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Sample records for ai based prediction

  1. Pre-processing in AI based Prediction of QSARs

    CERN Document Server

    Patri, Om Prasad

    2009-01-01

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

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

    Science.gov (United States)

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

    2015-11-01

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

  3. An SDR based AIS receiver for satellites

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  4. NASA space station automation: AI-based technology review

    Science.gov (United States)

    Firschein, O.; Georgeff, M. P.; Park, W.; Neumann, P.; Kautz, W. H.; Levitt, K. N.; Rom, R. J.; Poggio, A. A.

    1985-01-01

    Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures.

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

    Science.gov (United States)

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

    2006-05-01

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

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

    International Nuclear Information System (INIS)

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

  9. Assessing an AI knowledge-base for asymptomatic liver diseases.

    OpenAIRE

    Babic, A.; Mathiesen, U.; Hedin, K.; Bodemar, G.; Wigertz, O.

    1998-01-01

    Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented ...

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

    Science.gov (United States)

    Swanson, David J.

    1990-01-01

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

  11. 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.

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

    Science.gov (United States)

    Schultz, Roger D.; Stobie, Iain

    1991-01-01

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

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

    Science.gov (United States)

    Goldstein, Ira P.; Miller, Mark L.

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

  14. An AIS-Based E-mail Classification Method

    Science.gov (United States)

    Qing, Jinjian; Mao, Ruilong; Bie, Rongfang; Gao, Xiao-Zhi

    This paper proposes a new e-mail classification method based on the Artificial Immune System (AIS), which is endowed with good diversity and self-adaptive ability by using the immune learning, immune memory, and immune recognition. In our method, the features of spam and non-spam extracted from the training sets are combined together, and the number of false positives (non-spam messages that are incorrectly classified as spam) can be reduced. The experimental results demonstrate that this method is effective in reducing the false rate.

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

    OpenAIRE

    Koehler, Jana; Ottiger, Daniel

    2002-01-01

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

  16. AI-Based Schedulers in Manufacturing Practice: Report of a Panel Discussion

    OpenAIRE

    Kempf, Karl; Russell, Bruce; Sidhu, Sanjiv; Barrett, Stu

    1990-01-01

    There is a great disparity between the number of papers which have been published about AI-based manufacturing scheduling tools and the number of systems which are in daily use by manufacturing engineers. It is argued that this is not a reflection of inadequate AI technology, but is rather indicative of lack of a systems perspective by AI practitioners and their manufacturing customers. Case studies to support this perspective are presented by Carnegie Group as a builder of scheduling systems...

  17. 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...

  18. Teaching AI Search Algorithms in a Web-Based Educational System

    Science.gov (United States)

    Grivokostopoulou, Foteini; Hatzilygeroudis, Ioannis

    2013-01-01

    In this paper, we present a way of teaching AI search algorithms in a web-based adaptive educational system. Teaching is based on interactive examples and exercises. Interactive examples, which use visualized animations to present AI search algorithms in a step-by-step way with explanations, are used to make learning more attractive. Practice…

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

    Institute of Scientific and Technical Information of China (English)

    马枫; 初秀民; 严新平

    2012-01-01

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

  20. 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.

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

    Science.gov (United States)

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

    2015-08-01

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

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

    Science.gov (United States)

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

    2001-01-01

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

  3. AI Topics

    OpenAIRE

    Buchanan, Bruce G; Glick, Jonathan

    2002-01-01

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

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

    International Nuclear Information System (INIS)

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

  5. NASA space station automation: AI-based technology review. Executive summary

    Science.gov (United States)

    Firschein, O.; Georgeff, M. P.; Park, W.; Cheeseman, P. C.; Goldberg, J.; Neumann, P.; Kautz, W. H.; Levitt, K. N.; Rom, R. J.; Poggio, A. A.

    1985-01-01

    Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics.

  6. Ai ai ai (4/4 F)

    OpenAIRE

    2011-01-01

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

  7. 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

  8. AI-based alarm processing for a nuclear power plant

    International Nuclear Information System (INIS)

    A real-time expert system is implemented using artificial intelligence and object-oriented technology for alarm processing and presentation in a nuclear power plant. The knowledge base is constructed based on some schemes to process and display alarms to the plant operators. The activated alarms are dynamically prioritized by the reasoning rules, and then, presented on the process mimic overview and by some other means. To demonstrate the proposed system, the alarm processing and presentation is carried out in a simulated environment of the TMI-2 accident

  9. A Text Knowledge Base from the AI Handbook.

    Science.gov (United States)

    Simmons, Robert F.

    1987-01-01

    Describes a prototype natural language text knowledge system (TKS) that was used to organize 50 pages of a handbook on artificial intelligence as an inferential knowledge base with natural language query and command capabilities. Representation of text, database navigation, query systems, discourse structuring, and future research needs are…

  10. PERFORMANCE ANALYSIS OF AI BASED QOS SCHEDULER FOR MOBILE WIMAX

    OpenAIRE

    D. David Neels Pon Kumar; K. Murugesan

    2012-01-01

    Interest in broadband wireless access (BWA) has been growing due to increased user mobility and the need for data access at all times. IEEE 802.16e based WiMAX networks promise the best available quality of experience for mobile data service users. WiMAX networks incorporate several Quality of Service (QoS) mechanisms at the Media Access Control (MAC) level for guaranteed services for multimedia viz. data, voice and video. The problem of assuring QoS is how to allocate available resources amo...

  11. PERFORMANCE ANALYSIS OF AI BASED QOS SCHEDULER FOR MOBILE WIMAX

    Directory of Open Access Journals (Sweden)

    D. David Neels Pon Kumar

    2012-09-01

    Full Text Available Interest in broadband wireless access (BWA has been growing due to increased user mobility and the need for data access at all times. IEEE 802.16e based WiMAX networks promise the best available quality of experience for mobile data service users. WiMAX networks incorporate several Quality of Service (QoS mechanisms at the Media Access Control (MAC level for guaranteed services for multimedia viz. data, voice and video. The problem of assuring QoS is how to allocate available resources among users to meet the QoS criteria such as delay, delay jitter, fairness and throughput requirements. IEEE standard does not include a standard scheduling mechanism and leaves it for various implementer differentiations. Although a lot of the real-time and non real-time packet scheduling schemes has been proposed, it needs to be modified to apply to Mobile WiMAX system that supports five kinds of service classes. In this paper, we propose a novel Priority based Scheduling scheme that uses Artificial Intelligence to support various services by considering the QoS constraints of each class. The simulation results show that slow mobility does not affect the performances and faster mobility and the increment in users beyond a particular load have their say in defining average throughput, average per user throughput, fairness index, average end to end delay and average delay jitter. Nevertheless the results are encouraging that the proposed scheme provides QoS support for each class efficiently.

  12. 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.

  13. Global Coastal and Marine Spatial Planning (CMSP) from Space Based AIS Ship Tracking

    Science.gov (United States)

    Schwehr, K. D.; Foulkes, J. A.; Lorenzini, D.; Kanawati, M.

    2011-12-01

    All nations need to be developing long term integrated strategies for how to use and preserve our natural resources. As a part of these strategies, we must evalutate how communities of users react to changes in rules and regulations of ocean use. Global characterization of the vessel traffic on our Earth's oceans is essential to understanding the existing uses to develop international Coast and Marine Spatial Planning (CMSP). Ship traffic within 100-200km is beginning to be effectively covered in low latitudes by ground based receivers collecting position reports from the maritime Automatic Identification System (AIS). Unfortunately, remote islands, high latitudes, and open ocean Marine Protected Areas (MPA) are not covered by these ground systems. Deploying enough autonomous airborne (UAV) and surface (USV) vessels and buoys to provide adequate coverage is a difficult task. While the individual device costs are plummeting, a large fleet of AIS receivers is expensive to maintain. The global AIS coverage from SpaceQuest's low Earth orbit satellite receivers combined with the visualization and data storage infrastructure of Google (e.g. Maps, Earth, and Fusion Tables) provide a platform that enables researchers and resource managers to begin answer the question of how ocean resources are being utilized. Near real-time vessel traffic data will allow managers of marine resources to understand how changes to education, enforcement, rules, and regulations alter usage and compliance patterns. We will demonstrate the potential for this system using a sample SpaceQuest data set processed with libais which stores the results in a Fusion Table. From there, the data is imported to PyKML and visualized in Google Earth with a custom gx:Track visualization utilizing KML's extended data functionality to facilitate ship track interrogation. Analysts can then annotate and discuss vessel tracks in Fusion Tables.

  14. AIS authentication

    CERN Multimedia

    2006-01-01

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

  15. 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.

  16. 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.

  17. Novel AI-2 quorum sensing inhibitors in Vibrio harveyi identified through structure-based virtual screening.

    Science.gov (United States)

    Zhu, Peng; Peng, Hanjing; Ni, Nanting; Wang, Binghe; Li, Minyong

    2012-10-15

    In this letter, a high-throughput virtual screening was accomplished to identify potent inhibitors against AI-2 quorum sensing on the basis of Vibrio harveyi LuxPQ crystal structure. Seven compounds were found to inhibit AI-2 quorum sensing with IC(50) values in the micromolar range, and presented low cytotoxicity or no cytotoxicity in V. harveyi. PMID:22963763

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

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

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

    2016-01-01

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

  20. AI 3D Cybug Gaming

    OpenAIRE

    Ahmed, Zeeshan

    2010-01-01

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

  1. AI Magazine Poster: The AI Landscape

    OpenAIRE

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

    2008-01-01

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

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

    OpenAIRE

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

    2004-01-01

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

  3. Exploring Need-based AI Behaviour and its Effect on the Game Experience of Neverwinter Nights

    OpenAIRE

    Södergren, Gunnar

    2013-01-01

    Single Player Roleplaying Game (SPRPG) is a popular genre among players as well as developers, with recent blockbuster titles such as Skyrim by Bethesda and Mass E↵ect 3 by Bioware. In recent years, an occurrence that have been gaining a lot of attention is the development of more advanced and vivid Artificial Intelligence (AI) within these SPRPG and a lot of progress has been made towards making the Non-player Character (NPC) more vivacious and life-like. It is, however, still a common occurr...

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

    OpenAIRE

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

    2010-01-01

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

  5. 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

  6. 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

  7. 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

  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-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

  10. 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

  11. 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.

  12. 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。

  13. 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)

  14. 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

  15. Typical and atypical AIS. Pathogenesis.

    Science.gov (United States)

    Dudin, M; Pinchuk, D

    2012-01-01

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

  16. 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.

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

    Science.gov (United States)

    Teng, William; Lynnes, Christopher

    2014-01-01

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

  18. An Improved AIS Based E-mail Classification Technique for Spam Detection

    OpenAIRE

    Idris, Ismaila; Abdulhamid, Shafii Muhammad

    2014-01-01

    An improved email classification method based on Artificial Immune System is proposed in this paper to develop an immune based system by using the immune learning, immune memory in solving complex problems in spam detection. An optimized technique for e-mail classification is accomplished by distinguishing the characteristics of spam and non-spam that is been acquired from trained data set. These extracted features of spam and non-spam are then combined to make a single detector, therefore re...

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

    International Nuclear Information System (INIS)

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

  20. 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

  1. 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

  2. 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

  3. 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

  4. Research on the Evolutionary Strategy Based on AIS and Its Application on Numerical Integration

    Science.gov (United States)

    Bei, Li

    Based on the features of artificial immune system, a new evolutionary strategy is proposed in order to calculate the numerical integration of functions. This evolutionary strategy includes the mechanisms of swarm searching and constructing the fitness function. Finally, numerical examples are given for verifying the effectiveness of evolutionary strategy. The results show that the performance of evolutionary strategy is satisfactory and more accurate than traditional methods of numerical integration, such as trapezoid formula and Simpson formula.

  5. Classification techniques based on AI application to defect classification in cast aluminum

    Science.gov (United States)

    Platero, Carlos; Fernandez, Carlos; Campoy, Pascual; Aracil, Rafael

    1994-11-01

    This paper describes the Artificial Intelligent techniques applied to the interpretation process of images from cast aluminum surface presenting different defects. The whole process includes on-line defect detection, feature extraction and defect classification. These topics are discussed in depth through the paper. Data preprocessing process, as well as segmentation and feature extraction are described. At this point, algorithms employed along with used descriptors are shown. Syntactic filter has been developed to modelate the information and to generate the input vector to the classification system. Classification of defects is achieved by means of rule-based systems, fuzzy models and neural nets. Different classification subsystems perform together for the resolution of a pattern recognition problem (hybrid systems). Firstly, syntactic methods are used to obtain the filter that reduces the dimension of the input vector to the classification process. Rule-based classification is achieved associating a grammar to each defect type; the knowledge-base will be formed by the information derived from the syntactic filter along with the inferred rules. The fuzzy classification sub-system uses production rules with fuzzy antecedent and their consequents are ownership rates to every defect type. Different architectures of neural nets have been implemented with different results, as shown along the paper. In the higher classification level, the information given by the heterogeneous systems as well as the history of the process is supplied to an Expert System in order to drive the casting process.

