Sample records for ai based prediction

  1. Pre-processing in AI based Prediction of QSARs

    Patri, Om Prasad


    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.

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


    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

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


    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

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


    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

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


    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.


    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

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


    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

    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.

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


    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

    Swanson, David J.


    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

    McManus, John W.; Goodrich, Kenneth H.


    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

    Schultz, Roger D.; Stobie, Iain


    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.

    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

    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

    Koehler, Jana; Ottiger, Daniel


    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

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


    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

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter


    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

    Grivokostopoulou, Foteini; Hatzilygeroudis, Ioannis


    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

    马枫; 初秀民; 严新平


    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,


    Lean YU; Shouyang WANG; Kin Keung LAI


    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

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


    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

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


    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

    Buchanan, Bruce G; Glick, Jonathan


    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

    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

    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.


    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)


    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!




    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 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!AS-DB/AS-SU

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

    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.

    Simmons, Robert F.


    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…


    D. David Neels Pon Kumar; K. Murugesan


    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...


    D. David Neels Pon Kumar


    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

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


    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

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


    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


    Users are invited to use the NICE password for AIS authentication. As announced in CNL June-August 2006 (see 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 ( 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

    Anil Kumar Yadav; Prerna Gaur


    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

    Le Minh Duc


    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.

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


    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

    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

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


    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

    Ahmed, Zeeshan


    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

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


    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

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


    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

    Södergren, Gunnar


    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

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


    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

    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...// 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

    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.../ 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

    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.../ 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

    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...// 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

    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.../ 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

    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.../ 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

    I Putu Sindhu Asmara


    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



    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

    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.../ 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

    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.../ 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.

    Dudin, M; Pinchuk, D


    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

    孟凡君; 曹伟; 管志强


    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

    Teng, William; Lynnes, Christopher


    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

    Idris, Ismaila; Abdulhamid, Shafii Muhammad


    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

    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

    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 Representative seq. ID FC-AI...03P (Link to Original site) Representative DNA sequence >FC-AI03 (FC-AI03Q) /CSM/FC/FC-AI/*l*ivtlskv*qswyqvfcslmrftcwi*nv fht*ivhwsqhwhql*flqpivaiv*skapitifnlhmaslwif*ivl*sfvpfhiitmk sfkfspfvpqlki

  1. Dicty_cDB: FC-AI02 [Dicty_cDB

    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.../ 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

    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 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

    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 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

    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

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


    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

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


    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.

    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


    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

    Manfred Füllsack


    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

    Rich, Elaine


    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

    Full Text Available FC (Link to library) FC-AI22 (Link to dictyBase) - - - Contig-U15369-1 | Contig-U15732-1 FC-AI...22P (Link to Original site) FC-AI22F 583 FC-AI22Z 683 FC-AI22P 1266 - - Show FC-AI22 Library FC (...Link to library) Clone ID FC-AI22 (Link to dictyBase) Atlas ID - NBRP ID - dictyBase ID - Link to Contig Con...tig-U15369-1 | Contig-U15732-1 Original site URL Representative seq. ID FC-AI22P (Link to Original site) Representative DNA sequence >FC-AI22 (FC-AI

  11. Dicty_cDB: FC-AI08 [Dicty_cDB

    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 Representative seq. ID FC-AI08P (Link to Original site) Representative DNA sequence >FC-AI08 (FC-AI

  12. Dicty_cDB: FC-AI07 [Dicty_cDB

    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 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

    Aversa, Davide; Vassos, Stavros


    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

    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 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

    Thompson, Alan M.


    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

    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.../ 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

    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...// 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

    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.../ 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

    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.../ 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

    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 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

    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.../ 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

    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

    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

    Dobrev, Dimiter


    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


    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

    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

    Angel Garrido


    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

    Angel Garrido


    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

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


    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

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


    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

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


    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

    Lützen, Marie; Friis-Hansen, Peter


    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

    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

    Lieberman, Henry; Massachusetts Institute of Technology Media Lab


    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?

    Fisher, Robert B.


    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

    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

    Hitzer, Eckhard


    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

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


    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

    Luo, Tiejian; Xu, Guandong; Zhou, Jia


    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

    Brunskog, Jonas


    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

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


    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

    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

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


    The document consists of a publicly available web site ( 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

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


    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

    Smith, M.K. E-mail:; Bracken, D.S


    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

    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

    Smith, M K


    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

    LIKe-Ping; CHENTian-Lun; GAOZi-You


    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.

