WorldWideScience

Sample records for machine based operating

  1. Light-operated machines based on threaded molecular structures.

    Science.gov (United States)

    Credi, Alberto; Silvi, Serena; Venturi, Margherita

    2014-01-01

    Rotaxanes and related species represent the most common implementation of the concept of artificial molecular machines, because the supramolecular nature of the interactions between the components and their interlocked architecture allow a precise control on the position and movement of the molecular units. The use of light to power artificial molecular machines is particularly valuable because it can play the dual role of "writing" and "reading" the system. Moreover, light-driven machines can operate without accumulation of waste products, and photons are the ideal inputs to enable autonomous operation mechanisms. In appropriately designed molecular machines, light can be used to control not only the stability of the system, which affects the relative position of the molecular components but also the kinetics of the mechanical processes, thereby enabling control on the direction of the movements. This step forward is necessary in order to make a leap from molecular machines to molecular motors.

  2. MITS machine operations

    International Nuclear Information System (INIS)

    Flinchem, J.

    1980-01-01

    This document contains procedures which apply to operations performed on individual P-1c machines in the Machine Interface Test System (MITS) at AiResearch Manufacturing Company's Torrance, California Facility

  3. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

    Science.gov (United States)

    Deng, Li; Wang, Guohua; Chen, Bo

    2015-01-01

    In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  4. Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP

    Directory of Open Access Journals (Sweden)

    Li Deng

    2015-01-01

    Full Text Available In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming, using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model’s input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators’ operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

  5. Man-machine communication based on the computerized operator support system

    International Nuclear Information System (INIS)

    Sano, Y.; Fukumoto, A.; Seki, E.; Tai, I.; Mori, N.; Tsuchida, M.; Sato, N.

    1985-01-01

    Development of a man-machine communication system in a nuclear power plant has been performed, utilizing the new communication technologies and an advanced diagnosis system. In the course of elaborating the communication concept, selection and rearrangement of communication functions in a control room were made based on the human factors engineering. Guidelines and criteria for information display system and operational equipments were also studied and evaluated. The outline of the communication concept and some evaluation test results are described. (author)

  6. Operation and machine studies

    International Nuclear Information System (INIS)

    1992-01-01

    This annual report describes the GANIL (Grand accelerateur national d'ions lourds, Caen, France) operation and the machine studies realized in 1992. Metallic ions have been accelerated during 36 pc of the time; some were produced for the first time at GANIL: 125 Te, 52 Cr with ECR3, 181 Ta with ECR4. The various machine studies are: comparison of lifetimes of carbon sheets, charge exchange of very heavy ions in carbon foils and in the residual gas of the Ganil cyclotrons, commissioning of the new high intensity axial injection system for Ganil, tantalum acceleration with the new injector, a cyclotron as a mass spectrometer; other studies concerned: implementing the new control system, gettering flux measurement, energy deposited by neutrons and gamma rays in the cryogenic system of SISSI; latest developments on multicharged ECR ion sources, and an on-line isotopic separator test bench at Ganil

  7. Knowledge-based support for design and operational use of human-machine interfaces

    International Nuclear Information System (INIS)

    Johannsen, G.

    1994-01-01

    The possibilities for knowledge support of different human user classes, namely operators, operational engineers and designers of human-machine interfaces, are discussed. Several human-machine interface functionalities are briefly explained. The paper deals with such questions as which type of knowledge is needed for design and operation, how to represent it, where to get it from, how to process it, and how to consider and use it. The relationships between design and operational use are thereby emphasised. (author)

  8. DNA-based machines.

    Science.gov (United States)

    Wang, Fuan; Willner, Bilha; Willner, Itamar

    2014-01-01

    The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

  9. Energetic optimization of a piezo-based touch-operated button for man–machine interfaces

    International Nuclear Information System (INIS)

    Sun, Hao; De Vries, Theo J A; De Vries, Rene; Van Dalen, Harry

    2012-01-01

    This paper discusses the optimization of a touch-operated button for man–machine interfaces based on piezoelectric energy harvesting techniques. In the mechanical button, a common piezoelectric diaphragm, is assembled to harvest the ambient energy from the source, i.e. the operator’s touch. Under touch force load, the integrated diaphragm will have a bending deformation. Then, its mechanical strain is converted into the required electrical energy by means of the piezoelectric effect presented to the diaphragm. Structural design (i) makes the piezoceramic work under static compressive stress instead of static or dynamic tensile stress, (ii) achieves a satisfactory stress level and (iii) provides the diaphragm and the button with a fatigue lifetime in excess of millions of touch operations. To improve the button’s function, the effect of some key properties consisting of dimension, boundary condition and load condition on electrical behavior of the piezoelectric diaphragm are evaluated by electromechanical coupling analysis in ANSYS. The finite element analysis (FEA) results indicate that the modification of these properties could enhance the diaphragm significantly. Based on the key properties’ different contributions to the improvement of the diaphragm’s electrical energy output, they are incorporated into the piezoelectric diaphragm’s redesign or the structural design of the piezo-based button. The comparison of the original structure and the optimal result shows that electrical energy stored in the diaphragm and the voltage output are increased by 1576% and 120%, respectively, and the volume of the piezoceramic is reduced to 33.6%. These results will be adopted to update the design of the self-powered button, thus enabling a large decrease of energy consumption and lifetime cost of the MMI. (paper)

  10. Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

    Science.gov (United States)

    Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

    2016-06-01

    This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

  11. Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

    Science.gov (United States)

    Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

    2016-01-01

    This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively. PMID:27271840

  12. Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment.

    Science.gov (United States)

    Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S; Phoon, Sin Ye

    2016-06-07

    This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

  13. Optimization of machining parameters of turning operations based on multi performance criteria

    Directory of Open Access Journals (Sweden)

    N.K.Mandal

    2013-01-01

    Full Text Available The selection of optimum machining parameters plays a significant role to ensure quality of product, to reduce the manufacturing cost and to increase productivity in computer controlled manufacturing process. For many years, multi-objective optimization of turning based on inherent complexity of process is a competitive engineering issue. This study investigates multi-response optimization of turning process for an optimal parametric combination to yield the minimum power consumption, surface roughness and frequency of tool vibration using a combination of a Grey relational analysis (GRA. Confirmation test is conducted for the optimal machining parameters to validate the test result. Various turning parameters, such as spindle speed, feed and depth of cut are considered. Experiments are designed and conducted based on full factorial design of experiment.

  14. Effect of Machining Velocity in Nanoscale Machining Operations

    International Nuclear Information System (INIS)

    Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

    2015-01-01

    The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

  15. Towards Designing Graceful Degradation into Trajectory Based Operations: A Human-Machine System Integration Approach

    Science.gov (United States)

    Edwards, Tamsyn; Lee, Paul

    2017-01-01

    One of the most fundamental changes to the air traffic management system in NextGen is the concept of trajectory based operations (TBO). With the introduction of such change, system safety and resilience is a critical concern, in particular, the ability of systems to gracefully degrade. In order to design graceful degradation into a TBO envrionment, knowledge of the potential causes of degradation, and appropriate solutions, is required. In addition, previous research has predominantly explored the technological contribution to graceful degradation, frequently neglecting to consider the role of the human operator, specifically, air traffic controllers (ATCOs). This is out of step with real-world operations, and potentially limits an ecologically valid understanding of achieving graceful degradation in an air traffic control (ATC) environment. The following literature review aims to identify and summarize the literature to date on the potential causes of degradation in ATC and the solutions that may be applied within a TBO context, with a specific focus on the contribution of the air traffic controller. A framework of graceful degradation, developed from the literature, is presented. It is argued that in order to achieve graceful degradation within TBO, a human-system integration approach must be applied.

  16. Machine function based control code algebras

    NARCIS (Netherlands)

    Bergstra, J.A.

    Machine functions have been introduced by Earley and Sturgis in [6] in order to provide a mathematical foundation of the use of the T-diagrams proposed by Bratman in [5]. Machine functions describe the operation of a machine at a very abstract level. A theory of hardware and software based on

  17. Peak thrust operation of linear induction machines from parameter identification

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Z.; Eastham, T.R.; Dawson, G.E. [Queen`s Univ., Kingston, Ontario (Canada). Dept. of Electrical and Computer Engineering

    1995-12-31

    Various control strategies are being used to achieve high performance operation of linear drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

  18. Induction Machine with Improved Operating Performances for Electric Trucks. A FEM-Based Analysis

    Directory of Open Access Journals (Sweden)

    MUNTEANU, A.

    2010-05-01

    Full Text Available The paper presents a study concerning the performance developed by induction motors destined for motorization of heavy electric vehicles such as trucks. Taking into consideration the imposed restrictions, one presents, in a comparative manner, the main geometrical parameters which come of the classical design algorithms. A special attention is dedicated to the winding design, since it has to ensure two synchronous speeds corresponding to 16 and 8 poles, respectively. Moreover, the influence of the rotor slots shape for the improvement of the start-up is analyzed. Finally, a FEM-based study (approach based on finite element method is performed to put in view specific torque and slip values such as rated, start-up and pull-out ones.

  19. Human machine interface based on labview for vacuum system operation of cyclotron proton DECY-13 MeV

    International Nuclear Information System (INIS)

    Fajar Sidik Permana; Saminto; Kurnia Wibowo; Vika Arwida Fanita Sari

    2016-01-01

    Center of Accelerator Science and Technology (CAST), BATAN is designing DECY-13 MeV Proton Cyclotron. So far, this operation system has been conducted conventionally. In this research, an Human Machine Interface system has been successfully built for simplifying operation and monitoring pressure inside vacuum chamber of cyclotron DECY-13 MeV. HMI system is built with LabVIEW software and integrated with Programmable Logic Controller FX-2424 series and NI cRIO (NI-9025 and NI-9870) module. HMI system consist of turning on/of pumps (rotary and diffusion), opening/ closing valve automatically, and retrieving of data from sensor in real time. (author)

  20. Human-machine interface aspects and use of computer-based operator support systems in control room upgrades and new control room designs for nuclear power plants

    International Nuclear Information System (INIS)

    Berg, O.

    1997-01-01

    At the Halden Project efforts are made to explore the possibilities through design, development and validation of Computer-based Operator Support Systems (COSSes) which can assist the operators in different operational situations, ranging from normal operation to disturbance and accident conditions. The programme comprises four main activities: 1) verification and validation of safety critical software systems; 2) man-machine interaction research emphasizing improvements in man-machine interfaces on the basis of human factors studies; 3) computerized operator support systems assisting the operator in fault detection/diagnosis and planning of control actions; and 4) control room development providing a basis for retrofitting of existing control rooms and for the design of advanced concepts. The paper presents the status of this development programme, including descriptions of specific operator support functions implemented in the simulator-based, experimental control room at Halden (HAMMLAB, HAlden Man-Machine LABoratory). These operator aids comprise advanced alarms systems, diagnostic support functions, electronic procedures, critical safety functions surveillance and accident management support systems. The different operator support systems development at the Halden Project are tested and evaluated in HAMMLAB with operators from the Halden Reactor, and occasionally from commercial NPPs, as test subjects. These evaluations provide data on the merits of different operator support systems in an advanced control room setting, as well as on how such systems should be integrated to enhance operator performance. The paper discusses these aspects and the role of computerized operator support systems in plant operation based on the experience from this work at the Halden Project. 15 refs, 5 figs

  1. Human-machine interface aspects and use of computer-based operator support systems in control room upgrades and new control room designs for nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Berg, O [Institutt for Energiteknikk, OECD Halden Reactor Project (Netherlands)

    1997-07-01

    At the Halden Project efforts are made to explore the possibilities through design, development and validation of Computer-based Operator Support Systems (COSSes) which can assist the operators in different operational situations, ranging from normal operation to disturbance and accident conditions. The programme comprises four main activities: 1) verification and validation of safety critical software systems; 2) man-machine interaction research emphasizing improvements in man-machine interfaces on the basis of human factors studies; 3) computerized operator support systems assisting the operator in fault detection/diagnosis and planning of control actions; and 4) control room development providing a basis for retrofitting of existing control rooms and for the design of advanced concepts. The paper presents the status of this development programme, including descriptions of specific operator support functions implemented in the simulator-based, experimental control room at Halden (HAMMLAB, HAlden Man-Machine LABoratory). These operator aids comprise advanced alarms systems, diagnostic support functions, electronic procedures, critical safety functions surveillance and accident management support systems. The different operator support systems development at the Halden Project are tested and evaluated in HAMMLAB with operators from the Halden Reactor, and occasionally from commercial NPPs, as test subjects. These evaluations provide data on the merits of different operator support systems in an advanced control room setting, as well as on how such systems should be integrated to enhance operator performance. The paper discusses these aspects and the role of computerized operator support systems in plant operation based on the experience from this work at the Halden Project. 15 refs, 5 figs.

  2. Fault tolerant operation of switched reluctance machine

    Science.gov (United States)

    Wang, Wei

    The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and

  3. Operating System For Numerically Controlled Milling Machine

    Science.gov (United States)

    Ray, R. B.

    1992-01-01

    OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

  4. High speed operation of permanent magnet machines

    Science.gov (United States)

    El-Refaie, Ayman M.

    This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been

  5. Accessible engineering drawings for visually impaired machine operators.

    Science.gov (United States)

    Ramteke, Deepak; Kansal, Gayatri; Madhab, Benu

    2014-01-01

    An engineering drawing provides manufacturing information to a machine operator. An operator plans and executes machining operations based on this information. A visually impaired (VI) operator does not have direct access to the drawings. Drawing information is provided to them verbally or by using sample parts. Both methods have limitations that affect the quality of output. Use of engineering drawings is a standard practice for every industry; this hampers employment of a VI operator. Accessible engineering drawings are required to increase both independence, as well as, employability of VI operators. Today, Computer Aided Design (CAD) software is used for making engineering drawings, which are saved in CAD files. Required information is extracted from the CAD files and converted into Braille or voice. The authors of this article propose a method to make engineering drawings information directly accessible to a VI operator.

  6. Operator aid system for Dhruva fueling machine

    International Nuclear Information System (INIS)

    Misra, S.M.; Ramaswamy, L.R.; Gohel, N.; Bharadwaj, G.; Ranade, M.R.; Khadilkar, M.G.

    1997-01-01

    Systems with significant software contents are replacing the old hardware logic systems. These systems not only are versatile but are easy to make changes in the program. Extensive use of such systems in critical real-time operation environment warrants not only excessive training on simulators, documentation but also fault tolerant system to bring the operation to a safe state in case of error. With new graphic user software interface and advancement in personal computer hardware design, the dynamic status of the physical environment can be shown on the visual display at near real time. These visual aids along with the software covering all the interlocks aids an operator in his professional work. This paper highlights the operator aid system for Dhruva fueling machine. (author). 6 refs., 1 fig

  7. Model-based machine learning.

    Science.gov (United States)

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  8. Machine intelligence and knowledge bases

    Energy Technology Data Exchange (ETDEWEB)

    Furukawa, K

    1981-09-01

    The basic functions necessary in machine intelligence are a knowledge base and a logic programming language such as PROLOG using deductive reasoning. Recently inductive reasoning based on meta knowledge and default reasoning have been developed. The creative thought model of Lenit is reviewed and the concept of knowledge engineering is introduced. 17 references.

