statistical summary of Latency Position Reports has been presented. The navigation data recordings were conducted during 30 days of March 2014 from 19 vessels located in area of Gulf of Gdansk. On the base of Latency Position Reports it is possible to designate the availability of AIS system.

  18. 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

  19. Dicty_cDB: FC-AI13 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI13 (Link to dictyBase) - - - Contig-U16263-1 FC-AI13F (Li...nk to Original site) FC-AI13F 507 - - - - - - Show FC-AI13 Library FC (Link to library) Clone ID FC-AI13 (Li.../dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI13Q.Seq.d/ Representative seq. ID FC-AI...13F (Link to Original site) Representative DNA sequence >FC-AI13 (FC-AI13Q) /CSM/FC/FC-AI/FC-AI13Q.Seq....nificant alignments: (bits) Value FC-AI13 (FC-AI13Q) /CSM/FC/FC-AI/FC-AI13Q.Seq.d/ 963 0.0 VSA730 (VSA730Q)

  20. 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.

  1. Structure-based discovery and experimental verification of novel AI-2 quorum sensing inhibitors against Vibrio harveyi.

    Science.gov (United States)

    Li, Minyong; Ni, Nanting; Chou, Han-Ting; Lu, Chung-Dar; Tai, Phang C; Wang, Binghe

    2008-08-01

    Quorum sensing has been implicated in the control of pathologically relevant bacterial behavior such as secretion of virulence factors, biofilm formation, sporulation, and swarming motility. The AI-2 quorum sensing pathway is found in both gram-positive and gram-negative bacteria. Therefore, antagonizing AI-2 quorum sensing is a possible approach to modifying bacterial behaviour. However, efforts in developing inhibitors of AI-2-mediated quorum sensing are especially lacking. High-throughput virtual screening using the V. harveyi LuxP crystal structure identified two compounds that were found to antagonize AI-2-mediated quorum sensing in V. harveyi without cytotoxicity. The sulfone functionality of these inhibitors was identified as critical to their ability to mimic the natural ligand in their interactions with Arg 215 and Arg 310 of the active site.

  2. 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.

  3. 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

  4. 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.

  5. Interarea Power System Oscillations Damping via AI-based Referential Integrity Variable-Structure Control

    Science.gov (United States)

    Ebrahim, M. A.; Ramadan, H. S.

    2016-10-01

    The design of power system stabilizer (PSS) is load-dependent and needs continuous adjustment at each operating condition. This paper aims at introducing a robust non-fragile PSS for interconnected power systems. The proposed controller has the capability of adaptively tuning online its rule-base through a variable-structure direct adaptive control algorithm in order to rigorously attain the desired objectives. The PSS controller acts on damping the electromechanical modes of oscillations not only through a wide range of operating conditions but also in presence of different disturbances. Using MATLABTM-Simulink, simulation results significantly verify that the proposed controller provides favorable performance and efficiently contributes towards enhancing the system dynamic behavior when applied to the four machines two-area power system that mimics the typical system behavior in actual operation. The interaction between the variable-structure adaptive fuzzy-based power system stabilizer (VS-AFPSS) and the existed typical ones inside the interconnected power systems has been explicitly discussed. Compared to other conventional controllers, VS-AFPSS enables better damping characteristics to both local and inter-area oscillation modes considering different operating conditions and sever disturbances.

  6. 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

  7. 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.

  8. Dicty_cDB: FC-AI03 [Dicty_cDB

    Lifescience Database Archive (English)

    Full Text Available FC (Link to library) FC-AI03 (Link to dictyBase) - - - Contig-U15833-1 FC-AI03P (Li...nk to Original site) FC-AI03F 690 FC-AI03Z 651 FC-AI03P 1341 - - Show FC-AI03 Library FC (Link to library) Clone ID FC-AI...nal site URL http://dictycdb.biol.tsukuba.ac.jp/CSM/FC/FC-AI/FC-AI03Q.Seq.d/ Representative seq. ID FC-AI...03P (Link to Original site) Representative DNA sequence >FC-AI03 (FC-AI03Q) /CSM/FC/FC-AI/FC-AI...li*l*ivtlskv*qswyqvfcslmrftcwi*nv fht*ivhwsqhwhql*flqpivaiv*skapitifnlhmaslwif*ivl*sfvpfhiitmk sfkfspfvpqlki

  9. 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

  10. 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

  11. 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

  12. CASCADE: Introducing AI into CBT.

    Science.gov (United States)

    Hendley, R. J.; Jurascheck, N.

    1992-01-01

    Discusses changes in training requirements of commerce and industry in the United Kingdom and describes a project, CASCADE, that was developed to investigate and implement the introduction of artificial intelligence (AI) techniques into computer-based training (CBT). An overview of pilot projects in higher education settings is provided. (eight…

  13. AIS Data Base Generation.

    Science.gov (United States)

    1981-04-01

    in Bartlett [1932], and much earlier, by Kant in his Critique of Pure Reason (1787). Recent discussion of related issues can be found in ( Goffman 1974...University of Cali- fornia at Berkeley, 1975, pp. 123-131 Gotfman, Erving . [1974] Frame Analysis, New York: Harper and Row, 1974. Grishman, R. and L

  14. Computer vulnerability in information- based AIS and optimization of control%面向信息化的AIS计算机漏洞与控制优化

    Institute of Scientific and Technical Information of China (English)

    刘雪晶; 潘荣根; 聂铁铸

    2012-01-01

    AIS从构建开始到寿命结束都面临着各种风险,而计算机漏洞已成为信息化下AIS越来越致命的风险因素之一.针对信息化下AIS在应用程序设计、系统运行、数据库系统、网络系统等方面面临的计算机漏洞以及应采取的控制目标进行了分析和探讨.%AIS faces a variety of risks from its beginning stage to its ending of longevity, and the computer vulnerability has been one of the fatal risks in information - based AIS. An analysis is made of computer vulnerability in information- based AIS concerning application program design, system operation, database system and network system, and some control measures are proposed.

  15. T'ai Chi

    Science.gov (United States)

    ... who practice it wear a martial arts training uniform. T'ai chi is usually practiced barefoot or ... health problem. Is your schedule jam-packed with school, work, and social activities? Here are a few ...

  16. 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

  17. 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

  18. 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

  19. Prediction based on mean subset

    DEFF Research Database (Denmark)

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

    2002-01-01

    , 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......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...

  20. 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.

  1. 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

  2. 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...

  3. 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

  4. 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...... og enkeltpersoner med interesse for natur- og miljøforhold i Danmark. Yderligere oplysninger om Areal Informations Systemet, herunder adgangsforhold, kan fås på DMU's hjemmeside med følgende adresse: http://ais.dmu.dk/ Projektet er finansieret af Miljø- og Energiministeriet, mens en række...... (planlægning, beskyttelse), geologi (overfladegeologi og marin geologi), samt marin dybdemodel. # Etablering af dataadgang for Miljø- og Energiministeriets institutioner til AIS-data. # Afprøvning af systemet i forhold til konkrete projekter. # Formidling af produkterne til brugerkredsen....

  5. 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

  6. 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

  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-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

  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. 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

  11. 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....

  12. 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.

  13. Concentration of apolipoprotein B is comparable with the apolipoprotein B/apolipoprotein A-I ratio and better than routine clinical lipid measurements in predicting coronary heart disease mortality: findings from a multi-ethnic US population

    Science.gov (United States)

    Sierra-Johnson, Justo; Fisher, Rachel M.; Romero-Corral, Abel; Somers, Virend K.; Lopez-Jimenez, Francisco; Öhrvik, John; Walldius, Göran; Hellenius, Mai-Lis; Hamsten, Anders

    2009-01-01

    Aims Prospective studies indicate that apolipoprotein measurements predict coronary heart disease (CHD) risk; however, evidence is conflicting, especially in the US. Our aim was to assess whether measurements of apolipoprotein B (apoB) and apolipoprotein A-I (apoA-I) can improve the ability to predict CHD death beyond what is possible based on traditional cardiovascular (CV) risk factors and clinical routine lipid measurements. Methods and results We analysed prospectively associations of apolipoprotein measurements, traditional CV risk factors, and clinical routine lipid measurements with CHD mortality in a multi-ethnic representative subset of 7594 US adults (mean age 45 years; 3881 men and 3713 women, median follow-up 124 person-months) from the Third National Health and Nutrition Examination Survey mortality study. Multiple Cox-proportional hazards regression was applied. There were 673 CV deaths of which 432 were from CHD. Concentrations of apoB [hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.09–3.61], apoA-I (HR 0.48, 95% CI 0.27–0.85) and total cholesterol (TC) (HR 1.17, 95% CI 1.02–1.34) were significantly related to CHD death, whereas high density lipoprotein cholesterol (HDL-C) (HR 0.68, 95% CI 0.45–1.05) was borderline significant. Both the apoB/apoA-I ratio (HR 2.14, 95% CI 1.11–4.10) and the TC/HDL-C ratio (HR 1.10, 95% CI 1.04–1.16) were related to CHD death. Only apoB (HR 2.01, 95% CI 1.05–3.86) and the apoB/apoA-I ratio (HR 2.09, 95% CI 1.04–4.19) remained significantly associated with CHD death after adjusting for CV risk factors. Conclusion In the US population, apolipoprotein measurements significantly predict CHD death, independently of conventional lipids and other CV risk factors (smoking, dyslipidaemia, hypertension, obesity, diabetes and C-reactive protein). Furthermore, the predictive ability of apoB alone to detect CHD death was better than any of the routine clinical lipid measurements. Inclusion of apolipoprotein

  14. Parameters Characteristics Analysis of Ship Traffic Flow with Cellular Automata Model on AIS-based%基于 AIS 的元胞自动机模型的船舶交通流特征参数分析

    Institute of Scientific and Technical Information of China (English)

    冯宏祥; 孔凡邨; 肖英杰; 杨小军

    2014-01-01

    针对海上交通工程学中的密度-速度(流量)关系图“线性假设”的不严密性,利用基于 AIS的元胞自动机船舶交通流模型模拟再现船舶交通流,然后统计出船舶交通流密度-速度(流量)基本关系图,并给出其三相交通流理论的解释;模拟研究发现,船舶交通流包含自由流、同步流和拥挤流三种相态,相态之间的转换也包含自由流与同步流、同步流与堵塞流两种形式;船舶密度-速度之间并非简单的线性关系,船舶密度-流量之间也不是二次抛物线关系,而是不明确的多值关系。模拟方法和结论有助于解释一些复杂的水上交通现象。%Against the non-stringency of linear hypothsis in ship traffic density-velocity equations in marine traffic engineering ,and as per the ship cellular automata model on AIS-based ,simlation is car-ried ot to reproduce ship traffic flow of waterway in micro level .The simulation results are refined and new ship traffic density-velocity (flux ) non-linear equations are presented ;T he three-phase traffic the-ory explication is given .the simulation results show that ship traffic contains free flow ,synchronized flow and congestion flow ;and phase changes contain free 1 synchronized and synchronized 1 jam .The sim-ulation method and conclusions could help to explain some complex phenomena of marine traffic .

  15. 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.

  16. 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...

  17. Data-Based Predictive Control with Multirate Prediction Step

    Science.gov (United States)

    Barlow, Jonathan S.

    2010-01-01

    Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  18. Risk Reducing Effect of AIS Implementation on Collision Risk

    DEFF Research Database (Denmark)

    Lützen, Marie; Friis-Hansen, Peter

    2003-01-01

    AIS (Automatic Identification System) is a transponder system developed for sea traffic purposes. The system sends and receives important ship information and other safety-related information between other ships and shore-based AIS stations. The implementation of AIS has now been initiated and......, 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...

  19. 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...

  20. 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.

  1. 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.

  2. Preparation of mucosal nanoparticles and polymer-based inactivated vaccine for Newcastle disease and H9N2 AI viruses

    Directory of Open Access Journals (Sweden)

    Heba M. El Naggar

    2017-02-01

    Full Text Available Aim: To develop a mucosal inactivated vaccines for Newcastle disease (ND and H9N2 viruses to protect against these viruses at sites of infections through mucosal immunity. Materials and Methods: In this study, we prepared two new formulations for mucosal bivalent inactivated vaccine formulations for Newcastle and Avian Influenza (H9N2 based on the use of nanoparticles and polymer adjuvants. The prepared vaccines were delivered via intranasal and spray routes of administration in specific pathogen-free chickens. Cell-mediated and humoral immune response was measured as well as challenge trial was carried out. In addition, ISA71 water in oil was also evaluated. Results: Our results showed that the use of spray route as vaccination delivery method of polymer and nanoparticles MontanideTM adjuvants revealed that it enhanced the cell mediated immune response as indicated by phagocytic activity, gamma interferon and interleukin 6 responses and induced protection against challenge with Newcastle and Avian Influenza (H9N2 viruses. Conclusion: The results of this study demonstrate the potentiality of polymer compared to nanoparticles adjuvantes when used via spray route. Mass application of such vaccines will add value to improve the vaccination strategies against ND virus and Avian influenza viruses.

  3. Collective prediction based on community structure

    Science.gov (United States)

    Jiang, Yasong; Li, Taisong; Zhang, Yan; Yan, Yonghong

    2017-01-01

    Collective prediction algorithms have been used to improve performances when network structures are involved in prediction tasks. The training dataset of such tasks often contain information of content, links and labels, while the testing dataset have only content and link information. Conventional collective prediction algorithms conduct predictions based on the content of a node and the information of its direct neighbors with a base classifier. However, the information of some direct neighbor nodes may be not consistent with the target one. In addition, the information of indirect neighbors can be helpful when that of direct neighbors is scant. In this paper, instead of using information of direct neighbors, we propose to apply community structures in networks to prediction tasks. A community detection method is aggregated into the collective prediction process to improve prediction performance. Experimental results show that the proposed algorithm outperforms a number of standard prediction algorithms specially under conditions that labeled training dataset are limited.

  4. 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.

  5. 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.

  6. 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.

  7. Tactical AI in Real Time Strategy Games

    Science.gov (United States)

    2015-03-26

    a parent , and each parent can have multiple children based on what decision options are available at the parent node. The search evaluates each child...The real time strategy (RTS) tactical decision making problem is a difficult problem. It is generally more complex due to its high degree of time...evolutionary algorithms (MOEAs) in this tactical decision making problem allows an AI agent to make fast, effective solutions that do not require modification

  8. 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...

  9. 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

  10. Progesterone (CIDR)-based timed AI protocols using GnRH, porcine LH or estradiol cypionate for dairy heifers: ovarian and endocrine responses and pregnancy rates.

    Science.gov (United States)

    Ambrose, J D; Kastelic, J P; Rajamahendran, R; Aali, M; Dinn, N

    2005-10-15

    The overall objective was to compare the efficacy of GnRH, porcine LH (pLH) and estradiol cypionate (ECP), in a modified Ovsynch/fixed-time AI (FTAI) protocol that included a controlled internal drug [progesterone] release (CIDR) device. In Experiment 1, heifers received a CIDR on Day -10, and PGF (25mg) on Day -3. At CIDR insertion, heifers received 100 microg of GnRH (n=6), 0.5mg of ECP (n=6), 5.0mg of pLH (n=6) or 2 mL of saline (n=7); these treatments were repeated on Day -1, except for ECP, that was repeated on Day -2, concurrent with CIDR-removal. The 5.0 mg pLH was the least effective with a longer interval to ovulation than the other groups combined (102 versus 64 h; PpLH compared to all other groups (4.5 versus 10.3 ng/mL; PpLH (n=6; pLH-low), 25.0 mg pLH (n=6, pLH-high), or 100 microg GnRH (n=5; control). Heifers in the pLH-high group had greater (PpLH treatments did not differ (P>0.10). Area under the curve for LH (ng/32 h) was at least 50% greater (PpLH-treated heifers compared to GnRH-treated heifers (mean, 41.3, 56.3 and 20.3 for pLH-low, pLH-high and GnRH, respectively). Ovulation occurred in 15 of 17 heifers. Progesterone concentrations were higher on Days 9 and 14 in heifers given 25mg of pLH, suggesting enhanced CL function. In Experiment 3, 240 heifers were assigned to CIDR-based Ovsynch/FTAI protocols. The first and second hormonal treatments (with an intervening PGF treatment on Day -3) were GnRH/GnRH (100 microg), ECP/ECP (0.5 mg), pLH/pLH (12.5 mg) or GnRH/ECP, respectively; pregnancy rates were 58.7, 66.1, 45.9 and 48.3%, respectively (ECP/ECP>both pLH/pLH and GnRH/ECP; Pbased Ovsynch/FTAI protocols using either GnRH/GnRH or ECP/ECP yielded pregnancy rates about 20% points higher than previously reported for dairy heifers bred to Ovsynch/FTAI in the absence of a CIDR.