  6. ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

    OpenAIRE

    Kempka, Michał; Wydmuch, Marek; Runc, Grzegorz; Toczek, Jakub; Jaśkowski, Wojciech

    2016-01-01

    The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective...

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

    Science.gov (United States)

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

    2013-03-01

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

  8. 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.

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

    OpenAIRE

    Rich, Elaine

    1992-01-01

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

  10. Dicty_cDB: FC-AI22 [Dicty_cDB

    Lifescience Database Archive (English)

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  11. 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

  12. 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

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

    OpenAIRE

    Aversa, Davide; Vassos, Stavros

    2013-01-01

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

  14. 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

  15. Welcome to AI Magazine

    OpenAIRE

    Thompson, Alan M.

    1980-01-01

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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

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

    International Nuclear Information System (INIS)

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

  4. Formal Definition of AI

    OpenAIRE

    Dobrev, Dimiter

    2012-01-01

    A definition of Artificial Intelligence was proposed in [1] but this definition was not absolutely formal at least because the word "Human" was used. In this paper we will formalize the definition from [1]. The biggest problem in this definition was that the level of intelligence of AI is compared to the intelligence of a human being. In order to change this we will introduce some parameters to which AI will depend. One of this parameters will be the level of intelligence and we will define o...

  5. 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....

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

    International Nuclear Information System (INIS)

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

  7. AI and Mathematical Education

    OpenAIRE

    Angel Garrido

    2012-01-01

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

  8. 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.

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

    OpenAIRE

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

    2010-01-01

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

  10. 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...

  11. Analysis of received AIS data from a LEO Cubesat

    DEFF Research Database (Denmark)

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

    2014-01-01

    using their AIS broadcast signals as an indication of position by means of a simple dipole antenna. The SDR based receiver used on board the satellite is using a single chip front-end solution, which downconverts the AIS signal located around 162 MHz to an intermediate frequency at 200 kHz. This I....../Q signal is sampled with a 750 kSPS A/D converter and further processed by an Analog De- vices DSP. The algorithm first analyses the stream to identify possible AIS transmissions on the two different AIS channels. If likely transmissions are identified, the center frequency of the transmission is estimated...... mission of the satellite was to perform a feasibility study about the possibility of detecting AIS signals from a 1U cubesat in LEO. However, soon after launch it was found, that the AIS receiver performed very well and an improved algorithm which samples and decodes real-time was developed and uploaded...

  12. 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...

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

    International Nuclear Information System (INIS)

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

  14. User Interface Goals, AI Opportunities

    OpenAIRE

    Lieberman, Henry; Massachusetts Institute of Technology Media Lab

    2009-01-01

    This is an opinion piece about the relationship between the fields of human-computer interaction (HCI), and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier to use for people. But historically, they have disagreed about whether "intelligence" or "direct manipulation" is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces a...

  15. Is Computer Vision Still AI?

    OpenAIRE

    Fisher, Robert B.

    1994-01-01

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

  16. Monitoring severe accidents using AI techniques

    International Nuclear Information System (INIS)

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

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

    OpenAIRE

    Hitzer, Eckhard

    2013-01-01

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

  18. 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...

  19. 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

  20. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed on...

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

    Science.gov (United States)

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

    2002-07-01

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

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

    International Nuclear Information System (INIS)

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

  3. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Calorimeter prediction based on multiple exponentials

    Energy Technology Data Exchange (ETDEWEB)

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

    2002-05-21

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

  6. Calorimeter prediction based on multiple exponentials

    International Nuclear Information System (INIS)

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

  7. Calorimeter prediction based on multiple exponentials

    CERN Document Server

    Smith, M K

    2002-01-01

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

  8. Time Series Prediction Based on Chaotic Attractor

    Institute of Scientific and Technical Information of China (English)

    LIKe-Ping; CHENTian-Lun; GAOZi-You

    2003-01-01

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

  9. Knowledge-based fragment binding prediction.

    Science.gov (United States)

    Tang, Grace W; Altman, Russ B

    2014-04-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  10. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

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

  11. 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.

  12. 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.

  13. 2011 Tohoku tsunami runup hydrographs, ship tracks, upriver and overland flow velocities based on video, LiDAR and AIS measurements

    Science.gov (United States)

    Fritz, H. M.; Phillips, D. A.; Okayasu, A.; Shimozono, T.; Liu, H.; Takeda, S.; Mohammed, F.; Skanavis, V.; Synolakis, C.; Takahashi, T.

    2014-12-01

    The 2004 Indian Ocean tsunami marked the advent of survivor videos mainly from tourist areas in Thailand and basin-wide locations. Near-field video recordings on Sumatra's north tip at Banda Aceh were limited to inland areas a few kilometres off the beach (Fritz et al., 2006). The March 11, 2011, magnitude Mw 9.0 earthquake off the Tohoku coast of Japan caused catastrophic damage and loss of life resulting in the costliest natural disaster in recorded history. The mid-afternoon tsunami arrival combined with survivors equipped with cameras on top of vertical evacuation buildings provided numerous inundation recordings with unprecedented spatial and temporal resolution. High quality tsunami video recording sites at Yoriisohama, Kesennuma, Kamaishi and Miyako along Japan's Sanriku coast were surveyed, eyewitnesses interviewed and precise topographic data recorded using terrestrial laser scanning (TLS). The original video recordings were recovered from eyewitnesses and the Japanese Coast Guard (JCG). The analysis of the tsunami videos follows an adapted four step procedure (Fritz et al., 2012). Measured overland flow velocities during tsunami runup exceed 13 m/s at Yoriisohama. The runup hydrograph at Yoriisohama highlights the under sampling at the Onagawa Nuclear Power Plant (NPP) pressure gauge, which skips the shorter period second crest. Combined tsunami and runup hydrographs are derived from the videos based on water surface elevations at surface piercing objects and along slopes identified in the acquired topographic TLS data. Several hydrographs reveal a draw down to minus 10 m after a first wave crest exposing harbor bottoms at Yoriisohama and Kamaishi. In some cases ship moorings resist the main tsunami crest only to be broken by the extreme draw down. A multi-hour ship track for the Asia Symphony with the vessels complete tsunami drifting motion in Kamaishi Bay is recovered from the universal ship borne AIS (Automatic Identification System). Multiple

  14. Wavelet-based prediction of oil prices

    International Nuclear Information System (INIS)

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

  15. Applying AI tools to operational space environmental analysis

    Science.gov (United States)

    Krajnak, Mike; Jesse, Lisa; Mucks, John

    1995-01-01

    The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines

  16. 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.

  17. 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.

  18. 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.

  19. 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 ...

  20. Physically based prediction of earthquake induced landsliding

    Science.gov (United States)

    Marc, Odin; Meunier, Patrick; Hovius, Niels; Gorum, Tolga; Uchida, Taro

    2015-04-01

    Earthquakes are an important trigger of landslides and can contribute significantly to sedimentary or organic matter fluxes. We present a new physically based expression for the prediction of total area and volume of populations of earthquake-induced landslides. This model implements essential seismic processes, linking key parameters such as ground acceleration, fault size, earthquake source depth and seismic moment. To assess the model we have compiled and normalized a database of landslide inventories for 40 earthquakes. We have found that low landscape steepness systematically leads to overprediction of the total area and volume of landslides. When this effect is accounted for, the model is able to predict within a factor of 2 the landslide areas and associated volumes for about two thirds of the cases in our databases. This is a significant improvement on a previously published empirical expression based only on earthquake moment, even though the prediction of total landslide area is more difficult than that of volume because it is affected by additional parameters such as the depth and continuity of soil cover. Some outliers in terms of observed landslide intensity are likely to be associated with exceptional rock mass properties in the epicentral area. Others may be related to seismic source complexities ignored by the model. However, most cases in our catalogue seem to be relatively unaffected by these two effects despite the variety of lithologies and tectonic settings they cover. This makes the model suitable for integration into landscape evolution models, and application to the assessment of secondary hazards and risks associated with earthquakes.

  1. 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.

  2. Monitoring Severe Accidents Using AI Techniques

    International Nuclear Information System (INIS)

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

  3. SEKILAS TENTANG AVIAN INFLUENZA (AI)

    OpenAIRE

    Fauziah Elytha

    2011-01-01

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

  4. Organisational Intelligence and Distributed AI

    OpenAIRE

    Kirn, Stefan

    2008-01-01

    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...

  5. 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

  6. Base drag prediction on missile configurations

    Science.gov (United States)

    Moore, F. G.; Hymer, T.; Wilcox, F.

    1993-01-01

    New wind tunnel data have been taken, and a new empirical model has been developed for predicting base drag on missile configurations. The new wind tunnel data were taken at NASA-Langley in the Unitary Wind Tunnel at Mach numbers from 2.0 to 4.5, angles of attack to 16 deg, fin control deflections up to 20 deg, fin thickness/chord of 0.05 to 0.15, and fin locations from 'flush with the base' to two chord-lengths upstream of the base. The empirical model uses these data along with previous wind tunnel data, estimating base drag as a function of all these variables as well as boat-tail and power-on/power-off effects. The new model yields improved accuracy, compared to wind tunnel data. The new model also is more robust due to inclusion of additional variables. On the other hand, additional wind tunnel data are needed to validate or modify the current empirical model in areas where data are not available.

  7. 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.

  8. DMC modified algorithm based on time series prediction principle

    Institute of Scientific and Technical Information of China (English)

    齐维贵; 朱学莉

    2002-01-01

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

  9. Proceedings of conference on AI applications in physical sciences

    International Nuclear Information System (INIS)

    A Conference cum workshop on AI applications in Physical Sciences was organised by the Indian Physics Association at Bhabha Atomic Research Centre, Bombay during January 15-17, 1992. It was held in memory of Late Shri S.N. Seshadri, who was the moving spirit behind self reliance in instrumentation development for research and industry. The two day conference which was followed by one day workshop covered the following broad spectrum of topics in Artificial Intelligence: AI Tools and Techniques, Neural Networks, Robotics and Machine Vision, Fuzzy Control and Applications, Natural Language and Speech Processing, Knowledge based Systems, and AI and Allied applications. The conference dealt with recent advances and achievements in AI. It provided a forum for the exchange of valuable information and expertise in this fast emerging field. Over 200 scientists, engineers and computer professionals from various universities, R and D institutes and industries actively participated. 45 contributed papers and 8 invited talks were presented in the symposium. The volume contains selected papers which were contributed by the participants. Some of them dealt with AI applications in nuclear science and technology. (original)

  10. 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 of...... position. The SDR receiver developed to listen for these AIS signals also allows for sampling and storing of the raw intermediate frequency spectrum, which has been used in order to map channel utilization over the areas of interest for the mission, which is mainly the arctic regions. The SDR based...... receiver used onboard the satellite is using a single chip front-end solution, which down converts the AIS signal located around 162 MHz into an intermediate frequency, with a up to 200 kHz bandwidth. This I/F signal is sampled with a 750 kSPS A/D converter and further processed by an Analog Devices DSP...

  11. Tools and techniques for AIS Strategic Planning

    OpenAIRE

    Monod, Emmanuel; Watson, Richard

    2003-01-01

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

  12. 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.

  13. 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.

  14. 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,…

  15. 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…

  16. Activity Prediction: A Twitter-based Exploration

    OpenAIRE

    Weerkamp, W.; Rijke, de, M.

    2012-01-01

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

  17. 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.

  18. Theoretical bases analysis of scientific prediction on marketing principles

    OpenAIRE

    A.S. Rosohata

    2012-01-01

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

  19. 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.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Bangcheng Zhang

    2015-01-01

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

  2. Optimizing Water Treatment Systems Using Artificial Intelligence Based Tools

    OpenAIRE

    Pinto, Ana Mafalda; Fernandes, Ana; Vicente, Henrique; Neves, José

    2009-01-01

    Predictive modelling is a process used in predictive analytics to create a statistical model of future behaviour. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. On the other hand, Artificial Intelligence (AI) concerns itself with intelligent behaviour, i.e. the things that make us seem intelligent. Following this process of thinking, in this work the main goal is the assessment of the impact of using AI based tools for th...

  3. Feature Selection for Neural Network Based Stock Prediction

    Science.gov (United States)

    Sugunnasil, Prompong; Somhom, Samerkae

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

  4. Base Oils Biodegradability Prediction with Data Mining Techniques

    OpenAIRE

    Malika Trabelsi; Saloua Saidane; Sihem Ben Abdelmelek

    2010-01-01

    In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classifi...

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

    International Nuclear Information System (INIS)

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

  6. Crystal structure of truncated human apolipoprotein A-I suggests a lipid-bound conformation

    Science.gov (United States)

    Borhani, David W.; Rogers, Danise P.; Engler, Jeffrey A.; Brouillette, Christie G.