  9. State machine operation of the MICE cooling channel

    International Nuclear Information System (INIS)

    Hanlet, Pierrick

    2014-01-01

    The Muon Ionization Cooling Experiment (MICE) is a demonstration experiment to prove the feasibility of cooling a beam of muons for use in a Neutrino Factory and/or Muon Collider. The MICE cooling channel is a section of a modified Study II cooling channel which will provide a 10% reduction in beam emittance. In order to ensure a reliable measurement, MICE will measure the beam emittance before and after the cooling channel at the level of 1%, a relative measurement of 0.001. This renders MICE a precision experiment which requires strict controls and monitoring of all experimental parameters in order to control systematic errors. The MICE Controls and Monitoring system is based on EPICS and integrates with the DAQ, Data monitoring systems, and a configuration database. The cooling channel for MICE has between 12 and 18 superconductnig solenoid coils in 3 to 7 magnets, depending on the staged development of the experiment. The magnets are coaxial and in close proximity which requires coordinated operation of the magnets when ramping, responding to quench conditions, and quench recovery. To reliably manage the operation of the magnets, MICE is implementing state machines for each magnet and an over-arching state machine for the magnets integrated in the cooling channel. The state machine transitions and operating parameters are stored/restored to/from the configuration database and coupled with MICE Run Control. Proper implementation of the state machines will not only ensure safe operation of the magnets, but will help ensure reliable data quality. A description of MICE, details of the state machines, and lessons learned from use of the state machines in recent magnet training tests will be discussed.

  10. MONITORING DIAGNOSTIC INDICATORS DURING OPERATION OF A PRINT MACHIN

    Directory of Open Access Journals (Sweden)

    Jozef Dobránsky

    2015-11-01

    Full Text Available This article deals with monitoring diagnostic indicators during the operation of a machine used for production of packing materials with a print. It analyses low-frequency vibrations measured in individual spherical roller bearings in eight print positions. The rollers in these positions have a different pressure based on positioning these rollers in relation to the central roller. As a result, the article also deals with a correlation of pressure and level of measured low-frequency vibrations. The speed of the print machine (the speed of a line in meters per minute is a very important variable during its operation, this is why it is important to verify the values of vibrations in various speeds of the line, what can lead to revelation of one or more resonance areas. Moreover, it examines vibrations of the central roller drive and measurement of backlash of transmission cogs of this drive. Based on performed analyses recommendations for an operator of the machine have been conceived.

  11. Connection machine: a computer architecture based on cellular automata

    Energy Technology Data Exchange (ETDEWEB)

    Hillis, W D

    1984-01-01

    This paper describes the connection machine, a programmable computer based on cellular automata. The essential idea behind the connection machine is that a regular locally-connected cellular array can be made to behave as if the processing cells are connected into any desired topology. When the topology of the machine is chosen to match the topology of the application program, the result is a fast, powerful computing engine. The connection machine was originally designed to implement knowledge retrieval operations in artificial intelligence programs, but the hardware and the programming techniques are apparently applicable to a much larger class of problems. A machine with 100000 processing cells is currently being constructed. 27 references.

  12. Design and development of remotely operated coolant channel cutting machine

    International Nuclear Information System (INIS)

    Suthar, R.L.; Sinha, A.K.; Srikrishnamurty, G.

    1994-01-01

    One of the coolant tubes of Narora Atomic Power Station (NAPS) reactor needs to be removed. To remove a coolant tube, four cutting operations, (liner tube cutting, end-fitting cutting, machining of seal weld of bellow ring and finally coolant tube cutting) are required to be carried out. A remotely operated cutting machine to carry out all these operations has been designed and developed by Central Workshops. This machine is able to cut at the exact location because of numerically controlled axial and radial travel of tool. Only by changing the tool head and tool holder, same machine can be used for various types of cutting/machining operations. This report details the design, manufacture, assembly and testing work done on the machine. (author). 4 figs

  13. Investigations of Cutting Fluid Performance Using Different Machining Operations

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo; Belluco, Walter

    2002-01-01

    An analysis of cutting fluid performance in dif-ferent metal cutting operations is presented based on performance criteria, work material and fluid type. Cutting fluid performance was evaluated in turning, drilling, reaming and tapping operations, with respect to tool life, cutting forces and prod...... will get the same performance ranking for different metalworking fluids no matter what machining test is used, when the fluids are of the same type. Results show that this is mostly true for the water-based fluids on austenitic stainless steel while ranking did change depending on the test with straight......-gated. In the case of austenitic stainless steel as the workpiece material, results using the different operations under different cutting conditions show that the performance of vegetable oil based prod-ucts is superior or equal to that of mineral oil based products. The hypothesis was investigated that one...

  14. Machinability of nickel based alloys using electrical discharge machining process

    Science.gov (United States)

    Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

    2018-04-01

    The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

  15. Job Grading Standard for Machine Tool Operator, WG-3431.

    Science.gov (United States)

    Civil Service Commission, Washington, DC. Bureau of Policies and Standards.

    The standard covers nonsupervisory work involved in the set up, adjustment, and operation of conventional machine tools to perform machining operations in the manufacture and repair of castings, forgings, or parts from raw stock made of various metals, metal alloys, and other materials. A general description of the job at both the WG-8 and WG-9…

  16. Principles of control automation of soil compacting machine operating mechanism

    Science.gov (United States)

    Anatoly Fedorovich, Tikhonov; Drozdov, Anatoly

    2018-03-01

    The relevance of the qualitative compaction of soil bases in the erection of embankment and foundations in building and structure construction is given.The quality of the compactible gravel and sandy soils provides the bearing capability and, accordingly, the strength and durability of constructed buildings.It has been established that the compaction quality depends on many external actions, such as surface roughness and soil moisture; granulometry, chemical composition and degree of elasticity of originalfilled soil for compaction.The analysis of technological processes of soil bases compaction of foreign and domestic information sources showed that the solution of such important problem as a continuous monitoring of soil compaction actual degree in the process of machine operation carry out only with the use of modern means of automation. An effective vibrodynamic method of gravel and sand material sealing for the building structure foundations for various applications was justified and suggested.The method of continuous monitoring the soil compaction by measurement of the amplitudes and frequencies of harmonic oscillations on the compactible surface was determined, which allowed to determine the basic elements of facilities of soil compacting machine monitoring system of operating, etc. mechanisms: an accelerometer, a bandpass filter, a vibro-harmonics, an on-board microcontroller. Adjustable parameters have been established to improve the soil compaction degree and the soil compacting machine performance, and the adjustable parameter dependences on the overall indexhave been experimentally determined, which is the soil compaction degree.A structural scheme of automatic control of the soil compacting machine control mechanism and theoperation algorithm has been developed.

  17. Work stress of women in sewing machine operation.

    Science.gov (United States)

    Nag, A; Desai, H; Nag, P K

    1992-06-01

    The study examined the work stresses of 107 women who were engaged in sewing machine operation in small garment manufacturing units. Of the three types of sewing machines (motor-operated, full and half shuttle foot-operated), 74% of the machines were foot-operated, where throttle action of the lower limb is required to move the shuttle of the machine. The motor-operated machines were faster than the foot-operated machines. The short cycle sewing work involves repetitive action of hand and feet. The women had to maintain a constant seated position on a stool without backrest and the body inclined forward. Long-term sewing work had a cumulative load on the musculo-skeletal structures, including the vertebral column and reflected in the form of high prevalence of discomfort and pain in different body parts. About 68% of the women complained of back pain, among whom 35% reported a persistent low back pain. Common sewing work accident is piercing of the needle through the fingers, particularly the right forefingers. Unsatisfactory man-machine incompatibility, work posture and fatigue, improper coordination of eye, leg and hand are the major problems of the operators. The design mis-match of the work place may be significantly improved by taking women's anthropometric dimensions in modifying the workplace, i.e. the seat surface, seat height, work height, backrest, etc.

  18. Improvement of human operator vibroprotection system in the utility machine

    Science.gov (United States)

    Korchagin, P. A.; Teterina, I. A.; Rahuba, L. F.

    2018-01-01

    The article is devoted to an urgent problem of improving efficiency of road-building utility machines in terms of improving human operator vibroprotection system by determining acceptable values of the rigidity coefficients and resistance coefficients of operator’s cab suspension system elements and those of operator’s seat. Negative effects of vibration result in labour productivity decrease and occupational diseases. Besides, structure vibrations have a damaging impact on the machine units and mechanisms, which leads to reducing an overall service life of the machine. Results of experimental and theoretical research of operator vibroprotection system in the road-building utility machine are presented. An algorithm for the program to calculate dynamic impacts on the operator in terms of different structural and performance parameters of the machine and considering combination of external pertrubation influences was proposed.

  19. New developments in operator protection for forest machines

    Science.gov (United States)

    Robert B. Rummer; S. Taylor; M. Veal

    2003-01-01

    Mechanization of forest operations ha greatly improved saftey of woods work. However, increasing use of machines has introduced new hazards that must be addressed. Two of these hazards are rollover of swing-type forestry machines (currently excluded from standard protection) and the hazard of thrown objects from cutting devices. Ongoing research projects are developing...

  20. A consideration of the operation of automatic production machines.

    Science.gov (United States)

    Hoshi, Toshiro; Sugimoto, Noboru

    2015-01-01

    At worksites, various automatic production machines are in use to release workers from muscular labor or labor in the detrimental environment. On the other hand, a large number of industrial accidents have been caused by automatic production machines. In view of this, this paper considers the operation of automatic production machines from the viewpoint of accident prevention, and points out two types of machine operation - operation for which quick performance is required (operation that is not permitted to be delayed) - and operation for which composed performance is required (operation that is not permitted to be performed in haste). These operations are distinguished by operation buttons of suitable colors and shapes. This paper shows that these characteristics are evaluated as "asymmetric on the time-axis". Here, in order for workers to accept the risk of automatic production machines, it is preconditioned in general that harm should be sufficiently small or avoidance of harm is easy. In this connection, this paper shows the possibility of facilitating the acceptance of the risk of automatic production machines by enhancing the asymmetric on the time-axis.

  1. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  2. Machine utilisation and operation experience with Jet from 1983

    International Nuclear Information System (INIS)

    Green, B.J.; Chuilon, P.; Noble, B.; Saunders, R.; Webberley, D.

    1989-01-01

    The operation of JET commenced in June 1983 and is scheduled until the end of 1992. This seemingly long period is actually quite limited when compared with the time needed to implement and commission the planned machine enhancements, and pursue research and developments which result from the experiments. There is an ever-present urgency to make the best use of the machine. 1983-84 was a learning period and only in 1985 was it felt worthwhile to adopt double-shift day working. Data has been compiled and analysed for utilisation of the machine, delays in terms of time lost and systems involved, and number and frequency of machine pulses. This paper presents an overall picture of machine availability and utilisation. It describes the JET operational arrangements and the experience of system faults. Finally, it draws conclusions and identifies lessons learned which may be relevant to the next stage of fusion power development

  3. Machine utilisation and operation experience with JET from 1983

    International Nuclear Information System (INIS)

    Green, B.J.; Chuilon, P.; Noble, B.; Saunders, R.; Webberley, D.

    1989-01-01

    The operation of JET commenced in June 1983 and is scheduled until the end of 1992. This seemingly long period is actually quite limited when compared with the time needed to implement and commission the planned machine enhancements, and pursue research and developments which result from the experiments. There is an ever-present urgency to make the use of the machine. 1983-84 was a learning period and only in 1985 was it felt worthwhile to adopt double-shift day working. Data has been compiled and analysed for utilisation of the machine, delays in terms of time lost and system involved, and number and frequency of machine pulses. This paper presents an overall picture of machine availability and utilisation. It describes the JET operational arrangements and the experience of system faults. Finally, it draws conclusions and identifies lessons learned which may be relevant to the next stage of fusion power development. (author). 9 figs

  4. Development of an electrically operated cassava slicing machine

    Directory of Open Access Journals (Sweden)

    I. S. Aji

    2013-08-01

    Full Text Available Labor input in manual cassava chips processing is very high and product quality is low. This paper presents the design and construction of an electrically operated cassava slicing machine that requires only one person to operate. Efficiency, portability, ease of operation, corrosion prevention of slicing component of the machine, force required to slice a cassava tuber, capacity of 10 kg/min and uniformity in the size of the cassava chips were considered in the design and fabrication of the machine. The performance of the machine was evaluated with cassava of average length and diameter of 253 mm and 60 mm respectively at an average speed of 154 rpm. The machine produced 5.3 kg of chips of 10 mm length and 60 mm diameter in 1 minute. The efficiency of the machine was 95.6% with respect to the quantity of the input cassava. The chips were found to be well chipped to the designed thickness, shape and of generally similar size. Galvanized steel sheets were used in the cutting section to avoid corrosion of components. The machine is portable and easy to operate which can be adopted for cassava processing in a medium size industry.

  5. Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

    Science.gov (United States)

    Koptev, V. Yu

    2017-02-01

    The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

  6. Man-machine interfaces analysis system based on computer simulation

    International Nuclear Information System (INIS)

    Chen Xiaoming; Gao Zuying; Zhou Zhiwei; Zhao Bingquan

    2004-01-01

    The paper depicts a software assessment system, Dynamic Interaction Analysis Support (DIAS), based on computer simulation technology for man-machine interfaces (MMI) of a control room. It employs a computer to simulate the operation procedures of operations on man-machine interfaces in a control room, provides quantified assessment, and at the same time carries out analysis on operational error rate of operators by means of techniques for human error rate prediction. The problems of placing man-machine interfaces in a control room and of arranging instruments can be detected from simulation results. DIAS system can provide good technical supports to the design and improvement of man-machine interfaces of the main control room of a nuclear power plant

  7. A survey of machine readable data bases

    Science.gov (United States)

    Matlock, P.

    1981-01-01

    Forty-two of the machine readable data bases available to the technologist and researcher in the natural sciences and engineering are described and compared with the data bases and date base services offered by NASA.

  8. Thrown object testing of forest machine operator protective structures

    Science.gov (United States)

    S.E. Taylor; M.W. Veal; R.B. Rummer

    2003-01-01

    High-speed chains or rotating disks are commonly used to cut and process trees during forest harvesting operations. Mechanical failure or fatigue of these tools can lead to a potentially hazardous situation where fragments of chain or sawteeth are thrown through the operator enclosures on forest machines. This poster presentation discusses the development and...

  9. A Manually Operated Cassava Grating Machine | Odigboh | Nigerian ...

    African Journals Online (AJOL)

    The design and development of a manually operated cassava grating machine prototype are presented. The prototype grater is shown to be easy to operate at 30 - 45 rpm to give a product whose quality is as good as that from motorized graters at a throughput of 125 - 185 kg/h. The prototype grater is a powerful alternative ...

  10. Principles of machine operation and maintenance

    CERN Document Server

    Jeffrey, Dick

    2013-01-01

    This book explains how rotating machinery works, and the role of the maintenance engineer in ensuring its proper operation. Stress is laid on the need for the trainee engineer to develop skills in diagnosis and troubleshooting as well as practicalexpertise in maintenance procedures.