  11. 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

  12. Type Recognition of Air-Attack Target Based on VD-AiNet Cluster Algorithm%基于VD-AiNet聚类算法的空袭目标类型识别

    Institute of Scientific and Technical Information of China (English)

    范海雄; 刘付显

    2011-01-01

    To solve the problem of recognizing aerial defense and antimissile target type, based on the analysis of the primary air-attack target types, important useful factors and primary recognition principles , the vector distance primary artificial immune network cluster algorithm of artificial immune algorithm is used in the model of antibody swatch training. Furthermore, the side-by-side decision making model of antibody training and target recognition are established. Finally, the algorithm and model is validated with examples, proving the utility and effectiveness of the algorithm and model.%针对防空反导作战中空袭目标类型识别问题,在分析空袭目标的主要类型、识别指标及其识别原则的基础上,将人工免疫算法中矢量距人工免疫网络聚类算法应用于抗体样本训练模块,并建立了抗体训练和目标识别的并行决策模型.最后进行了算例验证,结果表明了算法和模型的可行性和有效性.

  13. 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...

  14. 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.

  15. 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.

  16. 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.

  17. 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 ...

  18. 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...

  19. AIS ASM Operational Integration Plan

    Science.gov (United States)

    2013-08-01

    River , WA; and the future Vessel Traffic Service systems being developed under PAWSS. Interfacing the AIS Transmit architecture with agencies that...USACE). The NMS provides a variety of important marine protection information to the mariner such as Seasonal Management Areas, Right Whale Listening...provides accurate real-time information such as water levels, currents, and other oceanographic and meteorological data. The USACE provide river lock

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Massive AIS Data Storage Method Based on ElasticSearch%基于ElasticSearch的海量AIS数据存储方法

    Institute of Scientific and Technical Information of China (English)

    郑义成; 莫钦华; 王海鸿

    2016-01-01

    船舶自动识别系统(AIS)数据具有海量性、时空性和小记录频繁更新等特性.面向海量AIS数据,提出了一种基于分布式集群的AIS数据存储方法.该方法对AIS数据存储索引结构进行了设计,通过对时间维按月切分,以及对空间范围聚类切分,构造了索引时空立方体,从而提升了时空查询效率.讨论了索引分片和副本设计与数据安全性和系统服务可靠性的关系,分析了时空立方体性能,并针对索引分片与副本数对索引性能的影响,进行了理论分析和试验验证.

  5. AiResearch QCGAT engine: Acoustic test results

    Science.gov (United States)

    Kisner, L. S.

    1980-01-01

    The noise levels of the quiet, general aviation turbofan (QCGAT) engine were measured in ground static noise tests. The static noise levels were found to be markedly lower than the demonstrably quiet AiResearch model TFE731 engine. The measured QCGAT noise levels were correlated with analytical noise source predictions to derive free-field component noise predictions. These component noise sources were used to predict the QCGAT flyover noise levels at FAR Part 36 conditions. The predicted flyover noise levels are about 10 decibels lower than the current quietest business jets.

  6. USACE AIS Transmit Technical Support Summary Report

    Science.gov (United States)

    2014-09-01

    USACE AIS Transmit Technical Support Summary Report Distribution Statement A: Approved for public release; distribution is unlimited...September 2014 Report No. CD-D-09-15 USACE AIS Transmit Technical Support Summary Report ii UNCLAS//Public | CG-926 RDC | I. Gonin et al. Public...States Coast Guard Research & Development Center 1 Chelsea Street New London, CT 06320 USACE AIS Transmit Technical Support Summary Report

  7. 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.

  8. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  9. Health Care, Capabilities, and AI Assistive Technologies

    NARCIS (Netherlands)

    Coeckelbergh, Mark

    2010-01-01

    Scenarios involving the introduction of artificially intelligent (AI) assistive technologies in health care practices raise several ethical issues. In this paper, I discuss four objections to introducing AI assistive technologies in health care practices as replacements of human care. I analyse them

  10. 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…

  11. 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,…

  12. 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

  13. 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.

  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

    The primary payloads on board the AAUSAT3 satellite are two different AIS receivers, one is a traditional hardware-based receiver, the other one is a software-defined radio receiver. The hardware-based receiver has been developed around an ADF-7020 transceiver, with an appropriate LNA in front...... 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...... at Aalborg University, basic telemetry and the first few AIS messages were downloaded. During the first 14 days of the mission, the two receivers managed to detect more than 100,000 different AIS messages from ships all around the world, and more than 35,000 of these messages have been successfully...

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

    DEFF Research Database (Denmark)

    Buchholz, Anke; Trapp, Stefan

    penetration as first crucial step can be modified by formulation whereas the active ingredient (AI) distribution within cells is usually solely determined by physicochemical properties. This passive AI distribution was calculated with the Fick-Nernst-Planck equation implemented in a cell model....... 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...

  16. 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.

  17. 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.

  18. 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 ...

  19. 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.

  20. 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...

  1. Artificial intelligence (AI) systems for interpreting complex medical datasets.

    Science.gov (United States)

    Altman, R B

    2017-02-09

    Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. The effect of type of vaginal insert and dose of pLH on embryo production, following fixed-time AI in a progestin-based superstimulatory protocol in Nelore cattle.

    Science.gov (United States)

    Nogueira, Marcelo F Gouveia; Fragnito, Paulo S; Trinca, Luzia A; Barros, Ciro M

    2007-02-01

    The objective was to analyze and report field data focusing on the effect of type of progesterone-releasing vaginal insert and dose of pLH on embryo production, following a superstimulatory protocol involving fixed-time artificial insemination (FTAI) in Nelore cattle (Bos taurus indicus). Donor heifers and cows (n = 68; 136 superstimulations over 2 years) received an intravaginal, progesterone-releasing insert (CIDR or DIB, with 1.9 or 1.0 g progesterone, respectively) and 3-4 mg of estradiol benzoate (EB) i.m. at random stages of the estrous cycle. Five days later (designated Day 0), cattle were superstimulated with a total of 120-200 mg of pFSH (Folltropin-V), given twice daily in decreasing doses from Days 0 to 3. All cattle received two luteolytic doses of PGF2alpha at 08:00 and 20:00 h on Day 2 and progesterone inserts were removed at 20:00 h on Day 3 (36 h after the first PGF2alpha injection). Ovulation was induced with pLH (Lutropin-V, 12.5 or 25 mg, i.m.) at 08:00 h on Day 4 with FTAI 12, 24 and in several cases, 36 h later. Embryos were recovered on Days 11 or 12, graded and transferred to synchronous recipients. Overall, the mean (+/-S.E.M.) number of total ova/embryos (13.3 +/- 0.8) and viable embryos (9.4 +/- 0.6) and pregnancy rate (43.5%; 528/1213) did not differ among groups, but embryo viability rate (overall, 70.8%) was higher in donors with a DIB (72.3%) than a CIDR (68.3%, P = 0.007). In conclusion, the administration of pLH 12 h after progesterone removal in a progestin-based superstimulatory protocol facilitated fixed-time AI in Nelore donors, with embryo production, embryo viability and pregnancy rates after embryo transfer, comparable to published results where estrus detection and AI was done. Results suggested a possible alternative, which would eliminate the need for estrus detection in donors.

  7. 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...

  8. 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.

  9. 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.

  10. Activity Prediction: A Twitter-based Exploration

    NARCIS (Netherlands)

    Weerkamp, W.; de Rijke, 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, th

  11. Two new Oxalis species (Oxalidaceae) from the Ai-Ais / Richtersveld Transfrontier Park, South Africa

    NARCIS (Netherlands)

    Roets, F.; Oberlander, K.C.; Dreyer, L.L.

    2012-01-01

    South Africa has a rich, but scantily surveyed, desert flora. Documenting annual and geophytic species in this biome is challenging, as they usually only flower after adequate precipitation, which is characteristically erratic and infrequent. Recent floristic surveys in the Ai-Ais / Richtersveld Tra

  12. Enhanced AIS receiver design for satellite reception

    Science.gov (United States)

    Clazzer, Federico; Lázaro, Francisco; Plass, Simon

    2016-12-01

    The possibility to detect Automatic Identification System (AIS) messages from low earth orbit (LEO) satellites paves the road for a plurality of new and unexplored services. Besides worldwide tracking of vessels, maritime traffic monitoring, analysis of vessel routes employing big data, and oceans monitoring are just few of the fields, where satellite-aided AIS is beneficial. Designed for ship-to-ship communication and collision avoidance, AIS satellite reception performs poorly in regions with a high density of vessels. This calls for the development of advanced satellite AIS receivers able to improve the decoding capabilities. In this context, our contribution focuses on the introduction of a new enhanced AIS receiver design and its performance evaluation. The enhanced receiver makes use of a coherent receiver for the low signal-to-noise ratio (SNR) region, while for medium to high SNRs, a differential Viterbi receiver is used. Additional novelty of our work is in the exploitation of previously decoded packets from one vessel that is still under the LEO reception range, to improve the vessel detection probability. The assessment of the performance against a common receiver is done making the use of a simple and tight model of the medium access (MAC) layer and the multi-packet reception (MPR) matrix for physical layer (PHY) representation. Performance results show the benefits of such enhanced receiver, especially when it is bundled with successive interference cancellation (SIC).

  13. 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.

  14. Heart Attack Prediction System Based Neural Arbitration

    OpenAIRE

    Helwan, Abdulkader

    2015-01-01

    Heart attack is an asymptomatic and epidemic medical condition that may suddenly occur and causes “death”. Therefore, it is a life-threatening condition and it should be detected before it occurs. Heart attack is so far predicted using the conventional ways of doctor’s examination and by performing some medical tests such as stress test, ECG, and heart CTScan etc. The coronary vessels constriction, the cholesterol levels in the arteries, and other attributes can be good indicators for making ...

  15. Strategic Team AI Path Plans: Probabilistic Pathfinding

    Directory of Open Access Journals (Sweden)

    Tng C. H. John

    2008-01-01

    Full Text Available This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002, in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006. We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

  16. 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.

  17. 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.

  18. 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.

  19. AIS Algorithm for Smart Antenna Application in WLAN

    Directory of Open Access Journals (Sweden)

    Evizal Abdul Kadir

    2015-07-01

    Full Text Available Increasing numbers of wireless local area networks (WLAN replacing wired networks have an impact on wireless network systems, causing issues such as interference. The smart antenna system is a method to overcome interference issues in WLANs. This paper proposes an artificial immune system (AIS for a switch beam smart antenna system. A directional antenna is introduced to aim the beam at the desired user. The antenna consists of 8 directional antennas, each of which covers 45 degrees, thus creating an omnidirectional configuration of which the beams cover 360 degrees. To control the beam switching, an inexpensive PIC 16F877 microchip was used. An AIS algorithm was implemented in the microcontroller, which uses the received radio signal strength of the mobile device as reference. This is compared for each of the eight beams, after which the AIS algorithm selects the strongest signal received by the system and the microcontroller will then lock to the desired beam. In the experiment a frequency of 2.4 GHz (ISM band was used for transmitting and receiving. A test of the system was conducted in an outdoor environment. The results show that the switch beam smart antenna worked fine based on locating the mobile device.

  20. 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......We employ size-based theoretical arguments to derive simple analytic predictions of ecological patterns and properties of natural communities: size-spectrum exponent, maximum trophic level, and susceptibility to invasive species. The predictions are brought about by assuming that an infinite number...... 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...

  1. 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)

  2. SNAP and AI Fuel Summary Report

    Energy Technology Data Exchange (ETDEWEB)

    Lords, R.E.

    1994-08-01

    The SNAP and AI Fuel Summary Report provides a detailed overview of treatment and storage of these fuels from fabrication through current storage including design parameters and reactor history. Chemical and physical characteristics are described, and potential indicators of as-stored fuel conditions are emphasized.

  3. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

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

    2016-01-01

    E)) and a size-structured fish community model. The models were compared with respect to predicted ecological consequences of fishing to identify commonalities and differences in model predictions for the California Current fish community. We compared the models regarding direct and indirect responses to fishing...... on one or more species. The size-based model predicted a higher fishing mortality needed to reach maximum sustainable yield than EwE for most species. The size-based model also predicted stronger top-down effects of predator removals than EwE. In contrast, EwE predicted stronger bottom-up effects...... 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...

  4. Transmission line icing prediction based on DWT feature extraction

    Science.gov (United States)

    Ma, T. N.; Niu, D. X.; Huang, Y. L.

    2016-08-01

    Transmission line icing prediction is the premise of ensuring the safe operation of the network as well as the very important basis for the prevention of freezing disasters. In order to improve the prediction accuracy of icing, a transmission line icing prediction model based on discrete wavelet transform (DWT) feature extraction was built. In this method, a group of high and low frequency signals were obtained by DWT decomposition, and were fitted and predicted by using partial least squares regression model (PLS) and wavelet least square support vector model (w-LSSVM). Finally, the final result of the icing prediction was obtained by adding the predicted values of the high and low frequency signals. The results showed that the method is effective and feasible in the prediction of transmission line icing.

  5. 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.

  6. Speed control model of ships entering and leaving ports based on AIS data%基于AIS信息的船舶进出港航速控制模型

    Institute of Scientific and Technical Information of China (English)

    肖潇; 邵哲平; 纪贤标; 陈玲玲

    2014-01-01

    To improve near-port channel efficiency and ship safety,a speed control model of ships ente-ring and leaving ports is built based on the regular,efficient and potential information on ships entering and leaving ports from a great deal of marine traffic characteristic information collected by Automatic Identification System (AIS). According to the marine traffic engineering theory and data mining technolo-gy,AIS real-time data collected in port waters is stored into a database,and then the database is pro-cessed by ETL (Extract Transform Load). The database is of the function of statistical analysis on every stage speed distribution of ships entering and leaving ports. A container terminal of Songyu Port in Xia-men is taken for example,and a corresponding database is built. The statistics on the speed of 120 000 t container carrier entering and leaving ports is made to obtain the speed distribution. Then,some sugges-tions are given according to the probability distribution characteristics of the speed. It provides a theoreti-cal basis for ships to automatically enter and leave ports,pilots to pilot ships,and port authority depart-ments to better supervise navigation environment.%为提升近港航道效率及船舶安全,从船舶自动识别系统(Automatic Identification System, AIS)集合的大量的海上交通特征信息中获取能够反映船舶进出港规律的、有效的、潜在的信息,建立船舶进出港航速控制模型。根据海上交通工程理论和数据挖掘技术,将实时采集的港口水域的AIS信息存放在数据库中,再通过ETL (Extract Transform Load )进行处理。该数据库具备统计分析船舶进出港各阶段航速分布的功能。以厦门嵩屿港集装箱码头为例,构建相应的数据库,对12万吨级集装箱船舶进出港的航速进行统计,获得此类船舶在进出港过程中的航速分布规律,并根据航速的概率分布特点给出建议航速,为船舶自动

  7. 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.

  8. 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.

  9. 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...

  10. Prediction based chaos control via a new neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Liqun [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Liu Wanyu [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China); Sun Guanghui [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)

    2008-11-17

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.

  11. Identification of two apolipoprotein variants, A-I Karatsu (Tyr 100-->His) and A-I Kurume (His 162-->Gln).

    Science.gov (United States)

    Moriyama, K; Sasaki, J; Matsunaga, A; Takada, Y; Kagimoto, M; Arakawa, K

    1996-02-01

    We identified two apolipoprotein (apo) A-I variants, using isoelectric focusing gel electrophoresis: apo A-I Karatsu, which had a relative charge of +1 compared to normal apo A-I4, and apo A-I Kurume, which had a relative charge of -1. Direct sequence analysis of the PCR-amplified DNA from the proband of apo A-I Karatsu revealed a single substitution of tyrosine (TAC) for histidine (CAC) at position 100. Sequence analysis of apo A-I Kurume revealed a single substitution of histidine (CAT) for glutamine (CAG) at position 162. Probands of these two mutants and limited family study showed no accelerated atherosclerosis.

  12. 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.

  13. 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.

  14. 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.

  15. 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 ...

  16. 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...

  17. Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions.

    Science.gov (United States)

    Ma, Jianfen; Hu, Yi; Loizou, Philipos C

    2009-05-01

    The articulation index (AI), speech-transmission index (STI), and coherence-based intelligibility metrics have been evaluated primarily in steady-state noisy conditions and have not been tested extensively in fluctuating noise conditions. The aim of the present work is to evaluate the performance of new speech-based STI measures, modified coherence-based measures, and AI-based measures operating on short-term (30 ms) intervals in realistic noisy conditions. Much emphasis is placed on the design of new band-importance weighting functions which can be used in situations wherein speech is corrupted by fluctuating maskers. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train, and street interferences). Of all the measures considered, the modified coherence-based measures and speech-based STI measures incorporating signal-specific band-importance functions yielded the highest correlations (r=0.89-0.94). The modified coherence measure, in particular, that only included vowel/consonant transitions and weak consonant information yielded the highest correlation (r=0.94) with sentence recognition scores. The results from this study clearly suggest that the traditional AI and STI indices could benefit from the use of the proposed signal- and segment-dependent band-importance functions.