    1997-01-01

    The structure of truncated human apolipoprotein A-I (apo A-I), the major protein component of high density lipoprotein, has been determined at 4-Å resolution. The crystals comprise residues 44–243 (exon 4) of apo A-I, a fragment that binds to lipid similarly to intact apo A-I and that retains the lipid-bound conformation even in the absence of lipid. The molecule consists almost entirely of a pseudo-continuous, amphipathic α-helix that is punctuated by kinks at regularly spaced proline residues; it adopts a shape similar to a horseshoe of dimensions 125 × 80 × 40 Å. Four molecules in the asymmetric unit associate via their hydrophobic faces to form an antiparallel four-helix bundle with an elliptical ring shape. Based on this structure, we propose a model for the structure of apo A-I bound to high density lipoprotein. PMID:9356442

  7. 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.

  8. 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. PMID:26185241

  9. Satellittbasert system for AIS-meldinger

    OpenAIRE

    Aas, Olav Ådne

    2006-01-01

    Innen 1.juli 2008 skal alle skip over 300 tonn i internasjonal trafikk og 500 tonn i nasjonal trafikk ha installert Automatic Identification System (AIS). AIS fungerer både som et antikollisjons og overvåkningssystem. I dag (2006) blir kun havområdet ut til 30 40 nautiske mil fra land overvåket ved bruk av AIS. For å øke rekkevidden til dette systemet, ser Forsvarets Forskningsinstitutt (FFI) på en løsning der det blir innført to nye segmenter: En bakkestasjon og en lavbanesatellitt. Det ...

  10. Consumer perceptions and reactions concerning AI

    OpenAIRE

    Figuié, Muriel; Food and Agriculture Organization of the United Nations

    2007-01-01

    This paper focuses on the results of different consumer surveys conducted between 2004 and 2006 with regard to consumers’ perceptions and reactions concerning AI in Vietnam, (mainly in Hanoi). The main results observed are as follows: A high proportion of consumers consider AI to be a food-related risk. However, over time,there has been a slight shift from a fear of consuming poultry to a fear of preparing it (slaughtering it). AI has had a profound effect on poultry consumption, even outside...

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

    OpenAIRE

    Mark Morris; Simon Colton; Robin Baumgarten

    2009-01-01

    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 generat...

  12. 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...

  13. 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.

  14. Model Based Predictive Control of a Fully Parallel Robot

    OpenAIRE

    Vivas, Oscar Andrès; Poignet, Philippe

    2003-01-01

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

  15. 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.

  16. From Constructionist to Constructivist A.I.

    OpenAIRE

    Thórisson, Kristinn R.

    2009-01-01

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

  17. An Optimization Problem for Predicting the Maximal Effect of Degradation of Mechanical Structures

    DEFF Research Database (Denmark)

    Achtziger, W.; Bendsøe, Martin P.; Taylor, J. E.

    2000-01-01

    gives insight in terms of a mechanical interpretation of the optimization problem. We derive an equivalent convex problem formulation and a convex dual problem, and for dyadic matrices A(i) a quadratic programming problem formulation is developed. A nontrivial numerical example is included, based on the......This paper deals with a nonlinear nonconvex optimization problem that models prediction of degradation in discrete or discretized mechanical structures. The mathematical difficulty lies in equality constraints of the form Σ(i=1)(m) 1/yi A(i) x=b, where A(i) are symmetric and positive semidefinite...

  18. Ensemble-based prediction of RNA secondary structures

    OpenAIRE

    Aghaeepour, Nima; Hoos, Holger H

    2013-01-01

    Background Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive,...

  19. Size-based predictions of food web patterns

    DEFF Research Database (Denmark)

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

    2014-01-01

    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 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 resistance. Our results show that the predicted size-spectrum exponent is borne out in the simulated food webs even with few species, albeit with a systematic bias. The predicted maximum trophic level turns out to be an upper limit since simulated food webs may have a lower number of...

  20. Prediction of Mortality Based on Facial Characteristics.

    Science.gov (United States)

    Delorme, Arnaud; Pierce, Alan; Michel, Leena; Radin, Dean

    2016-01-01

    Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person's photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 s. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. Participants were asked to press a button if they thought the person in a photo was living or deceased. Overall mean accuracy on this task was 53.8%, where 50% was expected by chance (p clairvoyance warrants further investigation. PMID:27242466

  1. 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...

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

    CERN Document Server

    Ward, B F L; Yost, S A

    2014-01-01

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

  3. Using intron position conservation for homology-based gene prediction.

    Science.gov (United States)

    Keilwagen, Jens; Wenk, Michael; Erickson, Jessica L; Schattat, Martin H; Grau, Jan; Hartung, Frank

    2016-05-19

    Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest.Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well.Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration. PMID:26893356

  4. POUR UNE PRISE EN COMPTE D'UN SECTEUR NEGLIGE EN DIDACTIQUE DU FRANÇAIS LANGUE ETRANGERE : LA FORMATION DE BASE

    OpenAIRE

    Etienne, Sophie

    2004-01-01

    La première partie de cette thèse éclaire la problématique posée : la formation de base vise l'insertion de publics hétérogènes à travers l'apprentissage de la langue, la didactique du FLE est concernée. La seconde partie propose quelques pistes pour une adéquation entre les besoins et les réponses formatives. Afin de mieux aborder la situation actuelle, le milieu instituant et le milieu institué de la formation de base, sont analysés à la lumière des dernières évolutions. Les formateurs doiv...

  5. 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.

  6. 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 ...

  7. Oil spills and AI: How to manage resources through simulation

    International Nuclear Information System (INIS)

    Today, in the Mediterranean theater of the Upper Tyrrhenian, the ecological risk involving oil installations is still quite high. This is due to the fact that valuable environmental and tourist areas exist together with large industrial and port structures; in particular, recent events have demonstrated the danger involving oil spills along the Ligurian coastline. This study proposes an approach to plan the operations that should be performed when accidents occur, based on the use of AI techniques

  8. Comparing model predictions for ecosystem-based management

    DEFF Research Database (Denmark)

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

    2016-01-01

    Ecosystem modeling is becoming an integral part of fisheries management, but there is a need to identify differences between predictions derived from models employed for scientific and management purposes. Here, we compared two models: a biomass-based food-web model (Ecopath with Ecosim (EwE)) and...... predictions, underscoring the importance of incorporating knowledge of model assumptions and limitation, possibly through using model ensembles, when providing model-based scientific advice to policy makers....

  9. CLINICAL DATABASE ANALYSIS USING DMDT BASED PREDICTIVE MODELLING

    Directory of Open Access Journals (Sweden)

    Srilakshmi Indrasenan

    2013-04-01

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

  10. Compact AIS substations with high availability

    Energy Technology Data Exchange (ETDEWEB)

    Solver, C.E.; Haglund, L.; Wahl, P. [ABB, Vasteras (Sweden)

    2008-07-01

    The changing electricity market offers new opportunities to change traditional practices in the design of substations. This paper described 2 new types of switching devices for air insulated (AIS) outdoor high voltage substations. Both the withdrawable circuit-breaker (WCB) and the disconnecting circuit-breaker (DCB) eliminate the need for conventional disconnectors. When conventional disconnectors are removed, the main circuits of the substation are simplified and the electrical power losses decrease significantly. WCB and DCB devices achieve significant space savings compared to traditional open-air solutions, and lead to higher overall availability of the substation. They normally require fewer foundations than traditional equipment, and have short and simple installation and commissioning procedures. Total life cycle cost and environmental impact is generally lower than for traditional equipment. In addition to high reliability and low maintenance, there are several environmental advantages to WCB and DCB devices. Substations equipped with WCB or DCB have smaller space requirements than conventional substations and use less steel structures and foundations. They have simpler cable connections and have lower electrical power losses. The amount of SF6 gas in the live-tank WCB and DCB designs is many times lower than in corresponding devices bases on gas-insulating substation (GIS) technology. 10 refs., 1 tab., 10 figs.

  11. [Artificial intelligence] AI for protection systems

    Energy Technology Data Exchange (ETDEWEB)

    Aggarwal, R.; Johns, A.

    1997-12-31

    The reliable operation of large power systems with small stability margins is highly dependent on control systems and protection devices. Progress in the field of microprocessor systems and demanding requirements in respect of the performance of protective relays are the reasons for digital device applications to power system protection. The superiority of numeric protection over its analogue alternatives is attributed to such factors as accurate extraction of the fundamental voltage and current components through filtering, functional benefits resulting from multi-processor design and extensive self-monitoring, etc. However, all these reasons have not led to a major impact on speed, sensitivity and selectivity of primary protective relays, and the gains are only marginal; this is so because conventional digital relays still rely on deterministic signal models and a heuristic approach for decision making, so that only a fraction of the information contained within voltage and current signals as well as knowledge about the plant to be protected is used. The performance of digital relays may be substantially improved if the decision making is based on elements of artificial intelligence (AI). (Author)

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

    Directory of Open Access Journals (Sweden)

    R. Lashkari

    2009-09-01

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

  13. The role of apolipoprotein AI domains in lipid binding

    OpenAIRE

    Davidson, W. Sean; Hazlett, Theodore; Mantulin, William W.; Jonas, Ana

    1996-01-01

    Apolipoprotein AI (apoAI) is the principal protein constituent of high density lipoproteins and it plays a key role in human cholesterol homeostasis; however, the structure of apoAI is not clearly understood. To test the hypothesis that apoAI is organized into domains, three deletion mutants of human apoAI expressed in Escherichia coli were studied in solution and in reconstituted high density lipoprotein particles. Each mutant lacked one of three specific regions ...

  14. Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)

    OpenAIRE

    Parsapoor, Mahboobeh

    2016-01-01

    This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning-Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest des...

  15. 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.

  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. 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.

  18. Human Posture and Movement Prediction based on Musculoskeletal Modeling

    DEFF Research Database (Denmark)

    Farahani, Saeed Davoudabadi

    2014-01-01

    Abstract This thesis explores an optimization-based formulation, so-called inverse-inverse dynamics, for the prediction of human posture and motion dynamics performing various tasks. It is explained how this technique enables us to predict natural kinematic and kinetic patterns for human posture...... almost no doubt that the objective function is to maximize the height reached by the body center of mass. But, finding the proper objective function for other motions is not always as straight forward as in jumping. The existence of a “right” criterion for different tasks or indeed whether any single...... performance criterion is capable of predicting realistic motions for a wide range of dynamic human movements remain open questions. In this thesis, we investigated the validity of different physiology-based cost functions for the prediction of kinematic and kinetic patterns for different human postures and...

  19. 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.

  20. Predictive PID Control Based on GPC Control of Inverted Pendulum

    Directory of Open Access Journals (Sweden)

    Safa Bouhajar

    2014-05-01

    Full Text Available Having regard to the large application of the inverted pendulum in robotic system, this study is interested in controlling this process with two strategies of controls. The first proposed control is the state feedback with an observer based on the Generalized Predictive Control (GPC algorithm. In the second proposed control we used the characteristic of predictive control GPC to improve the performance of the classical PID controller. The obtained results have been discussed and compared; the simulation results obtained by the predictive PID control are mentioned.

  1. Neural Network Predictive Control Based Power System Stabilizer

    OpenAIRE

    Ali Mohamed Yousef

    2012-01-01

    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 ...

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

    OpenAIRE

    2014-01-01

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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    Science.gov (United States)

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

    2012-01-01

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

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

    OpenAIRE

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

    2011-01-01

    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 ...

  9. 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.

  10. AI reference LMFBR steam-generator development

    International Nuclear Information System (INIS)

    The Design Data Sheets summarize the key parameters being used in the design and analysis of the AI Prototype LMFBR Steam Generator. These Data Sheets supplement SDD-097-330-002, Steam Generator System, 1450 psi Steam Conditions. This document will serve as the baseline design data control until a GE/RRD approved steam generator specification with ordering data is received

  11. SNAP and AI Fuel Summary Report

    International Nuclear Information System (INIS)

    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

  12. 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.

  13. 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.

  14. 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.

  15. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

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

    2006-01-01

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

  16. 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.

  17. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

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

    OpenAIRE

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

    1983-01-01

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

  20. 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%.

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

    OpenAIRE

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

    2014-01-01

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

  2. 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.

  3. Network planning tool based on network classification and load prediction

    OpenAIRE

    Hammami, Seif eddine; Afifi, Hossam; Marot, Michel; Gauthier, Vincent

    2016-01-01

    Real Call Detail Records (CDR) are analyzed and classified based on Support Vector Machine (SVM) algorithm. The daily classification results in three traffic classes. We use two different algorithms, K-means and SVM to check the classification efficiency. A second support vector regression (SVR) based algorithm is built to make an online prediction of traffic load using the history of CDRs. Then, these algorithms will be integrated to a network planning tool which will help cellular operators...

  4. Predicting Difficult Laparoscopic Cholecystectomy Based on Clinicoradiological Assessment

    Science.gov (United States)

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

    2015-01-01

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

  5. 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.

  6. 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.