  11. Comparison the machinability of Inconel 718, Inconel 625 and Monel 400 in hot turning operation

    Directory of Open Access Journals (Sweden)

    Asit Kumar Parida

    2018-06-01

    Full Text Available In the present paper, three nickel base alloys (Inconel 718, Inconel 625 and Monel-400 have been studied for chip formation in the hot turning process using flame heating. Cutting force, tool life, chip morphology, tool wear, and surface integrity (surface roughness and microhardness beneath the machined surface have been determined in both room and hot temperature conditions (300 °C and 600 °C. Flame heating (Liquefied petroleum gas and oxygen along with turning operation has been utilized for machining of three materials. It was observed that significant reduction of cutting force, tool wear, chatter formation, surface roughness and increase tool life, chip tool contact length, etc., for all three nickel base alloys in hot machining compared to room temperature machining. Keywords: Hot turning, Nickel base alloys, Machinability, Cutting forces, Tool wear

  12. A Mobile Robot for Emergency Operation of Fuel Exchange Machine

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Yongchil; Lee, Sunguk; Kim, Changhoi; Shin, Hochul; Jung, Seungho; Choi, Changhwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2007-07-01

    A Pressurized Heavy Water Reactor (PHWR) uses a heavy water as the coolant and moderator because it does not attenuate the neutron inside the reactor, which makes it possible to use natural uranium for nuclear fuels. However, since the uranium ratio is too low within the natural uranium, the reactor should be refueled everyday while the reactor is working. For that purpose, there is a fuel exchange machine. However as the time passes by, the durability and reliability become a problem. While the fuel handling machine exchanges the reactor fuel, it can be stuck to the pressure tube attached in the Calandra. Although this kind of situation is rarely happen, it can make the reactor be shutdown for normalizing the operation. Since the refueling is performed while the reactor is working, the radiation level is extremely high and the machine can be located at a high position up to nine meters from the floor, that is, the human worker can not approach the machine, so the fuel handling machine should be released remotely. To cope with this situation, the fuel handling machine has a manual drive mechanism at the rear side of it as shown in the circled images. If the worker can handle these manual drive mechanisms, the fuel handling machine can be released form the pressure tube. The KAERI had developed a long-reach manipulator system with a telescophic mast mechanism which can be deployed in the basement of the reactor room and manipulate the manual lever of the fuel exchange machine. Since the manipulator is located in the basement, there are several problems for its application such that the plug hole should be removed before the operation and the vibration of the mast mechanism make it difficult to locate the end effecter of the manipulator.

  13. A Mobile Robot for Emergency Operation of Fuel Exchange Machine

    International Nuclear Information System (INIS)

    Seo, Yongchil; Lee, Sunguk; Kim, Changhoi; Shin, Hochul; Jung, Seungho; Choi, Changhwan

    2007-01-01

    A Pressurized Heavy Water Reactor (PHWR) uses a heavy water as the coolant and moderator because it does not attenuate the neutron inside the reactor, which makes it possible to use natural uranium for nuclear fuels. However, since the uranium ratio is too low within the natural uranium, the reactor should be refueled everyday while the reactor is working. For that purpose, there is a fuel exchange machine. However as the time passes by, the durability and reliability become a problem. While the fuel handling machine exchanges the reactor fuel, it can be stuck to the pressure tube attached in the Calandra. Although this kind of situation is rarely happen, it can make the reactor be shutdown for normalizing the operation. Since the refueling is performed while the reactor is working, the radiation level is extremely high and the machine can be located at a high position up to nine meters from the floor, that is, the human worker can not approach the machine, so the fuel handling machine should be released remotely. To cope with this situation, the fuel handling machine has a manual drive mechanism at the rear side of it as shown in the circled images. If the worker can handle these manual drive mechanisms, the fuel handling machine can be released form the pressure tube. The KAERI had developed a long-reach manipulator system with a telescophic mast mechanism which can be deployed in the basement of the reactor room and manipulate the manual lever of the fuel exchange machine. Since the manipulator is located in the basement, there are several problems for its application such that the plug hole should be removed before the operation and the vibration of the mast mechanism make it difficult to locate the end effecter of the manipulator

  14. Development of Web-based Virtual Training Environment for Machining

    Science.gov (United States)

    Yang, Zhixin; Wong, S. F.

    2010-05-01

    With the booming in the manufacturing sector of shoe, garments and toy, etc. in pearl region, training the usage of various facilities and design the facility layout become crucial for the success of industry companies. There is evidence that the use of virtual training may provide benefits in improving the effect of learning and reducing risk in the physical work environment. This paper proposed an advanced web-based training environment that could demonstrate the usage of a CNC machine in terms of working condition and parameters selection. The developed virtual environment could provide training at junior level and advanced level. Junior level training is to explain machining knowledge including safety factors, machine parameters (ex. material, speed, feed rate). Advanced level training enables interactive programming of NG coding and effect simulation. Operation sequence was used to assist the user to choose the appropriate machining condition. Several case studies were also carried out with animation of milling and turning operations.

  15. Dual voltage source inverter topology extending machine operating range

    NARCIS (Netherlands)

    Gerrits, T.; Wijnands, C.G.E.; Paulides, J.J.H.; Duarte, J.L.

    2012-01-01

    Field weakening operation of an electrical machine is a conventional method to extend the angular velocity range of a system above the peak output voltage of the inverter. A downside, however, is that an increased reactive current is required that creates losses but no output torque. A dual voltage

  16. The 1989 progress report of GANIL: Operations and machine studies

    International Nuclear Information System (INIS)

    1990-03-01

    The 1989 progress report of the GANIL (French acronym for National Large Accelerator of Heavy Ions) is presented. The studies and the operations performed on the accelerator during the 10th July to the 18th December are summarized. The machine's operating time, the time required in the starting step and the time available for the users are examined. Several technical studies performed are reported. The results obtained after the energy increase operation are satisfactory. The beam intensity was increased of about a factor of 10 [fr

  17. Ergonomic requirements for the operation of machines and technical equipment

    Directory of Open Access Journals (Sweden)

    Górny Adam

    2017-01-01

    Full Text Available In order to operate machinery and equipment safely, it is critical for the solutions in place to conform to design-related and operating requirements. Design-related requirements are primarily the responsibility of machine designers/developers and manufacturers. Operating requirements should be satisfied by employers, who are responsible for ensuring safe working conditions for their employees. Under applicable laws, machinery and equipment should be designed, produced and then operated without placing excessive loads on workers and in keeping with machine functionality and intended use. One should also ensure that machinery and equipment can be maintained and adjusted without exposing their operators to hazards. Ergonomic criteria are an integral part of such requirements. They ensure that human users and operators of technical equipment are enabled to function properly. Design-related requirements are viewed as a priority safety consideration. While they facilitate the use of technical tools, the actual safety of employees ultimately depends on the satisfaction of specific requirements during operation.

  18. Molecular machines operating on the nanoscale: from classical to quantum

    Directory of Open Access Journals (Sweden)

    Igor Goychuk

    2016-03-01

    Full Text Available The main physical features and operating principles of isothermal nanomachines in the microworld, common to both classical and quantum machines, are reviewed. Special attention is paid to the dual, constructive role of dissipation and thermal fluctuations, the fluctuation–dissipation theorem, heat losses and free energy transduction, thermodynamic efficiency, and thermodynamic efficiency at maximum power. Several basic models are considered and discussed to highlight generic physical features. This work examines some common fallacies that continue to plague the literature. In particular, the erroneous beliefs that one should minimize friction and lower the temperature for high performance of Brownian machines, and that the thermodynamic efficiency at maximum power cannot exceed one-half are discussed. The emerging topic of anomalous molecular motors operating subdiffusively but very efficiently in the viscoelastic environment of living cells is also discussed.

  19. Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

    Science.gov (United States)

    Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

    2014-01-01

    Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

  20. Operating point resolved loss computation in electrical machines

    Directory of Open Access Journals (Sweden)

    Pfingsten Georg Von

    2016-03-01

    Full Text Available Magnetic circuits of electromagnetic energy converters, such as electrical machines, are nowadays highly utilized. This proposition is intrinsic for the magnetic as well as the electric circuit and depicts that significant enhancements of electrical machines are difficult to achieve in the absence of a detailed understanding of underlying effects. In order to improve the properties of electrical machines the accurate determination of the locally distributed iron losses based on idealized model assumptions solely is not sufficient. Other loss generating effects have to be considered and the possibility being able to distinguish between the causes of particular loss components is indispensable. Parasitic loss mechanisms additionally contributing to the total losses originating from field harmonics, non-linear material behaviour, rotational magnetizations, and detrimental effects caused by the manufacturing process or temperature, are not explicitly considered in the common iron-loss models, probably even not specifically contained in commonly used calibration factors. This paper presents a methodology being able to distinguish between different loss mechanisms and enables to individually consider particular loss mechanisms in the model of the electric machine. A sensitivity analysis of the model parameters can be performed to obtain information about which decisive loss origin for which working point has to be manipulated by the electromagnetic design or the control of the machine.

  1. Machine-based mapping of innovation portfolios

    NARCIS (Netherlands)

    de Visser, Matthias; Miao, Shengfa; Englebienne, Gwenn; Sools, Anna Maria; Visscher, Klaasjan

    2017-01-01

    Machine learning techniques show a great promise for improving innovation portfolio management. In this paper we experiment with different methods to classify innovation projects of a high-tech firm as either explorative or exploitative, and compare the results with a manual, theory-based mapping of

  2. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  3. A nanoplasmonic switch based on molecular machines

    KAUST Repository

    Zheng, Yue Bing

    2009-06-01

    We aim to develop a molecular-machine-driven nanoplasmonic switch for its use in future nanophotonic integrated circuits (ICs) that have applications in optical communication, information processing, biological and chemical sensing. Experimental data show that an Au nanodisk array, coated with rotaxane molecular machines, switches its localized surface plasmon resonances (LSPR) reversibly when it is exposed to chemical oxidants and reductants. Conversely, bare Au nanodisks and disks coated with mechanically inert control compounds, do not display the same switching behavior. Along with calculations based on time-dependent density functional theory (TDDFT), these observations suggest that the nanoscale movements within surface-bound "molecular machines" can be used as the active components in plasmonic devices. ©2009 IEEE.

  4. Timer-based data acquisitioning of creep testing machines

    International Nuclear Information System (INIS)

    Rana, M.A.; Farooq, M.A.; Ali, L.

    1998-01-01

    Duration of a creep test may be short or long term extending over several years. Continuous operation of a computer for automatic data acquisition of creep testing machines is useless. Timer based data acquisitioning of the machines already interface with IBM-Pc/AT and compatibles has been streamlined for economical use of the computer. A locally designed and fabricated timer has been introduced in the system in this regard to meet the requirements of the system. The timer switches on the computer according to pre scheduled interval of time of capture creep data in Real time. The periodically captured data is logged on the hard disk for analysis and report generation. (author)

  5. 29 CFR 570.62 - Occupations involved in the operation of bakery machines (Order 11).

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 3 2010-07-01 2010-07-01 false Occupations involved in the operation of bakery machines... Health or Well-Being § 570.62 Occupations involved in the operation of bakery machines (Order 11). Link... following occupations involved in the operation of power-driven bakery machines are particularly hazardous...

  6. Preliminary Test of Upgraded Conventional Milling Machine into PC Based CNC Milling Machine

    International Nuclear Information System (INIS)

    Abdul Hafid

    2008-01-01

    CNC (Computerized Numerical Control) milling machine yields a challenge to make an innovation in the field of machining. With an action job is machining quality equivalent to CNC milling machine, the conventional milling machine ability was improved to be based on PC CNC milling machine. Mechanically and instrumentally change. As a control replacing was conducted by servo drive and proximity were used. Computer programme was constructed to give instruction into milling machine. The program structure of consists GUI model and ladder diagram. Program was put on programming systems called RTX software. The result of up-grade is computer programming and CNC instruction job. The result was beginning step and it will be continued in next time. With upgrading ability milling machine becomes user can be done safe and optimal from accident risk. By improving performance of milling machine, the user will be more working optimal and safely against accident risk. (author)

  7. Operating limits on commutator dc machines used as electromechanical capacitors

    International Nuclear Information System (INIS)

    Thullen, P.

    1981-01-01

    This paper presents data gathered in testing 28 rebuilt motors and discusses the impact of possible variation of characteristics among the machines of the operation of the facility. It also discusses commutation limits and their relationship to the economics and reliability of the system. When installed, the system will consist of 30 motors and will have a rating of 50 kA, 1.68 kV corresponding to 84 MVA at a peak power of 42 MW. The cost of this supply will be near $6/kVA which continues to compare favorably with the present $30/kVA cost of alternators

  8. Component based modelling of piezoelectric ultrasonic actuators for machining applications

    International Nuclear Information System (INIS)

    Saleem, A; Ahmed, N; Salah, M; Silberschmidt, V V

    2013-01-01

    Ultrasonically Assisted Machining (UAM) is an emerging technology that has been utilized to improve the surface finishing in machining processes such as turning, milling, and drilling. In this context, piezoelectric ultrasonic transducers are being used to vibrate the cutting tip while machining at predetermined amplitude and frequency. However, modelling and simulation of these transducers is a tedious and difficult task. This is due to the inherent nonlinearities associated with smart materials. Therefore, this paper presents a component-based model of ultrasonic transducers that mimics the nonlinear behaviour of such a system. The system is decomposed into components, a mathematical model of each component is created, and the whole system model is accomplished by aggregating the basic components' model. System parameters are identified using Finite Element technique which then has been used to simulate the system in Matlab/SIMULINK. Various operation conditions are tested and performed to demonstrate the system performance

  9. Modification and Performance Evaluation of a Low Cost Electro-Mechanically Operated Creep Testing Machine

    OpenAIRE

    John J. MOMOH; Lanre Y. SHUAIB-BABATA; Gabriel O. ADELEGAN

    2010-01-01

    Existing mechanically operated tensile and creep testing machine was modified to a low cost, electro-mechanically operated creep testing machine capable of determining the creep properties of aluminum, lead and thermoplastic materials as a function of applied stress, time and temperature. The modification of the testing machine was necessitated by having an electro-mechanically operated creep testing machine as a demonstration model ideal for use and laboratory demonstrations, which will prov...

  10. Ergonomics issues among sewing machine operators in the textile manufacturing industry in Botswana.

    Science.gov (United States)

    Sealetsa, O J; Thatcher, A

    2011-01-01

    Universally musculoskeletal disorders are among the leading causes of low productivity in today's work environment. The situation is reportedly even worse in developing countries with appalling working conditions in many industries. In addition, there is often an acute lack of awareness of ergonomics issues, education and training programmes, and certification within developing countries. Numerous studies internationally have highlighted musculoskeletal risk factors associated with the textile industry and garment-making jobs because of highly repetitive work in awkward work postures. The objective of this study was to identify and describe possible ergonomics deficiencies in the workstation of sewing machine operators in a textile industry in Botswana as well as their perception of workload and bodily discomfort. This study focused on one textile manufacturing factory in Botswana where 157 female sewing machine operators were recruited as participants. A modified Corlett and Bishop body map questionnaire and the NASA TLX were administered and relevant anthropometric and workplace layout measurements were collected. The results of the study revealed a high prevalence of musculoskeletal disorders. Back, neck and shoulder discomfort are highly prevalent among these sewing machine operators. This study proposes intervention strategies including the re-design of the workstations and seating and the provision of training in basic ergonomics principles for improving the work-life of these operators and provides a base for further research on the prevalence of musculoskeletal disorders among sewing machine operators in developing countries.

  11. Behavioral simulation of a nuclear power plant operator crew for human-machine system design

    International Nuclear Information System (INIS)

    Furuta, K.; Shimada, T.; Kondo, S.

    1999-01-01

    This article proposes an architecture of behavioral simulation of an operator crew in a nuclear power plant including group processes and interactions between the operators and their working environment. An operator model was constructed based on the conceptual human information processor and then substantiated as a knowledge-based system with multiple sets of knowledge base and blackboard, each of which represents an individual operator. From a trade-off between reality and practicality, we adopted an architecture of simulation that consists of the operator, plant and environment models in order to consider operator-environment interactions. The simulation system developed on this framework and called OCCS was tested using a scenario of BWR plant operation. The case study showed that operator-environment interactions have significant effects on operator crew performance and that they should be considered properly for simulating behavior of human-machine systems. The proposed architecture contributed to more realistic simulation in comparison with an experimental result, and a good prospect has been obtained that computer simulation of an operator crew is feasible and useful for human-machine system design. (orig.)

  12. Safety in unlimited power supply. Method and means of parallel operation of flywheel aggregates. [parallel operation of flywheel machines

    Energy Technology Data Exchange (ETDEWEB)

    Krause, E [Struever (A.) K.G., Hamburg (Germany, F.R.)