  18. Prediction of Betavoltaic Battery Output Parameters Based on SEM Measurements

    Directory of Open Access Journals (Sweden)

    E.B. Yakimov

    2016-12-01

    Full Text Available The approach for the prediction of betavoltaic battery output parameters based on EBIC investigations of semiconductor converters of beta-radiation energy into electric power is presented. Using this approach the parameters of battery based on porous Si are calculated. These parameters are compared with those of battery based on a planar Si p-n junction.

  19. Predicting links based on knowledge dissemination in complex network

    Science.gov (United States)

    Zhou, Wen; Jia, Yifan

    2017-04-01

    Link prediction is the task of mining the missing links in networks or predicting the next vertex pair to be connected by a link. A lot of link prediction methods were inspired by evolutionary processes of networks. In this paper, a new mechanism for the formation of complex networks called knowledge dissemination (KD) is proposed with the assumption of knowledge disseminating through the paths of a network. Accordingly, a new link prediction method-knowledge dissemination based link prediction (KDLP)-is proposed to test KD. KDLP characterizes vertex similarity based on knowledge quantity (KQ) which measures the importance of a vertex through H-index. Extensive numerical simulations on six real-world networks demonstrate that KDLP is a strong link prediction method which performs at a higher prediction accuracy than four well-known similarity measures including common neighbors, local path index, average commute time and matrix forest index. Furthermore, based on the common conclusion that an excellent link prediction method reveals a good evolving mechanism, the experiment results suggest that KD is a considerable network evolving mechanism for the formation of complex networks.

  20. 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.

  1. Fatty acid modulation of autoinducer (AI-2) influenced growth and macrophage invasion by Salmonella Typhimurium.

    Science.gov (United States)

    Widmer, Kenneth W; Jesudhasan, Palmy; Pillai, Suresh D

    2012-03-01

    Autoinducer-2 (AI-2) is a small molecule that is involved in bacterial cell-to-cell signaling whose precursor formation is mediated by luxS. A luxS mutant of Salmonella Typhimurium PJ002 (ΔluxS) was grown in glucose-containing M-9 minimal medium supplemented with varying concentrations (1×, 10×, and 100×) of long-chain fatty acids (linoleic acid, oleic acid, palmitic acid, and stearic acid) to study the influence of fatty acids on growth rate and macrophage invasion. Additionally, in vitro synthesized AI-2 was added to this medium to identify the influence of AI-2 on S. Typhimurium PJ002 (ΔluxS) growth rate and macrophage invasion. The growth rate constant (k) for each experimental treatment was determined based on OD₆₀₀ values recorded during 12 h of incubation. There was a significant (p=0.01) increase in the growth rate of S. Typhimurium PJ002 (ΔluxS) in the presence of AI-2 when compared to the phosphate-buffered saline (PBS) control. However, fatty acids either singly or in a mixture were unable to influence AI-2's effect on growth rate. The presence of AI-2 significantly (p=0.02) decreased the invasiveness of S. Typhimurium PJ002 (ΔluxS) towards the murine macrophage cell line, RAW 264.7. However, the fatty acid mixture was able to reverse this reduction and restore invasiveness to background levels. These results suggest that, while AI-2 may enhance the growth rate and reduce macrophage invasion by the luxS mutant S. Typhimurium PJ002 (ΔluxS), fatty acids may influence the virulence in S. Typhimurium (PJ002) by modulating AI-2 activity.

  2. Deploying Embodied AI into Virtual Worlds

    Science.gov (United States)

    Burden, David J. H.

    The last two years have seen the start of commercial activity within virtual worlds. Unlike computer games where Non-Player-Character avatars are common, in most virtual worlds they are the exception — and until recently in Second Life they were non-existent. However there is real commercial scope for Als in these worlds — in roles from virtual sales staff and tutors to personal assistants. Deploying an embodied AI into a virtual world offers a unique opportunity to evaluate embodied Als, and to develop them within an environment where human and computer are on almost equal terms. This paper presents an architecture being used for the deployment of chatbot driven avatars within the Second Life virtual world, looks at the challenges of deploying an AI within such a virtual world, the possible implications for the Turing Test, and identifies research directions for the future.

  3. 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.

  4. 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…

  5. 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.

  6. 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.

  7. Effects of alloying elements on magnetic properties of bulk nanocrystalUne Fe3AI based materials%合金元素对块体纳米晶Fe3Al材料磁学性能的影响

    Institute of Scientific and Technical Information of China (English)

    喇培清; 胡苏磊; 白亚平; 魏玉鹏

    2012-01-01

    The bulk nanocrystalline Fe3A1 and Fe3AI with NilOwt%, Cr10wt%, MnlOwt% and NilOwt% - Cu2wt% elements were prepared by aluminothermic reaction, respectively. The hysteresis loop was measured by vibrating sample magnetometer (VSM) ,the crystal structures of the samples were characterized by X - ray diffractometer (XRD) and the average grain sizes were calculated. Results show that the magnetic hysteresis loop of the alloys have tilting shape and long narrow, which indicates that the hysteresis loss is small. The satu- ration magnetization Ms of the Fe3A1 with 10wt% Ni is larger and the residual magnetization Mr and coercivity He are smaller than those of other alloys and demonstrate good soft magnetic property. The grain size of Fe3 A1 becomes Smaller after adding alloying elements, but the magnetic property changes a lot. It could be concluded that the effect of alloying elements on OUS. magnetic property of nanocrystalline Fe3 A1 based materials is obvi-%为了研究合金元素对块体纳米晶Fe3Al材料磁学性能的影响,通过铝热反应熔化法制备了纳米晶Fe3Al以及分别含Ni质量分数10%、Cr质量分数10%、Mn质量分数10%和含Ni质量分数10%-Cu质量分数2%的块体纳米晶Fe3Al.在振动样品磁强计(VSM)上测得合金的磁滞回线,分析其磁性能,采用X射线衍射仪进行结构分析和平均晶粒尺寸计算.结果表明:各样品的磁滞回线呈倾斜状且狭长,磁滞损耗很小;含Ni质量分数10%的样品饱和磁化强度Ms较大,剩余磁化强度Mr和矫顽力Hc较其他样品最小,具有较好的软磁性能;添加合金元素后几种材料的晶粒尺寸变小,磁性能有较大变化,合金元素对纳米晶Fe3Al块体材料的磁性能影响明显.

  8. Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation.

    Science.gov (United States)

    Sun, Pingping; Chen, Wenhan; Huang, Yanxin; Wang, Hongyan; Ma, Zhiqiang; Lv, Yinghua

    2011-06-16

    Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.

  9. Epitope Prediction Based on Random Peptide Library Screening: Benchmark Dataset and Prediction Tools Evaluation

    Directory of Open Access Journals (Sweden)

    Yanxin Huang

    2011-06-01

    Full Text Available Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09–0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.

  10. 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...... day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models......, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each...

  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. The OASE project: Object-based Analysis and Seamless prediction

    Science.gov (United States)

    Troemel, Silke; Wapler, Kathrin; Bick, Theresa; Diederich, Malte; Deneke, Hartwig; Horvath, Akos; Senf, Fabian; Simmer, Clemens; Simon, Juergen

    2013-04-01

    The research group on Object-based Analysis and SEamless prediction (OASE) is part of the Hans Ertel Centre for Weather Research (HErZ). The group consists of scientists at the Meteorological Institute, University of Bonn, the Leibniz-Institute for Tropospheric Research in Leipzig and the German Weather Service. OASE addresses seamless prediction of convective events from nowcasting to daily predictions by combining radar/satellite compositing and tracking with high-resolution model-based ensemble generation and prediction. While observation-based nowcasting provides good results for lead times between 0-1 hours, numerical weather prediction addresses lead times between 3-21 hours. Especially the discontinuity between 1-3 hours needs to be addressed. Therefore a central goal of the project is a near real-time high-resolved unprecedented data base. A radar and satellite remote sensing-driven 3D observation-microphysics composite covering Germany, currently under development, contains gridded observations and estimated microphysical quantities. Observations and microphysics are intertwined via forward operators and estimated inverse relations, which also provide uncertainties for model ensemble initialisations. The lifetime evolution of dynamics and microphysics in (severe) convective storms is analysed based on 3D scale-space tracking. An object-based analysis condenses the information contained in the dynamic 3D distributions of observables and related microphysics into descriptors, which will allow identifying governing processes leading to the formation and evolution of severe weather events. The object-based approach efficiently characterises and quantifies the process structure and life cycles of severe weather events, and facilitates nowcasting and the generation and initialisation of model prediction ensembles. Observation-based nowcasting will exploit the dual-composite based 3D feature detection and tracking to generate a set of predictions (observation-based

  13. 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.

  14. 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.

  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. Endometrial cancer risk prediction including serum-based biomarkers

    DEFF Research Database (Denmark)

    Fortner, Renée T; Hüsing, Anika; Kühn, Tilman;

    2017-01-01

    Endometrial cancer risk prediction models including lifestyle, anthropometric, and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case......-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum...... concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines, and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at pdiscrimination was assessed using...

  18. Integrated Software Architecture-Based Reliability Prediction for IT Systems

    OpenAIRE

    Brosch, Franz

    2012-01-01

    With the increasing importance of reliability in business and industrial IT systems, new techniques for architecture-based software reliability prediction are becoming an integral part of the development process. This dissertation thesis introduces a novel reliability modelling and prediction technique that considers the software architecture with its component structure, control and data flow, recovery mechanisms, its deployment to distributed hardware resources and the system´s usage p...

  19. 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.

  20. 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.

  1. 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).

  2. Maritime surveillance with synthetic aperture radar (SAR) and automatic identification system (AIS) onboard a microsatellite constellation

    Science.gov (United States)

    Peterson, E. H.; Zee, R. E.; Fotopoulos, G.

    2012-11-01

    New developments in small spacecraft capabilities will soon enable formation-flying constellations of small satellites, performing cooperative distributed remote sensing at a fraction of the cost of traditional large spacecraft missions. As part of ongoing research into applications of formation-flight technology, recent work has developed a mission concept based on combining synthetic aperture radar (SAR) with automatic identification system (AIS) data. Two or more microsatellites would trail a large SAR transmitter in orbit, each carrying a SAR receiver antenna and one carrying an AIS antenna. Spaceborne AIS can receive and decode AIS data from a large area, but accurate decoding is limited in high traffic areas, and the technology relies on voluntary vessel compliance. Furthermore, vessel detection amidst speckle in SAR imagery can be challenging. In this constellation, AIS broadcasts of position and velocity are received and decoded, and used in combination with SAR observations to form a more complete picture of maritime traffic and identify potentially non-cooperative vessels. Due to the limited transmit power and ground station downlink time of the microsatellite platform, data will be processed onboard the spacecraft. Herein we present the onboard data processing portion of the mission concept, including methods for automated SAR image registration, vessel detection, and fusion with AIS data. Georeferencing in combination with a spatial frequency domain method is used for image registration. Wavelet-based speckle reduction facilitates vessel detection using a standard CFAR algorithm, while leaving sufficient detail for registration of the filtered and compressed imagery. Moving targets appear displaced from their actual position in SAR imagery, depending on their velocity and the image acquisition geometry; multiple SAR images acquired from different locations are used to determine the actual positions of these targets. Finally, a probabilistic inference

  3. Decadal prediction of Sahel rainfall using dynamics-based indices

    Science.gov (United States)

    Otero, Noelia; Mohino, Elsa; Gaetani, Marco

    2016-12-01

    At decadal time scales, the capability of state-of-the-art atmosphere-ocean coupled climate models in predicting the precipitation in Sahel is assessed. A set of 14 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is selected and two experiments are analysed, namely initialized decadal hindcasts and forced historical simulations. Considering the strong linkage of the atmospheric circulation signatures over West Africa with the rainfall variability, this study aims to investigate the potential of using wind fields for decadal predictions. Namely, a West African monsoon index (WAMI) is defined, based on the coherence of low (925 hPa) and high (200 hPa) troposphere wind fields, which accounts for the intensity of the monsoonal circulation. A combined empirical orthogonal functions analysis is applied to explore the wind fields' covariance modes, and a set of indices is defined on the basis of the identified patterns. The WAMI predictive skill is assessed by comparing WAMI from coupled models with WAMI from reanalysis products and with a standardized precipitation index (SPI) from observations. Results suggest that the predictive skill is highly model dependent and it is strongly related to the WAMI definition. In addition, hindcasts are more skilful than historical simulations in both deterministic and probability forecasts, which suggests an added value of initialization for decadal predictability. Moreover, coupled models are more skilful in predicting the observed SPI than the WAMI obtained from reanalysis. WAMI performance is also compared with decadal predictions from CMIP5 models based on a Sahelian precipitation index, and an improvement in predictive skill is observed in some models when WAMI is used. Therefore, we conclude that dynamics-based indices are potentially more effective for decadal prediction of precipitation in Sahel than precipitation-based indices for those models in which Sahel rainfall variability is not well

  4. Signature prediction for model-based automatic target recognition

    Science.gov (United States)

    Keydel, Eric R.; Lee, Shung W.

    1996-06-01

    The moving and stationary target recognition (MSTAR) model- based automatic target recognition (ATR) system utilizes a paradigm which matches features extracted form an unknown SAR target signature against predictions of those features generated from models of the sensing process and candidate target geometries. The candidate target geometry yielding the best match between predicted and extracted features defines the identify of the unknown target. MSTAR will extend the current model-based ATR state-of-the-art in a number of significant directions. These include: use of Bayesian techniques for evidence accrual, reasoning over target subparts, coarse-to-fine hypothesis search strategies, and explicit reasoning over target articulation, configuration, occlusion, and lay-over. These advances also imply significant technical challenges, particularly for the MSTAR feature prediction module (MPM). In addition to accurate electromagnetics, the MPM must provide traceback between input target geometry and output features, on-line target geometry manipulation, target subpart feature prediction, explicit models for local scene effects, and generation of sensitivity and uncertainty measures for the predicted features. This paper describes the MPM design which is being developed to satisfy these requirements. The overall module structure is presented, along with the specific deign elements focused on MSTAR requirements. Particular attention is paid to design elements that enable on-line prediction of features within the time constraints mandated by model-driven ATR. Finally, the current status, development schedule, and further extensions in the module design are described.

  5. [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.

  6. 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.

  7. 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 p......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...

  8. Revisiting AI-2 quorum sensing inhibitors: direct comparison of alkyl-DPD analogues and a natural product fimbrolide.

    Science.gov (United States)

    Lowery, Colin A; Abe, Takumi; Park, Junguk; Eubanks, Lisa M; Sawada, Daisuke; Kaufmann, Gunnar F; Janda, Kim D

    2009-11-04

    Quorum sensing (QS) systems have been discovered in a wide variety of bacteria, and mediate both intra- and interspecies communication. The AI-2-based QS system represents the most studied of these proposed interspecies systems and has been shown to regulate diverse functions such as bioluminescence, expression of virulence factors, and biofilm formation. As such, the development of modulatory compounds, both agonists and antagonists, is of great interest for the study of unknown AI-2-based QS systems and the potential treatment of bacterial infections. The fimbrolide class of natural products has exhibited excellent inhibitory activity against AI-2-based QS and as such may be considered the "gold standard" of AI-2 inhibitors. Thus, we sought to include a fimbrolide as a control compound for our recently developed alkyl-DPD panel of AI-2 modulators. Herein, we present a revised synthesis of a commonly studied fimbrolide as well as a direct comparison between the fimbrolide and alkyl-DPD analogues. We demonstrate that our alkyl-DPD analogues are more potent inhibitors of QS in both Vibrio harveyi and Salmonella typhimurium, the two organisms with defined AI-2 QS systems, and in doing so call into question the widely accepted use of fimbrolide-derived compounds as the "gold standard" of AI-2 inhibition.

  9. 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.

  10. 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.

  11. The effect of genealogy-based haplotypes on genomic prediction

    DEFF Research Database (Denmark)

    Edriss, Vahid; Fernando, Rohan L.; Su, Guosheng

    2013-01-01

    Background Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression...... on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using...... local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (pi) of the haplotype covariates had zero effect...

  12. Generalized ESO and Predictive Control Based Robust Autopilot Design

    Directory of Open Access Journals (Sweden)

    Bhavnesh Panchal

    2016-01-01

    Full Text Available A novel continuous time predictive control and generalized extended state observer (GESO based acceleration tracking pitch autopilot design is proposed for a tail controlled, skid-to-turn tactical missile. As the dynamics of missile are significantly uncertain with mismatched uncertainty, GESO is employed to estimate the state and uncertainty in an integrated manner. The estimates are used to meet the requirement of state and to robustify the output tracking predictive controller designed for nominal system. Closed loop stability for the controller-observer structure is established. An important feature of the proposed design is that it does not require any specific information about the uncertainty. Also the predictive control design yields the feedback control gain and disturbance compensation gain simultaneously. Effectiveness of GESO in estimation of the states and uncertainties and in robustifying the predictive controller in the presence of parametric uncertainties, external disturbances, unmodeled dynamics, and measurement noise is illustrated by simulation.