  7. Predicting carcinogenicity of organic compounds based on CPDB.

    Science.gov (United States)

    Wu, Xiuchao; Zhang, Qingzhu; Wang, Hui; Hu, Jingtian

    2015-11-01

    Cancer is a major killer of human health and predictions for the carcinogenicity of chemicals are of great importance. In this article, predictive models for the carcinogenicity of organic compounds using QSAR methods for rats and mice were developed based on the data from CPDB. The models was developed based on the data of specific target site liver and classified according to sex of rats and mice. Meanwhile, models were also classified according to whether there is a ring in the molecular structure in order to reduce the diversity of molecular structure. Therefore, eight local models were developed in the final. Taking into account the complexity of carcinogenesis and in order to obtain as much information, DRAGON descriptors were selected as the variables used to develop models. Fitting ability, robustness and predictive power of the models were assessed according to the OECD principles. The external predictive coefficients for validation sets of each model were in the range of 0.711-0.906, and for the whole data in each model were all greater than 0.8, which represents that all models have good predictivity. In order to study the mechanism of carcinogenesis, standardized regression coefficients were calculated for all predictor variables. In addition, the effect of animal sex on carcinogenesis was compared and a trend that female showed stronger tolerance for cancerogen than male in both species was appeared. PMID:26070146

  8. 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.

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

    Science.gov (United States)

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

    2007-09-01

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

  10. 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.

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

    Institute of Scientific and Technical Information of China (English)

    LI Ji; ZHANG Hongyue

    2005-01-01

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

  12. 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.

  13. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-03-01

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

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

    OpenAIRE

    Young-Joo Lee; Soojin Cho

    2016-01-01

    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 ...

  16. miRNA-target prediction based on transcriptional regulation

    Directory of Open Access Journals (Sweden)

    Fujiwara Toyofumi

    2013-02-01

    Full Text Available Abstract Background microRNAs (miRNAs are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes. A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules. Results Here, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method. Conclusions This is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.

  17. 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.

  18. CD-Based Indices for Link Prediction in Complex Network.

    Science.gov (United States)

    Wang, Tao; Wang, Hongjue; Wang, Xiaoxia

    2016-01-01

    Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks. PMID:26752405

  19. 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…

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

    International Nuclear Information System (INIS)

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

  1. 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.

  2. 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%.

  3. Structure based activity prediction of HIV-1 reverse transcriptase inhibitors.

    Science.gov (United States)

    de Jonge, Marc R; Koymans, Lucien M H; Vinkers, H Maarten; Daeyaert, Frits F D; Heeres, Jan; Lewi, Paul J; Janssen, Paul A J

    2005-03-24

    We have developed a fast and robust computational method for prediction of antiviral activity in automated de novo design of HIV-1 reverse transcriptase inhibitors. This is a structure-based approach that uses a linear relation between activity and interaction energy with discrete orientation sampling and with localized interaction energy terms. The localization allows for the analysis of mutations of the protein target and for the separation of inhibition and a specific binding to the enzyme. We apply the method to the prediction of pIC(50) of HIV-1 reverse transcriptase inhibitors. The model predicts the activity of an arbitrary compound with a q(2) of 0.681 and an average absolute error of 0.66 log value, and it is fast enough to be used in high-throughput computational applications. PMID:15771460

  4. 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.

  5. A structure-based model for predicting serum albumin binding.

    Directory of Open Access Journals (Sweden)

    Katrina W Lexa

    Full Text Available One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.

  6. The Attribute for Hydrocarbon Prediction Based on Attenuation

    International Nuclear Information System (INIS)

    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

  7. 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.

  8. 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.

  9. Microporosity Prediction and Validation for Ni-based Superalloy Castings

    Science.gov (United States)

    Guo, J.; Beckermann, C.; Carlson, K.; Hirvo, D.; Bell, K.; Moreland, T.; Gu, J.; Clews, J.; Scott, S.; Couturier, G.; Backman, D.

    2015-06-01

    Microporosityin high performance aerospace castings can reduce mechanical properties and consequently degrade both component life and durability. Therefore, casting engineers must be able to both predict and reduce casting microporosity. A dimensionless Niyama model has been developed [1] that predicts local microporosity by accounting for local thermal conditions during casting as well as the properties and solidification characteristics of the cast alloy. Unlike the well-known Niyama criterion, application of the dimensionless Niyama model avoids the need to find a threshold Niyama criterion below which shrinkage porosity forms - a criterion which can be determined only via extensive alloy dependent experimentation. In the present study, the dimensionless Niyama model is integrated with a commercial finite element casting simulation software, which can now more accurately predict the location-specific shrinkage porosity volume fraction during solidification of superalloy castings. These microporosity predictions are validated by comparing modelled results against radiographically and metallographically measured porosity for several Ni-based superalloy equiaxed castings that vary in alloy chemistry with a focus on plates of changing draft angle and thickness. The simulation results agree well with experimental measurements. The simulation results also show that the dimensionless Niyama model can not only identify the location but also the average volume fraction of microporosity distribution in these equiaxed investment cast Ni-based superalloy experiments of relatively simple geometry.

  10. 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.

  11. 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.

  12. Radar Image Processing and AIS Target Fusion

    OpenAIRE

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

    2015-01-01

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

  13. Strategic Team AI Path Plans: Probabilistic Pathfinding

    OpenAIRE

    Chaudhari, Narendra S.; Edmond C. Prakash; John, Tng C. H.

    2008-01-01

    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 i...

  14. AI & Law, logic and argument schemes

    OpenAIRE

    Prakken, Henry

    2006-01-01

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

  15. 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.

  16. 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.

  17. A Prediction Model for Membrane Proteins Using Moments Based Features

    Science.gov (United States)

    Butt, Ahmad Hassan; Khan, Sher Afzal; Jamil, Hamza; Rasool, Nouman; Khan, Yaser Daanial

    2016-01-01

    The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies. PMID:26966690

  18. Prediction-based Adaptation (PRADA) Algorithm for Modulation and Coding

    CERN Document Server

    Lin, Shou-Pon; Lin, Wei-Ting; Yeh, Ping-Cheng; Su, Hsuan-Jung

    2010-01-01

    In this paper, we propose a novel adaptive modulation and coding (AMC) algorithm dedicated to reduce the feedback frequency of the channel state information (CSI). There have been already plenty of works on AMC so as to exploit the bandwidth more efficiently with the CSI feedback to the transmitter. However, in some occasions, frequent CSI feedback is not favorable in these systems. This work considers finite-state Markov chain (FSMC) based channel prediction to alleviate the feedback while maximizing the overall throughput. We derive the close-form of the frame error rate (FER) based on channel prediction using limited CSI feedback. In addition, instead of switching settings according to the CSI, we also provide means to combine both CSI and FER as the switching parameter. Numerical results illustrate that the average throughput of the proposed algorithm has significant performance improvement over fixed modulation and coding while the CSI feedback being largely reduced.

  19. Predictive Control Using Short-Term Prediction Method Based on chaos theory

    International Nuclear Information System (INIS)

    In this paper, an active vibration control method for nonlinear mechanical systems is discussed. The control forces are determined by using the future values of the system obtained by the short-term prediction method based on chaos theory. The authors call such a control method a predictive control method. This method is applied to a pendulum system forced by a sinusoidal torque at the supported point as a numerical example here. The equation of motion for the system becomes nonlinear one when the swing angle is large. The angular displacements near future are used to calculate the control forces. Particularly, the methods to get the optimal sampling period, the forward horizon and the backward horizon are presented here and the effectiveness of the methods are examined numerically

  20. 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.

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

    OpenAIRE

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

    2010-01-01

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

  2. Prediction Research of Red Tide Based on Improved FCM

    OpenAIRE

    Xiaomei Hu; Dong Wang; Hewei Qu; Xinran Shi

    2016-01-01

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

  3. 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.

  4. BLANNOTATOR: enhanced homology-based function prediction of bacterial proteins

    Directory of Open Access Journals (Sweden)

    Kankainen Matti

    2012-02-01

    Full Text Available Abstract Background Automated function prediction has played a central role in determining the biological functions of bacterial proteins. Typically, protein function annotation relies on homology, and function is inferred from other proteins with similar sequences. This approach has become popular in bacterial genomics because it is one of the few methods that is practical for large datasets and because it does not require additional functional genomics experiments. However, the existing solutions produce erroneous predictions in many cases, especially when query sequences have low levels of identity with the annotated source protein. This problem has created a pressing need for improvements in homology-based annotation. Results We present an automated method for the functional annotation of bacterial protein sequences. Based on sequence similarity searches, BLANNOTATOR accurately annotates query sequences with one-line summary descriptions of protein function. It groups sequences identified by BLAST into subsets according to their annotation and bases its prediction on a set of sequences with consistent functional information. We show the results of BLANNOTATOR's performance in sets of bacterial proteins with known functions. We simulated the annotation process for 3090 SWISS-PROT proteins using a database in its state preceding the functional characterisation of the query protein. For this dataset, our method outperformed the five others that we tested, and the improved performance was maintained even in the absence of highly related sequence hits. We further demonstrate the value of our tool by analysing the putative proteome of Lactobacillus crispatus strain ST1. Conclusions BLANNOTATOR is an accurate method for bacterial protein function prediction. It is practical for genome-scale data and does not require pre-existing sequence clustering; thus, this method suits the needs of bacterial genome and metagenome researchers. The method and a

  5. 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%.

  6. 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. PMID:25899615

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

    Directory of Open Access Journals (Sweden)

    Sanguthevar Rajasekaran

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

  8. 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.

  9. 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.

  10. Link prediction based on local information considering preferential attachment

    Science.gov (United States)

    Zeng, Shan

    2016-02-01

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

  11. 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.

  12. 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.

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

    DEFF Research Database (Denmark)

    Lunde, Asger; Brix, Anne Floor

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

  14. Will AI in pigs become more efficient?

    Science.gov (United States)

    Roca, J; Parrilla, I; Bolarin, A; Martinez, E A; Rodriguez-Martinez, H

    2016-07-01

    AI is commercially applied worldwide to breed pigs, yielding fertility outcomes similar to those of natural mating. However, it is not fully efficient, as only liquid-stored semen is used, with a single boar inseminating about 2000 sows yearly. The use of liquid semen, moreover, constrains international trade and slows genetic improvement. Research efforts, reviewed hereby, are underway to reverse this inefficient scenario. Special attention is paid to studies intended to decrease the number of sperm used per pregnant sow, facilitating the practical use of sexed frozen-thawed semen in swine commercial insemination programs. PMID:26723133

  15. Roadmap Toward a Predictive Performance-based Commercial Energy Code

    Energy Technology Data Exchange (ETDEWEB)

    Rosenberg, Michael I.; Hart, Philip R.

    2014-10-01

    Energy codes have provided significant increases in building efficiency over the last 38 years, since the first national energy model code was published in late 1975. The most commonly used path in energy codes, the prescriptive path, appears to be reaching a point of diminishing returns. The current focus on prescriptive codes has limitations including significant variation in actual energy performance depending on which prescriptive options are chosen, a lack of flexibility for designers and developers, and the inability to handle control optimization that is specific to building type and use. This paper provides a high level review of different options for energy codes, including prescriptive, prescriptive packages, EUI Target, outcome-based, and predictive performance approaches. This paper also explores a next generation commercial energy code approach that places a greater emphasis on performance-based criteria. A vision is outlined to serve as a roadmap for future commercial code development. That vision is based on code development being led by a specific approach to predictive energy performance combined with building specific prescriptive packages that are designed to be both cost-effective and to achieve a desired level of performance. Compliance with this new approach can be achieved by either meeting the performance target as demonstrated by whole building energy modeling, or by choosing one of the prescriptive packages.

  16. A new splitting-based displacement prediction approach for location-based services

    OpenAIRE

    Daoud, Mohammad Sh.; Ayesh, Aladdin; Hopgood, Adrian A.; Al-Fayoumi, Mustafa

    2011-01-01

    In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Several location prediction models have been proposed to enhance and increase the relevance of the information retrieved by users of mobile information systems, but none of them studied the relationship between accuracy rate of prediction and the performance of the model in terms of consuming resources and constraints of mobile devices. Most of the ...

  17. 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.

  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. PMID:26117822

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

    International Nuclear Information System (INIS)

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

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

    Directory of Open Access Journals (Sweden)

    Isabel Pôças

    2015-12-01

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

  2. Phosphate-based glasses: Prediction of acoustical properties

    Science.gov (United States)

    El-Moneim, Amin Abd

    2016-04-01

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

  3. Dissecting phenotypic variation among AIS patients

    International Nuclear Information System (INIS)

    We have created genital skin fibroblast cell lines directly from three patients in a Chinese family affected by androgen insensitivity syndrome (AIS). All patients in the family share an identical AR Arg840Cys mutant but show different disease phenotypes. By using the cell lines, we find that the mutation has not influenced a normal androgen-binding capacity at 37 deg C but has reduced the affinity for androgens and may cause thermolability of the androgen-receptor complex. The impaired nuclear trafficking of the androgen receptor in the cell lines is highly correlated with the severity of donors' disease phenotype. The transactivity of the mutant is substantially weakened and the extent of the reduced transactivity reflects severity of the donors' disease symptom. Our data reveal that although etiology of AIS is monogenic and the mutant may alter the major biological functions of its wild allele, the function of the mutant AR can also be influenced by the different genetic backgrounds and thus explains the divergent disease phenotypes

  4. Radar Image Processing and AIS Target Fusion

    Directory of Open Access Journals (Sweden)

    F. Heymann

    2015-09-01

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

  5. AIS spectra of desert shrub canopies

    Science.gov (United States)

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

    1986-01-01

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

  6. 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...... is built upon a fixed number of overlapping word sequences (n-grams). But it would be difficult to decide what kind of representation of gaze data (unit of n-grams) would correlate more with cognitive effort associated with reading. Moreover, the randomness associated with gaze data also accounts for data...... sparsity, making it difficult for gaze based n-gram models to handle real test scenarios. It has already been seen that some important eye-movement phenomena are captured better by scanpaths than considering individual fixations, saccades and pauses. In this talk, we propose and validate an n-gram based...