    1975-11-01

    A special type of Diesel emergency generator sets, i.e., with flywheel machines is described. Construction and operation of a flywheel machine are described and reasons are given for a possible or necessary parallel operation. The basic requirements for parallel operation are explained and the intrinsic operation is described. Special designs are also presented.

  13. MACHINE PROTECTION SYSTEM FOR CONCURRENT OPERATION OF RHIC AND BLIP

    International Nuclear Information System (INIS)

    WILINSKI, M.; BELLAVIA, S.; GLENN, J.W.; MAUSNER, L.F.; UNGER, K.L.

    2005-01-01

    The Brookhaven 200MeV linac is a multipurpose machine used to inject low intensity polarized protons for RHIC (Relativistic Heavy Ion Collider), as well as to inject high intensity protons to BLIP (Brookhaven Linac Isotope Producer), a medical isotope production facility. If high intensity protons were injected to RHIC by mistake, administrative radiation limits could be exceeded or sensitive electronics could be damaged. In the past, the changeover from polarized proton to high intensity proton operation has been a lengthy process, thereby never allowing the two programs to run simultaneously. To remedy this situation and allow concurrent operation of RHIC and BLIP, an active interlock system has been designed to monitor current levels in the AGS using two current transformers with fail safe circuitry and associated electronics to inhibit beam to RHIC if high intensity currents are detected

  14. Machine Protection System for Concurrent Operation of RHIC and BLIP

    CERN Document Server

    Wilinski, Michelle; Glenn, Joseph; Mausner, Leonard; Unger, Kerry

    2005-01-01

    The Brookhaven 200 MeV linac is a multipurpose machine used to inject low intensity polarized protons ultimately ending up in RHIC as well as to inject high intensity protons to BLIP, a medical isotope production facility. If high intensity protons were injected to RHIC by mistake, administrative radiation limits could be exceeded or sensitive electronics could be damaged. In the past, the changeover from polarized proton to high intensity proton operation has been a lengthy process, thereby never allowing the two programs to run simultaneously. To remedy this situation and allow for concurrent operation of RHIC and BLIP, an active interlock system has been designed to monitor current levels in the AGS using two current transformers with fail safe circuitry and associated electronics to inhibit beam to RHIC if high intensity is detected.

  15. Efficient operation of anisotropic synchronous machines for wind energy systems

    International Nuclear Information System (INIS)

    Eldeeb, Hisham; Hackl, Christoph M.; Kullick, Julian

    2016-01-01

    This paper presents an analytical solution for the Maximum-Torque-per-Ampere (MTPA) operation of synchronous machines (SM) with anisotropy and magnetic cross-coupling for the application in wind turbine systems and airborne wind energy systems. For a given reference torque, the analytical MTPA solution provides the optimal stator current references which produce the desired torque while minimizing the stator copper losses. From an implementation point of view, the proposed analytical method is appealing in terms of its fast online computation (compared to classical numerical methods) and its efficiency enhancement of the electrical drive system. The efficiency of the analytical MTPA operation, with and without consideration of cross-coupling, is compared to the conventional method with zero direct current. (paper)

  16. Optimization of Surface Finish in Turning Operation by Considering the Machine Tool Vibration using Taguchi Method

    Directory of Open Access Journals (Sweden)

    Muhammad Munawar

    2012-01-01

    Full Text Available Optimization of surface roughness has been one of the primary objectives in most of the machining operations. Poor control on the desired surface roughness generates non conforming parts and results into increase in cost and loss of productivity due to rework or scrap. Surface roughness value is a result of several process variables among which machine tool condition is one of the significant variables. In this study, experimentation was carried out to investigate the effect of machine tool condition on surface roughness. Variable used to represent machine tool\\'s condition was vibration amplitude. Input parameters used, besides vibration amplitude, were feed rate and insert nose radius. Cutting speed and depth of cut were kept constant. Based on Taguchi orthogonal array, a series of experimentation was designed and performed on AISI 1040 carbon steel bar at default and induced machine tool\\'s vibration amplitudes. ANOVA (Analysis of Variance, revealed that vibration amplitude and feed rate had moderate effect on the surface roughness and insert nose radius had the highest significant effect on the surface roughness. It was also found that a machine tool with low vibration amplitude produced better surface roughness. Insert with larger nose radius produced better surface roughness at low feed rate.

  17. Concept of Operations for a Virtual Machine for C3I Applications

    National Research Council Canada - National Science Library

    Bagrodia, Rajive

    1997-01-01

    .... This 12-month research endeavor, entitled "Concept of Operations for a Virtual Machine for C31 Applications," examined issues in using a concurrent virtual machine for the design of C31 applications...

  18. Computerized operator support system with new man-machine interface for BWR power plants

    International Nuclear Information System (INIS)

    Monta, K.; Naito, N.; Sugawara, M.; Sato, N.; Mori, N.; Tai, I.; Fukumoto, A.; Tsuchida, M.

    1984-01-01

    Improvement of the man-machine interface of nuclear power plants is an important contribution to the further enhancement of operational safety. In addition, recent advances in computer technology seem to offer the greatest opportunity to date for achieving improvement in the man-machine interface. The development of a computerized operator support system for BWRs has been undertaken since 1980 with the support of the Japanese Government. The conceptual design of this system is based on the role of the operators. The main functions are standby system management, disturbance analysis and post-trip operational guidance. The objective of the standby system management is to monitor the standby status of the engineered safety feature during normal operation to assure its proper functioning at the onset of emergency situations. The disturbance analysis system detects disturbances in the plant in their early stages and informs the plant operators about, for example, the cause of the disturbances, the plant status and possible propagations. Consequently, operators can take corrective actions to prevent unnecessary plant shutdown. The objective of the post trip operational guide is to support operators in diagnosis and corrective action after a plant trip. Its functions are to monitor the performance of the engineered safety feature, to identify the plant status and to guide the appropriate corrective action to achieve safe plant shutdown. The information from the computerized operator support system is supplied to operators through a colour CRT operator console. The authors have evaluated the performance of various new man-machine interfacing tools and proposed a new operator console design. A prototype system has been developed and verification/validation is proceeding with a BWR plant simulator. (author)

  19. Voice based gender classification using machine learning

    Science.gov (United States)

    Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

    2017-11-01

    Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

  20. STUDY OF TRANSIENT AND STATIONARY OPERATION MODES OF SYNCHRONOUS SYSTEM CONSISTING IN TWO MACHINES

    Directory of Open Access Journals (Sweden)

    V. S. Safaryan

    2017-01-01

    Full Text Available The solution of the problem of reliable functioning of an electric power system (EPS in steady-state and transient regimes, prevention of EPS transition into asynchronous regime, maintenance and restoration of stability of post-emergency processes is based on formation and realization of mathematical models of an EPS processes. During the functioning of electric power system in asynchronous regime, besides the main frequencies, the currents and voltages include harmonic components, the frequencies of which are multiple of the difference of main frequencies. At the two-frequency asynchronous regime the electric power system is being made equivalent in a form of a two-machine system, functioning for a generalized load. In the article mathematical models of transient process of a two-machine system in natural form and in d–q coordinate system are presented. The mathematical model of two-machine system is considered in case of two windings of excitement at the rotors. Also, in the article varieties of mathematical models of EPS transient regimes (trivial, simple, complete are presented. Transient process of a synchronous two-machine system is described by the complete model. The quality of transient processes of a synchronous machine depends on the number of rotor excitation windings. When there are two excitation windings on the rotor (dual system of excitation, the mathematical model of electromagnetic transient processes of a synchronous machine is represented in a complex form, i.e. in coordinate system d, q, the current of rotor being represented by a generalized vector. In asynchronous operation of a synchronous two-machine system with two excitation windings on the rotor the current and voltage systems include only harmonics of two frequencies. The mathematical model of synchronous steady-state process of a two-machine system is also provided, and the steady-state regimes with different structures of initial information are considered.

  1. Passivity-Based Control of Electric Machines

    Energy Technology Data Exchange (ETDEWEB)

    Nicklasson, P.J.

    1996-12-31

    This doctoral thesis presents new results on the design and analysis of controllers for a class of electric machines. Nonlinear controllers are derived from a Lagrangian model representation using passivity techniques, and previous results on induction motors are improved and extended to Blondel-Park transformable machines. The relation to conventional techniques is discussed, and it is shown that the formalism introduced in this work facilitates analysis of conventional methods, so that open questions concerning these methods may be resolved. In addition, the thesis contains the following improvements of previously published results on the control of induction motors: (1) Improvement of a passivity-based speed/position controller, (2) Extension of passivity-based (observer-less and observer-based) controllers from regulation to tracking of rotor flux norm, (3) An extension of the classical indirect FOC (Field-Oriented Control) scheme to also include global rotor flux norm tracking, instead of only torque tracking and rotor flux norm regulation. The design is illustrated experimentally by applying the proposed control schemes to a squirrel-cage induction motor. The results show that the proposed methods have advantages over previous designs with respect to controller tuning, performance and robustness. 145 refs., 21 figs.

  2. A Perspective on Remote Handling Operations and Human Machine Interface for Remote Handling in Fusion

    International Nuclear Information System (INIS)

    Haist, B.; Hamilton, D.; Sanders, St.

    2006-01-01

    A large-scale fusion device presents many challenges to the remote handling operations team. This paper is based on unique operational experience at JET and gives a perspective on remote handling task development, logistics and resource management, as well as command, control and human-machine interface systems. Remote operations require an accurate perception of a dynamic environment, ideally providing the operators with the same unrestricted knowledge of the task scene as would be available if they were actually at the remote work location. Traditional camera based systems suffer from a limited number of viewpoints and also degrade quickly when exposed to high radiation. Virtual Reality and Augmented Reality software offer great assistance. The remote handling system required to maintain a tokamak requires a large number of different and complex pieces of equipment coordinating to perform a large array of tasks. The demands on the operator's skill in performing the tasks can escalate to a point where the efficiency and safety of operations are compromised. An operations guidance system designed to facilitate the planning, development, validation and execution of remote handling procedures is essential. Automatic planning of motion trajectories of remote handling equipment and the remote transfer of heavy loads will be routine and need to be reliable. This paper discusses the solutions developed at JET in these areas and also the trends in management and presentation of operational data as well as command, control and HMI technology development offering the potential to greatly assist remote handling in future fusion machines. (author)

  3. VIRTUAL MODELING OF A NUMERICAL CONTROL MACHINE TOOL USED FOR COMPLEX MACHINING OPERATIONS

    Directory of Open Access Journals (Sweden)

    POPESCU Adrian

    2015-11-01

    Full Text Available This paper presents the 3D virtual model of the numerical control machine Modustar 100, in terms of machine elements. This is a CNC machine of modular construction, all components allowing the assembly in various configurations. The paper focused on the design of the subassemblies specific to the axes numerically controlled by means of CATIA v5, which contained different drive kinematic chains of different translation modules that ensures translation on X, Y and Z axis. Machine tool development for high speed and highly precise cutting demands employment of advanced simulation techniques witch it reflect on cost of total development of the machine.

  4. Rule based systems for big data a machine learning approach

    CERN Document Server

    Liu, Han; Cocea, Mihaela

    2016-01-01

    The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

  5. CrN-based wear resistant hard coatings for machining and forming tools

    Energy Technology Data Exchange (ETDEWEB)

    Yang, S; Cooke, K E; Teer, D G [Teer Coatings Ltd, West Stone House, Berry Hill Industrial Estate, Droitwich, Worcestershire WR9 9AS (United Kingdom); Li, X [School of Metallurgy and Materials, University of Birmingham, Birmingham B15 2TT (United Kingdom); McIntosh, F [Rolls-Royce plc, Inchinnan, Renfrewshire PA4 9AF, Scotland (United Kingdom)

    2009-05-21

    Highly wear resistant multicomponent or multilayer hard coatings, based on CrN but incorporating other metals, have been developed using closed field unbalanced magnetron sputter ion plating technology. They are exploited in coated machining and forming tools cutting and forming of a wide range of materials in various application environments. These coatings are characterized by desirable properties including good adhesion, high hardness, high toughness, high wear resistance, high thermal stability and high machining capability for steel. The coatings appear to show almost universal working characteristics under operating conditions of low and high temperature, low and high machining speed, machining of ordinary materials and difficult to machine materials, and machining under lubricated and under minimum lubricant quantity or even dry conditions. These coatings can be used for cutting and for forming tools, for conventional (macro-) machining tools as well as for micromachining tools, either as a single coating or in combination with an advanced, self-lubricating topcoat.

  6. A Function-Behavior-State Approach to Designing Human Machine Interface for Nuclear Power Plant Operators

    Science.gov (United States)

    Lin, Y.; Zhang, W. J.

    2005-02-01

    This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.

  7. Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

    OpenAIRE

    Ming Cheng; Le Sun; Giuseppe Buja; Lihua Song

    2015-01-01

    The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator perm...

  8. Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

    Directory of Open Access Journals (Sweden)

    Ming Cheng

    2015-09-01

    Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

  9. Reduction of work-related musculoskeletal risk factors following ergonomics education of sewing machine operators.

    Science.gov (United States)

    Bulduk, Sıdıka; Bulduk, Emre Özgür; Süren, Tufan

    2017-09-01

    Work-related musculoskeletal disorders (WMSDs) are a major hazard for sewing machine operators. Ergonomics education is recommended for reducing musculoskeletal disorders at workstations. This study aimed to evaluate the effect of an ergonomics education in reducing the exposure to risk factors for WMSDs among sewing machine operators. In this study of 278 workers, their exposure to the risk of WMSDs was assessed using the quick exposure check scale prior to them attending an ergonomics education programme and then again 3 months after the programme. The scores for risk exposure before the education programme were moderate for back (static) and back (dynamic), high for shoulder/arm and very high for wrist/hand and neck. The results obtained 3 months later were low for back (static) and shoulder/arm, and moderate for back (dynamic), wrist/hand and neck. Based on our results, ergonomics education can reduce the exposure to risk factors for WMSDs in the workplace.

  10. Cognitive task analysis of nuclear power plant operators for man-machine interface design

    International Nuclear Information System (INIS)

    Itoh, J.I.; Yoshimura, S.; Ohtsuka, T.

    1990-01-01

    This paper aims to ascertain and further develop design guidelines for a man-machine interface compatible with plant operators' problem solving strategies. As the framework for this study, operator's information processing activities were modeled, based on J. Rasmussen's framework for cognitive task analysis. Two experiments were carried out. One was an experiment aimed at gaining an understanding of internal mechanisms involved in mistakes and slips which occurred in operators' responses to incidents and accidents. As a result of fifteen cases of operator performance analysis, sixty one human errors were identified. Further analysis of the errors showed that frequently occurring error mechanisms were absent-mindedness, lack of recognition of patterns in diagnosis and failed procedure formulation due to memory lapses. The other kind of experiment was carried out to identify the envelope of trajectories for the operator's search in the problem space consisting of the two dimensions of means-ends and whole-part relations while dealing with transients. Two cases of experimental sessions were conducted with the thinking-aloud method. From analyses based on verbal protocols, trajectories of operator's search were derived, covering from the whole plant level through the component level in the whole-part dimension and covering from the functional purpose level through the physical form level in the means-ends dimension. The findings obtained from these analyses serve as a basis for developing design guidelines for man-machine interfaces in control rooms of nuclear power plants

  11. Prototype-based models in machine learning.

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

  12. Electrical gearbox equivalent by means of dynamic machine operation

    NARCIS (Netherlands)

    Gerrits, T.; Wijnands, C.G.E.; Paulides, J.J.H.; Duarte, J.L.