  13. 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%.

  14. 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.

  15. 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

  16. Fuzzy-Based Trust Prediction Model for Routing in WSNs

    Directory of Open Access Journals (Sweden)

    X. Anita

    2014-01-01

    Full Text Available The cooperative nature of multihop wireless sensor networks (WSNs makes it vulnerable to varied types of attacks. The sensitive application environments and resource constraints of WSNs mandate the requirement of lightweight security scheme. The earlier security solutions were based on historical behavior of neighbor but the security can be enhanced by predicting the future behavior of the nodes in the network. In this paper, we proposed a fuzzy-based trust prediction model for routing (FTPR in WSNs with minimal overhead in regard to memory and energy consumption. FTPR incorporates a trust prediction model that predicts the future behavior of the neighbor based on the historical behavior, fluctuations in trust value over a period of time, and recommendation inconsistency. In order to reduce the control overhead, FTPR received recommendations from a subset of neighbors who had maximum number of interactions with the requestor. Theoretical analysis and simulation results of FTPR protocol demonstrate higher packet delivery ratio, higher network lifetime, lower end-to-end delay, and lower memory and energy consumption than the traditional and existing trust-based routing schemes.

  17. Fuzzy Prediction for Traffic Flow Based on Delta Test

    OpenAIRE

    2016-01-01

    This paper presents a novel approach to one-step-forward prediction of traffic flow based on fuzzy reasoning. The successful construction of a competent fuzzy inference system of Sugeno type largely relies on proper choice of input dimension and accurate estimation of structure parameters and rules. The first issue is addressed with a proposed method, based on δ-test, which can simultaneously determine input dimension and reduce noise level. In response to the second issue, two clustering tec...

  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. 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.

  20. 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.

  1. SVM-based prediction of caspase substrate cleavage sites

    Directory of Open Access Journals (Sweden)

    Ranganathan Shoba

    2006-12-01

    Full Text Available Abstract 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 machines (SVM algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i the tetrapeptide cleavage sites, (ii the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. Conclusion This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

  2. Blind test of physics-based prediction of protein structures.

    Science.gov (United States)

    Shell, M Scott; Ozkan, S Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A

    2009-02-01

    We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. PPA BASED PREDICTION-CORRECTION METHODS FOR MONOTONE VARIATIONAL INEQUALITIES

    Institute of Scientific and Technical Information of China (English)

    He Bingsheng; Jiang Jianlin; Qian Maijian; Xu Ya

    2005-01-01

    In this paper we study the proximal point algorithm (PPA) based predictioncorrection (PC) methods for monotone variational inequalities. Each iteration of these methods consists of a prediction and a correction. The predictors are produced by inexact PPA steps. The new iterates are then updated by a correction using the PPA formula. We present two profit functions which serve two purposes: First we show that the profit functions are tight lower bounds of the improvements obtained in each iteration. Based on this conclusion we obtain the convergence inexactness restrictions for the prediction step. Second we show that the profit functions are quadratically dependent upon the step lengths, thus the optimal step lengths are obtained in the correction step. In the last part of the paper we compare the strengths of different methods based on their inexactness restrictions.

  8. 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.

  9. 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...

  10. Prediction of tectonically deformed coal based on lithologic seismic information

    Science.gov (United States)

    Li, Juanjuan; Pan, Dongming; Cui, Ruofei; Ding, Enjie; Zhang, Wei; Hu, Mingshun

    2016-02-01

    Owing to the differences in physical properties between tectonically deformed coal (TDC) and primary coal, lithologic seismic inversion methods were adopted to identify the coal structure type, including probabilistic neural network (PNN) inversion, elastic impedance (EI) inversion and simultaneous inversion methods. Based on poststack and prestack gathers, the inversion methods were applied to calculate lithologic seismic information, which included porosity, acoustic impedance, elastic impedance, λ × ρ and μ × ρ data. The inversion results were then analysed to evaluate the development potential of TDC. The research showed that the lithology inversion results, which indicated the potential zone of development areas of the coal, were all basically identical and a comprehensive prediction factor (the linear lithologic information combination) was proposed to effectively predict the development potential. Therefore, the prediction of TDC by lithologic seismic information could provide a scientific basis for both coal mining safety and the development potential of large-scale coalbed methane resources.

  11. 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%.

  12. New Approaches for Channel Prediction Based on Sinusoidal Modeling

    Directory of Open Access Journals (Sweden)

    Ekman Torbjörn

    2007-01-01

    Full Text Available Long-range channel prediction is considered to be one of the most important enabling technologies to future wireless communication systems. The prediction of Rayleigh fading channels is studied in the frame of sinusoidal modeling in this paper. A stochastic sinusoidal model to represent a Rayleigh fading channel is proposed. Three different predictors based on the statistical sinusoidal model are proposed. These methods outperform the standard linear predictor (LP in Monte Carlo simulations, but underperform with real measurement data, probably due to nonstationary model parameters. To mitigate these modeling errors, a joint moving average and sinusoidal (JMAS prediction model and the associated joint least-squares (LS predictor are proposed. It combines the sinusoidal model with an LP to handle unmodeled dynamics in the signal. The joint LS predictor outperforms all the other sinusoidal LMMSE predictors in suburban environments, but still performs slightly worse than the standard LP in urban environments.

  13. 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.

  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. Prediction of Breast Cancer using Rule Based Classification

    Directory of Open Access Journals (Sweden)

    Nagendra Kumar SINGH

    2015-12-01

    Full Text Available The current work proposes a model for prediction of breast cancer using the classification approach in data mining. The proposed model is based on various parameters, including symptoms of breast cancer, gene mutation and other risk factors causing breast cancer. Mutations have been predicted in breast cancer causing genes with the help of alignment of normal and abnormal gene sequences; then predicting the class label of breast cancer (risky or safe on the basis of IF-THEN rules, using Genetic Algorithm (GA. In this work, GA has used variable gene encoding mechanisms for chromosomes encoding, uniform population generations and selects two chromosomes by Roulette-Wheel selection technique for two-point crossover, which gives better solutions. The performance of the model is evaluated using the F score measure, Matthews Correlation Coefficient (MCC and Receiver Operating Characteristic (ROC by plotting points (Sensitivity V/s 1- Specificity.

  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. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-03-13

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  18. Temperature-based bioclimatic parameters can predict nematode metabolic footprints.

    Science.gov (United States)

    Bhusal, Daya Ram; Tsiafouli, Maria A; Sgardelis, Stefanos P

    2015-09-01

    Nematode metabolic footprints (MFs) refer to the lifetime amount of metabolized carbon per individual, indicating a connection to soil food web functions and eventually to processes supporting ecosystem services. Estimating and managing these at a convenient scale requires information upscaling from the soil sample to the landscape level. We explore the feasibility of predicting nematode MFs from temperature-based bioclimatic parameters across a landscape. We assume that temperature effects are reflected in MFs, since temperature variations determine life processes ranging from enzyme activities to community structure. We use microclimate data recorded for 1 year from sites differing by orientation, altitude and vegetation cover. At the same sites we estimate MFs for each nematode trophic group. Our models show that bioclimatic parameters, specifically those accounting for temporal variations in temperature and extremities, predict most of the variation in nematode MFs. Higher fungivorous and lower bacterivorous nematode MFs are predicted for sites with high seasonality and low isothermality (sites of low vegetation, mostly at low altitudes), indicating differences in the relative contribution of the corresponding food web channels to the metabolism of carbon across the landscape. Higher plant-parasitic MFs were predicted for sites with high seasonality. The fitted models provide realistic predictions of unknown cases within the range of the predictor's values, allowing for the interpolation of MFs within the sampled region. We conclude that upscaling of the bioindication potential of nematode communities is feasible and can provide new perspectives not only in the field of soil ecology but other research areas as well.

  19. 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.

  20. 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.

  1. AIS for Misbehavior Detection in Wireless Sensor Networks: Performance and Design Principles

    CERN Document Server

    Drozda, Martin; Szczerbicka, Helena

    2009-01-01

    A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices (nodes) are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We show that (i) mechanism of the AIS have to be carefully applied in order to avoid security weaknesses, (ii) the choice of genes and their interaction have a profound influence on the performance of the AIS, (iii) randomly created detectors do not comply with limitations imposed by communications protocols and (iv) the data traffic pattern seems not to impact significantly the overall performance. We identified a specific MAC layer based gene that showed to be especially useful for detection; genes measure a network's perfor...

  2. GIS-BASED PREDICTION OF HURRICANE FLOOD INUNDATION

    Energy Technology Data Exchange (ETDEWEB)

    JUDI, DAVID [Los Alamos National Laboratory; KALYANAPU, ALFRED [Los Alamos National Laboratory; MCPHERSON, TIMOTHY [Los Alamos National Laboratory; BERSCHEID, ALAN [Los Alamos National Laboratory

    2007-01-17

    A simulation environment is being developed for the prediction and analysis of the inundation consequences for infrastructure systems from extreme flood events. This decision support architecture includes a GIS-based environment for model input development, simulation integration tools for meteorological, hydrologic, and infrastructure system models and damage assessment tools for infrastructure systems. The GIS-based environment processes digital elevation models (30-m from the USGS), land use/cover (30-m NLCD), stream networks from the National Hydrography Dataset (NHD) and soils data from the NRCS (STATSGO) to create stream network, subbasins, and cross-section shapefiles for drainage basins selected for analysis. Rainfall predictions are made by a numerical weather model and ingested in gridded format into the simulation environment. Runoff hydrographs are estimated using Green-Ampt infiltration excess runoff prediction and a 1D diffusive wave overland flow routing approach. The hydrographs are fed into the stream network and integrated in a dynamic wave routing module using the EPA's Storm Water Management Model (SWMM) to predict flood depth. The flood depths are then transformed into inundation maps and exported for damage assessment. Hydrologic/hydraulic results are presented for Tropical Storm Allison.

  3. 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.

  4. 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.

  5. ENS-AI: Un sistema experto para la enseñanza

    Directory of Open Access Journals (Sweden)

    Clara BARROSO JEREZ

    2009-11-01

    Full Text Available The E.S. being developed in named ENS-AI, acronym for ENSeñanza Artificial Intilligence. ENS-AI will work as support and guide system for education practice. It is being implemented in the expert system environment MILORD-II, developed in the Institut d'Investigació en Intel.ligencia Artificial (IIIA of the C.S.I.C. MILORD-II provides a mechanism to represent heuristic rules weighted by linguistic certainty values, based on the use of many-valued logics. This facility allows to represent poor structured knowledge domains, such as in the education case.

  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. 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.

  8. 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...

  9. 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.

  10. 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.

  11. Ac Synchronous Servo Based On The Armature Voltage Prediction Model

    Science.gov (United States)

    Hoshino, Akihiro; Kuromaru, Hiroshi; Kobayashi, Shinichi

    1987-10-01

    A new control method of the AC synchro-nous servo-system (Brushless DC servo-system) is discussed. The new system is based on the armature voltage prediction model. Without a resolver-digital-conver-ter nor a tachometer-generator, the resolver provides following three signals to the system immediately, they are the current command, the induced voltage, and the rotor speed. The new method realizes a simple hardware configuration. Experimental results show a good performance of the system.

  12. Network Based Prediction Model for Genomics Data Analysis*

    OpenAIRE

    Huang, Ying; Wang, Pei

    2012-01-01

    Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. ...

  13. 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.

  14. Fuzzy Prediction for Traffic Flow Based on Delta Test

    Directory of Open Access Journals (Sweden)

    Yang Wang

    2016-01-01

    Full Text Available This paper presents a novel approach to one-step-forward prediction of traffic flow based on fuzzy reasoning. The successful construction of a competent fuzzy inference system of Sugeno type largely relies on proper choice of input dimension and accurate estimation of structure parameters and rules. The first issue is addressed with a proposed method, based on δ-test, which can simultaneously determine input dimension and reduce noise level. In response to the second issue, two clustering techniques, based on nearest-neighbor clustering and Gaussian mixture models, are successively employed to determine the antecedent parameters and rules, and the estimation for the consequent parameters is achieved by the least square estimation technique. A number of experiments have been performed on the one-week data of traffic flow to evaluate the proposed approach in terms of denosing, prediction performances, overfitting, and so forth. The experimental results have demonstrated that the proposed prediction approach is effective in removing noise and constructing a competent and compact fuzzy inference system without significant overfitting.

  15. Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

    Science.gov (United States)

    2015-07-01

    Learning -based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking Chris J. Ostafew Institute for Aerospace Studies... learning from experience. Schoellig et al. (2012) and Ostafew et al. (2013) present ILC algorithms for quadrotors and mobile robots, respectively...presents a Learning -based Nonlinear Model Predictive Control (LB-NMPC) algo- rithm for a path-repeating, mobile robot negotiating large-scale, GPS

  16. 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...... as Ai’s artwork and life....

  17. 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...... as Ai’s artwork and life....

  18. Ontology-Based Prediction and Prioritization of Gene Functional Annotations.

    Science.gov (United States)

    Chicco, Davide; Masseroli, Marco

    2016-01-01

    Genes and their protein products are essential molecular units of a living organism. The knowledge of their functions is key for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. The association of a gene or protein with its functions, described by controlled terms of biomolecular terminologies or ontologies, is named gene functional annotation. Very many and valuable gene annotations expressed through terminologies and ontologies are available. Nevertheless, they might include some erroneous information, since only a subset of annotations are reviewed by curators. Furthermore, they are incomplete by definition, given the rapidly evolving pace of biomolecular knowledge. In this scenario, computational methods that are able to quicken the annotation curation process and reliably suggest new annotations are very important. Here, we first propose a computational pipeline that uses different semantic and machine learning methods to predict novel ontology-based gene functional annotations; then, we introduce a new semantic prioritization rule to categorize the predicted annotations by their likelihood of being correct. Our tests and validations proved the effectiveness of our pipeline and prioritization of predicted annotations, by selecting as most likely manifold predicted annotations that were later confirmed.

  19. Porosity prediction of calcium phosphate cements based on chemical composition.

    Science.gov (United States)

    Öhman, Caroline; Unosson, Johanna; Carlsson, Elin; Ginebra, Maria Pau; Persson, Cecilia; Engqvist, Håkan

    2015-07-01

    The porosity of calcium phosphate cements has an impact on several important parameters, such as strength, resorbability and bioactivity. A model to predict the porosity for biomedical cements would hence be a useful tool. At the moment such a model only exists for Portland cements. The aim of this study was to develop and validate a first porosity prediction model for calcium phosphate cements. On the basis of chemical reaction, molar weight and density of components, a volume-based model was developed and validated using calcium phosphate cement as model material. 60 mol% β-tricalcium phosphate and 40 mol% monocalcium phosphate monohydrate were mixed with deionized water, at different liquid-to-powder ratios. Samples were set for 24 h at 37°C and 100% relative humidity. Thereafter, samples were dried either under vacuum at room temperature for 24 h or in air at 37 °C for 7 days. Porosity and phase composition were determined. It was found that the two drying protocols led to the formation of brushite and monetite, respectively. The model was found to predict well the experimental values and also data reported in the literature for apatite cements, as deduced from the small absolute average residual errors (brushite, monetite and apatite cements. The model gives a good estimate of the final porosity and has the potential to be used as a porosity prediction tool in the biomedical cement field.

  20. 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%.

  1. 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

  2. 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.

  3. 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…

  4. 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

  5. Quality guaranteed aggregation based model predictive control and stability analysis

    Institute of Scientific and Technical Information of China (English)

    LI DeWei; XI YuGeng

    2009-01-01

    The input aggregation strategy can reduce the online computational burden of the model predictive controller. But generally aggregation based MPC controller may lead to poor control quality. Therefore, a new concept, equivalent aggregation, is proposed to guarantee the control quality of aggregation based MPC. From the general framework of input linear aggregation, the design methods of equivalent aggregation are developed for unconstrained and terminal zero constrained MPC, which guarantee the actual control inputs exactly to be equal to that of the original MPC. For constrained MPC, quasi-equivalent aggregation strategies are also discussed, aiming to make the difference between the control inputs of aggregation based MPC and original MPC as small as possible. The stability conditions are given for the quasi-equivalent aggregation based MPC as well.

  6. 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%)

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

    Science.gov (United States)

    Venkata Sastry, N N; Bhunia, Avijit; Sundararajan, T; Das, Sarit K

    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 (∼ ± 5%).

  8. Imputation for transcription factor binding predictions based on deep learning

    Science.gov (United States)

    Qin, Qian

    2017-01-01

    Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding. PMID:28234893

  9. 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.

  10. 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

    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...... 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...