  7. A Prediction-based Smart Meter Data Generator

    DEFF Research Database (Denmark)

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

    2016-01-01

    , 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...... with Gaussian white noise. In this paper, we comprehensively evaluate the efficiency and effectiveness of the proposed method based on a real-world energy data set.......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...

  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. 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.

  10. Intelligent condition-based prediction of machinery reliability

    Science.gov (United States)

    Heng, Aiwina; Tan, Andy C. C.; Mathew, Joseph; Montgomery, Neil; Banjevic, Dragan; Jardine, Andrew K. S.

    2009-07-01

    The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models that attempt to forecast machinery health based on condition data. This paper presents a novel approach for incorporating population characteristics information and suspended condition trending data of historical units into prognosis. The population characteristics information extracted from statistical failure distribution enables longer-range prognosis. The accurate modelling of suspended data is also found to be of great importance, since in practice machines are rarely allowed to run to failure and hence data are commonly suspended. The proposed model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density function (PDF) estimator. The trained network is capable of estimating the future survival probabilities of an operating asset when a series of condition indices are inputted. The output survival probabilities collectively form an estimated survival curve. Pump vibration data were used for model validation. The proposed model was compared with two similar models that neglect suspended data, as well as with a conventional time series prediction model. The results support our hypothesis that the proposed model can predict more accurately and further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis.

  11. 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

    AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital electrocar......AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital...... electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Framingham formula was used for heart rate-correction of the QT interval. Data on medication, comorbidity, and outcomes were retrieved from administrative registries. During a median follow-up period of 6...... interval resulted in the worst prognosis for men whereas in women, a very short QTc interval was equivalent in risk to a borderline prolonged QTc interval. The effect of the QTc interval on the absolute risk of CVD was most pronounced in the elderly and in those with cardiovascular disease whereas...

  12. 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...

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

    International Nuclear Information System (INIS)

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

  14. AIC - An AI-system for Combination of senses

    OpenAIRE

    Håkansson, Anne

    2013-01-01

    AI-complete systems developed today, are commonly used for solving different artificial intelligence problems.A problem is a typical image recognition or speech recognition, but it can also be language processing, as wellas, other complex systems dealing with general problem solving. However, no AI-complete system, whichmodels the human brain or behavior, can exist without looking at the totality of the whole situation and, andhence, incorporating an AI-computerized sensory systems into a tot...

  15. 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...

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

    OpenAIRE

    Barroso Jerez, Clara

    2009-01-01

    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 poo...

  17. 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.

  18. 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

  19. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    Science.gov (United States)

    Kovalenko, Andriy

    2014-08-01

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

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

    OpenAIRE

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

    2015-01-01

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

  1. 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).

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

    Science.gov (United States)

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

    2015-02-01

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

  3. Parkinson's disease prediction using diffusion-based atlas approach

    Science.gov (United States)

    Teodorescu, Roxana O.; Racoceanu, Daniel; Smit, Nicolas; Cretu, Vladimir I.; Tan, Eng K.; Chan, Ling L.

    2010-03-01

    We study Parkinson's disease (PD) using an automatic specialized diffusion-based atlas. A total of 47 subjects, among who 22 patients diagnosed clinically with PD and 25 control cases, underwent DTI imaging. The EPIs have lower resolution but provide essential anisotropy information for the fiber tracking process. The two volumes of interest (VOI) represented by the Substantia Nigra and the Putamen are detected on the EPI and FA respectively. We use the VOIs for the geometry-based registration. We fuse the anatomical detail detected on FA image for the putamen volume with the EPI. After 3D fibers growing on the two volumes, we compute the fiber density (FD) and the fiber volume (FV). Furthermore, we compare patients based on the extracted fibers and evaluate them according to Hohen&Yahr (H&Y) scale. This paper introduces the method used for automatic volume detection and evaluates the fiber growing method on these volumes. Our approach is important from the clinical standpoint, providing a new tool for the neurologists to evaluate and predict PD evolution. From the technical point of view, the fusion approach deals with the tensor based information (EPI) and the extraction of the anatomical detail (FA and EPI).

  4. Interesting Metrics Based Adaptive Prediction Technique for Knowledge Discovery

    OpenAIRE

    G. Anbukkarasy; N. Sairam

    2013-01-01

    Prediction is considered as an important factor to predict the future results from the existing information. Decision tree methodology is widely used for predicting the results. But this is not efficient to handle the large, heterogeneous or multi-featured type of data sources. So an adaptive prediction method is proposed by combining the statistical analysis approach of the data mining methods along with the decision tree prediction methodology. So when dealing with large and multi-server ba...

  5. 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.

  6. 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.

  7. Prediction of spectral acceleration response ordinates based on PGA attenuation

    Science.gov (United States)

    Graizer, V.; Kalkan, E.

    2009-01-01

    Developed herein is a new peak ground acceleration (PGA)-based predictive model for 5% damped pseudospectral acceleration (SA) ordinates of free-field horizontal component of ground motion from shallow-crustal earthquakes. The predictive model of ground motion spectral shape (i.e., normalized spectrum) is generated as a continuous function of few parameters. The proposed model eliminates the classical exhausted matrix of estimator coefficients, and provides significant ease in its implementation. It is structured on the Next Generation Attenuation (NGA) database with a number of additions from recent Californian events including 2003 San Simeon and 2004 Parkfield earthquakes. A unique feature of the model is its new functional form explicitly integrating PGA as a scaling factor. The spectral shape model is parameterized within an approximation function using moment magnitude, closest distance to the fault (fault distance) and VS30 (average shear-wave velocity in the upper 30 m) as independent variables. Mean values of its estimator coefficients were computed by fitting an approximation function to spectral shape of each record using robust nonlinear optimization. Proposed spectral shape model is independent of the PGA attenuation, allowing utilization of various PGA attenuation relations to estimate the response spectrum of earthquake recordings.

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

    Science.gov (United States)

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

    2016-06-01

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

  9. 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....

  10. 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....

  11. 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.  

  12. Optimization of arterial age prediction models based in pulse wave

    International Nuclear Information System (INIS)

    We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff

  13. 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...

  14. 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...

  15. 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.

  16. Operational Numerical Weather Prediction systems based on Linux cluster architectures

    International Nuclear Information System (INIS)

    The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

  17. 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.

  18. 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.

  19. The Ai project: historical and ecological contexts.

    Science.gov (United States)

    Matsuzawa, Tetsuro

    2003-12-01

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

  20. 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)

  1. FACE: the barefaced facts of AI potency

    International Nuclear Information System (INIS)

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

  2. SNP annotation-based whole genomic prediction and selection

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  3. Future of AI application to electric power field

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-08-20

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

  4. 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.

  5. 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…

  6. 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

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

    Science.gov (United States)

    Badua, Frank

    2008-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    LiYanling; ZhaoXuanmin; XieWenxian

    2005-01-01

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

  9. 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.

  10. Precision of incidence predictions based on Poisson distributed observations.

    Science.gov (United States)

    Hakulinen, T; Dyba, T

    1994-08-15

    Disease incidence predictions are useful for a number of administrative and scientific purposes. The simplest ones are made using trend extrapolation, on either an arithmetic or a logarithmic scale. This paper shows how approximate confidence prediction intervals can be calculated for such predictions, both for the total number of cases and for the age-adjusted incidence rates, by assuming Poisson distribution of the age and period specific numbers of incident cases. Generalizations for prediction models, for example, using power families and extra-Poisson variation, are also presented. Cancer incidence predictions for the Stockholm-Gotland Oncological Region in Sweden are used as an example. PMID:7973230

  11. HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Z. Ghaemi

    2015-12-01

    Full Text Available 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.

  12. 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.

  13. 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.

  14. Prediction

    OpenAIRE

    Woollard, W.J.

    2006-01-01

    In this chapter we will look at the ways in which you can use ICT in the classroom to support hypothesis and prediction and how modern technology is enabling: pattern seeking, extrapolation and interpolation to meet the challenges of the information explosion of the 21st century.

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  16. Bayesian Predictive Densities Based on Latent Information Priors

    OpenAIRE

    Komaki, Fumiyasu

    2010-01-01

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

  17. 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.

  18. Link Strength Prediction Using Transaction-Based Matrix Factorization

    OpenAIRE

    Bozorgkhan, Ali

    2013-01-01

    The revolution of social networks and methods of analyzing them have attracted interest in many research fields. Predicting whether a friendship holds in a social network between two individuals or not, link prediction, has been a heavily researched topic in the last decade. In this research I've investigated a related problem, link strength prediction: how to assign strengths to friendship links. A basic approach would be matrix factorization applied to only friendship ratings. However, the ...

  19. Synthesis and evaluation of thiazolidinedione and dioxazaborocane analogues as inhibitors of AI-2 quorum sensing in Vibrio harveyi.

    Science.gov (United States)

    Brackman, Gilles; Al Quntar, Abed Al Aziz; Enk, Claes D; Karalic, Izet; Nelis, Hans J; Van Calenbergh, Serge; Srebnik, Morris; Coenye, Tom

    2013-02-01

    Two focused libraries based on two types of compounds, that is, thiazolidinediones and dioxazaborocanes were designed. Structural resemblances can be found between thiazolidinediones and well-known furanone type quorum sensing (QS) inhibitors such as N-acylaminofuranones, and/or acyl-homoserine lactone signaling molecules, while dioxazaborocanes structurally resemble previously reported oxazaborolidine derivatives which antagonized autoinducer 2 (AI-2) binding to its receptor. Because of this, we hypothesized that these compounds could affect AI-2 QS in Vibrio harveyi. Although all compounds blocked QS, the thiazolidinediones were the most active AI-2 QS inhibitors, with EC(50) values in the low micromolar range. Their mechanism of inhibition was elucidated by measuring the effect on bioluminescence in a series of V. harveyi QS mutants and by DNA-binding assays with purified LuxR protein. The active compounds neither affected bioluminescence as such nor the production of AI-2. Instead, our results indicate that the thiazolidinediones blocked AI-2 QS in V. harveyi by decreasing the DNA-binding ability of LuxR. In addition, several dioxazaborocanes were found to block AI-2 QS by targeting LuxPQ. PMID:23286963

  20. Forbush Decrease Prediction Based on Remote Solar Observations

    Science.gov (United States)

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

    2016-01-01

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

  1. Coal Calorific Value Prediction Based on Projection Pursuit Principle

    Directory of Open Access Journals (Sweden)

    QI Minfang

    2012-10-01

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

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

    OpenAIRE

    Benkert, Pascal

    2007-01-01

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

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

    OpenAIRE

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

    2010-01-01

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

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

    Science.gov (United States)

    Nelson, Matthew Paul

    1998-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Young-Joo Lee

    2016-03-01

    Full Text Available 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.

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

    Science.gov (United States)

    Lee, Young-Joo; Cho, Soojin

    2016-01-01

    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. PMID:26950125

  7. Model-based Purchase Predictions for Large Assortments

    NARCIS (Netherlands)

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

    2016-01-01

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

  8. Containment code comparison exercise on experiment ThAI TH7

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-07-01

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

  9. Containment code comparison exercise on experiment ThAI TH7

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2004-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Kurgan Lukasz

    2008-10-01

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

  12. AI-2 signalling is induced by acidic shock in probiotic strains of Lactobacillus spp.

    Science.gov (United States)

    Moslehi-Jenabian, Saloomeh; Gori, Klaus; Jespersen, Lene

    2009-11-15

    Survival and ability to respond to various environmental stresses such as low pH are important factors for lactobacilli for their function as probiotics. LuxS-mediated quorum sensing mechanism, which is based on the production of universal signal molecule called autoinducer-2 (AI-2), regulates important physiological traits and a variety of adaptive processes in different bacteria. The aim of this study was to investigate the effect of acidic stress on LuxS-mediated quorum sensing (AI-2 signalling) in four probiotic strains of different Lactobacillus species. Initially, the production of AI-2-like molecule was investigated in four strains of Lactobacillus spp. at standard growth conditions using Vibrio harveyi bioluminescence assay. Species variation in AI-2 activity was observed. AI-2 activity started at early-exponential growth phase and increased during the mid-exponential phase concomitant with the reduction of pH, reaching maximum at late exponential phase (L. rhamnosus GG) or at stationary phase (L. salivarius UCC118, L. acidophilus NCFM and L. johnsonii NCC533). Acidic shock experiments were conducted on L. rhamnosus GG and L. acidophilus NCFM after exposure to different acidic shocks (pH 5.0, 4.0 and 3.0) and to pH 6.5 as control, measuring AI-2 activity and transcription of the luxS gene. AI-2 activity increased by lowering the pH in a dose dependent manner and was negatively influenced by acid adaptation. In both species, the luxS gene was repressed after exposure to pH 6.5 as control. However, after acidic shock (pH 4.0) a transient response of luxS gene was observed and the transcription augmented over time, reaching a maximum level and decreased subsequently. Acid adaptation of cells attenuated the transcription of this gene. Based on the observations done in the present study, the luxS gene appears to have a clear role in acidic stress response in probiotic lactobacilli. This might be important in the survival of these bacteria during the passage

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

    Science.gov (United States)

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

    2009-12-01

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

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

    OpenAIRE

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

    2014-01-01

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

  15. 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.