    2011-01-01

    A dynamic propulsion system using variable torque/speed characteristics, designed to realize a machine integrated equivalent of a gearbox is presented. This is achieved through adaption of the machine characteristics, and driven by specialized power electronics, leading to a reconfigurable stator

  13. An expert machine tools selection system for turning operation

    NARCIS (Netherlands)

    Tan, C.F.; Khalil, S.N.; Karjanto, J.; Wahidin, L.S.; Chen, W.; Rauterberg, G.W.M.

    2015-01-01

    The turning machining process is an important process in the manufacturing industry. It is important to select the right tool for the turning process so that the manufacturing cost will be decreased. The main objective of this research is to select the most suitable machine tools with respect to

  14. Distance based control system for machine vision-based selective spraying

    NARCIS (Netherlands)

    Steward, B.L.; Tian, L.F.; Tang, L.

    2002-01-01

    For effective operation of a selective sprayer with real-time local weed sensing, herbicides must be delivered, accurately to weed targets in the field. With a machine vision-based selective spraying system, acquiring sequential images and switching nozzles on and off at the correct locations are

  15. Correlation of cutting fluid performance in different machining operations

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo; Belluco, Walter

    2001-01-01

    An analysis of cutting fluid performance in different metal cutting operations is presented, based on experimental investigations in which type of operation, performance criteria, work material, and fluid type are considered. Cutting fluid performance was evaluated in turning, drilling, reaming...... investigated. Results show that correlation of cutting fluid performance in different operations exists, within the same group of cutting fluids, in the case of stainless steel as workpiece material. Under the tested conditions, the average correlation coefficients between efficiency parameters with different...... operations on austenitic stainless steel lied in the range 0.87-0.97 for waterbased fluids and 0.79-0.89 for straight oils. A similar correlation could not be found for the other workpiece materials investigated in this work. A rationalisation of cutting fluid performance tests is suggested....

  16. 76 FR 174 - International Business Machines (IBM), Global Sales Operations Organization, Sales and...

    Science.gov (United States)

    2011-01-03

    ... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-74,575; TA-W-74,575D] International Business Machines (IBM), Global Sales Operations Organization, Sales and Distribution Business Manager Roles; One Teleworker Located in Charleston, WV; International Business Machines (IBM), Global Sales Operations Organization, Sales and...

  17. Musculoskeletal disorders of the neck and shoulders in female sewing machine operators: prevalence, incidence, and prognosis

    DEFF Research Database (Denmark)

    Kaergaard, A.; Andersen, JH

    2000-01-01

    .8% among sewing machine operators compared with 9.0% and 2.2%, respectively, among controls. The presence of the disorders was strongly associated with a self perception of poor general health. Although myofascial pain syndrome showed a U shaped association with years as a sewing machine operator, rotator...

  18. Making and Operating Molecular Machines: A Multidisciplinary Challenge.

    Science.gov (United States)

    Baroncini, Massimo; Casimiro, Lorenzo; de Vet, Christiaan; Groppi, Jessica; Silvi, Serena; Credi, Alberto

    2018-02-01

    Movement is one of the central attributes of life, and a key feature in many technological processes. While artificial motion is typically provided by macroscopic engines powered by internal combustion or electrical energy, movement in living organisms is produced by machines and motors of molecular size that typically exploit the energy of chemical fuels at ambient temperature to generate forces and ultimately execute functions. The progress in several areas of chemistry, together with an improved understanding of biomolecular machines, has led to the development of a large variety of wholly synthetic molecular machines. These systems have the potential to bring about radical innovations in several areas of technology and medicine. In this Minireview, we discuss, with the help of a few examples, the multidisciplinary aspects of research on artificial molecular machines and highlight its translational character.

  19. enhancement of the performan machine operating in t nt

    African Journals Online (AJOL)

    eobe

    damper windings, the main and auxiliary wind being located on the ... t power achievable by capacitance injection, the transfer field machine ent induction motor ... e as a function of rotor the saliency in the ... auxiliary supply plant. Figure 1: ...

  20. A Review of Virtual Machine Attack Based on Xen

    Directory of Open Access Journals (Sweden)

    Ren xun-yi

    2016-01-01

    Full Text Available Virtualization technology as the foundation of cloud computing gets more and more attention because the cloud computing has been widely used. Analyzing the threat with the security of virtual machine and summarizing attack about virtual machine based on XEN to predict visible security hidden recently. Base on this paper can provide a reference for the further research on the security of virtual machine.

  1. Development of nuclear power plant operator simulator for man-machine interface evaluation

    International Nuclear Information System (INIS)

    Nakagawa, Takashi; Nakatani, Yoshio; Sasaki, Kazunori; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko; Hasegawa, Akira.

    1997-01-01

    The operational safety in nuclear power plants depends strongly on man machine interfaces (MMI), such as assignment of equipment on control boards and operation procedures in emergency situations. Therefore, the evaluation and analysis methods for the MMI are important. In order for the methods to be practical, the methods should be executed in each step of design and be easy for designers to use. We aim to develop SEAMAID system: a computer supported system for evaluating and analyzing the MMI by simulating the interaction between the operator and the machine. In this paper, we discuss problems of the conventional methods and the required functions of the operator simulator for the SEAMAID. The operator simulator executes not human errors but correct behavior which follows the operational procedure. The SEAMAID evaluates the MMI by finding potential human errors which could occur in the simulated interactions and points out the problematic interaction parts which could induce human errors. We construct the operator simulator by combining the human model which was proposed by Prof. Reason, and the knowledge base model based on the Petri net model. This simulator can treat frequency parameter which represents the degree of frequency of using a certain knowledge. We conduct two sample simulations in different frequency parameters in the same scenario. These simulation results show that even if the operator behaves correctly following the procedure, there are alternative task sequences. We verify that the simulated interactions are in good agreement with the actual one. Also, we propose the method to point out the problematic parts in the interactions based on the working memory consumption. (author)

  2. Development of nuclear power plant operator simulator for man-machine interface evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Nakagawa, Takashi; Nakatani, Yoshio; Sasaki, Kazunori [Mitsubishi Electric Corp., Amagasaki, Hyogo (Japan). Industrial Electronics and Systems Development Lab.; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko; Hasegawa, Akira

    1997-08-01

    The operational safety in nuclear power plants depends strongly on man machine interfaces (MMI), such as assignment of equipment on control boards and operation procedures in emergency situations. Therefore, the evaluation and analysis methods for the MMI are important. In order for the methods to be practical, the methods should be executed in each step of design and be easy for designers to use. We aim to develop SEAMAID system: a computer supported system for evaluating and analyzing the MMI by simulating the interaction between the operator and the machine. In this paper, we discuss problems of the conventional methods and the required functions of the operator simulator for the SEAMAID. The operator simulator executes not human errors but correct behavior which follows the operational procedure. The SEAMAID evaluates the MMI by finding potential human errors which could occur in the simulated interactions and points out the problematic interaction parts which could induce human errors. We construct the operator simulator by combining the human model which was proposed by Prof. Reason, and the knowledge base model based on the Petri net model. This simulator can treat frequency parameter which represents the degree of frequency of using a certain knowledge. We conduct two sample simulations in different frequency parameters in the same scenario. These simulation results show that even if the operator behaves correctly following the procedure, there are alternative task sequences. We verify that the simulated interactions are in good agreement with the actual one. Also, we propose the method to point out the problematic parts in the interactions based on the working memory consumption. (author)

  3. A Performance Survey on Stack-based and Register-based Virtual Machines

    OpenAIRE

    Fang, Ruijie; Liu, Siqi

    2016-01-01

    Virtual machines have been widely adapted for high-level programming language implementations and for providing a degree of platform neutrality. As the overall use and adaptation of virtual machines grow, the overall performance of virtual machines has become a widely-discussed topic. In this paper, we present a survey on the performance differences of the two most widely adapted types of virtual machines - the stack-based virtual machine and the register-based virtual machine - using various...

  4. Research on machine learning framework based on random forest algorithm

    Science.gov (United States)

    Ren, Qiong; Cheng, Hui; Han, Hai

    2017-03-01

    With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

  5. THE INFLUENCE SEWING MACHINE ON FOOTWEAR MOCCASIN ECONOMIC INDICATORS AI ASSEMBLY OPERATIONS

    Directory of Open Access Journals (Sweden)

    MALCOCI Marina

    2014-05-01

    Full Text Available With the evolution of time changes, grows and improves, construction, form, rationality and argumentation usefulness of various types of footwear, namely machines. In the present paper analyzes a number of sewing machines, bound for achieving moccasin shoes. With sewing machines can perform a wide variety of stitches, they create an aesthetically pleasing, but all at once enable product diversification with minimum expenses. Sewing machines moccasins are distinguished by technological parameters, number of stitches, design and affordability. Sewing operation carried out in these machines is carried out within 72 seconds to manual operation - 22 minutes. A flow diagram mechanical requires a reduced number of workers (e.g., 3 workers, to a manual flow diagram - 38 workers. Labour productivity in the use of sewing machines increase by 10 times, and operation cost decreases from 3,7 to 5,7 lei. Regardless of the sewing moccasins construction company helps to increase productivity, quality completion of the operation, ie products, reducing the time required to manufacture the products, shortening manufacturing cycle. Among the cars analyzed, the most recommended sewing machine as OS 7700 P Global company because it represents the best technical features. Sewing machines for manufacturing footwear moccasin were implemented in Moldova in 2010, at the "Cristina Mold Rom Simpex" in Chisinau. Because, company management understood beneficial role of sewing moccasins on quality operation, but also on other economic indicators. Currently the majority of footwear enterprises in Moldova sewing moccasins are done manually. One problem is the high price of sewing machines moccasins.

  6. Modeling human-machine interactions for operations room layouts

    Science.gov (United States)

    Hendy, Keith C.; Edwards, Jack L.; Beevis, David

    2000-11-01

    The LOCATE layout analysis tool was used to analyze three preliminary configurations for the Integrated Command Environment (ICE) of a future USN platform. LOCATE develops a cost function reflecting the quality of all human-human and human-machine communications within a workspace. This proof- of-concept study showed little difference between the efficacy of the preliminary designs selected for comparison. This was thought to be due to the limitations of the study, which included the assumption of similar size for each layout and a lack of accurate measurement data for various objects in the designs, due largely to their notional nature. Based on these results, the USN offered an opportunity to conduct a LOCATE analysis using more appropriate assumptions. A standard crew was assumed, and subject matter experts agreed on the communications patterns for the analysis. Eight layouts were evaluated with the concepts of coordination and command factored into the analysis. Clear differences between the layouts emerged. The most promising design was refined further by the USN, and a working mock-up built for human-in-the-loop evaluation. LOCATE was applied to this configuration for comparison with the earlier analyses.

  7. Earth-moving equipment as base machines in forest work. Final report of an NSR project

    Energy Technology Data Exchange (ETDEWEB)

    Johansson, Jerry [ed.

    1997-12-31

    Excavators have been used for forest draining for a long time in the Nordic countries. Only during the 1980s they were introduced as base machines for other forest operations, such as mounding, processing, harvesting, and road construction and road maintenance. Backhoe loaders were introduced in forestry at a somewhat later stage and to a smaller degree. The number of this type of base machines in forestry is so far small and is increasing very slowly. The NSR project `Earth moving equipment as base machines in forest work` started in 1993 and the project ended in 1995. The objective of the project was to obtain an overall picture of this type of machines up to a point where the logs are at landing site, ready for transportation to the industry. The project should cover as many aspects as possible. In order to obtain this picture, the main project was divided into sub projects. The sub projects separately described in this volume are (1) Excavators in ditching operations and site preparation, (2) Backhoe loaders in harvesting operations, (3) Excavators in wood cutting operations, (4) Tracked excavators in forestry operations, (5) Crawler versus wheeled base machines for single-grip harvester, and (6) Soil changes - A comparison between a wheeled and a tracked forest machine

  8. A nanoplasmonic switch based on molecular machines

    KAUST Repository

    Zheng, Yue Bing; Yang, Ying-Wei; Jensen, Lasse; Fang, Lei; Juluri, Bala Krishna; Weiss, Paul S.; Stoddart, J. Fraser; Huang, Tony Jun

    2009-01-01

    We aim to develop a molecular-machine-driven nanoplasmonic switch for its use in future nanophotonic integrated circuits (ICs) that have applications in optical communication, information processing, biological and chemical sensing. Experimental

  9. Machine learning based analysis of cardiovascular images

    NARCIS (Netherlands)

    Wolterink, JM

    2017-01-01

    Cardiovascular diseases (CVDs), including coronary artery disease (CAD) and congenital heart disease (CHD) are the global leading cause of death. Computed tomography (CT) and magnetic resonance imaging (MRI) allow non-invasive imaging of cardiovascular structures. This thesis presents machine

  10. Density Based Support Vector Machines for Classification

    OpenAIRE

    Zahra Nazari; Dongshik Kang

    2015-01-01

    Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used cl...

  11. Operational reliability of the GPK and the 4PP-2 heading machines

    Energy Technology Data Exchange (ETDEWEB)

    Ivanov, N.A.; Demchenko, N.T.

    1985-09-01

    Reliability of the GPK and 4PP-2 heading machines used in 98 development workings in 40 coal mines is analyzed. Failure analysis was based on records of 199 heading machines after overhauls. The mean-time-between-failures of the GPK and the 4PP-2 was 270 min (93 t) and 155 min (35 t), availability coefficient was 0.83 and 0.77 respectively. Reliability of the GPK on the average was higher than that of the 4PP-2. The mean-time-to-overhaul of the GPK was 17.7 months (54,000 t), of the 4PP-2 - 18 months (59,000 t). Time between overhauls in the case of the GPK was 14 months (47,200 t), in the case of the 4PP-2 it was 14.5 months (38,300 t). During the 17.7 months between overhauls the GPK failed 580 times, the repair operations lasted 530 h. During the 18 month time to overhaul the 4PP-2 failed 1600 times, the repair operations lasted 1300 h. Reliability of major elements of the 2 heading machines is analyzed: cutters, materials handling equipment, electrical equipment, hydraulic systems, dust suppression systems, etc.

  12. Optimal interference code based on machine learning

    Science.gov (United States)

    Qian, Ye; Chen, Qian; Hu, Xiaobo; Cao, Ercong; Qian, Weixian; Gu, Guohua

    2016-10-01

    In this paper, we analyze the characteristics of pseudo-random code, by the case of m sequence. Depending on the description of coding theory, we introduce the jamming methods. We simulate the interference effect or probability model by the means of MATLAB to consolidate. In accordance with the length of decoding time the adversary spends, we find out the optimal formula and optimal coefficients based on machine learning, then we get the new optimal interference code. First, when it comes to the phase of recognition, this study judges the effect of interference by the way of simulating the length of time over the decoding period of laser seeker. Then, we use laser active deception jamming simulate interference process in the tracking phase in the next block. In this study we choose the method of laser active deception jamming. In order to improve the performance of the interference, this paper simulates the model by MATLAB software. We find out the least number of pulse intervals which must be received, then we can make the conclusion that the precise interval number of the laser pointer for m sequence encoding. In order to find the shortest space, we make the choice of the greatest common divisor method. Then, combining with the coding regularity that has been found before, we restore pulse interval of pseudo-random code, which has been already received. Finally, we can control the time period of laser interference, get the optimal interference code, and also increase the probability of interference as well.

  13. The Abstract Machine Model for Transaction-based System Control

    Energy Technology Data Exchange (ETDEWEB)

    Chassin, David P.