  11. Fault prediction of fighter based on nonparametric density estimation

    Institute of Scientific and Technical Information of China (English)

    Zhang Zhengdao; Hu Shousong

    2005-01-01

    Fighters and other complex engineering systems have many characteristics such as difficult modeling and testing, multiple working situations, and high cost. Aim at these points, a new kind of real-time fault predictor is designed based on an improved k-nearest neighbor method, which needs neither the math model of system nor the training data and prior knowledge. It can study and predict while system's running, so that it can overcome the difficulty of data acquirement. Besides, this predictor has a fast prediction speed, and the false alarm rate and missing alarm rate can be adjusted randomly. The method is simple and universalizable. The result of simulation on fighter F-16 proved the efficiency.

  12. 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.

  13. 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...

  14. Rutting Prediction in Asphalt Pavement Based on Viscoelastic Theory

    Directory of Open Access Journals (Sweden)

    Nahi Mohammed Hadi

    2016-01-01

    Full Text Available Rutting is one of the most disturbing failures on the asphalt roads due to the interrupting it is caused to the drivers. Predicting of asphalt pavement rutting is essential tool leads to better asphalt mixture design. This work describes a method of predicting the behaviour of various asphalt pavement mixes and linking these to an accelerated performance testing. The objective of this study is to develop a finite element model based on viscoplastic theory for simulating the laboratory testing of asphalt mixes in Hamburg Wheel Rut Tester (HWRT for rutting. The creep parameters C1, C2 and C3 are developed from the triaxial repeated load creep test at 50°C and at a frequency of 1 Hz and the modulus of elasticity and Poisson’ s ratio determined at the same temperature. Viscoelastic model (creep model is adopted using a FE simulator (ANSYS in order to calculate the rutting for various mixes under a uniform loading pressure of 500 kPa. An eight-node with a three Degrees of Freedom (UX, UY, and UZ Element is used for the simulation. The creep model developed for HWRT tester was verified by comparing the predicted rut depths with the measured one and by comparing the rut depth with ABAQUS result from literature. Reasonable agreement can be obtained between the predicted rut depths and the measured one. Moreover, it is found that creep model parameter C1 and C3 have a strong relationship with rutting. It was clear that the parameter C1 strongly influences rutting than the parameter C3. Finally, it can be concluded that creep model based on finite element method can be used as an effective tool to analyse rutting of asphalt pavements.

  15. Data of evolutionary structure change: 1AIFB-2AI0K [Confc[Archive

    Lifescience Database Archive (English)

    Full Text Available ignment> 0 n> 1AIF n>B 1AI... CA 272 n> 2AI0 n>K ...n>2AI0Kn> VAHPASSTKVD EEE EEEE...1AIFB-2AI0K 1AIF 2AI0 B K EVKLQESGGGLVQPGGSMKLSCVASGFTFNNYWMSWVRQ...SCAASGFTFRNYGMSWVRQTPEKRLEWVAAIS--GNSLYTSYPDSVKGRFTISRDNAKNNLYLQMSSLRSEDTALYFCARH

  16. Analysis of Sequence Based Classifier Prediction for HIV Subtypes

    Directory of Open Access Journals (Sweden)

    S. Santhosh Kumar

    2012-10-01

    Full Text Available Human immunodeficiency virus (HIV is a lent virus that causes acquired immunodeficiency syndrome (AIDS. The main drawback in HIV treatment process is its sub type prediction. The sub type and group classification of HIV is based on its genetic variability and location. HIV can be divided into two major types, HIV type 1 (HIV-1 and HIV type 2 (HIV-2. Many classifier approaches have been used to classify HIV subtypes based on their group, but some of cases are having two groups in one; in such cases the classification becomes more complex. The methodology used is this paper based on the HIV sequences. For this work several classifier approaches are used to classify the HIV1 and HIV2. For implementation of the work a real time patient database is taken and the patient records are experimented and the final best classifier is identified with quick response time and least error rate.

  17. LÉVY-BASED ERROR PREDICTION IN CIRCULAR SYSTEMATIC SAMPLING

    Directory of Open Access Journals (Sweden)

    Kristjana Ýr Jónsdóttir

    2013-06-01

    Full Text Available In the present paper, Lévy-based error prediction in circular systematic sampling is developed. A model-based statistical setting as in Hobolth and Jensen (2002 is used, but the assumption that the measurement function is Gaussian is relaxed. The measurement function is represented as a periodic stationary stochastic process X obtained by a kernel smoothing of a Lévy basis. The process X may have an arbitrary covariance function. The distribution of the error predictor, based on measurements in n systematic directions is derived. Statistical inference is developed for the model parameters in the case where the covariance function follows the celebrated p-order covariance model.

  18. 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.

  19. 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.

  20. Bathymetry Prediction Based on the Admittance Theory of Gravity Anomalies

    Directory of Open Access Journals (Sweden)

    OUYANG Mingda

    2015-10-01

    Full Text Available Based on the admittance theory of gravity anomalies, the method of bathymetry prediction was studied in detail in this paper. In frequency domains, the correlation between gravity anomalies and bathymetry was analyzed, which suggests that the wavelength band correlated strongly was in a range of 20—300 km, this band was appropriated to inverse bathymetry by gravity anomalies. Took the Emperor Chain as an example, the uncompensated admittance model and flexural isostatic admittance model were used for researching, respectively, the included parameter of crust thickness and effective elastic thickness were calculated by the isostatic response function. As the down continuation factor was unstable, a high-cut filter was proposed in the inversion procedure to ensure convergence of series. The results showed that, the admittance theory of gravity anomalies can be used effectively in the bathymetry prediction, the predicted result was real and reliable, the relative precision was approximately 6%, which was equal to ETOPO1 model, and the detailed feature of sea floor which was not showed in ETOPO1 model can also be depicted; the precisions were not so well in areas of ocean mountains intensively distributed because of the complexion of the sea floor.

  1. 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.

  2. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    Science.gov (United States)

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  3. 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...

  4. 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

  5. Stand diameter distribution modelling and prediction based on Richards function.

    Directory of Open Access Journals (Sweden)

    Ai-guo Duan

    Full Text Available The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM or maximum likelihood estimates method (MLEM were applied to estimate the parameters of models, and the parameter prediction method (PPM and parameter recovery method (PRM were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1 R distribution presented a more accurate simulation than three-parametric Weibull function; (2 the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3 the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4 the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.

  6. Stand diameter distribution modelling and prediction based on Richards function.

    Science.gov (United States)

    Duan, Ai-guo; Zhang, Jian-guo; Zhang, Xiong-qing; He, Cai-yun

    2013-01-01

    The objective of this study was to introduce application of the Richards equation on modelling and prediction of stand diameter distribution. The long-term repeated measurement data sets, consisted of 309 diameter frequency distributions from Chinese fir (Cunninghamia lanceolata) plantations in the southern China, were used. Also, 150 stands were used as fitting data, the other 159 stands were used for testing. Nonlinear regression method (NRM) or maximum likelihood estimates method (MLEM) were applied to estimate the parameters of models, and the parameter prediction method (PPM) and parameter recovery method (PRM) were used to predict the diameter distributions of unknown stands. Four main conclusions were obtained: (1) R distribution presented a more accurate simulation than three-parametric Weibull function; (2) the parameters p, q and r of R distribution proved to be its scale, location and shape parameters, and have a deep relationship with stand characteristics, which means the parameters of R distribution have good theoretical interpretation; (3) the ordinate of inflection point of R distribution has significant relativity with its skewness and kurtosis, and the fitted main distribution range for the cumulative diameter distribution of Chinese fir plantations was 0.4∼0.6; (4) the goodness-of-fit test showed diameter distributions of unknown stands can be well estimated by applying R distribution based on PRM or the combination of PPM and PRM under the condition that only quadratic mean DBH or plus stand age are known, and the non-rejection rates were near 80%, which are higher than the 72.33% non-rejection rate of three-parametric Weibull function based on the combination of PPM and PRM.

  7. Phosphate-based glasses: Prediction of acoustical properties

    Energy Technology Data Exchange (ETDEWEB)

    El-Moneim, Amin Abd, E-mail: aminabdelmoneim@hotmail.com

    2016-04-15

    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-P{sub 2}O{sub 5}, Li{sub 2}O-TeO{sub 2}-B{sub 2}O{sub 3}-P{sub 2}O{sub 5}, TiO{sub 2}-Na{sub 2}O-CaO-P{sub 2}O{sub 5} and Cr{sub 2}O{sub 3}-doped Na{sub 2}O-ZnO-P{sub 2}O{sub 5} 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-P{sub 2}O{sub 5} glasses at 10 MHz frequency and in quaternary Li{sub 2}O-TeO{sub 2}-B{sub 2}O{sub 3}-P{sub 2}O{sub 5}, TiO{sub 2}-Na{sub 2}O-CaO-P{sub 2}O{sub 5} and Cr{sub 2}O{sub 3}-Na{sub 2}O-ZnO-P{sub 2}O{sub 5} 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.

  8. 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.

  9. Forbush Decrease Prediction Based on Remote Solar Observations

    Science.gov (United States)

    Dumbovic, Mateja; Vrsnak, Bojan; Calogovic, Jasa

    2016-04-01

    We study the relation between remote observations of coronal mass ejections (CMEs), their associated solar flares and short-term depressions in the galactic cosmic-ray flux (so called Forbush decreases). Statistical relations between Forbush decrease magnitude and several CME/flare parameters are examined. In general we find that Forbush decrease magnitude is larger for faster CMEs with larger apparent width, which is associated with stronger flares that originate close to the center of the solar disk and are (possibly) involved in a CME-CME interaction. The statistical relations are quantified and employed to forecast expected Forbush decrease magnitude range based on the selected remote solar observations of the CME and associated solar flare. Several verification measures are used to evaluate the forecast method. We find that the forecast is most reliable in predicting whether or not a CME will produce a Forbush decrease with a magnitude >3 %. The main advantage of the method is that it provides an early prediction, 1-4 days in advance. Based on the presented research, an online forecast tool was developed (Forbush Decrease Forecast Tool, FDFT) available at Hvar Observatory web page: http://oh.geof.unizg.hr/FDFT/fdft.php. We acknowledge the support of Croatian Science Foundation under the project 6212 „Solar and Stellar Variability" and of European social fond under the project "PoKRet".

  10. 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.

  11. Analyst-to-Analyst Variability in Simulation-Based Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Romero, Vicente J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-02-01

    This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.

  12. Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    LIU Yuan-ning; DONG Hao; ZHANG Hao; WANG Gang; LI Zhi; CHEN Hui-ling

    2011-01-01

    A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to e ncoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

  13. Link prediction based on a semi-local similarity index

    Institute of Scientific and Technical Information of China (English)

    Bai Meng; Hu Ke; Tang Yi

    2011-01-01

    Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks.Recently,many algorithms have been proposed based on various node-similarity measures.Among these measures,the common neighbour index,the resource allocation index,and the local path index,stemming from different source,have been proved to have relatively high accuracy and low computational effort.In this paper,we propose a similarity index by combining the resource allocation index and the local path index.Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one.Based on the same idea of the present index,we develop its corresponding weighted version and test it on several weighted networks.It is found that,except for the USAir network,the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index.Due to the improved accuracy and the still low computational complexity,the indices may be useful for link prediction.

  14. Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment

    Science.gov (United States)

    Deouell, Leon Y.

    2017-01-01

    Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction. PMID:28187128

  15. Distance matrix-based approach to protein structure prediction.

    Science.gov (United States)

    Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

    2009-03-01

    Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

  16. Ship Emission Inventories in Estuary of the Yangtze River Using Terrestrial AIS Data

    Directory of Open Access Journals (Sweden)

    Xin Yao

    2016-12-01

    Full Text Available Estuary forms a transition zone between inland river and open sea. In China, the estuary of the Yangtze River plays a vital role in connecting the inland and oversea shipping, and witnesses heavy vessel traffic in the recent decades. Nowadays, more attentions have been directed to the issue of ship pollution in busy waterways. In order to investigate the ship emission inventory, this paper presents an Automatic Identification System(AIS based method. AIS data is the realistic data of vessel traffic including dynamic information (position, speed, course, etc. and static information (ship type, dimensions, name, etc.. According to ship dimensions, the power of engines is estimated for different ship types. By using AIS based bottom-up approach, ship emission inventories and shares of air pollutants and GHGs (Greenhouse gases are developed. Spatial distribution of ship emissions is illustrated in the form of heat map. As a case study, the emission inventories are analyzed using AIS data of 2010 in the estuary, and following results are made:(1 shares of the emission are cruise ships 6.59%, bulk carriers 5.16%, container ships 52.96%, tankers 15.16%, fishing ships 9.16%, other ships 10.97%; (2 CO2 is the dominant part of the emission. (3 Areas of highest emission intensity are generally clustered around the South Channel, the North Channel and ports in the vicinity. The proposed method is promising because it is derived from the AIS data which contains not only real data of individual ship but also vessel traffic situation in the study area. It can server as a reference for other researchers and policy makers working in this field.

  17. 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).

  18. 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.

  19. 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

  20. 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

  1. 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...

  2. Recent progresses on AI-2 bacterial quorum sensing inhibitors.

    Science.gov (United States)

    Zhu, Peng; Li, Minyong

    2012-01-01

    Quorum sensing (QS) is a communication procedure that predominates gene expression in response to cell density and fluctuations in the neighboring environment as a result of discerning molecules termed autoinducers (AIs). It has been embroiled that QS can govern bacterial behaviors such as the secretion of virulence factors, biofilm formation, bioluminescence production, conjugation, sporulation and swarming motility. Autoinducer 2 (AI-2), a QS signaling molecule brought up to be involved in interspecies communication, exists in both gram-negative and -positive bacteria. Therefore, novel approaches to interrupt AI-2 quorum sensing are being recognized as next generation antimicrobials. In the present review article, we summarized recent progresses on AI-2 bacterial quorum sensing inhibitors and discussed their potential as the antibacterial agents.

  3. 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.

  4. Neural Network Based Popularity Prediction For IPTV System

    Directory of Open Access Journals (Sweden)

    Jun Li

    2012-12-01

    Full Text Available Internet protocol television (IPTV, being an emerging Internet application, plays an important and indispensable role in our daily life. In order to maximize user experience and on the same time to minimize service cost, we must take into pay attention to how to reduce the storage and transport costs. A lot of previous work has been done before to do this. There is a challenging problem in this: how to predict the popularities of videos as accurate as possible. To solve the problem, this paper presents a Neural Network model for the popularity prediction of the programs in the IPTV system. And we use the actual historical logs to validate our method. The historical logs are divided to two parts, one is used to train the neural network by extract input/output vectors, and the other part is used to verify the model. The experimental results from our validation show the Neural Network based method can gain better accuracy than the comparative method.

  5. Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

    Science.gov (United States)

    Manta, Asimina; Gresil, Matthieu; Soutis, Constantinos

    2017-04-01

    In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

  6. A nonlinear regression model-based predictive control algorithm.

    Science.gov (United States)

    Dubay, R; Abu-Ayyad, M; Hernandez, J M

    2009-04-01

    This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

  7. Precipitation Prediction in North Africa Based on Statistical Downscaling

    Science.gov (United States)

    Molina, J. M.; Zaitchik, B.

    2013-12-01

    variability of the observed local process. Also, a split-window approach is used in the cross-validation stage for comparison purposes of the monthly regression schemes, and different pre-processing alternatives of the precipitation records are implemented to reduce the strong skewness observed in the periodic distribution functions. Preliminary results show that bootstrapping approaches like those based on K-Nearest Neighbors (K-NN) resampling improves the preservation of the historical variability, for which the GLM methods exhibit important limitations. It has been also observed the important role that plays both the teleconnections analysis and the normalization pre-processing in the prediction skill. It is expected that the methodologies from this research can be extrapolated to other regions and time scales for the study of climate change impact and water resources management.

  8. A Prediction-based Smart Meter Data Generator

    DEFF Research Database (Denmark)

    Iftikhar, Nadeem; Nordbjerg, Finn Ebertsen

    2016-01-01

    With the prevalence of cloud computing and Internet 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 decisionmaking....... 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 data set as seed. The generator generates data using a prediction-based method that depends on historical energy consumption patterns...

  9. A Prediction-based Smart Meter Data Generator

    DEFF Research Database (Denmark)

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

    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...

  10. 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...

  11. 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.

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

    DEFF Research Database (Denmark)

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

    Estimation of structural damage from known increase in the fundamental period of a structure after an earthquake or prediction of degradation of stiffness and strength for known damage requires reliable correlations between these response functionals. This study proposes a modified Clough......-Johnston SDOF oscillator to establish these correlations in case of a simple elasto-plastic oscillator. It is assumed that the oscillator closely describes the response of a given multi-degree-of-freedom system in its fundamental mode throughout the duration of the excitation. The proposed model considers...... 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...

  13. 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.

  14. 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.  

  15. Prediction-based association control scheme in dense femtocell networks

    Science.gov (United States)

    Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond

    2017-01-01

    The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992

  16. 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.