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

    DEFF Research Database (Denmark)

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

    2001-01-01

    The application and capability of the friction theory (f-theory) for viscosity predictions of hydrocarbon fluids is further illustrated by predicting the viscosity of binary and ternary liquid mixtures composed of n-alkanes ranging from n-pentane to n-decane for wide ranges of temperature and from...... low to high pressures. In the f-theory viscosity predictions the SRK and the PRSV EOS have respectively been used. Further, a comparison with the widely used LBC viscosity model shows that better results are obtained with the f-theory models. The obtained AAD% is within or close to the experimental...

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

    Science.gov (United States)

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

    2013-01-01

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

  18. 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.

  19. 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.

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

    OpenAIRE

    Ugochukwu, Francoise; Okafor, Peter

    2004-01-01

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

  1. 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...

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

    CERN Document Server

    De Silva, Anthony Mihirana

    2015-01-01

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

  3. 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.

  4. 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.

  5. Safety prediction for basic components of safety critical software based on static testing

    International Nuclear Information System (INIS)

    The purpose of this work is to develop a safety prediction method, with which we can predict the risk of software components based on static testing results at the early development stage. The predictive model combines the major factor with the quality factor for the components, both of which are calculated based on the measures proposed in this work. The application to a safety-critical software system demonstrates the feasibility of the safety prediction method. (authors)

  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. Safety prediction for basic components of safety-critical software based on static testing

    International Nuclear Information System (INIS)

    The purpose of this work is to develop a safety prediction method, with which we can predict the risk of software components based on static testing results at the early development stage. The predictive model combines the major factor with the quality factor for the components, which are calculated based on the measures proposed in this work. The application to a safety-critical software system demonstrates the feasibility of the safety prediction method. (authors)

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

    OpenAIRE

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

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an im...

  9. Accurate Multisteps Traffic Flow Prediction Based on SVM

    OpenAIRE

    Zhang Mingheng; Zhen Yaobao; Hui Ganglong; Chen Gang

    2013-01-01

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

  10. Real time highway traffic prediction based on dynamic demand modeling

    OpenAIRE

    Bernhardsson, Viktor; Ringdahl, Rasmus

    2014-01-01

    Traffic problems caused by congestion are increasing in cities all over the world. As a traffic management tool traffic predictions can be used in order to make prevention actions against traffic congestion. There is one software for traffic state estimations called Mobile Millennium Stockholm (MMS) that are a part of a project for estimate real-time traffic information.In this thesis a framework for running traffic predictions in the MMS software have been implemented and tested on a stretch...

  11. A Hidden Genetic Layer Based Neural Network for Mobility Prediction

    OpenAIRE

    L. Velmurugan; P. Thangaraj

    2012-01-01

    Problem statement: With numerous wireless devices increasingly connecting to the internet, WLAN infrastructure planning becomes very important to maintain desired quality of services. For maintaining desired quality of service it is desirable to know the movement pattern of users. Mobility prediction involves finding the mobile device's next access point as it moves through the wireless network. Hidden Markov models and Bayesian approach have been proposed to predict the next hop. Approach: I...

  12. SVM-based prediction of caspase substrate cleavage sites

    OpenAIRE

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

    2006-01-01

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

  13. Performance of Local Information Based Link Prediction: A Sampling Perspective

    OpenAIRE

    Zhao, Jichang; Feng, Xu; Dong, Li; Liang, Xiao; Xu, Ke

    2011-01-01

    Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained from kinds of sampling methods. Contrarily, in the previous literature, in order to evaluate the performance of the prediction, the known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the diverse sampling approaches beyond. However, different sampling methods might lead to different missing li...

  14. Neuro Fuzzy based Techniques for Predicting Stock Trends

    Directory of Open Access Journals (Sweden)

    Hemanth Kumar P.

    2012-07-01

    Full Text Available In this paper we discuss about Prediction of stock market returns.Artificial neural networks (ANNs have been popularly applied to finance problems such as stock exchange index prediction, bankruptcy prediction and corporate bond classification. An ANN model essentially mimics the learning capability of the human brain. A Fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. HereNeuro Fuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes in the context of stocks and options trading. The methodologies are tested with actual financial data and shown considerable promise as a decision making and planning tool. In this paper methods are designed to predict 10-15 days of stock returns in advance.

  15. Effective protein conformational sampling based on predicted torsion angles.

    Science.gov (United States)

    Yang, Yuedong; Zhou, Yaoqi

    2016-04-30

    Protein structure prediction is a long-standing problem in molecular biology. Due to lack of an accurate energy function, it is often difficult to know whether the sampling algorithm or the energy function is the most important factor for failure of locating near-native conformations of proteins. This article examines the size dependence of sampling effectiveness by using a perfect "energy function": the root-mean-squared distance from the target native structure. Using protein targets up to 460 residues from critical assessment of structure prediction techniques (CASP11, 2014), we show that the accuracy of near native structures sampled is relatively independent of protein sizes but strongly depends on the errors of predicted torsion angles. Even with 40% out-of-range angle prediction, 2 Å or less near-native conformation can be sampled. The result supports that the poor energy function is one of the bottlenecks of structure prediction and predicted torsion angles are useful for overcoming the bottleneck by restricting the sampling space in the absence of a perfect energy function. © 2015 Wiley Periodicals, Inc. PMID:26696379

  16. 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...

  17. 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.

  18. Creating an AI modeling application for designers and developers

    Science.gov (United States)

    Houlette, Ryan; Fu, Daniel; Jensen, Randy

    2003-09-01

    Simulation developers often realize an entity's AI by writing a program that exhibits the intended behavior. These behaviors are often the product of design documents written by designers. These individuals, while possessing a vast knowledge of the subject matter, might not have any programming knowledge whatsoever. To address this disconnect between design and subsequent development, we have created an AI application whereby a designer or developer sketches an entity's AI using a graphical "drag and drop" interface to quickly articulate behavior using a UML-like representation of state charts. Aside from the design-level benefits, the application also features a runtime engine that takes the application's data as input along with a simulation or game interface, and makes the AI operational. We discuss our experience in creating such an application for both designer and developer.

  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. Research on bearing life prediction based on support vector machine and its application

    International Nuclear Information System (INIS)

    Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

  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. Using AI Planning Techniques for Army Small Unit Operations

    OpenAIRE

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

    1999-01-01

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

  5. Giving the AI definition a form suitable for the engineer

    OpenAIRE

    Dobrev, Dimiter

    2013-01-01

    Artificial Intelligence - what is this? That is the question! In earlier papers we already gave a formal definition for AI, but if one desires to build an actual AI implementation, the following issues require attention and are treated here: the data format to be used, the idea of Undef and Nothing symbols, various ways for defining the "meaning of life", and finally, a new notion of "incorrect move". These questions are of minor importance in the theoretical discussion, but we already know t...

  6. 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.

  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. Annotation-Based Whole Genomic Prediction and Selection

    DEFF Research Database (Denmark)

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

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

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

    OpenAIRE

    Lei Yang; Xianglong Tang

    2014-01-01

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

  12. 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....

  13. 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.

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

    Directory of Open Access Journals (Sweden)

    Lin Li

    Full Text Available 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.

  15. Sequence-based feature prediction and annotation of proteins

    DEFF Research Database (Denmark)

    Juncker, Agnieszka; Jensen, Lars J.; Pierleoni, Andrea; Bernsel, Andreas; Tress, Michael L.; Bork, Peer; Von Heijne, Gunnar; Valencia, Alfonso; A Ouzounis, Christos; Casadio, Rita; Brunak, Søren

    2009-01-01

    A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome....

  16. Well-log based prediction of thermal conductivity

    DEFF Research Database (Denmark)

    Fuchs, Sven; Förster, Andrea

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

  17. 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.

  18. Sustainable long-term conservation of rare cattle breeds using rotational AI sires

    Directory of Open Access Journals (Sweden)

    Avon Laurent

    2008-07-01

    Full Text Available Abstract The development of inbreeding in rotation breeding schemes, sequentially using artificial insemination (AI sires over generations, was investigated for a full AI scheme. Asymptotic prediction formulae of inbreeding coefficients were established when the first rotation list of AI sires (possibly related was in use. Simulated annealing provided the optimal rotation order of sires within this list, when the sires were related. These methods were also used for subsequent rotation lists, needed by the exhaustion of semen stores for the first bulls. Simulation was carried out starting with groups of independent sires, with different sizes. To generate a yearly inbreeding rate substantially lower than 0.05% (considered to be within reach by conventional conservation schemes using frequent replacements, the results obtained showed that the number of sires should be at least 10–15 and that the same sires should be used during at least 50 years. The ultimate objective was to examine the relevance of implementing rotation in breeding schemes on the actual rare French cattle breeds under conservation. The best candidate for such a test was the Villard-de-Lans breed (27 bulls and 73 000 doses for only 340 females and it turned out to be the best performer with an inbreeding coefficient of only 7.4% after 500 years and five different sire lists. Due to the strong requirements on semen stores and on the stability of population size, actual implementation of this kind of conservation scheme was recommended only in special ('niche' cattle populations.

  19. 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.

  20. 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.

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

    OpenAIRE

    Jizhao Liu; Quan Wang

    2015-01-01

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

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

    OpenAIRE

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

    2014-01-01

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

  3. 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...

  4. 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.

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

    OpenAIRE

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

    2011-01-01

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

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

    OpenAIRE

    Mohammad Ehsan Basiri; Ahmad Reza Naghsh-Nilchi; Nasser Ghasem-Aghaee

    2014-01-01

    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 designi...

  7. Stand Diameter Distribution Modelling and Prediction Based on Richards Function

    OpenAIRE

    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) we...

  8. Online Forums Hotspot Prediction Based on Sentiment Analysis

    OpenAIRE

    V. Murali Bhaskarn; K. Nirmala Devi

    2012-01-01

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

  9. Predicting solar radiation based on available weather indicators

    Science.gov (United States)

    Sauer, Frank Joseph

    Solar radiation prediction models are complex and require software that is not available for the household investor. The processing power within a normal desktop or laptop computer is sufficient to calculate similar models. This barrier to entry for the average consumer can be fixed by a model simple enough to be calculated by hand if necessary. Solar radiation modeling has been historically difficult to predict and accurate models have significant assumptions and restrictions on their use. Previous methods have been limited to linear relationships, location restrictions, or input data limits to one atmospheric condition. This research takes a novel approach by combining two techniques within the computational limits of a household computer; Clustering and Hidden Markov Models (HMMs). Clustering helps limit the large observation space which restricts the use of HMMs. Instead of using continuous data, and requiring significantly increased computations, the cluster can be used as a qualitative descriptor of each observation. HMMs incorporate a level of uncertainty and take into account the indirect relationship between meteorological indicators and solar radiation. This reduces the complexity of the model enough to be simply understood and accessible to the average household investor. The solar radiation is considered to be an unobservable state that each household will be unable to measure. The high temperature and the sky coverage are already available through the local or preferred source of weather information. By using the next day's prediction for high temperature and sky coverage, the model groups the data and then predicts the most likely range of radiation. This model uses simple techniques and calculations to give a broad estimate for the solar radiation when no other universal model exists for the average household.

  10. Project Cost Prediction Model Based on Fuzzy Theory

    OpenAIRE

    Hailing Sun; Tianbao Su

    2013-01-01

    Project cost is the main factor contributing to the cost of construction, which accounts for a large proportion of the total investment. So strengthening the management and control of the project cost will be beneficial for the improvement of international competitiveness and conservation of social resource for construction enterprises. The article applies the basic principles of fuzzy mathematics to project cost prediction, and makes use of the evaluation information of typical project chara...

  11. Lasso based feature selection for malaria risk exposure prediction

    OpenAIRE

    Kouwayè, Bienvenue; Fonton, Noël; Rossi, Fabrice

    2015-01-01

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

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

    OpenAIRE

    R. Bhaskaran; 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 constr...

  13. Predicting Active Users' Personality Based on Micro-Blogging Behaviors

    OpenAIRE

    Lin LI; 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 845 micro-blogging behavioral fe...