    2003-01-31

    Recent work applying statistical mechanics to economic modeling has demonstrated the effectiveness of using thermodynamic theory to address the complexities of large scale economic systems. Transaction-based control systems depend on the conjecture that when control of thermodynamic systems is based on price-mediated strategies (e.g., auctions, markets), the optimal allocation of resources in a market-based control system results in an emergent optimal control of the thermodynamic system. This paper proposes an abstract machine model as the necessary precursor for demonstrating this conjecture and establishes the dynamic laws as the basis for a special theory of emergence applied to the global behavior and control of complex adaptive systems. The abstract machine in a large system amounts to the analog of a particle in thermodynamic theory. The permit the establishment of a theory dynamic control of complex system behavior based on statistical mechanics. Thus we may be better able to engineer a few simple control laws for a very small number of devices types, which when deployed in very large numbers and operated as a system of many interacting markets yields the stable and optimal control of the thermodynamic system.

  14. Empirical Studies On Machine Learning Based Text Classification Algorithms

    OpenAIRE

    Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

    2011-01-01

    Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

  15. BEBP: An Poisoning Method Against Machine Learning Based IDSs

    OpenAIRE

    Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi

    2018-01-01

    In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...

  16. Detection of Watermelon Seeds Exterior Quality based on Machine Vision

    OpenAIRE

    Xiai Chen; Ling Wang; Wenquan Chen; Yanfeng Gao

    2013-01-01

    To investigate the detection of watermelon seeds exterior quality, a machine vision system based on least square support vector machine was developed. Appearance characteristics of watermelon seeds included area, perimeter, roughness, minimum enclosing rectangle and solidity were calculated by image analysis after image preprocess.The broken seeds, normal seeds and high-quality seeds were distinguished by least square support vector machine optimized by genetic algorithm. Compared to the grid...

  17. Operation of micro and molecular machines: a new concept with its origins in interface science.

    Science.gov (United States)

    Ariga, Katsuhiko; Ishihara, Shinsuke; Izawa, Hironori; Xia, Hong; Hill, Jonathan P

    2011-03-21

    A landmark accomplishment of nanotechnology would be successful fabrication of ultrasmall machines that can work like tweezers, motors, or even computing devices. Now we must consider how operation of micro- and molecular machines might be implemented for a wide range of applications. If these machines function only under limited conditions and/or require specialized apparatus then they are useless for practical applications. Therefore, it is important to carefully consider the access of functionality of the molecular or nanoscale systems by conventional stimuli at the macroscopic level. In this perspective, we will outline the position of micro- and molecular machines in current science and technology. Most of these machines are operated by light irradiation, application of electrical or magnetic fields, chemical reactions, and thermal fluctuations, which cannot always be applied in remote machine operation. We also propose strategies for molecular machine operation using the most conventional of stimuli, that of macroscopic mechanical force, achieved through mechanical operation of molecular machines located at an air-water interface. The crucial roles of the characteristics of an interfacial environment, i.e. connection between macroscopic dimension and nanoscopic function, and contact of media with different dielectric natures, are also described.

  18. Effect of operational conditions of electroerosion machining on the surface microgeometry parameters of steels and alloys

    International Nuclear Information System (INIS)

    Foteev, N.K.

    1976-01-01

    Studies the influence of pulse duration and a series of operating conditions of a ShGI-40-440 spark-machining generator on changes in the basic surface microgeometry characteristics of components of stainless steel 1Kh18N10T, steel St 45 and hard alloy T14K8. The microgeometry characteristics of spark-machined surfaces differ significantly from the corresponding characteristics of surfaces machined by cutting and vibro-rolling

  19. Energy-saving operation of a converter-fed synchronous machine

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, I.; Veszpremi, K. [Technical University of Budapest, Department of Electrical Machines and Drives, Budapest (Hungary)

    1997-12-31

    In the Converter-Fed Synchronous Machine (CFSM) there is no need for forced commutation the CL and CF converters operate with line commutation, the CM is commutated by the machine voltages. This drive is applied mainly for large power and high speed controlled drives. Considering the large power, the investigation of energy-saving operation is important. As in the DC and the induction motor drives the energy-saving operation is implemented by flux control. (orig.) 6 refs.

  20. Dynamic Scheduling Of Batch Operations With Non-Identical Machines

    NARCIS (Netherlands)

    van der Zee, D.J.; van Harten, A.; Schuur, P.C.

    1997-01-01

    Batch-wise production is found in many industries. A good example of production systems which process products batch-wise are the ovens found in aircraft industry and in semiconductor manufacturing. These systems mostly consist of multiple machines of different types, given the range and volumes of

  1. Dynamic scheduling of batch operations with non-identical machines

    NARCIS (Netherlands)

    van der Zee, D.J.; van Harten, Aart; Schuur, Peter

    1997-01-01

    Batch-wise production is found in many industries. A good example of production systems which process products batch-wise are the ovens found in aircraft industry and in semiconductor manufacturing. These systems mostly consist of multiple machines of different types, given the range and volumes of

  2. Development and operating performance of the refuelling machine of the Fugen

    International Nuclear Information System (INIS)

    Kaneko, Jun; Kasai, Yoshimitsu; Takeshita, Norito; Ohta, Takeo

    1985-01-01

    In the advanced thermal reactor ''Fugen'' power station, with the refuelling machine the fuel replacement during operation is made through the reactor bottom. Its design was started in 1967 and up to 1975 various tests were conducted. Fugen's refuelling machine has thus been used from the initial fuel loading in 1978 and handled so far about 1300 fuel assemblies in seven times of the refuelling. In the stage of Fugen operation there occurred failure of the grab drive due to crud, etc. At present, with such troubles all eliminated, the refuelling machine is in steady operation with proper maintenance. The results with Fugen's refuelling machine are reflected in the development of the refuelling machine for the demonstration ATR. (Mori, K.)

  3. Machine Learning Based Classifier for Falsehood Detection

    Science.gov (United States)

    Mallikarjun, H. M.; Manimegalai, P., Dr.; Suresh, H. N., Dr.

    2017-08-01

    The investigation of physiological techniques for Falsehood identification tests utilizing the enthusiastic aggravations started as a part of mid 1900s. The need of Falsehood recognition has been a piece of our general public from hundreds of years back. Different requirements drifted over the general public raising the need to create trick evidence philosophies for Falsehood identification. The established similar addressing tests have been having a tendency to gather uncertain results against which new hearty strategies are being explored upon for acquiring more productive Falsehood discovery set up. Electroencephalography (EEG) is a non-obtrusive strategy to quantify the action of mind through the anodes appended to the scalp of a subject. Electroencephalogram is a record of the electric signs produced by the synchronous activity of mind cells over a timeframe. The fundamental goal is to accumulate and distinguish the important information through this action which can be acclimatized for giving surmising to Falsehood discovery in future analysis. This work proposes a strategy for Falsehood discovery utilizing EEG database recorded on irregular people of various age gatherings and social organizations. The factual investigation is directed utilizing MATLAB v-14. It is a superior dialect for specialized registering which spares a considerable measure of time with streamlined investigation systems. In this work center is made on Falsehood Classification by Support Vector Machine (SVM). 72 Samples are set up by making inquiries from standard poll with a Wright and wrong replies in a diverse era from the individual in wearable head unit. 52 samples are trained and 20 are tested. By utilizing Bluetooth based Neurosky’s Mindwave kit, brain waves are recorded and qualities are arranged appropriately. In this work confusion matrix is derived by matlab programs and accuracy of 56.25 % is achieved.

  4. Modification and Performance Evaluation of a Low Cost Electro-Mechanically Operated Creep Testing Machine

    Directory of Open Access Journals (Sweden)

    John J. MOMOH

    2010-12-01

    Full Text Available Existing mechanically operated tensile and creep testing machine was modified to a low cost, electro-mechanically operated creep testing machine capable of determining the creep properties of aluminum, lead and thermoplastic materials as a function of applied stress, time and temperature. The modification of the testing machine was necessitated by having an electro-mechanically operated creep testing machine as a demonstration model ideal for use and laboratory demonstrations, which will provide an economical means of performing standard creep experiments. The experimental result is a more comprehensive understanding of the laboratory experience, as the technology behind the creep testing machine, the test methodology and the response of materials loaded during experiment are explored. The machine provides a low cost solution for Mechanics of Materials laboratories interested in creep testing experiment and demonstration but not capable of funding the acquisition of commercially available creep testing machines. Creep curves of strain versus time on a thermoplastic material were plotted at a stress level of 1.95MPa, 3.25MPa and 4.55MPa and temperature of 20oC, 40oC and 60oC respectively. The machine is satisfactory since it is always ready for operation at any given time.

  5. Operating a pneumatic-mechanical flotation machine for coal-slurry flotation

    Energy Technology Data Exchange (ETDEWEB)

    Shmaenok, N M; Bedran, N G; Konstantinov, V K; Kochetkov, Yu I; Sysoev, V S

    1976-01-01

    The FPM-GMO-1.6 pneumatic-mechanical flotation machine is easy to operate and regulate and maintains a high throughput at a low energy consumption. The flotation process is stable, the quality of the concentrate and tailings satisfactory, and the selectivity of separation high. The machine cannot handle coal slurries at higher throughputs because the rate of froth removal is too low across the entire flotation front. Experience on the pneumatic-mechanical flotation machine at the ''Kolosnikovskaya'' Central Washery indicates that a similar machine should be developed for a throughput of 1000 m/sup 3//hr.

  6. Methods, systems and apparatus for controlling third harmonic voltage when operating a multi-space machine in an overmodulation region

    Science.gov (United States)

    Perisic, Milun; Kinoshita, Michael H; Ranson, Ray M; Gallegos-Lopez, Gabriel

    2014-06-03

    Methods, system and apparatus are provided for controlling third harmonic voltages when operating a multi-phase machine in an overmodulation region. The multi-phase machine can be, for example, a five-phase machine in a vector controlled motor drive system that includes a five-phase PWM controlled inverter module that drives the five-phase machine. Techniques for overmodulating a reference voltage vector are provided. For example, when the reference voltage vector is determined to be within the overmodulation region, an angle of the reference voltage vector can be modified to generate a reference voltage overmodulation control angle, and a magnitude of the reference voltage vector can be modified, based on the reference voltage overmodulation control angle, to generate a modified magnitude of the reference voltage vector. By modifying the reference voltage vector, voltage command signals that control a five-phase inverter module can be optimized to increase output voltages generated by the five-phase inverter module.

  7. Rule-based machine translation for Aymara

    NARCIS (Netherlands)

    Coler, Matthew; Homola, Petr; Jones, Mari

    2014-01-01

    This paper presents the ongoing result of an approach developed by the collaboration of a computational linguist with a field linguist that addresses one of the oft-overlooked keys to language maintenance: the development of modern language-learning tools. Although machine translation isn’t commonly

  8. AC Loss Analysis of MgB2-Based Fully Superconducting Machines

    Science.gov (United States)

    Feddersen, M.; Haran, K. S.; Berg, F.

    2017-12-01

    Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

  9. Development and evaluation of intelligent machine tools based on knowledge evolution in M2M environment

    International Nuclear Information System (INIS)

    Kim, Dong Hoon; Song, Jun Yeob; Lee, Jong Hyun; Cha, Suk Keun

    2009-01-01

    In the near future, the foreseen improvement in machine tools will be in the form of a knowledge evolution-based intelligent device. The goal of this study is to develop intelligent machine tools having knowledge-evolution capability in Machine to Machine (M2M) wired and wireless environment. The knowledge evolution-based intelligent machine tools are expected to be capable of gathering knowledge autonomously, producing knowledge, understanding knowledge, applying reasoning to knowledge, making new decisions, dialoguing with other machines, etc. The concept of the knowledge-evolution intelligent machine originated from the process of machine control operation by the sense, dialogue and decision of a human expert. The structure of knowledge evolution in M2M and the scheme for a dialogue agent among agent-based modules such as a sensory agent, a dialogue agent and an expert system (decision support agent) are presented in this paper, and work-offset compensation from thermal change and recommendation of cutting condition are performed on-line for knowledge-evolution verification

  10. Experimental apparatus and its operational characteristics for MHD rotating machine with superconducting rotor

    International Nuclear Information System (INIS)

    Katsurai, Makoto; Karasaki, Takashi; Sekiguchi, Tadashi; Matsuda, Shoji; Ichikawa, Hayao.

    1976-01-01

    This paper presents the construction and operational characteristics of the experimental apparatus of MHD rotating machine with superconducting rotor, which has the electromechanical energy conversion function based on the inductive interactions between travelling magnetic field produced by the rotor and MHD working fluid. The machine consists of a rotating-dewar type superconducting rotor and a coaxially rotating metal cylinder which simulates the liquid metal MHD working fluid, and the both of them are driven separately by speed-controlled driving motors. The superconducting magnets installed in the rotor has the 8 shaped winding whose outer diameter is 11 cm and hight is 11 cm, and with the excitation current of 200 A (rating), it produces screw type magnetic field in the inductive interaction region of the cylinder with the peak value of 0.2 Wb/m 2 , whereas the average field strength reaches almost 4 Wb/m 2 inside the winding. In this condition, mutual interaction force is 30 N in the peripheral direction and 8 N in the axial direction and the total driving power of motors is 1,300 W when the relative rotation speed of the rotor and the cylinder is 800 rpm. Observed characteristics of this machine are for the most part in agreement with those estimated by the theoretical analysis. (auth.)

  11. 49 CFR 214.355 - Training and qualification in on-track safety for operators of roadway maintenance machines.

    Science.gov (United States)

    2010-10-01

    ... operators of roadway maintenance machines. 214.355 Section 214.355 Transportation Other Regulations Relating... operators of roadway maintenance machines. (a) The training and qualification of roadway workers who operate roadway maintenance machines shall include, as a minimum: (1) Procedures to prevent a person from being...

  12. Methods and means for the in-house training of mining machine operators

    Directory of Open Access Journals (Sweden)

    Velikanov Vladimir

    2017-01-01

    Full Text Available This study investigates the quality issue of the in-house training process for mining machine operators. The authors prove the urgency of the designated problem. The changes in modern society, as well as the development of science and technology have a direct impact on the vocational education system. This paper describes the main aspects of the in-house training process of mining machine operators; define the essence, structure, contents, and main directions of its revitalization. The following solutions are proposed in order to improve the quality of the in-house training process: to use the original method based on a rating system of the operator knowledge evaluation, active and interactive forms of using modern training technologies. The authors conducted testing techniques in mining enterprises with the aim of confirming the adequacy of the suggested approaches. The results are given in the work. It was proposed that the methods and tools integration has a positive impact on professional training system.

  13. Are there intelligent Turing machines?

    OpenAIRE

    Bátfai, Norbert

    2015-01-01

    This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...

  14. Time cycle calculation procedure for the special crew during the mining mobile machine complex operation

    International Nuclear Information System (INIS)

    Shmurygin, V; Lukyanov, V; Maslovsky, A

    2015-01-01

    The relevance of the research is specified by the necessity to optimize the delft mobile tunneling equipment operation. Target of the research is tunneling time cycle justification for the special crew during the mining mobile machine complex operation. Methods of the research included the consideration of operation organization schemes in the drifting face and effective use of the mobile equipment during mine exploratory working operations. Time cycle calculation procedures for major processes have been considered. This has been done for the special crew during the mobile machine complex operations for several working faces and various organization schemes

  15. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi; Ramahaleomiarantsoa, Jacques F.; Nounou, Mohamed N.; Nounou, Hazem N.

    2016-01-01

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM

  16. Hazard perception and occupational injuries in the welders and lathe machine operators of Rawalpindi and Islamabad.

    Science.gov (United States)

    Shaikh, M A

    2001-02-01

    To study the prevalence of occupational injuries in the welders and lathe machine operators and their hazard perception. This study was conducted in the welders and lathe machine operators working in the welding and metal working shops in Rawalpindi and Islamabad. A cross-sectional survey was conducted by two trained health interviewers using uniform questionnaire with both close and open-ended questions. Two hundred and eight welders and 104 lathe machine operators were interviewed. Thirty nine (18.7%) welders and 27 (26%) lathe machine operators reported an injury in the past three months, while 63 (30.3%) welders and 76 (73.8%) lathe machine operators reported sustaining an injury in the past twelve months. However, only half of the welders and 31 (29.8%) lathe machine operators believed that their occupation was hazardous for health. For effective public health policy there is a need preventive education and enforcement of safety regulations for the informal occupational sector in Pakistan.