  17. Fast rule-based bioactivity prediction using associative classification mining

    Directory of Open Access Journals (Sweden)

    Yu Pulan

    2012-11-01

    Full Text Available Abstract Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM, which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR, classification based on multiple association rules (CMAR and classification based on association rules (CBA are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB, mutagenicity and hERG (the human Ether-a-go-go-Related Gene blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM methods, and produce highly interpretable models.

  18. Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

    Institute of Scientific and Technical Information of China (English)

    唐圣金; 郭晓松; 于传强; 周志杰; 周召发; 张邦成

    2014-01-01

    Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

  19. Rainfall events prediction using rule-based fuzzy inference system

    Science.gov (United States)

    Asklany, Somia A.; Elhelow, Khaled; Youssef, I. K.; Abd El-wahab, M.

    2011-07-01

    We are interested in rainfall events prediction by applying rule-based reasoning and fuzzy logic. Five parameters: relative humidity, total cloud cover, wind direction, temperature and surface pressure are the input variables for our model, each has three membership functions. The data used is twenty years METAR data for Cairo airport station (HECA) [1972-1992] 30° 3' 29″ N, 31° 13' 44″ E. and five years METAR data for Mersa Matruh station (HEMM) 31° 20' 0″ N, 27° 13' 0″ E. Different models for each station were constructed depending on the available data sets. Among the overall 243 possibilities we have based our models on one hundred eighteen fuzzy IF-THEN rules and fuzzy reasoning. The output variable which has four membership functions, takes values from zero to one hundred corresponding to the percentage for rainfall events given for every hourly data. We used two skill scores to verify our results, the Brier score and the Friction score. The results are in high agreements with the recorded data for the stations with increasing in output values towards the real time rain events. All implementation are done with MATLAB 7.9.

  20. 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.

  1. Predicting the reference evapotranspiration based on tensor decomposition

    Science.gov (United States)

    Misaghian, Negin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Mohammadi, Kasra

    2016-09-01

    Most of the available models for reference evapotranspiration (ET0) estimation are based upon only an empirical equation for ET0. Thus, one of the main issues in ET0 estimation is the appropriate integration of time information and different empirical ET0 equations to determine ET0 and boost the precision. The FAO-56 Penman-Monteith, adjusted Hargreaves, Blaney-Criddle, Priestley-Taylor, and Jensen-Haise equations were utilized in this study for estimating ET0 for two stations of Belgrade and Nis in Serbia using collected data for the period of 1980 to 2010. Three-order tensor is used to capture three-way correlations among months, years, and ET0 information. Afterward, the latent correlations among ET0 parameters were found by the multiway analysis to enhance the quality of the prediction. The suggested method is valuable as it takes into account simultaneous relations between elements, boosts the prediction precision, and determines latent associations. Models are compared with respect to coefficient of determination (R 2), mean absolute error (MAE), and root-mean-square error (RMSE). The proposed tensor approach has a R 2 value of greater than 0.9 for all selected ET0 methods at both selected stations, which is acceptable for the ET0 prediction. RMSE is ranged between 0.247 and 0.485 mm day-1 at Nis station and between 0.277 and 0.451 mm day-1 at Belgrade station, while MAE is between 0.140 and 0.337 mm day-1 at Nis and between 0.208 and 0.360 mm day-1 at Belgrade station. The best performances are achieved by Priestley-Taylor model at Nis station (R 2 = 0.985, MAE = 0.140 mm day-1, RMSE = 0.247 mm day-1) and FAO-56 Penman-Monteith model at Belgrade station (MAE = 0.208 mm day-1, RMSE = 0.277 mm day-1, R 2 = 0.975).

  2. 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.

  3. 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.

  4. Mission activities planning for a Hermes mission by means of AI-technology

    Science.gov (United States)

    Pape, U.; Hajen, G.; Schielow, N.; Mitschdoerfer, P.; Allard, F.

    1993-01-01

    Mission Activities Planning is a complex task to be performed by mission control centers. AI technology can offer attractive solutions to the planning problem. This paper presents the use of a new AI-based Mission Planning System for crew activity planning. Based on a HERMES servicing mission to the COLUMBUS Man Tended Free Flyer (MTFF) with complex time and resource constraints, approximately 2000 activities with 50 different resources have been generated, processed, and planned with parametric variation of operationally sensitive parameters. The architecture, as well as the performance of the mission planning system, is discussed. An outlook to future planning scenarios, the requirements, and how a system like MARS can fulfill those requirements is given.

  5. Data of evolutionary structure change: 1AIFH-2AI0K [Confc[Archive

    Lifescience Database Archive (English)

    Full Text Available > 0 n> 1AIF n>H ...n>1AIFHn> VAHPA-SSTKV n> 2AI0 n>K 2AI...1AIFH-2AI0K 1AIF 2AI0 H K EVKLQESGGGLVQPGGSMKLSCVASGFTFNNYWMSWVRQ...SCAASGFTFRNYGMSWVRQTPEKRLEWVAAIS--GNSLYTSYPDSVKGRFTISRDNAKNNLYLQMSSLRSEDTALYFCARH

  6. Data of evolutionary structure change: 1AIFL-2AI0L [Confc[Archive

    Lifescience Database Archive (English)

    Full Text Available /ss_2> 0 n> 1AIF L...n> 1AIFLn> VSSSI----SSSNL 2AI0 Ln> 2AI0L...1AIFL-2AI0L 1AIF 2AI0 L L DIQLTQSPAFMAASPGEKVTITCSVSSSI----SSSNLH...line> SER CA 273 SER CA 260 ASN CA 337 LEU CA 408

  7. Data of evolutionary structure change: 1AIFA-2AI0O [Confc[Archive

    Lifescience Database Archive (English)

    Full Text Available s_2> 0 n> 1AIF A...n> 1AIFAn> VSSSI----SSSNL n> 2AI0 n>O 2AI...1AIFA-2AI0O 1AIF 2AI0 A O DIQLTQSPAFMAASPGEKVTITCSVSSSI----SSSNLH...e>SER CA 251 SER CA 276 SER CA 258 ASN CA 337 LEU CA 410

  8. Data of evolutionary structure change: 1AIFA-2AI0L [Confc[Archive

    Lifescience Database Archive (English)

    Full Text Available 2> 0 n> 1AIF n>A ...n>1AIFAn> VSSSI----SSSNL...n> 2AI0 n>L 2AI...1AIFA-2AI0L 1AIF 2AI0 A L DIQLTQSPAFMAASPGEKVTITCSVSSSI----SSSNLH...SER CA 251 SER CA 276 SER CA 258 ASN CA 337 LEU CA 410

  9. 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...

  10. NPCs Vote! Changing Voter Reactions Over Time Using the Extreme AI Personality Engine

    OpenAIRE

    Georgeson, Jeffrey

    2016-01-01

    Can non-player characters have human-realistic personalities, changing over time depending on input from those around them? And can they have different reactions and thoughts about different people? Using Extreme AI, a psychology-based personality engine using the Five Factor model of personality, I answer these questions by creating personalities for 100 voters and allowing them to react to two politicians to see if the NPC voters' choice of candidate develops in a realistic-seeming way, bas...

  11. 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.

  12. 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.

  13. 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.  

  14. Gene function prediction based on the Gene Ontology hierarchical structure.

    Science.gov (United States)

    Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

    2014-01-01

    The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

  15. Optimization of condition-based asset management using a predictive health model

    NARCIS (Netherlands)

    Bajracharya, G.; Koltunowicz, T.; Negenborn, R.R.; Papp, Z.; Djairam, D.; De Schutter, B.; Smit, J.J.

    2009-01-01

    In this paper, a model predictive framework is used to optimize the operation and maintenance actions of power system equipment based on the predicted health sate of this equipment. In particular, this framework is used to predict the health state of transformers based on their usage. The health sta

  16. 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...

  17. 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 fro...

  18. 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.

  19. Research on road topology based mobility prediction schemes

    Institute of Scientific and Technical Information of China (English)

    Chen Lin; Chen Hongzhong; Jiang Changjun

    2007-01-01

    Geographic routing has been introduced in mobile ad hoc networks and sensor networks . But its performance suffers greatly from mobility- induced location errors that can cause Lost Link (LLNK) and LOOP problems. Thus various mobility prediction algorithms have been proposed to mitigate the errors, but sometimes their prediction errors are substantial . A novel mobility prediction technique that incorporates both mobile positioning information and road topology knowledge was presented. Furthermore, the performance of the scheme was evaluated via simulations , along with two other schemes , namely , Linear Velocity Prediction ( LVP) and Weighted Velocity Prediction ( WVP) for comparison purpose . The results of simulation under Manhattan mobility model show that the proposed scheme could track the movement of a node well and hence provide noticeable improvement over LVP and MVP.

  20. 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...

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. The effect of hot-rolling on chill-cast AI-AI3Ni, chill-cast AI-AI2Cu, and Unidirectionally Solidified AI-AI3Ni Eutectic Alloys

    Science.gov (United States)

    Jardine, F. S. J.; Cantor, B.

    1986-11-01

    The effect of hot-rolling on the mechanical properties and microstructures of chill-cast Al-Al3Ni, chill-cast Al-Al2Cu, and unidirectionally solidified Al-Al3Ni eutectic alloys has been studied. The chill-cast eutectic alloys were produced by casting into preheated mild steel molds placed on copper chills. This system promoted growth along the length of the ingot and not radially from the mold wall. Cellular microstructures resulted with good alignment of Al3Ni fibers or Al2Cu lamellae within the cells and an interfiber/lamellar spacing of ~ 1 /urn. In contrast, the Al-Al3Ni eutectic alloy was also unidirectionally solidified at a growth rate of 3 x 10-1 m s-1 in a conventional horizontal crystal grower. This produced well-aligned Al3Ni fibers with an interfiber spacing of 1.2 ώm. Both the unidirectionally solidified and chill-cast Al-Al3Ni eutectic alloy can be hot-rolled at 773 K to reductions in area of greater than 95 pct. Deformation was achieved by Al3Ni fiber fracturing followed by separation of the broken fiber fragments in the rolling direction. Additionally, for the chill-cast eutectic the cellular microstructure disappeared and the Al3Ni fibers were homogeneously distributed throughout the matrix, after area reductions of 60 to 70 pct. In both cases, the eutectic microstructure was deformed with a constant volume fraction of Al3Ni/unit volume being maintained during rolling. The chill-cast Al-Al2Cu eutectic alloy can be hot-rolled at 773 K to an area reduction of ~50 pct, after the continuous brittle Al2Cu phase within the cells has been ‘broken up’ by coarsening at high temperature. The variations of room temperature tensile properties for the chill-cast and unidirectionally solidified eutectic alloys were measured as a function of reduction of thickness during hot-rolling and the results were compared with predicted strengths from discontinuous fiber reinforcement theory.

  9. 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.

  10. 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.

  11. 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 ...

  12. 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...

  13. 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.

  14. 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

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

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    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.

  16. A Comprehensive Propagation Prediction Model Comprising Microfacet Based Scattering and Probability Based Coverage Optimization Algorithm

    OpenAIRE

    A. S. M. Zahid Kausar; Ahmed Wasif Reza; Lau Chun Wo; Harikrishnan Ramiah

    2014-01-01

    Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented...

  17. A Comprehensive Propagation Prediction Model Comprising Microfacet Based Scattering and Probability Based Coverage Optimization Algorithm

    OpenAIRE

    Kausar, A. S. M. Zahid; Reza, Ahmed Wasif; Wo, Lau Chun; Ramiah, Harikrishnan

    2014-01-01

    Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented ...

  18. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

    OpenAIRE

    Chen, Yang; Xu, Rong

    2015-01-01

    Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cros...

  19. 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.

  20. Simulation-Based Performance Evaluation of Predictive-Hashing Based Multicast Authentication Protocol

    Directory of Open Access Journals (Sweden)

    Seonho Choi

    2012-12-01

    Full Text Available A predictive-hashing based Denial-of-Service (DoS resistant multicast authentication protocol was proposed based upon predictive-hashing, one-way key chain, erasure codes, and distillation codes techniques [4, 5]. It was claimed that this new scheme should be more resistant to various types of DoS attacks, and its worst-case resource requirements were derived in terms of coarse-level system parameters including CPU times for signature verification and erasure/distillation decoding operations, attack levels, etc. To show the effectiveness of our approach and to analyze exact resource requirements in various attack scenarios with different parameter settings, we designed and implemented an attack simulator which is platformindependent. Various attack scenarios may be created with different attack types and parameters against a receiver equipped with the predictive-hashing based protocol. The design of the simulator is explained, and the simulation results are presented with detailed resource usage statistics. In addition, resistance level to various types of DoS attacks is formulated with a newly defined resistance metric. By comparing these results to those from another approach, PRABS [8], we show that the resistance level of our protocol is greatly enhanced even in the presence of many attack streams.

  1. Efficient prediction of co-complexed proteins based on coevolution.

    Directory of Open Access Journals (Sweden)

    Damien M de Vienne

    Full Text Available The prediction of the network of protein-protein interactions (PPI of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.

  2. 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.

  3. 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.

  4. 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.

  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. 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

  7. 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.  

  8. Theoretical Explanation of Return Predictability Based on Stock Price Formulation

    Institute of Scientific and Technical Information of China (English)

    Guo Lei; Wu Chongfeng; Wang Xinrong

    2006-01-01

    To find out which factors determine stock return and to give rational explanation of return predictability, according to the principle of stock price formulation, the trend of stock price is obtained by use of option pricing method. The trend of stock price is put into reconstructing CAPM (capital asset pricing model) beta; it is concluded that the firm-specific biases and the scale biases potentially induce return predictability. In addition, through the relation between the biases structure and the intrinsic value, an appropriate theoretic explanation is supplied for three-factor pricing model proposed by Fama and French.

  9. 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.

  10. 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.

  11. Predictive Models of Alcohol Use Based on Attitudes and Individual Values

    Science.gov (United States)

    Del Castillo Rodríguez, José A. García; López-Sánchez, Carmen; Soler, M. Carmen Quiles; Del Castillo-López, Álvaro García; Pertusa, Mónica Gázquez; Campos, Juan Carlos Marzo; Inglés, Cándido J.

    2013-01-01

    Two predictive models are developed in this article: the first is designed to predict people' attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The…

  12. 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.…

  13. 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.…

  14. 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...

  15. 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...

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

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  17. Bayesian model-based cluster analysis for predicting macrofaunal communities

    NARCIS (Netherlands)

    Braak, ter C.J.F.; Hoijtink, H.; Akkermans, W.; Verdonschot, P.F.M.

    2003-01-01

    To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster ana

  18. Stability analysis of generalized predictive control based on Kleinman's controllers

    Institute of Scientific and Technical Information of China (English)

    DING Baocang; XI Yugeng

    2004-01-01

    With Kleinman's controller, its extended form and Riccati iteration as analyzing tools, the stability of GPC under various parameter cases is discussed. The overall closed-loop stability conclusions of GPC in equivalence with Kleinman's controller are obtained, which cover some existing results and provide the theoretical foundation for stable design of predictive control.

  19. Binaural intelligibility prediction based on the speech transmission index

    NARCIS (Netherlands)

    Wijngaarden, S.J. van; Drullman, R.

    2008-01-01

    Although the speech transmission index STI is a well-accepted and standardized method for objective prediction of speech intelligibility in a wide range of environments and applications, it is essentially a monaural model. Advantages of binaural hearing in speech intelligibility are disregarded. In

  20. Flexible nurse staffing based on hourly bed census predictions

    NARCIS (Netherlands)

    Kortbeek, N.; Braaksma, A.; Burger, C.A.J.; Bakker, P.J.M.; Boucherie, R.J.

    2012-01-01

    Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method t

  1. Flexible nurse staffing based on hourly bed census predictions

    NARCIS (Netherlands)

    Kortbeek, N.; Braaksma, A.; Burger, C.A.J.; Bakker, P.J.M.; Boucherie, R.J.

    2015-01-01

    Workloads in nursing wards depend highly on patient arrivals and lengths of stay, both of which are inherently variable. Predicting these workloads and staffing nurses accordingly are essential for guaranteeing quality of care in a cost-effective manner. This paper introduces a stochastic method tha

  2. 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.

  3. Experience-based model predictive control using reinforcement learning

    NARCIS (Netherlands)

    Negenborn, R.R.; De Schutter, B.; Wiering, M.A.; Hellendoorn, J.

    2004-01-01

    Model predictive control (MPC) is becoming an increasingly popular method to select actions for controlling dynamic systems. TraditionallyMPC uses a model of the system to be controlled and a performance function to characterize the desired behavior of the system. The MPC agent finds actions over a

  4. 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.

  5. State-based Communication on Time-predictable Multicore Processors

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo; Schoeberl, Martin; Sparsø, Jens

    2016-01-01

    of tasks on other cores. Assuming a specific time-predictable multicore processor, we evaluate how the read and write primitives of the five algorithms contribute to the worst-case execution time of the communicating tasks. Each of the five algorithms has specific capabilities that make them suitable...