    Tang, Grace W; Altman, Russ B


    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

    Maadikhah, Elham; Erfani, Nasrollah


    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



    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

    Kiyomi Cerezo Takahash


    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

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


    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

    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

    Krajnak, Mike; Jesse, Lisa; Mucks, John


    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



    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

    Tarek Khalifa


    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

    Endang W. Bachtiar


    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

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


    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

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


    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.




    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

    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


    Fauziah Elytha


    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

    Kirn, Stefan


    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

    Gao Fei; Zhang Xiaohui


    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

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


    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

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


    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

    齐维贵; 朱学莉


    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

    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

    Larsen, Jesper Abildgaard; Mortensen, Hans Peter


    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

    Monod, Emmanuel; Watson, Richard


    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

    Julian Gonzalez


    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

    Jing Lu


    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

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


    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.

    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

    Weerkamp, W.; Rijke, de, M.


    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

    Rongtai Cai; Yanjie Wang


    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

    A.S. Rosohata


    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

    A.S. Rosohata


    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

    Hardie Kim R


    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

    Bangcheng Zhang


    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

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


    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

    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

    Malika Trabelsi; Saloua Saidane; Sihem Ben Abdelmelek


    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

    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

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


    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

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


    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.

    Russell, Stuart; Bohannon, John


    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

    Aas, Olav Ådne


    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

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


    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

    Mark Morris; Simon Colton; Robin Baumgarten


    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

    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

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


    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

    Vivas, Oscar Andrès; Poignet, Philippe


    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

    Young, David


    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.

    Thórisson, Kristinn R.


    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

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


    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

    Aghaeepour, Nima; Hoos, Holger H


    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

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


    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.

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


    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

    Prasath, Guru; Jørgensen, John Bagterp


    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

    Ward, B F L; Yost, S A


    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.

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


    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


    Etienne, Sophie


    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

    Lei JIA


    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

    ... 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

    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

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


    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....


    Srilakshmi Indrasenan


    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

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


    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

    Aggarwal, R.; Johns, A.


    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

    R. Lashkari


    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

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


    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)

    Parsapoor, Mahboobeh


    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

    Yan Zhu


    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

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


    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

    Xiangrong Feng


    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

    Farahani, Saeed Davoudabadi


    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

    SHAO Liang-shan; FU Gui-xiang


    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

    Safa Bouhajar


    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

    Ali Mohamed Yousef


    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


    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

    Milan Vukićević


    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

    Zhang Mingheng


    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

    赵兴吉; 都定元; 孔令文; 张为民; 谭远康; 苏泓洁; 马丁


    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

    Robin Baumgarten


    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

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


    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

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


    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

    Bin Hong


    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

    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

    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

    Lords, R.E.


    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

    M. Dikpati


    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

    Pauzi, Herrini; Abdullah, Lazim


    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

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


    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

    Wenbiao Li


    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.


    Lu Kaining; Jin Zhigang; Zou Jun


    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

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


    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.

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


    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

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


    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

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


    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

    TONG Liang; LU Ji-lian


    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

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


    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

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


    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

    Wang Haowei; Xu Tingxue; Mi Qiaoli


    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

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


    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.

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


    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

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


    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.

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


    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

    Shouwei Li


    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

    LI Ji; ZHANG Hongyue


    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

    LIDong-Mei; WANGZheng-Ou


    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

    LI Dong-Mei; WANG Zheng-Ou


    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

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


    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

    Young-Joo Lee; Soojin Cho


    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

    Fujiwara Toyofumi


    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

    Lai Luhua


    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.

    Wang, Tao; Wang, Hongjue; Wang, Xiaoxia


    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?

    Vatanasakdakul, Savanid; Aoun, Chadi


    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

    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

    Ekman Torbjörn


    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

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


    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.

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


    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

    Xiaomei Hu


    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.

    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

    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

    Essam El. Seidy


    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

    Ali Mohamed Yousef


    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

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


    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

    Meng Li


    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

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


    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

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


    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

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


    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

    Prakken, Henry


    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

    Zhejing BA; Youxian SUN


    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

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


    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

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


    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

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


    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

    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


    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

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


    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

    Xiaomei Hu; Dong Wang; Hewei Qu; Xinran Shi


    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

    Hong Wang


    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

    Kankainen Matti


    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

    Ahmed Khalafallah


    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.

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


    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.

    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

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


    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

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


    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

    Zeng, Shan


    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.