  17. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

  18. Machine learning based switching model for electricity load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

    2008-06-15

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

  19. Machine learning based switching model for electricity load forecasting

    International Nuclear Information System (INIS)

    Fan Shu; Chen Luonan; Lee, Weijen

    2008-01-01

    In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

  20. State Machine Framework And Its Use For Driving LHC Operational states

    CERN Document Server

    Misiowiec, M; Solfaroli Camilloci, M

    2011-01-01

    The LHC follows a complex operational cycle with 12 major phases that include equipment tests, preparation, beam injection, ramping and squeezing, finally followed by the physics phase. This cycle is modelled and enforced with a state machine, whereby each operational phase is represented by a state. On each transition, before entering the next state, a series of conditions is verified to make sure the LHC is ready to move on. The State Machine framework was developed to cater for building independent or embedded state machines. They safely drive between the states executing tasks bound to transitions and broadcast related information to interested parties. The framework encourages users to program their own actions. Simple configuration management allows the operators to define and maintain complex models themselves. An emphasis was also put on easy interaction with the remote state machine instances through standard communication protocols. On top of its core functionality, the framework offers a transparen...

  1. Experimental investigation of the tip based micro/nano machining

    Science.gov (United States)

    Guo, Z.; Tian, Y.; Liu, X.; Wang, F.; Zhou, C.; Zhang, D.

    2017-12-01

    Based on the self-developed three dimensional micro/nano machining system, the effects of machining parameters and sample material on micro/nano machining are investigated. The micro/nano machining system is mainly composed of the probe system and micro/nano positioning stage. The former is applied to control the normal load and the latter is utilized to realize high precision motion in the xy plane. A sample examination method is firstly introduced to estimate whether the sample is placed horizontally. The machining parameters include scratching direction, speed, cycles, normal load and feed. According to the experimental results, the scratching depth is significantly affected by the normal load in all four defined scratching directions but is rarely influenced by the scratching speed. The increase of scratching cycle number can increase the scratching depth as well as smooth the groove wall. In addition, the scratching tests of silicon and copper attest that the harder material is easier to be removed. In the scratching with different feed amount, the machining results indicate that the machined depth increases as the feed reduces. Further, a cubic polynomial is used to fit the experimental results to predict the scratching depth. With the selected machining parameters of scratching direction d3/d4, scratching speed 5 μm/s and feed 0.06 μm, some more micro structures including stair, sinusoidal groove, Chinese character '田', 'TJU' and Chinese panda have been fabricated on the silicon substrate.

  2. Operator-machine interface at a large laser-fusion facility

    International Nuclear Information System (INIS)

    Sutton, J.G.; Howell, J.A.

    1982-01-01

    The operator-machine interface at the Antares Laser Facility provides the operator with a means of controlling the laser system and obtaining operational and performance information. The goal of this interface is to provide an operator with access to the control system in a comfortable way, and to facilitate meeting operational requirements. We describe the philosophy and requirements behind this interface, the hardware used in building it, and the software environment

  3. WORK PRECARIOUSNESS: ERGONOMIC RISKS TO OPERATORS OF MACHINES ADAPTED FOR FOREST HARVESTING

    Directory of Open Access Journals (Sweden)

    Stanley Schettino

    Full Text Available ABSTRACT This study aimed to assess different types of machines adapted for mechanized forest harvesting activities in order to quantify the degree of compliance with ergonomic principles applicable to forest machines, as well as the ergonomic risks to which workers are exposed. The following machines were evaluated: a feller buncher adapted into a wheel loader; a mini skidder coupled to an agricultural tractor; and a forest loader adapted to an agricultural tractor; operating in the states of Paraná and Minas Gerais. Biomechanical working conditions were assessed by applying a checklist for simplified assessment of the workplace biomechanical conditions. The forced postures assessment was performed using the REBA - "Rapid Entire Body Assessment" method. In turn, ergonomic classification was through guidelines contained in the ergonomic classification manual "Ergonomic Guidelines for Forest Machines". Moreover, the environmental factors noise, temperature and vibration to which the operators of these machines were exposed were assessed. The results showed all assessed machines had ergonomic standards below those indicated in all assessed aspects, mainly related to access and dimensions of the workplace, need to adopt forced postures during working hours, and exposure to environmental factors assessed above tolerance limits. It is concluded that machines adapted for use in forest harvesting processes have shown significant gaps in relation to ergonomic aspects, presenting high and imminent risk of development of occupational diseases in their operators.

  4. Design of an ARM-based Automatic Rice-Selling Machine for Cafeterias

    Directory of Open Access Journals (Sweden)

    Zhiliang Kang

    2016-02-01

    Full Text Available To address the problems of low selling efficiency, poor sanitation conditions, labor-intensive requirement, and quick rice cooling speed in manual rice selling in cafeterias, especially in colleges and secondary schools, this paper presented an Advanced RISC Machines (ARM microprocessor-based rice-selling machine for cafeterias. The machines consisted of a funnel-shaped rice bin, a thermal insulation box, and a conveying and scattering mechanism. Moreover, this machine exerts fuzzy control over stepper motor rpm, and the motor drives the conveyor belt with a scraper to scatter rice, deliver it, and keep it warm. Apart from an external 4*4 keyboard, a point of sale (POS machine, an ARM process and a pressure sensor, the machine is also equipped with card swiping and weighting mechanisms to achieve functions of card swiping payment and precise measurement, respectively. In addition, detection of the right amount of rice and the alarm function are achieved using an ultrasonic sensor and a beeper, respectively. The presence of the rice container on the rice outlet is detected by an optoelectronic switch. Results show that this rice-selling machine achieves precise measurement, quick card swiping, fast rice selling, stable operation, and good rice heat preservation. Therefore, the mechanical design enables the machine to achieve its goals.

  5. A reliability-based preventive maintenance methodology for the projection spot welding machine

    Directory of Open Access Journals (Sweden)

    Fayzimatov Ulugbek

    2018-06-01

    Full Text Available An effective operations of a projection spot welding (PSW machine is closely related to the effec-tiveness of the maintenance. Timely maintenance can prevent failures and improve reliability and maintainability of the machine. Therefore, establishing the maintenance frequency for the welding machine is one of the most important tasks for plant engineers. In this regard, reliability analysis of the welding machine can be used to establish preventive maintenance intervals (PMI and to identify the critical parts of the system. In this reliability and maintainability study, analysis of the PSW machine was carried out. The failure and repair data for analysis were obtained from automobile manufacturing company located in Uzbekistan. The machine was divided into three main sub-systems: electrical, pneumatic and hydraulic. Different distributions functions for all sub-systems was tested and their parameters tabulated. Based on estimated parameters of the analyzed distribu-tions, PMI for the PSW machines sub-systems at different reliability levels was calculated. Finally, preventive measures for enhancing the reliability of the PSW machine sub-systems are suggested.

  6. Applying Multi-Class Support Vector Machines for performance assessment of shipping operations: The case of tanker vessels

    DEFF Research Database (Denmark)

    Pagoropoulos, Aris; Møller, Anders H.; McAloone, Tim C.

    2017-01-01

    of feature selection algorithms. Afterwards, a model based on Multi- Class Support Vector Machines (SVM) was constructed and the efficacy of the approach is shown through the application of a test set. The results demonstrate the importance and benefits of machine learning algorithms in driving energy....... Identifying the potential of behavioural savings can be challenging, due to the inherent difficulty in analysing the data and operationalizing energy efficiency within the dynamic operating environment of the vessels. This article proposes a supervised learning model for identifying the presence of energy...

  7. The importance of layout and configuration data for flexibility during commissionning and operation of the LHC machine protection systems

    CERN Document Server

    Mariethoz, Julien; Le Roux, Pascal; Bernard, Frederic; Harrison, Robert; Zerlauth, Markus

    2006-01-01

    Due to the large stored energies in both magnets and particle beams, the Large Hadron Collider (LHC) requires a large inventory of machine protection systems, as e.g. powering interlock systems, based on a series of distributed industrial controllers for the protection of the more than 10'000 normal and superconducting magnets. Such systems are required to be at the same time fast, reliable and secure but also flexible and configurable to allow for automated commissioning, remote monitoring and optimization during later operation. Based on the generic hardware architecture of the LHC machine protection systems presented at EPAC 2002 [2] and ICALEPS 2003, the use of configuration data for protection systems in view of the required reliability and safety is discussed. To achieve the very high level of reliability, it is required to use a coherent description of the layout of the accelerator components and of the associated machine protection architecture and their logical interconnections. Mechanisms to guarant...

  8. Man machine interaction for operator information systems : a general purpose display package on PC/AT

    International Nuclear Information System (INIS)

    Chandra, A.K.; Dubey, B.P.; Deshpande, S.V.; Vaidya, U.W.; Khandekar, A.B.

    1991-01-01

    Several operator information systems for nuclear plants have been developed at Reactor Control Division of BARC and these have involved extensive operator interaction to extract the maximum information from the systems. Each of these systems used a different scheme for operator interaction. A composite package has now been developed on PC/AT with EGA/VGA for use with any system to obviate the necessity to develop new software for each project. This permits information to be displayed in various formats viz. trend and history curves, tabular data, bar graphs and core matrix (both for 235 and 500 MWe cores). It also allows data to be printed and plotted using multi colour plotter. This package thus integrates all the features of the earlier systems. It also integrates the operator interaction scheme. It uses window based pull down menus to select parameters to be fed into a particular display format. Within any display format the operator has significant flexibility to modify the selected parameters using context dependent soft keys. The package also allows data to be retrieved in machine readable form. This report describes the various user friendly functions implemented and also the design of the system software. (author). 1 tab., 10 fig., 3 refs

  9. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

  10. A support vector machine (SVM) based voltage stability classifier

    Energy Technology Data Exchange (ETDEWEB)

    Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

    2007-07-01

    Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

  11. Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

    OpenAIRE

    Sefik Ilkin Serengil; Alper Ozpinar

    2017-01-01

    Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. In...

  12. IoT Security Techniques Based on Machine Learning

    OpenAIRE

    Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

    2018-01-01

    Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

  13. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  14. Machine-vision based optofluidic cell sorting

    DEFF Research Database (Denmark)

    Glückstad, Jesper; Bañas, Andrew

    the available light and creating 2D or 3D beam distributions aimed at the positions of the detected cells. Furthermore, the beam shaping freedom provided by GPC can allow optimizations in the beam’s propagation and its interaction with the laser catapulted and sorted cells....... machine vision1. This approach is gentler, less invasive and more economical compared to conventional FACS-systems. As cells are less responsive to plastic or glass objects commonly used in the optical manipulation literature2, and since laser safety would be an issue in clinical use, we develop efficient...... approaches in utilizing lasers and light modulation devices. The Generalized Phase Contrast (GPC) method3-9 that can be used for efficiently illuminating spatial light modulators10 or creating well-defined contiguous optical traps11 is supplemented by diffractive techniques capable of integrating...

  15. Creation of operation algorithms for combined operation of anti-lock braking system (ABS) and electric machine included in the combined power plant

    Science.gov (United States)

    Bakhmutov, S. V.; Ivanov, V. G.; Karpukhin, K. E.; Umnitsyn, A. A.

    2018-02-01

    The paper considers the Anti-lock Braking System (ABS) operation algorithm, which enables the implementation of hybrid braking, i.e. the braking process combining friction brake mechanisms and e-machine (electric machine), which operates in the energy recovery mode. The provided materials focus only on the rectilinear motion of the vehicle. That the ABS task consists in the maintenance of the target wheel slip ratio, which depends on the tyre-road adhesion coefficient. The tyre-road adhesion coefficient was defined based on the vehicle deceleration. In the course of calculated studies, the following operation algorithm of hybrid braking was determined. At adhesion coefficient ≤0.1, driving axle braking occurs only due to the e-machine operating in the energy recovery mode. In other cases, depending on adhesion coefficient, the e-machine provides the brake torque, which changes from 35 to 100% of the maximum available brake torque. Virtual tests showed that values of the wheel slip ratio are close to the required ones. Thus, this algorithm makes it possible to implement hybrid braking by means of the two sources creating the brake torque.

  16. Metrological Aspects of Surface Topographies Produced by Different Machining Operations Regarding Their Potential Functionality

    Directory of Open Access Journals (Sweden)

    Żak Krzysztof

    2017-06-01

    Full Text Available This paper presents a comprehensive methodology for measuring and characterizing the surface topographies on machined steel parts produced by precision machining operations. The performed case studies concern a wide spectrum of topographic features of surfaces with different geometrical structures but the same values of the arithmetic mean height Sa. The tested machining operations included hard turning operations performed with CBN tools, grinding operations with Al2O3 ceramic and CBN wheels and superfinish using ceramic stones. As a result, several characteristic surface textures with the Sa roughness parameter value of about 0.2 μm were thoroughly characterized and compared regarding their potential functional capabilities. Apart from the standard 2D and 3D roughness parameters, the fractal, motif and frequency parameters were taken in the consideration.

  17. Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

    Science.gov (United States)

    Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

    2017-06-01

    Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

  18. Effect of unbalanced voltage on windings temperature, operational life and load carrying capacity of induction machine

    Energy Technology Data Exchange (ETDEWEB)

    Gnacinski, P. [Gdynia Maritime University, Department of Ship Electrical Power Engineering, Morska Street 83, 81-225 Gdynia (Poland)

    2008-04-15

    This paper investigates the influence of the CVUF angle on the windings temperature rise and the derating factor of an induction machine supplied with unbalanced voltage. The effect of simultaneous voltage unbalance and harmonics on its operational life is analyzed as well. The results of calculations and experimental investigations are presented for two induction cage machines of rated power 3 and 5.5 kW. (author)

  19. OPERATING OF MOBILE MACHINE UNITS SYSTEM USING THE MODEL OF MULTICOMPONENT COMPLEX MOVEMENT

    Directory of Open Access Journals (Sweden)

    A. Lebedev

    2015-07-01

    Full Text Available To solve the problems of mobile machine units system operating it is proposed using complex multi-component (composite movement physical models. Implementation of the proposed method is possible by creating of automatic operating systems of fuel supply to the engines using linear accelerometers. Some examples to illustrate the proposed method are offered.

  20. Operating of mobile machine units system using the model of multicomponent complex movement

    OpenAIRE

    A. Lebedev; R. Kaidalov; N. Artiomov; M. Shulyak; M. Podrigalo; D. Abramov; D. Klets

    2015-01-01

    To solve the problems of mobile machine units system operating it is proposed using complex multi-component (composite) movement physical models. Implementation of the proposed method is possible by creating of automatic operating systems of fuel supply to the engines using linear accelerometers. Some examples to illustrate the proposed method are offered.

  1. Optimization of operating schedule of machines in granite industry using evolutionary algorithms

    International Nuclear Information System (INIS)

    Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

    2014-01-01

    Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

  2. Building a Mechanism for the Function of an Innovation-Oriented Machine-Building Enterprise in Operating the Development

    Directory of Open Access Journals (Sweden)

    Boiarynova Кateryna О.