  6. 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

  7. 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...

  8. 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.

  9. Predicting Academic Performance Based on Students' Blog and Microblog Posts

    NARCIS (Netherlands)

    Dascalu, Mihai; Popescu, Elvira; Becheru, Alexandru; Crossley, Scott; Trausan-Matu, Stefan

    2016-01-01

    This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in th

  10. 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.

  11. 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.

  12. 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.

  13. Prediction of ventricular fibrillation based on nonlinear multi-parameter

    Institute of Scientific and Technical Information of China (English)

    SI Junfeng; NING Xinbao; ZHOU Lingling; ZHANG Song

    2003-01-01

    Ventricular fibrillation (VF) caused by myocardial ischemia is one of the leading factors of death attributed to cardiovascular diseases. It is particularly significant to predict VF and gain valuable time for clinic therapy. Fivedogs are taken as the research objects and a VF model is introduced. The nonlinear characteristics of the ECGs before and after VF are investigated with nonlinear multi-parame- ter analysis methods, Gaussian kernel (GK) correlation estimation algorithm and Lyapunov exponent estimation algorithm. Correlation entropy h2is also presented. The results indicate that there are three parameters which will change at the same time with the conditions of myocardial ischemia, and any changes of a single parameter may be caused by other factors and mislead the judgment. Multi-parameter analysis is more reliable to reveal the heart conditions,and to predict VF without misjudgments.

  14. Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction.

    Science.gov (United States)

    Rahman, Julia; Mondal, Md Nazrul Islam; Islam, Md Khaled Ben; Hasan, Md Al Mehedi

    2016-12-18

    For the importance of protein subcellular localization in different branches of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays a most vital role in protein subcellular localization prediction specially in case of machine learning techniques. Single feature representation-like pseudo amino acid composition (PseAAC), physiochemical property models (PPM), and amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problems, we have proposed two feature fusion representations, AAIDPAAC and PPMPAAC, to work with Support Vector Machine classifiers, which fused PseAAC with PPM and AAID accordingly. We have evaluated the performance for both single and fused feature representation of a Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.

  15. Pathway-Based Genomics Prediction using Generalized Elastic Net.

    Directory of Open Access Journals (Sweden)

    Artem Sokolov

    2016-03-01

    Full Text Available We present a novel regularization scheme called The Generalized Elastic Net (GELnet that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

  16. Pathway-Based Genomics Prediction using Generalized Elastic Net

    Science.gov (United States)

    Sokolov, Artem; Carlin, Daniel E.; Paull, Evan O.; Baertsch, Robert; Stuart, Joshua M.

    2016-01-01

    We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach. PMID:26960204

  17. 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.

  18. Optimality principles for model-based prediction of human gait.

    Science.gov (United States)

    Ackermann, Marko; van den Bogert, Antonie J

    2010-04-19

    Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.

  19. Pathway-Based Genomics Prediction using Generalized Elastic Net.

    Science.gov (United States)

    Sokolov, Artem; Carlin, Daniel E; Paull, Evan O; Baertsch, Robert; Stuart, Joshua M

    2016-03-01

    We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

  20. Validation of Biomarker-based risk prediction models

    OpenAIRE

    Taylor, Jeremy M.G.; Ankerst, Donna P.; Andridge, Rebecca R.

    2008-01-01

    The increasing availability and use of predictive models to facilitate informed decision making highlights the need for careful assessment of the validity of these models. In particular, models involving biomarkers require careful validation for two reasons: issues with overfitting when complex models involve a large number of biomarkers, and inter-laboratory variation in assays used to measure biomarkers. In this paper we distinguish between internal and external statistical validation. Inte...

  1. 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...

  2. 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.

  3. 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...

  4. 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'…

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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.

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

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund

    2012-01-01

    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-based rel...... for the phenotypes conditional on the observed markers, whereas the second term is the likelihood for t he observed markers. The performance of the proposed method is studied on simulated data examples....... that it may be important that a single-step method is based on a model conditional on the observed markers. When data are from routine evaluation systems, selection affects the allele frequencies, and therefore both observed markers and observed phenotypes contain information about allele frequencies...... 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...

  13. A Method for Driving Route Predictions Based on Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Ning Ye

    2015-01-01

    Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

  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. 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. 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.

  17. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

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

    1998-01-01

    -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...

  18. 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...

  19. 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.

  20. Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games

    DEFF Research Database (Denmark)

    Hintze, Arend; Olson, Randal; Lehman, Joel Anthony

    2016-01-01

    (i.e. agents subject to fewer generations of evolution) make for easier opponents, while highly-evolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating...... agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept...... opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences....

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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...

  7. Production and characterization of rhamnolipids from Pseudomonas aeruginosa san ai

    OpenAIRE

    Rikalovic Milena G.; Gojgic-Cvijovic Gordana; Vrvic Miroslav M.; Karadzic Ivanka

    2012-01-01

    Production and characterization of rhamnolipid biosurfactant obtained by strain Pseudomonas aeruginosa san ai was investigated. With regard to carbon and nitrogen source several media were tested to enhance production of rhamnolipids. Phosphate-limited proteose peptone-ammonium salt (PPAS) medium supplemented with sun flower oil as a source of carbon and mineral ammonium chloride and peptone as a nitrogen source greatly improved rhamnolipid production, from 0.15 on basic PPAS (C/N ratio...

  8. 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%

  9. Ontology-based tools to expedite predictive model construction.

    Science.gov (United States)

    Haug, Peter; Holmen, John; Wu, Xinzi; Mynam, Kumar; Ebert, Matthew; Ferraro, Jeffrey

    2014-01-01

    Large amounts of medical data are collected electronically during the course of caring for patients using modern medical information systems. This data presents an opportunity to develop clinically useful tools through data mining and observational research studies. However, the work necessary to make sense of this data and to integrate it into a research initiative can require substantial effort from medical experts as well as from experts in medical terminology, data extraction, and data analysis. This slows the process of medical research. To reduce the effort required for the construction of computable, diagnostic predictive models, we have developed a system that hybridizes a medical ontology with a large clinical data warehouse. Here we describe components of this system designed to automate the development of preliminary diagnostic models and to provide visual clues that can assist the researcher in planning for further analysis of the data behind these models.

  10. Prediction of close binarity based on planetary nebula morphology

    CERN Document Server

    Miszalski, B; Jones, D; Santander-García, M; Rodríguez-Gil, P; Rubio-Díez, M M

    2010-01-01

    A thorough search of the OGLE-III microlensing project has more than doubled the total sample of PNe known to have close binary central stars. These discoveries have enabled close binary induced morphological trends to be revealed for the first time. Canonical bipolar nebulae, low-ionisation structures and polar outflows are all identified within the sample and are provisionally associated with binarity. We have embarked upon a large photometric monitoring program using the Flemish Mercator telescope to simultaneously test the predictive power of these morphological features and to find more close binaries. Early results are very positive with at least five binaries found so far. This suggests our method is an effective means to expedite the construction of a statistically significant sample of close binary shaped nebulae. Such an authoritative sample will be essential to quantify the degree to which close binary nuclei may shape PNe.

  11. Predicting bicycle setup for children based on anthropometrics and comfort.

    Science.gov (United States)

    Grainger, Karl; Dodson, Zoe; Korff, Thomas

    2017-03-01

    Bicycling is a popular activity for children. In order for children to enjoy cycling and to minimize injury, it is important that they are positioned appropriately on the bicycle. The purpose of this study was therefore to identify a suitable bicycle setup for children aged between 7 and 16 years which accommodates developmental differences in anthropometrics, flexibility and perceptions of comfort. Using an adjustable bicycle fitting rig, we found the most comfortable position of 142 children aged 7 to 16. In addition, a number of anthropometric measures were recorded. Seat height and the horizontal distance between seat and handlebars were strongly predictable (R(2) > 0.999, p bicycle manufacturers.

  12. 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.

  13. An IoT Based Predictive Connected Car Maintenance Approach

    Directory of Open Access Journals (Sweden)

    Rohit Dhall

    2017-03-01

    Full Text Available Internet of Things (IoT is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.

  14. Extracting Uranium from Seawater: Promising AI Series Adsorbents

    Energy Technology Data Exchange (ETDEWEB)

    Das, S.; Oyola, Y.; Mayes, R. T.; Janke, C. J.; Kuo, L. -J.; Gill, G.; Wood, J. R.; Dai, S.

    2016-04-20

    A new series of adsorbents (AI10 through AI17) were successfully developed at ORNL by radiation induced graft polymerization (RIGP) of acrylonitrile (AN) and vinylphosphonic acid (VPA) (at different mole to mole ratios) onto high surface area polyethylene fiber, with high degrees of grafting (DOG) varying from 110 to 300%. The grafted nitrile groups were converted to amidoxime groups by reaction with 5 wt % hydroxylamine at 80 °C for 72 h. The amidoximated adsorbents were then conditioned with 0.44 M KOH at 80 °C followed by screening at ORNL with prescreening brine spiked with 8 ppm uranium. Uranium adsorption capacities in prescreening ranged from 171 to 187 g-U/kg-ads irrespective of percent DOG. The performance of the adsorbents with respect to uranium adsorption in natural seawater was also investigated using flow-throughcolumn testing at the Pacific Northwest National Laboratory (PNNL). Three hours of KOH conditioning led to higher uranium uptake than 1 h of conditioning. The adsorbent AI11, containing AN and VPA at the mole ratio of 3.52, emerged as the potential candidate for the highest uranium adsorption (3.35 g-U/kg-ads.) after 56 days of exposure in seawater flow-through-columns. The rate of vanadium adsorption over uranium linearly increased throughout the 56 days of exposure. The total mass of vanadium uptake was ~5 times greater than uranium after 56 days.

  15. Apolipoproteins A-I and B in Kuwaiti children.

    Science.gov (United States)

    Moussa, M A; Shaltout, A A; Nkansa-Dwamena, D; Mourad, M

    1998-01-01

    To assess the relation of apolipoproteins (Apos) A-I and B (the carrier proteins for high and low density lipoprotein cholesterol, respectively) with the degree of obesity, body fat distribution, serum lipids, glucose and insulin levels, a case-control study was carried out and included 460 Kuwaiti obese children, 6-13 years old, matched by age and sex to 460 normal-weight controls. Obese children were ascertained in a representative cross-sectional study of 2,400 school children. The Apo A-I levels were not different between obese and non-obese boys, while they were significantly lower in obese girls (p < 0.01). The Apo B mean concentrations were significantly higher in obese boys and girls (p < 0.001), while the Apo A-I:B ratio was significantly lower in obese children (p < 0.001). Apo A-I levels were positively correlated with total cholesterol, high- and low-density lipoprotein cholesterol, but were not correlated with very low-density lipoprotein cholesterol, triglycerides, insulin, glucose or insulin:glucose ratio. Apo B levels were negatively correlated with high-density lipoprotein cholesterol and positively correlated with insulin and insulin:glucose ratio (p < 0.01) in obese children. The study documented an adverse Apo profile in obese Kuwaiti children. Since Apo changes are correctable through management of obesity, their identification in childhood offers prospects for prevention of early onset atherogenesis in adulthood.

  16. Le système d’identification automatique (AIS

    Directory of Open Access Journals (Sweden)

    Arnaud Serry

    2015-12-01

    Full Text Available 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 circulations maritimes, spécialement pour la communauté scientifique ou les acteurs portuaires. Cet état de l’art est réalisé dans le cadre de la mise en œuvre d’une plateforme permettant de reconstituer les itinéraires des navires en utilisant les signaux AIS.

  17. 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...

  18. 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.

  19. Artificial interfaces ("AI") in surgery: historic development, current status and program implementation in the public health sector.

    Science.gov (United States)

    Healy, Donagh A; Murphy, Shane P; Burke, John P; Coffey, John C

    2013-06-01

    The past two decades have seen considerable advances in the application of artificial interfaces (AI) in surgery. Several have been developed including AESOP (Automated Endoscopic System for Optimal Positioning), Zeus and the Da Vinci Surgical System (DVSS). Whilst each has advantages DVSS is being used increasingly across multiple surgical specialities. These developments generate many challenges in an era where the emphasis is increasingly on safer and cost-effective surgery. Whilst the role of DVSS is firmly established in urologic and gynaecologic surgery, the role of DVSS in gastrointestinal surgery is evolving. Recent data indicate that it is at least as oncologically effective, whilst providing numerous benefits (e.g. reduced conversion and complication rates) over traditional laparoscopic approaches. The increasing adoption of AI/DVSS worldwide places institutes and health sectors under increasing pressure to adopt and develop such programs. This article provides (1) an update on the current status of AI in surgery in general and in colorectal surgery and (2) an appraisal of the cost implications of the establishment and implementation of AI/DVSS-based provisions in the public health sector. The numerous challenges faced generate many opportunities in the implementation of present and future surgical technologies.

  20. Apolipoprotein A-I configuration and cell cholesterol efflux activity of discoidal lipoproteins depend on the reconstitution process.

    Science.gov (United States)

    Cuellar, Luz Ángela; Prieto, Eduardo Daniel; Cabaleiro, Laura Virginia; Garda, Horacio Alberto

    2014-01-01

    Discoidal high-density lipoproteins (D-HDL) are critical intermediates in reverse cholesterol transport. Most of the present knowledge of D-HDL is based on studies with reconstituted lipoprotein complexes of apolipoprotein A-I (apoA-I) obtained by cholate dialysis (CD). D-HDL can also be generated by the direct microsolubilization (DM) of phospholipid vesicles at the gel/fluid phase transition temperature, a process mechanistically similar to the "in vivo" apoAI lipidation via ABCA1. We compared the apoA-I configuration in D-HDL reconstituted with dimyristoylphosphatidylcholine by both procedures using fluorescence resonance energy transfer measurements with apoA-I tryptophan mutants and fluorescently labeled cysteine mutants. Results indicate that apoA-I configuration in D-HDL depends on the reconstitution process and are consistent with a "double belt" molecular arrangement with different helix registry. As reported by others, a configuration with juxtaposition of helices 5 of each apoAI monomer (5/5 registry) predominates in D-HDL obtained by CD. However, a configuration with helix 5 of one monomer juxtaposed with helix 2 of the other (5/2 registry) would predominate in D-HDL generated by DM. Moreover, we also show that the kinetics of cholesterol efflux from macrophage cultures depends on the reconstitution process, suggesting that apoAI configuration is important for this HDL function.

  1. Cell Based GIS as Cellular Automata for Disaster Spreading Predictions and Required Data Systems

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-03-01

    Full Text Available A method for prediction and simulation based on the Cell Based Geographic Information System(GIS as Cellular Automata (CA is proposed together with required data systems, in particular metasearch engine usage in an unified way. It is confirmed that the proposed cell based GIS as CA has flexible usage of the attribute information that is attached to the cell in concert with location information and does work for disaster spreading simulation and prediction.

  2. Orbital period variation study of the low-mass Algol eclipsing binary AI Draconis

    Directory of Open Access Journals (Sweden)

    Magdy A. Hanna

    2013-06-01

    Full Text Available Orbital period changes for the Algol-type eclipsing binary AI Dra were studied based on the analysis of its observed times of light minimum. The period variation showed cyclic changes in the interval from JD. ≈ 2436000 to JD. ≈ 2447500 and a secular period increase rate (dP/dt = 2.44 × 10−7 d/year starting from JD. ≈ 2448500 up to 2455262, in a time scale equals to 5 × 106 year.

  3. 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.

  4. 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.

  5. 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...

  6. A Comprehensive Propagation Prediction Model Comprising Microfacet Based Scattering and Probability Based Coverage Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    A. S. M. Zahid Kausar

    2014-01-01

    Full Text Available Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented by microfacets, for which it becomes possible to compute the scattering field in all possible directions. New optimization techniques, like dual quadrant skipping (DQS and closest object finder (COF, are implemented for fast characterization of wireless communications and making the ray tracing technique more efficient. In conjunction with the ray tracing technique, probability based coverage optimization algorithm is accumulated with the ray tracing technique to make a compact solution for indoor propagation prediction. The proposed technique decreases the ray tracing time by omitting the unnecessary objects for ray tracing using the DQS technique and by decreasing the ray-object intersection time using the COF technique. On the other hand, the coverage optimization algorithm is based on probability theory, which finds out the minimum number of transmitters and their corresponding positions in order to achieve optimal indoor wireless coverage. Both of the space and time complexities of the proposed algorithm surpass the existing algorithms. For the verification of the proposed ray tracing technique and coverage algorithm, detailed simulation results for different scattering factors, different antenna types, and different operating frequencies are presented. Furthermore, the proposed technique is verified by the experimental results.

  7. 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.

  8. 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.

  9. 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.

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

    OpenAIRE

    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...

  11. 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

  12. 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.

  13. 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...

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Thematic and spatial resolutions affect model-based predictions of tree species distribution.

    Directory of Open Access Journals (Sweden)

    Yu Liang

    Full Text Available Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance. We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

  20. 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.