  14. 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.

  15. 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...

  16. Prediction of adiabatic bubbly flows in TRACE using the interfacial area transport equation

    International Nuclear Information System (INIS)

    The conventional thermal-hydraulic reactor system analysis codes utilize a two-field, two-fluid formulation to model two-phase flows. To close this model, static flow regime transition criteria and algebraic relations are utilized to estimate the interfacial area concentration (ai). To better reflect the continuous evolution of two-phase flow, an experimental version of TRACE is being developed which implements the interfacial area transport equation (IATE) to replace the flow regime based approach. Dynamic estimation of ai is provided through the use of mechanistic models for bubble coalescence and disintegration. To account for the differences in bubble interactions and drag forces, two-group bubble transport is sought. As such, Group 1 accounts for the transport of spherical and distorted bubbles, while Group 2 accounts for the cap, slug, and churn-turbulent bubbles. Based on this categorization, a two-group IATE applicable to the range of dispersed two-phase flows has been previously developed. Recently, a one-group, one-dimensional, adiabatic IATE has been implemented into the TRACE code with mechanistic models accounting for: (1) bubble breakup due to turbulent impact of an eddy on a bubble, (2) bubble coalescence due to random collision driven by turbulent eddies, and (3) bubble coalescence due to the acceleration of a bubble in the wake region of a preceding bubble. To demonstrate the enhancement of the code's capability using the IATE, experimental data for ai, void fraction, and bubble velocity measured by a multi-sensor conductivity probe are compared to both the IATE and flow regime based predictions. In total, 50 air-water vertical co-current upward and downward bubbly flow conditions in pipes with diameters ranging from 2.54 to 20.32 cm are evaluated. It is found that TRACE, using the conventional flow regime relation, always underestimates ai. Moreover, the axial trend of the ai prediction is always quasi-linear because ai in the conventional

  17. 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

  18. A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

    OpenAIRE

    Jui-Le Chen; Chun-Wei Tsai; Ming-Chao Chiang; Chu-Sing Yang

    2013-01-01

    The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking predictio...

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

    International Nuclear Information System (INIS)

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

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

    Science.gov (United States)

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

    2016-01-01

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

  1. 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...

  2. Predicting magnetostructural trends in FeRh-based ternary systems

    Science.gov (United States)

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

    2013-09-01

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

  3. 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 be a...

  4. 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.

  5. 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'…

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

    OpenAIRE

    Hedberg, Sara R.

    2006-01-01

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

  7. 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.

  8. On flare predictability based on sunspot group evolution

    CERN Document Server

    Korsos, Marianna; Erdelyi, Robert; Baranyi, Tunde

    2015-01-01

    The forecast method introduced by Kors\\'os et al.(2014) is generalised from the horizontal magnetic gradient (GM), defined between two opposite polarity spots, to all spots within an appropriately defined region close to the magnetic neutral line of an active region. This novel approach is not limited to searching for the largest GM of two single spots as in previous methods. Instead, the pre-flare conditions of the evolution of spot groups is captured by the introduction of the weighted horizontal magnetic gradient, or W_GM. This new proxy enables the potential of forecasting flares stronger than M5. The improved capability includes (i) the prediction of flare onset time and (ii) an assessment whether a flare is followed by another event within about 18 hours. The prediction of onset time is found to be more accurate here. A linear relationship is established between the duration of converging motion and the time elapsed from the moment of closest position to that of the flare onset of opposite polarity spot...

  9. Online Forums Hotspot Prediction Based on Sentiment Analysis

    Directory of Open Access Journals (Sweden)

    V. Murali Bhaskarn

    2012-01-01

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

  10. 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.

  11. Block-Based Parallel Intra Prediction Scheme for HEVC

    Directory of Open Access Journals (Sweden)

    Jie Jiang

    2012-08-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2010-01-01

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

  13. 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...

  14. Discovering Knowledge from AIS Database for Application in VTS

    Science.gov (United States)

    Tsou, Ming-Cheng

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

  15. 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...

  16. Evidence-based gene predictions in plant genomes

    Science.gov (United States)

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

  17. 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.

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

    International Nuclear Information System (INIS)

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

  19. Prediction of Stock Returns Based on Cross-Sectional Multivariable Model

    Science.gov (United States)

    Yamada, Shinya; Takahashi, Shinsuke; Funabashi, Motohisa

    A new prediction method of stock returns was constructed from a cross-sectional multivariable model where explanatory variables are current financial indexes and an explained variable is a future stock return. To achieve precise prediction, explanatory variables were appropriately selected over time based on various test statistics and optimization of a performance index of expected portfolio return. A long-short portfolio, in which stocks with high predicted return were bought and stocks with low predicted return were sold short, was constructed to evaluate the proposed method. The simulation test showed that the proposed prediction method was effective to achieve high portfolio performance.

  20. Life Prediction of DC Motor using Time Series Analysis based on Accelerated Degradation Testing

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-12-01

    Full Text Available This study presents a method of life prediction for DC motor using time series modeling procedure based on DC motor accelerated degradation testing data. DC motor accelerated degradation data are treated as time series and stochastic process are utilized to describe the degradation process for life prediction. An accelerated degradation test is processed for DC motor until they failed and the accelerated degradation data are collected for life prediction. A comparison between the predicted lifetime and the real lifetime of DC motors is processed and the results show that the life prediction of DC motors using time series analysis is effective.

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

    Science.gov (United States)

    Yan, Zheng; Wang, Jun

    2015-04-01

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

  2. RCM Based Failure-Prediction System for Equipment

    International Nuclear Information System (INIS)

    Power plants have many components and equipment. It is difficult for operators to know the time of failure or the equipment that fails. Plants incur heavy economic losses due to unexpected failure. The equipment in power plants is constantly monitored by various sensors and instruments. However, prevention of failure is very difficult. Therefore, engineers are developing many types of failure-alarm systems that can detect the abnormal functioning of equipment. Such failure-alarm systems inform only about the abnormal functioning of equipment and do not indicate the cause of failure or the parts that have failed. In this study, we have developed a failure-prediction system that can provide details on the cause of trouble and the maintenance method

  3. 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 the...... design yield strength and ductility supply as two input parameters which must be estimated over a narrow range of ductility supply from a frequency degradation curve. This curve is to be identified from a set of recorded excitation and response time-histories. The proposed model has been used to obtain...

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

    OpenAIRE

    Yan Zhu; Guanghua Zhang; Jing Qiu

    2013-01-01

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

  5. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    -linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  6. Route Prediction Based Vehicular Mobility Management Scheme for VANET

    OpenAIRE

    DaeWon Lee; Yoon-Ho Kim; HwaMin Lee

    2014-01-01

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

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

    OpenAIRE

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

    2010-01-01

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

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

    OpenAIRE

    Xiaonan Zhao; Chunping Hou; Qing Wang

    2013-01-01

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

  9. 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.

  10. 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.

  11. 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.

  12. Lossless compression of hyperspectral images using adaptive edge-based prediction

    Science.gov (United States)

    Wang, Keyan; Wang, Liping; Liao, Huilin; Song, Juan; Li, Yunsong

    2013-09-01

    By fully exploiting the high correlation of the pixels along an edge, a new lossless compression algorithm for hyperspectral images using adaptive edge-based prediction is presented in order to improve compression performance. The proposed algorithm contains three modes in prediction: intraband prediction, interband prediction, and no prediction. An improved median predictor (IMP) with diagonal edge detection is adopted in the intraband mode. And in the interband mode, an adaptive edge-based predictor (AEP) is utilized to exploit the spectral redundancy. The AEP, which is driven by the strong interband structural similarity, applies an edge detection first to the reference band, and performs a local edge analysis to adaptively determine the optimal prediction context of the pixel to be predicted in the current band, and then calculates the prediction coefficients by least-squares optimization. After intra/inter prediction, all predicted residuals are finally entropy coded. For a band with no prediction mode, all the pixels are directly entropy coded. Experimental results show that the proposed algorithm improves the lossless compression ratio for both standard AVIRIS 1997 hyperspectral images and the newer CCSDS test images.

  13. 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.

  14. 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.

  15. 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...

  16. What AI Pratitioners Should Know about the Law Part Two

    OpenAIRE

    Frank, Steven J.

    1988-01-01

    This is Part 2 of a two-part article and discusses issues of tort liability and the use of computers in the courtroom. [The legal dimensions of topics covered in this part are given comprehensive attention by the author in Tort Adjudication and the Emergence of Artificial Intelligence Software, 21 Suffolk University Law Review 623 (1987)]. Part 1 of this article, which appeared in the Spring 1988 issue of AI Magazine, discussed steps that developers of AI systems can take to protect their eff...

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

    Science.gov (United States)

    Tsang, Herbert H; Wiese, Kay C

    2015-01-01

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

  18. Demonstration of ripple-based index for predicting fast-scale instability in switching power converters

    OpenAIRE

    Rodríguez Vilamitjana, Enric; Alarcón Cot, Eduardo José; El Aroudi, Abdelali

    2009-01-01

    In this paper a simplified model based on the exact discrete-time map of a buck switching power converter with proportional control, which captures all its dynamics, allows deriving a closed-form stability condition for predicting fast-scale instability boundary. This condition analytically demonstrates the validity of the recently proposed ripple-based index to predict fast-scale period-doubling, hitherto based on an a priori hypothesis and simulation validation, thereby demonstrating ...

  19. 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.

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

    Institute of Scientific and Technical Information of China (English)

    初秀民; 刘潼; 马枫; 刘兴龙; 钟鸣

    2014-01-01

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

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Doina Jitca

    2012-10-01

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

  3. Temporal prediction errors modulate task-switching performance

    OpenAIRE

    Limongi, Roberto; Silva, Angélica M.; Góngora-Costa, Begoña

    2015-01-01

    We have previously shown that temporal prediction errors (PEs, the differences between the expected and the actual stimulus’ onset times) modulate the effective connectivity between the anterior cingulate cortex and the right anterior insular cortex (rAI), causing the activity of the rAI to decrease. The activity of the rAI is associated with efficient performance under uncertainty (e.g., changing a prepared behavior when a change demand is not expected), which leads to hypothesize that tempo...

  4. Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction

    Science.gov (United States)

    Gern, Frank H.

    2012-01-01

    This paper describes a scalable structural model suitable for Hybrid Wing Body (HWB) centerbody analysis and optimization. The geometry of the centerbody and primary wing structure is based on a Vehicle Sketch Pad (VSP) surface model of the aircraft and a FLOPS compatible parameterization of the centerbody. Structural analysis, optimization, and weight calculation are based on a Nastran finite element model of the primary HWB structural components, featuring centerbody, mid section, and outboard wing. Different centerbody designs like single bay or multi-bay options are analyzed and weight calculations are compared to current FLOPS results. For proper structural sizing and weight estimation, internal pressure and maneuver flight loads are applied. Results are presented for aerodynamic loads, deformations, and centerbody weight.

  5. 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.

  6. 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.

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

    OpenAIRE

    DU Jia-li; YU Ping-fang

    2012-01-01

    Natural language understanding (NLU) focusing onmachine reading comprehension is a branch of natural language processing (NLP). The domain of the developing NLU system covers from sentence decoding to text understanding and the automatic decoding of GP sentence belongs to the domain of NLU system. GP sentence is a special linguistic phenomenon in which processing breakdown and backtracking are two key features. Ifthe syntax-based system can present the special features of GP sentence and de...

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

    OpenAIRE

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

    2014-01-01

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

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

    OpenAIRE

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

    2015-01-01

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

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

    OpenAIRE

    Jiming Wang; Jin Ning; Ye Fei

    2012-01-01

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

  11. 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.

  12. Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning.

    Science.gov (United States)

    Byrne, Kaileigh A; Davis, Tyler; Worthy, Darrell A

    2016-07-01

    Dopaminergic genes play an important role in cognitive function. DRD2 and DARPP-32 dopamine receptor gene polymorphisms affect striatal dopamine binding potential, and the Val158Met single-nucleotide polymorphism of the COMT gene moderates dopamine availability in the pFC. Our study assesses the role of these gene polymorphisms on performance in two rule-based category learning tasks. Participants completed unidimensional and conjunctive rule-based tasks. In the unidimensional task, a rule along a single stimulus dimension can be used to distinguish category members. In contrast, a conjunctive rule utilizes a combination of two dimensions to distinguish category members. DRD2 C957T TT homozygotes outperformed C allele carriers on both tasks, and DARPP-32 AA homozygotes outperformed G allele carriers on both tasks. However, we found an interaction between COMT and task type where Met allele carriers outperformed Val homozygotes in the conjunctive rule task, but both groups performed equally well in the unidimensional task. Thus, striatal dopamine binding may play a critical role in both types of rule-based tasks, whereas prefrontal dopamine binding is important for learning more complex conjunctive rule tasks. Modeling results suggest that striatal dopaminergic genes influence selective attention processes whereas cortical genes mediate the ability to update complex rule representations. PMID:26918585

  13. 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.