  1. 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.

  2. Experimental method to predict avalanches based on neural networks

    Directory of Open Access Journals (Sweden)

    V. V. Zhdanov

    2016-01-01

    Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.

  3. 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.

  4. A Geometrical-based Vertical Gain Correction for Signal Strength Prediction of Downtilted Base Station Antennas in Urban Areas

    DEFF Research Database (Denmark)

    Rodriguez, Ignacio; Nguyen, Huan Cong; Sørensen, Troels Bundgaard

    2012-01-01

    , with electrical antenna downtilt in the range from 0 to 10 degrees, as well as predictions based on ray-tracing and 3D building databases covering the measurement area. Although the calibrated ray-tracing predictions are highly accurate compared with the measured data, the combined LOS/NLOS COST-WI model...

  5. The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

    Science.gov (United States)

    Anil, Duygu

    2008-01-01

    In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

  6. 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

  7. 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 ea...

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. RNA-RNA interaction prediction based on multiple sequence alignments

    CERN Document Server

    Li, Andrew X; Qin, Jing; Reidys, Christian M

    2010-01-01

    Recently, $O(N^6)$ time and $O(N^4)$ space dynamic programming algorithms have become available that compute the partition function of RNA-RNA interaction complexes for pairs of RNA sequences. These algorithms and the biological requirement of more reliable interactions motivate to utilize the additional information contained in multiple sequence alignments and to generalize the above framework to the partition function and base pairing probabilities for multiple sequence alignments.

  13. 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

  14. Observational attachment theory-based parenting measures predict children's attachment narratives independently from social learning theory-based measures.

    Science.gov (United States)

    Matias, Carla; O'Connor, Thomas G; Futh, Annabel; Scott, Stephen

    2014-01-01

    Conceptually and methodologically distinct models exist for assessing quality of parent-child relationships, but few studies contrast competing models or assess their overlap in predicting developmental outcomes. Using observational methodology, the current study examined the distinctiveness of attachment theory-based and social learning theory-based measures of parenting in predicting two key measures of child adjustment: security of attachment narratives and social acceptance in peer nominations. A total of 113 5-6-year-old children from ethnically diverse families participated. Parent-child relationships were rated using standard paradigms. Measures derived from attachment theory included sensitive responding and mutuality; measures derived from social learning theory included positive attending, directives, and criticism. Child outcomes were independently-rated attachment narrative representations and peer nominations. Results indicated that Attachment theory-based and Social Learning theory-based measures were modestly correlated; nonetheless, parent-child mutuality predicted secure child attachment narratives independently of social learning theory-based measures; in contrast, criticism predicted peer-nominated fighting independently of attachment theory-based measures. In young children, there is some evidence that attachment theory-based measures may be particularly predictive of attachment narratives; however, no single model of measuring parent-child relationships is likely to best predict multiple developmental outcomes. Assessment in research and applied settings may benefit from integration of different theoretical and methodological paradigms.

  15. Classification and predictions of RNA pseudoknots based on topological invariants

    Science.gov (United States)

    Vernizzi, Graziano; Orland, Henri; Zee, A.

    2016-10-01

    We propose a new topological characterization of ribonucleic acid (RNA) secondary structures with pseudoknots based on two topological invariants. Starting from the classic arc representation of RNA secondary structures, we consider a model that couples both (i) the topological genus of the graph and (ii) the number of crossing arcs of the corresponding primitive graph. We add a term proportional to these topological invariants to the standard free energy of the RNA molecule, thus obtaining a novel free-energy parametrization that takes into account the abundance of topologies of RNA pseudoknots observed in RNA databases.

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  20. Accuracy assessment of the UT1 prediction method based on 100-year series analysis

    CERN Document Server

    Malkin, Z; Tolstikov, A

    2013-01-01

    A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole coordinates. The method is based on construction of a general polyharmonic model of the variations of the Earth rotation parameters using all the data available for the last 80-100 years, and modified autoregression technique. In this presentation, a detailed comparison was made of real-time UT1 predictions computed making use of this method in 2006-2010 with simultaneous predictions computed at the International Earth Rotation and Reference Systems Service (IERS). Obtained results have shown that proposed method provides better accuracy at different prediction lengths.

  1. Fatigue Life Prediction of Ductile Iron Based on DE-SVM Algorithm

    Science.gov (United States)

    Yiqun, Ma; Xiaoping, Wang; lun, An

    the model, predicting fatigue life of ductile iron, based on SVM (Support Vector Machine, SVM) has been established. For it is easy to fall into local optimum during parameter optimization of SVM, DE (Differential Evolution algorithm, DE) algorithm was adopted to optimize to improve prediction precision. Fatigue life of ductile iron is predicted combining with concrete examples, and simulation experiment to optimize SVM is conducted adopting GA (Genetic Algorithm), ACO (Ant Colony Optimization) and POS (Partial Swarm Optimization). Results reveal that DE-SVM algorithm is of a better prediction performance.

  2. 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.

  3. 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.

  4. The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

    DEFF Research Database (Denmark)

    Sabuncu, Mert R.; Van Leemput, Koen

    2011-01-01

    to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes. Experiments on age......This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed...... prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy....

  5. Probabilistic prediction of fatigue damage based on linear fracture mechanics

    Directory of Open Access Journals (Sweden)

    M. Krejsa

    2017-01-01

    Full Text Available Paper describes in detail and gives example of the probabilistic assessment of a steel structural element subject to fatigue load, particular attention being paid to cracks from the edge and those from surface. Fatigue crack damage depends on a number of stress range cycles. Three sizes are important for the characteristics of the propagation of fatigue cracks - the initial size, detectable size and acceptable size. The theoretical model of fatigue crack progression in paper is based on a linear fracture mechanics. When determining the required degree of reliability, it is possible to specify the time of the first inspection of the construction which will focus on the fatigue damage. Using a conditional probability, times for subsequent inspections can be determined. For probabilistic calculation of fatigue crack progression was used the original and new probabilistic methods - the Direct Optimized Probabilistic Calculation (“DOProC”, which is based on optimized numerical integration. The algorithm of the probabilistic calculation was applied in the FCProbCalc code (“Fatigue Crack Probabilistic Calculation”, using which is possible to carry out the probabilistic modelling of propagation of fatigue cracks in a user friendly environment very effectively.

  6. Factor analysis and predictive validity of microcomputer-based tests

    Science.gov (United States)

    Kennedy, R. S.; Baltzley, D. R.; Turnage, J. J.; Jones, M. B.

    1989-01-01

    11 tests were selected from two microcomputer-based performance test batteries because previously these tests exhibited rapid stability (less than 10 min, of practice) and high retest reliability efficiencies (r greater than 0.707 for each 3 min. of testing). The battery was administered three times to each of 108 college students (48 men and 60 women) and a factor analysis was performed. Two of the three identified factors appear to be related to information processing ("encoding" and "throughput/decoding"), and the third named an "output/speed" factor. The spatial, memory, and verbal tests loaded on the "encoding" factor and included Grammatical Reasoning, Pattern Comparison, Continuous Recall, and Matrix Rotation. The "throughput/decoding" tests included perceptual/numerical tests like Math Processing, Code Substitution, and Pattern Comparison. The output speed factor was identified by Tapping and Reaction Time tests. The Wonderlic Personnel Test was group administered before the first and after the last administration of the performance tests. The multiple Rs in the total sample between combined Wonderlic as a criterion and less than 5 min. of microcomputer testing on Grammatical Reasoning and Math Processing as predictors ranged between 0.41 and 0.52 on the three test administrations. Based on these results, the authors recommend a core battery which, if time permits, would consist of two tests from each factor. Such a battery is now known to permit stable, reliable, and efficient assessment.

  7. Episodic memories predict adaptive value-based decision-making.

    Science.gov (United States)

    Murty, Vishnu P; FeldmanHall, Oriel; Hunter, Lindsay E; Phelps, Elizabeth A; Davachi, Lila

    2016-05-01

    Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory-specifically item versus associative memory-in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to reengage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to reengage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations-such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior.

  8. 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...

  9. 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...

  10. 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...

  11. 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

  12. 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.

  13. FEASIBILITY STUDY OF ESTABLISHING AN ARTIFICIAL INSEMINATION (AI CENTER FOR CARABAOS IN SAN ILDEFONSO, BULACAN, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    F.Q. Arrienda II

    2014-10-01

    Full Text Available The productivity of the carabao subsector is influenced by several constraints such as social,technical, economic and policy factors. The need to enhance the local production of carabaos will helplocal farmers to increase their income. Thus, producing thorough breeds of carabaos and improving itgenetically is the best response to these constraints. This study was conducted to present the feasibilitystudy of establishing an Artificial Insemination (AI Center and its planned area of operation in Brgy.San Juan, Ildefonso, Bulacan. The market, production, organizational and financial viability of operatingthe business would also be evaluated. This particular study will provide insights in establishing an AICenter. Included in this study is the identification of anticipated problems that could affect the businessand recommendation of specific courses of action to counteract these possible problems. Primary datawere obtained through interviews with key informants from the Philippine. Carabao Center (PCC. Togain insights about the present status of an AI Center, interviews with the technicians of PCC and privatefarm were done to get additional information. Secondary data were acquired from various literatures andfrom San Ildefonso Municipal Office. The proposed area would be 1,500 square meters that would beallotted for the laboratory and bullpen. The AI Center will operate six days a week and will be openedfrom 8 AM until 5 PM. However, customers or farmers can call the technicians beyond the office hoursin case of emergency. The total initial investment of Php 3,825,417.39 is needed in establishing the AICenter. The whole amount will be sourced from the owner’s equity. Financial projection showed an IRRof 30% with a computed NPV of Php 2,415,597.00 and a payback period of 3.97 years. Based on all themarket, technical, organizational, financial factors, projections and data analysis, it is said that thisbusiness endeavor is viable and feasible.

  14. Une hiérarchie privative pour le français

    Directory of Open Access Journals (Sweden)

    Tifrit Ali

    2014-07-01

    Full Text Available Nous présentons dans cet article deux hypothèses concernant la structuration du système des obstruantes du français. Nous explicitons les procédures menant à ces propositions où l’hypothèse des paires minimales (Pairwise Algorithm s’oppose à la méthode des divisions successives (Successive Division Algorithm. Cette dernière, bien qu’étant la seule permettant de rendre compte du contraste parce qu’elle ne maintient que les traits actifs dans les processus, montre des difficultés à motiver la structure proposée. Nous montrons que ces apories découlent de l’utilisation de primitives binaires. Par conséquent, nous défendons l’hypothèse qu’une représentation unaire des segments du français dans un cadre contrastiviste (Dresher 2009 est possible et nécessaire. Sur la base des données diachroniques, nous montrons que seule l’utilisation de primitives privatives permet d’expliquer et de justifier les mécanismes atteignant les obstruantes dans la lénition. Nous démontrons que, à l’instar des autres lieux de place, les fricatives palatales et les occlusives dorsales partagent une même structure interne qui ne se différencie que par le mode. Cette proposition constitue une révision majeure des représentations classiques du français. Elle a aussi pour corollaire de reconsidérer l’utilisation des éléments : nous montrons qu’ils ne peuvent constituer une simple traduction des traits binaires mais doivent être à même d’engrammer la dimension syllabique sans laquelle ils ne sauraient s’exprimer.

  15. 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.

  16. 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...

  17. Does spatial locative comprehension predict landmark-based navigation?

    Directory of Open Access Journals (Sweden)

    Laura Piccardi

    Full Text Available In the present study we investigated the role of spatial locative comprehension in learning and retrieving pathways when landmarks were available and when they were absent in a sample of typically developing 6- to 11-year-old children. Our results show that the more proficient children are in understanding spatial locatives the more they are able to learn pathways, retrieve them after a delay and represent them on a map when landmarks are present in the environment. These findings suggest that spatial language is crucial when individuals rely on sequences of landmarks to drive their navigation towards a given goal but that it is not involved when navigational representations based on the geometrical shape of the environment or the coding of body movements are sufficient for memorizing and recalling short pathways.

  18. Does spatial locative comprehension predict landmark-based navigation?

    Science.gov (United States)

    Piccardi, Laura; Palermo, Liana; Bocchi, Alessia; Guariglia, Cecilia; D'Amico, Simonetta

    2015-01-01

    In the present study we investigated the role of spatial locative comprehension in learning and retrieving pathways when landmarks were available and when they were absent in a sample of typically developing 6- to 11-year-old children. Our results show that the more proficient children are in understanding spatial locatives the more they are able to learn pathways, retrieve them after a delay and represent them on a map when landmarks are present in the environment. These findings suggest that spatial language is crucial when individuals rely on sequences of landmarks to drive their navigation towards a given goal but that it is not involved when navigational representations based on the geometrical shape of the environment or the coding of body movements are sufficient for memorizing and recalling short pathways.

  19. 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.

  20. 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.

  1. A PREDICTION BASED ALL-ZERO BLOCK DETECTION METHOD FOR H.263

    Institute of Scientific and Technical Information of China (English)

    Tian Dong; Yao Zhiheng; Shen Lansun

    2002-01-01

    In this letter, a novel method based on Temporal and Spatial Prediction (TSP) to detect all-zero DCT coefficients based on temporal and spatial prediction between neighboring blocks is proposed. The presented algorithm uses the knowledge of all-zero block distribution in the previous frame combined with the Sum of Absolute Difference (SAD) of the corresponding macroblock as a criterion. The algorithm almost needs no additional computation, and it shows an excellent overall detection performance in simulations.

  2. A PREDICTION BASED ALL—ZERO BLOCK DETECTION METHOD FOR H.263

    Institute of Scientific and Technical Information of China (English)

    TianDong; YaoZhiheng; 等

    2002-01-01

    In this letter,a novel method based on Temporal and Spatial Prediction(TSP)to detect all-zero DCT coefficients based on temporal and spatial prediction between neighboring blocks is proposed.The presented algorithm uses the knowledge of all-zero block distribution in the previous frame combined with the Sum of Absolute Difference(SAD)of the corresponding macroblock as a criterion .The algorithm almost needs no additional computation ,and it shows an excellent overall detection performance in simulations.

  3. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  4. 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.

  5. Prediction Uncertainty Analyses for the Combined Physically-Based and Data-Driven Models

    Science.gov (United States)

    Demissie, Y. K.; Valocchi, A. J.; Minsker, B. S.; Bailey, B. A.

    2007-12-01

    The unavoidable simplification associated with physically-based mathematical models can result in biased parameter estimates and correlated model calibration errors, which in return affect the accuracy of model predictions and the corresponding uncertainty analyses. In this work, a physically-based groundwater model (MODFLOW) together with error-correcting artificial neural networks (ANN) are used in a complementary fashion to obtain an improved prediction (i.e. prediction with reduced bias and error correlation). The associated prediction uncertainty of the coupled MODFLOW-ANN model is then assessed using three alternative methods. The first method estimates the combined model confidence and prediction intervals using first-order least- squares regression approximation theory. The second method uses Monte Carlo and bootstrap techniques for MODFLOW and ANN, respectively, to construct the combined model confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the intervals. The performance of these approaches is compared with Generalized Likelihood Uncertainty Estimation (GLUE) and Calibration-Constrained Monte Carlo (CCMC) intervals of the MODFLOW predictions alone. The results are demonstrated for a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory. This case study comprises structural, parameter, and measurement uncertainties. The preliminary results indicate that the proposed three approaches yield comparable confidence and prediction intervals, thus making the computationally efficient first-order least-squares regression approach attractive for estimating the coupled model uncertainty. These results will be compared with GLUE and CCMC results.

  6. Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

    Directory of Open Access Journals (Sweden)

    Moumita Saha

    2016-01-01

    Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

  7. 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....

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Shiraishi, Satomi; Moore, Kevin L., E-mail: kevinmoore@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California 92093 (United States)

    2016-01-15

    Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D{sub clin} − D{sub pred}. The mean (〈δD{sub r}〉), standard deviation (σ{sub δD{sub r}}), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r{sub PTV}) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r{sub PTV} ∈ [ − 6, 30] mm. The

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation.

    Science.gov (United States)

    Li, Xin; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2017-01-02

    Prediction of tissue damage under thermal loads plays important role for thermal ablation planning. A new methodology is presented in this paper by combing non-Fourier bio-heat transfer, constitutive elastic mechanics as well as non-rigid motion of dynamics to predict and analyze thermal distribution, thermal-induced mechanical deformation and thermal-mechanical damage of soft tissues under thermal loads. Simulations and comparison analysis demonstrate that the proposed methodology based on the non-Fourier bio-heat transfer can account for the thermal-induced mechanical behaviors of soft tissues and predict tissue thermal damage more accurately than classical Fourier bio-heat transfer based model.

  18. 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

    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...... to 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...

  19. 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.

  20. Fragment-based prediction of skin sensitization using recursive partitioning.

    Science.gov (United States)

    Lu, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

    2011-09-01

    Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 (precursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.