  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. 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...

  16. 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.

  17. 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.

  18. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    Science.gov (United States)

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning. PMID:24578640

  19. 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.

  20. 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

  1. Energy savings in mobile broadband network based on load predictions

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels Bundgaard;

    2012-01-01

    wireless networks. To save energy in MBNs, one of the options is to turn off parts of the network equipment in areas where traffic falls below a specific predefined threshold. This paper looks at a methodology for identifying periods of the day when cells or sites carrying low traffic are candidates for...... being totally or partly switched off, given that the decrease in service quality can be controlled gracefully when the sites are switched off. Based on traffic data from an operational network, potential average energy savings of approximately 30% with some few low traffic cells/sites reaching up to 99......% energy savings can be identified....

  2. 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.

  3. 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.

  4. Prediction of creamy mouthfeel based on texture attribute ratings of dairy desserts

    NARCIS (Netherlands)

    Weenen, H.; Jellema, R.H.; Wijk, de R.A.

    2006-01-01

    A quantitative predictive model for creamy mouthfeel in dairy desserts was developed, using PLS multivariate analysis of texture attributes. Based on 40 experimental custard desserts, a good correlation was obtained between measured and predicted creamy mouthfeel ratings. The model was validated by

  5. 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

  6. Predicting Classroom Achievement from Active Responding on a Computer-Based Groupware System.

    Science.gov (United States)

    Shin, Jongho; Deno, Stanley L.; Robinson, Steven L.; Marston, Douglas

    2000-01-01

    The predictive validity of active responding on a computer-based groupware system was examined with 48 second graders. Results showed that active responding correlated highly with initial and final performance measures and that active responding contributed significantly to predicting final performance when initial performance was controlled.…

  7. Predicting the Attitude Flow in Dialogue Based on Multi-Modal Speech Cues

    DEFF Research Database (Denmark)

    Juel Henrichsen, Peter; Allwood, Jens

    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 of reali...

  8. 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.

  9. 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.

  10. Theoretically predicted Fox-7 based new high energy density molecules

    Science.gov (United States)

    Ghanta, Susanta

    2016-08-01

    Computational investigation of CHNO based high energy density molecules (HEDM) are designed with FOX-7 (1, 1-dinitro 2, 2-diamino ethylene) skeleton. We report structures, stability and detonation properties of these new molecules. A systematic analysis is presented for the crystal density, activation energy for nitro to nitrite isomerisation and the C-NO2 bond dissociation energy of these molecules. The Atoms in molecules (AIM) calculations have been performed to interpret the intra-molecular weak H-bonding interactions and the stability of C-NO2 bonds. The structure optimization, frequency and bond dissociation energy calculations have been performed at B3LYP level of theory by using G03 quantum chemistry package. Some of the designed molecules are found to be more promising HEDM than FOX-7 molecule, and are proposed to be candidate for synthetic purpose.

  11. 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...

  12. 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...

  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. 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.…

  15. The hidden dangers of experimenting in distributed AI

    NARCIS (Netherlands)

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

    2006-01-01

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

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

    International Nuclear Information System (INIS)

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

  17. Man-machine communication in reactor control using AI methods

    International Nuclear Information System (INIS)

    In the last years the interest in process control has expecially focused on problems of man-machine communication. It depends on its great importance to process performance and user acceptance. Advanced computerized operator aids, e.g. in nuclear power plants, are as well as their man-machine interface. In the Central Institute for Nuclear Research in Rossendorf a computerized operator support system for nuclear power plants is designed, which is involved in a decentralized process automation system. A similar but simpler system, the Hierarchical Informational System (HIS) at the Rossendorf Research Reactor, works with a computer controlled man-machine interface, based on menu. In the special case of the disturbance analysis program SAAP-2, which is included in the HIS, the limits of menu techniques are obviously. Therefore it seems to be necessary and with extended hard- and software possible to realize an user controlled natural language interface using Artificial Intelligence (AI) methods. The draft of such a system is described. It should be able to learn during a teaching phase all phrases and their meanings. The system will work on the basis of a self-organizing, associative data structure. It is used to recognize a great amount of words which are used in language analysis. Error recognition and, if possible, correction is done by means of a distance function in the word set. Language analysis should be carried out with a simplified word class controlled functional analysis. With this interface it is supposed to get experience in intelligent man-machine communication to enhance operational safety in future. (author)

  18. 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.

  19. The current bases for roof fall prediction at WIPP and a preliminary prediction for SPDV Room 2

    International Nuclear Information System (INIS)

    This document presents the current bases for roof fall prediction at the Waste Isolation Pilot Plant (WIPP) and a preliminary prediction of the date of a roof fall in SPDV Test Room 2. The ability to correctly assess the stability of the excavations at the WIPP is necessary to protect the safety of site workers, the environment, and the integrity of in situ experiments that use transuranic mixed waste. Roof fall is the extreme case of instability. Although roof falls have been allowed to occur unused, barricaded rooms so that the pre-collapse behavior of this room could be studied. This document presents a discussion of some deformation mechanisms that can be expected around excavations in bedded salt at the WIPP. The geomechanical instrument data and fracture maps from the Site and Preliminary Design Validation (SPDV) room area have been analyzed to determine the deformation history of the rooms and to identify precursors to the SPDV Room 1 roof fall. The deformation history of the excavations as recorded by the instruments was then correlated with these proposed deformation mechanisms, providing a basis for prediction of roof falls in other locations. Finally, the means used at the WIPP to identify and monitor unstable ground are discussed. Throughout this document, ''Room 1 '' and ''Room 2'' refer to SPDV Rooms 1 and 2 unless otherwise stated

  20. Prediction Method of Speech Recognition Performance Based on HMM-based Speech Synthesis Technique

    Science.gov (United States)

    Terashima, Ryuta; Yoshimura, Takayoshi; Wakita, Toshihiro; Tokuda, Keiichi; Kitamura, Tadashi

    We describe an efficient method that uses a HMM-based speech synthesis technique as a test pattern generator for evaluating the word recognition rate. The recognition rates of each word and speaker can be evaluated by the synthesized speech by using this method. The parameter generation technique can be formulated as an algorithm that can determine the speech parameter vector sequence O by maximizing P(O¦Q,λ) given the model parameter λ and the state sequence Q, under a dynamic acoustic feature constraint. We conducted recognition experiments to illustrate the validity of the method. Approximately 100 speakers were used to train the speaker dependent models for the speech synthesis used in these experiments, and the synthetic speech was generated as the test patterns for the target speech recognizer. As a result, the recognition rate of the HMM-based synthesized speech shows a good correlation with the recognition rate of the actual speech. Furthermore, we find that our method can predict the speaker recognition rate with approximately 2% error on average. Therefore the evaluation of the speaker recognition rate will be performed automatically by using the proposed method.

  1. 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...

  2. 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...

  3. 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...

  4. 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.

  5. 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.

  6. Context Prediction of Mobile Users Based on Time-Inferred Pattern Networks: A Probabilistic Approach

    OpenAIRE

    Yong-Hyuk Kim; Yourim Yoon

    2013-01-01

    We present a probabilistic method of predicting context of mobile users based on their historic context data. The presented method predicts general context based on probability theory through a novel graphical data structure, which is a kind of weighted directed multigraphs. User context data are transformed into the new graphical structure, in which each node represents a context or a combined context and each directed edge indicates a context transfer with the time weight inferred from corr...

  7. A Local Energy Consumption Prediction-Based Clustering Protocol for Wireless Sensor Networks

    OpenAIRE

    Jiguo Yu; Li Feng; Lili Jia; Xin Gu; Dongxiao Yu

    2014-01-01

    Clustering is a fundamental and effective technique for utilizing sensor nodes’ energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also p...

  8. Leveraging Long-Term Predictions and Online-Learning in Agent-based Multiple Person Tracking

    OpenAIRE

    Liu, Wenxi; Chan, Antoni B.; Lau, Rynson W. H.; Manocha, Dinesh

    2014-01-01

    We present a multiple-person tracking algorithm, based on combining particle filters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion. In addition to position and velocity, our tracking algorithm can estimate the internal goals (desired destination or desired velocity) of the tracked pedestrian in an online manner, thus removing the need to specify this information beforehand. Furthermore, we leverage the longer-term predictions of...

  9. Analysis of energy-based algorithms for RNA secondary structure prediction

    OpenAIRE

    Hajiaghayi Monir; Condon Anne; Hoos Holger H

    2012-01-01

    Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andr...

  10. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied as...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  11. Comparison Of Short Term Rainfall Forecasts For Model Based Flow Prediction In Urban Drainage Systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren Liedtke; Poulsen, Troels Sander; Bøvith, Thomas;

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied as...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  12. Predicting Pharmacokinetics of Drugs Using Physiologically Based Modeling—Application to Food Effects

    OpenAIRE

    Parrott, N.; Lukacova, V.; Fraczkiewicz, G.; Bolger, M. B.

    2009-01-01

    Our knowledge of the major mechanisms underlying the effect of food on drug absorption allows reliable qualitative prediction based on biopharmaceutical properties, which can be assessed during the pre-clinical phase of drug discovery. Furthermore, several recent examples have shown that physiologically based absorption models incorporating biorelevant drug solubility measurements can provide quite accurate quantitative prediction of food effect. However, many molecules currently in developme...

  13. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    OpenAIRE

    Peng Liu; Lei Yang; Daming Shi; Xianglong Tang

    2015-01-01

    A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction net...

  14. Predicted Link Expiration Time Based Connected Dominating Sets for Mobile Ad hoc Networks

    OpenAIRE

    Pervis Fly; Natarajan Meghanathan

    2010-01-01

    We propose an algorithm to determine stable connecteddominating sets (CDS), based on the predicted link expiration time(LET), for mobile ad hoc networks (MANETs). The proposed LETbasedCDS algorithm is the first such algorithm that constructs a CDSbased on edge weights represented by predicted link expiration time,rather the traditional approach of using node weights like the wellknownmaximum density-based CDS (MaxD-CDS) algorithm. Theconstruction of the LET-CDS starts with the inclusion of th...

  15. Structure Based Predictive Model for Coal Char Combustion

    Energy Technology Data Exchange (ETDEWEB)

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

    This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed

  16. HYPLOSP: a knowledge-based approach to protein local structure prediction.

    Science.gov (United States)

    Chen, Ching-Tai; Lin, Hsin-Nan; Sung, Ting-Yi; Hsu, Wen-Lian

    2006-12-01

    Local structure prediction can facilitate ab initio structure prediction, protein threading, and remote homology detection. However, the accuracy of existing methods is limited. In this paper, we propose a knowledge-based prediction method that assigns a measure called the local match rate to each position of an amino acid sequence to estimate the confidence of our method. Empirically, the accuracy of the method correlates positively with the local match rate; therefore, we employ it to predict the local structures of positions with a high local match rate. For positions with a low local match rate, we propose a neural network prediction method. To better utilize the knowledge-based and neural network methods, we design a hybrid prediction method, HYPLOSP (HYbrid method to Protein LOcal Structure Prediction) that combines both methods. To evaluate the performance of the proposed methods, we first perform cross-validation experiments by applying our knowledge-based method, a neural network method, and HYPLOSP to a large dataset of 3,925 protein chains. We test our methods extensively on three different structural alphabets and evaluate their performance by two widely used criteria, Maximum Deviation of backbone torsion Angle (MDA) and Q(N), which is similar to Q(3) in secondary structure prediction. We then compare HYPLOSP with three previous studies using a dataset of 56 new protein chains. HYPLOSP shows promising results in terms of MDA and Q(N) accuracy and demonstrates its alphabet-independent capability. PMID:17245815

  17. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model

    Science.gov (United States)

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-01-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking. PMID:27498888

  18. Prediction of Filamentous Sludge Bulking using a State-based Gaussian Processes Regression Model.

    Science.gov (United States)

    Liu, Yiqi; Guo, Jianhua; Wang, Qilin; Huang, Daoping

    2016-01-01

    Activated sludge process has been widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, stable operation of activated sludge process is often compromised by the occurrence of filamentous bulking. The aim of this study is to build a proper model for timely diagnosis and prediction of filamentous sludge bulking in an activated sludge process. This study developed a state-based Gaussian Process Regression (GPR) model to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), in such a way that the evolution of SVI can be predicted over multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prediction of filamentous bulking sludge with real-time SVI prediction was tested through a simulation study. The results showed that the proposed methodology was capable of predicting future SVIs with good accuracy, thus providing sufficient time for predicting and controlling filamentous sludge bulking. PMID:27498888

  19. Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties

    Directory of Open Access Journals (Sweden)

    Bing Niu

    2013-01-01

    Full Text Available It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test.

  20. Prediction of substrate-enzyme-product interaction based on molecular descriptors and physicochemical properties.

    Science.gov (United States)

    Niu, Bing; Huang, Guohua; Zheng, Linfeng; Wang, Xueyuan; Chen, Fuxue; Zhang, Yuhui; Huang, Tao

    2013-01-01

    It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test. PMID:24455714