    2017-07-01

    Full Text Available The article is aimed at developing and substantiating a mechanism for the function of an innovation-oriented machine-building enterprise in operating the development in order to increase effectuality in the space of ecosystem of operation and to satisfy the economic interests of development. The article proposes a mechanism for the function of an innovation-oriented machine-building enterprise in operating the development, its structure and building based on a business model; operating the development through responsibility centers; functional transformation of economic interest of actors in the ecosystem of function into the economic interest of enterprise; use of economic-institutional regulators and management technologies. Implementation of the mechanism would ensure the value of enterprise, the ability to manage development with the prolonged satisfaction of the joint economic interest, the ability to operate the economic interest of actors in the ecosystem, and maintenance the development mode in the operating process. Prospect for further research will be development of technologies for implementation of the proposed mechanism in the machine-building enterprises.

  3. Workforce Optimization for Bank Operation Centers: A Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Sefik Ilkin Serengil

    2017-12-01

    Full Text Available Online Banking Systems evolved and improved in recent years with the use of mobile and online technologies, performing money transfer transactions on these channels can be done without delay and human interaction, however commercial customers still tend to transfer money on bank branches due to several concerns. Bank Operation Centers serve to reduce the operational workload of branches. Centralized management also offers personalized service by appointed expert employees in these centers. Inherently, workload volume of money transfer transactions changes dramatically in hours. Therefore, work-force should be planned instantly or early to save labor force and increase operational efficiency. This paper introduces a hybrid multi stage approach for workforce planning in bank operation centers by the application of supervised and unsu-pervised learning algorithms. Expected workload would be predicted as supervised learning whereas employees are clus-tered into different skill groups as unsupervised learning to match transactions and proper employees. Finally, workforce optimization is analyzed for proposed approach on production data.

  4. Gait rehabilitation machines based on programmable footplates.

    Science.gov (United States)

    Schmidt, Henning; Werner, Cordula; Bernhardt, Rolf; Hesse, Stefan; Krüger, Jörg

    2007-02-09

    Gait restoration is an integral part of rehabilitation of brain lesioned patients. Modern concepts favour a task-specific repetitive approach, i.e. who wants to regain walking has to walk, while tone-inhibiting and gait preparatory manoeuvres had dominated therapy before. Following the first mobilization out of the bed, the wheelchair-bound patient should have the possibility to practise complex gait cycles as soon as possible. Steps in this direction were treadmill training with partial body weight support and most recently gait machines enabling the repetitive training of even surface gait and even of stair climbing. With treadmill training harness-secured and partially relieved wheelchair-mobilised patients could practise up to 1000 steps per session for the first time. Controlled trials in stroke and SCI patients, however, failed to show a superior result when compared to walking exercise on the floor. Most likely explanation was the effort for the therapists, e.g. manually setting the paretic limbs during the swing phase resulting in a too little gait intensity. The next steps were gait machines, either consisting of a powered exoskeleton and a treadmill (Lokomat, AutoAmbulator) or an electromechanical solution with the harness secured patient placed on movable foot plates (Gait Trainer GT I). For the latter, a large multi-centre trial with 155 non-ambulatory stroke patients (DEGAS) revealed a superior gait ability and competence in basic activities of living in the experimental group. The HapticWalker continued the end effector concept of movable foot plates, now fully programmable and equipped with 6 DOF force sensors. This device for the first time enables training of arbitrary walking situations, hence not only the simulation of floor walking but also for example of stair climbing and perturbations. Locomotor therapy is a fascinating new tool in rehabilitation, which is in line with modern principles of motor relearning promoting a task-specific repetitive

  5. Gait rehabilitation machines based on programmable footplates

    Directory of Open Access Journals (Sweden)

    Bernhardt Rolf

    2007-02-01

    Full Text Available Abstract Background Gait restoration is an integral part of rehabilitation of brain lesioned patients. Modern concepts favour a task-specific repetitive approach, i.e. who wants to regain walking has to walk, while tone-inhibiting and gait preparatory manoeuvres had dominated therapy before. Following the first mobilization out of the bed, the wheelchair-bound patient should have the possibility to practise complex gait cycles as soon as possible. Steps in this direction were treadmill training with partial body weight support and most recently gait machines enabling the repetitive training of even surface gait and even of stair climbing. Results With treadmill training harness-secured and partially relieved wheelchair-mobilised patients could practise up to 1000 steps per session for the first time. Controlled trials in stroke and SCI patients, however, failed to show a superior result when compared to walking exercise on the floor. Most likely explanation was the effort for the therapists, e.g. manually setting the paretic limbs during the swing phase resulting in a too little gait intensity. The next steps were gait machines, either consisting of a powered exoskeleton and a treadmill (Lokomat, AutoAmbulator or an electromechanical solution with the harness secured patient placed on movable foot plates (Gait Trainer GT I. For the latter, a large multi-centre trial with 155 non-ambulatory stroke patients (DEGAS revealed a superior gait ability and competence in basic activities of living in the experimental group. The HapticWalker continued the end effector concept of movable foot plates, now fully programmable and equipped with 6 DOF force sensors. This device for the first time enables training of arbitrary walking situations, hence not only the simulation of floor walking but also for example of stair climbing and perturbations. Conclusion Locomotor therapy is a fascinating new tool in rehabilitation, which is in line with modern principles

  6. An intelligent human-machine system based on an ecological interface design concept

    International Nuclear Information System (INIS)

    Naito, N.

    1995-01-01

    It seems both necessary and promising to develop an intelligent human-machine system, considering the objective of the human-machine system and the recent advance in cognitive engineering and artificial intelligence together with the ever-increasing importance of human factor issues in nuclear power plant operation and maintenance. It should support human operators in their knowledge-based behaviour and allow them to cope with unanticipated abnormal events, including recovery from erroneous human actions. A top-down design approach has been adopted based on cognitive work analysis, and (1) an ecological interface, (2) a cognitive model-based advisor and (3) a robust automatic sequence controller have been established. These functions have been integrated into an experimental control room. A validation test was carried out by the participation of experienced operators and engineers. The results showed the usefulness of this system in supporting the operator's supervisory plant control tasks. ((orig.))

  7. Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

    Science.gov (United States)

    Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

    2018-04-01

    The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

  8. A New Type of Tea Baking Machine Based on Pro/E Design

    Science.gov (United States)

    Lin, Xin-Ying; Wang, Wei

    2017-11-01

    In this paper, the production process of wulong tea was discussed, mainly the effect of baking on the quality of tea. The suitable baking temperature of different tea was introduced. Based on Pro/E, a new type of baking machine suitable for wulong tea baking was designed. The working principle, mechanical structure and constant temperature timing intelligent control system of baking machine were expounded. Finally, the characteristics and innovation of new baking machine were discussed.The mechanical structure of this baking machine is more simple and reasonable, and can use the heat of the inlet and outlet, more energy saving and environmental protection. The temperature control part adopts fuzzy PID control, which can improve the accuracy and response speed of temperature control and reduce the dependence of baking operation on skilled experience.

  9. War Machine: Media and Technology during Operation Allied Force

    OpenAIRE

    Bobic, N

    2015-01-01

    One significant aspect of military interventions is that violence in binary geographies, which have an implied colonial discourse (such as Serbia), often involve the dialectics of construction and erasure, meaning that absence and presence of destruction and violence run side by side. This paper investigates the ways that technology and media were instrumentalised in miniaturising evidence and reducing the visibility of destruction during NATO’s Operation Allied Force in Serbia and Kosovo. Wh...

  10. Dictionary Based Machine Translation from Kannada to Telugu

    Science.gov (United States)

    Sindhu, D. V.; Sagar, B. M.

    2017-08-01

    Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

  11. First operational experience with the LHC machine protection system when operating with beam energies beyond the 100MJ range

    CERN Document Server

    Assmann, R; Ferro-Luzzi, M; Goddard, B; Lamont, M; Schmidt, R; Siemko, A; Uythoven, J; Wenninger, J; Zerlauth, M

    2012-01-01

    The Large Hadron Collider (LHC) at CERN has made remarkable progress during 2011, surpassing its ambitious goal for the year in terms of luminosity delivered to the LHC experiments. This achievement was made possible by a progressive increase of beam intensities by more than 5 orders of magnitude during the first months of operation, reaching stored beam energies beyond the 100MJ range at the end of the year, less than a factor of 4 from the nominal design value. The correct functioning of the machine protection systems is vital during the different operational phases, for initial operation and even more when approaching nominal beam parameters where already a small fraction of the stored energy is sufficient to damage accelerator equipment or experiments in case of uncontrolled beam loss. Safe operation of the machine in presence of such high intensity proton beams is guaranteed by the interplay of many different systems: beam dumping system, beam interlocks, beam instrumentation, equipment monitoring, colli...

  12. Machine learning versus knowledge based classification of legal texts

    NARCIS (Netherlands)

    de Maat, E.; Krabben, K.; Winkels, R.; Winkels, R.G.F.

    2010-01-01

    This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but

  13. Machine Learning Based Diagnosis of Lithium Batteries

    Science.gov (United States)

    Ibe-Ekeocha, Chinemerem Christopher

    The depletion of the world's current petroleum reserve, coupled with the negative effects of carbon monoxide and other harmful petrochemical by-products on the environment, is the driving force behind the movement towards renewable and sustainable energy sources. Furthermore, the growing transportation sector consumes a significant portion of the total energy used in the United States. A complete electrification of this sector would require a significant development in electric vehicles (EVs) and hybrid electric vehicles (HEVs), thus translating to a reduction in the carbon footprint. As the market for EVs and HEVs grows, their battery management systems (BMS) need to be improved accordingly. The BMS is not only responsible for optimally charging and discharging the battery, but also monitoring battery's state of charge (SOC) and state of health (SOH). SOC, similar to an energy gauge, is a representation of a battery's remaining charge level as a percentage of its total possible charge at full capacity. Similarly, SOH is a measure of deterioration of a battery; thus it is a representation of the battery's age. Both SOC and SOH are not measurable, so it is important that these quantities are estimated accurately. An inaccurate estimation could not only be inconvenient for EV consumers, but also potentially detrimental to battery's performance and life. Such estimations could be implemented either online, while battery is in use, or offline when battery is at rest. This thesis presents intelligent online SOC and SOH estimation methods using machine learning tools such as artificial neural network (ANN). ANNs are a powerful generalization tool if programmed and trained effectively. Unlike other estimation strategies, the techniques used require no battery modeling or knowledge of battery internal parameters but rather uses battery's voltage, charge/discharge current, and ambient temperature measurements to accurately estimate battery's SOC and SOH. The developed

  14. 29 CFR 570.55 - Occupations involved in the operation of power-driven woodworking machines (Order 5).

    Science.gov (United States)

    2010-07-01

    ... woodworking machines (Order 5). 570.55 Section 570.55 Labor Regulations Relating to Labor (Continued) WAGE AND... woodworking machines (Order 5). Link to an amendment published at 75 FR 28455, May 20, 2010. (a) Finding and declaration of fact. The following occupations involved in the operation of power-driven wood-working machines...

  15. Particle size of radioactive aerosols generated during machine operation in high-energy proton accelerators

    International Nuclear Information System (INIS)

    Oki, Yuichi; Kanda, Yukio; Kondo, Kenjiro; Endo, Akira

    2000-01-01

    In high-energy accelerators, non-radioactive aerosols are abundantly generated due to high radiation doses during machine operation. Under such a condition, radioactive atoms, which are produced through various nuclear reactions in the air of accelerator tunnels, form radioactive aerosols. These aerosols might be inhaled by workers who enter the tunnel just after the beam stop. Their particle size is very important information for estimation of internal exposure doses. In this work, focusing on typical radionuclides such as 7 Be and 24 Na, their particle size distributions are studied. An aluminum chamber was placed in the EP2 beam line of the 12-GeV proton synchrotron at High Energy Accelerator Research Organization (KEK). Aerosol-free air was introduced to the chamber, and aerosols formed in the chamber were sampled during machine operation. A screen-type diffusion battery was employed in the aerosol-size analysis. Assuming that the aerosols have log-normal size distributions, their size distributions were obtained from the radioactivity concentrations at the entrance and exit of the diffusion battery. Radioactivity of the aerosols was measured with Ge detector system, and concentrations of non-radioactive aerosols were obtained using condensation particle counter (CPC). The aerosol size (radius) for 7 Be and 24 Na was found to be 0.01-0.04 μm, and was always larger than that for non-radioactive aerosols. The concentration of non-radioactive aerosols was found to be 10 6 - 10 7 particles/cm 3 . The size for radioactive aerosols was much smaller than ordinary atmospheric aerosols. Internal doses due to inhalation of the radioactive aerosols were estimated, based on the respiratory tract model of ICRP Pub. 66. (author)

  16. Machine vision based quality inspection of flat glass products

    Science.gov (United States)

    Zauner, G.; Schagerl, M.

    2014-03-01

    This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little `image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

  17. The reduction methods of operator's radiation dose for portable dental X-ray machines.

    Science.gov (United States)

    Cho, Jeong-Yeon; Han, Won-Jeong

    2012-08-01

    This study was aimed to investigate the methods to reduce operator's radiation dose when taking intraoral radiographs with portable dental X-ray machines. Two kinds of portable dental X-ray machines (DX3000, Dexcowin and Rextar, Posdion) were used. Operator's radiation dose was measured with an 1,800 cc ionization chamber (RadCal Corp.) at the hand level of X-ray tubehead and at the operator's chest and waist levels with and without the backscatter shield. The operator's radiation dose at the hand level was measured with and without lead gloves and with long and short cones. The backscatter shield reduced operator's radiation dose at the hand level of X-ray tubehead to 23 - 32%, the lead gloves to 26 - 31%, and long cone to 48 - 52%. And the backscatter shield reduced operator's radiation dose at the operator's chest and waist levels to 0.1 - 37%. When portable dental X-ray systems are used, it is recommended to select X-ray machine attached with a backscatter shield and a long cone and to wear the lead gloves.

  18. Employing a virtual reality tool to explicate tacit knowledge of machine operations

    NARCIS (Netherlands)

    Vasenev, Alexandr; Hartmann, Timo; Doree, Andries G.; Hassani, F.

    2013-01-01

    The quality and durability of asphalted roads strongly depends on the final step in the road construction process; the profiling and compaction of the fresh spread asphalt. During compaction machine operators continuously make decisions on how to proceed with the compaction accounting for

  19. Machine Repairers and Operators. Reprinted from the Occupational Outlook Handbook, 1978-79 Edition.

    Science.gov (United States)

    Bureau of Labor Statistics (DOL), Washington, DC.

    Focusing on machine repairers and operators, this document is one in a series of forty-one reprints from the Occupational Outlook Handbook providing current information and employment projections for individual occupations and industries through 1985. The specific occupations covered in this document include appliance repairers,…

  20. Technology and Jobs: Computer-Aided Design. Numerical-Control Machine-Tool Operators. Office Automation.

    Science.gov (United States)

    Stanton, Michael; And Others

    1985-01-01

    Three reports on the effects of high technology on the nature of work include (1) Stanton on applications and implications of computer-aided design for engineers, drafters, and architects; (2) Nardone on the outlook and training of numerical-control machine tool operators; and (3) Austin and Drake on the future of clerical occupations in automated…

  1. Using machine learning to accelerate sampling-based inversion

    Science.gov (United States)

    Valentine, A. P.; Sambridge, M.

    2017-12-01

    In most cases, a complete solution to a geophysical inverse problem (including robust understanding of the uncertainties associated with the result) requires a sampling-based approach. However, the computational burden is high, and proves intractable for many problems of interest. There is therefore considerable value in developing techniques that can accelerate sampling procedures.The main computational cost lies in evaluation of the forward operator (e.g. calculation of synthetic seismograms) for each candidate model. Modern machine learning techniques-such as Gaussian Processes-offer a route for constructing a computationally-cheap approximation to this calculation, which can replace the accurate solution during sampling. Importantly, the accuracy of the approximation can be refined as inversion proceeds, to ensure high-quality results.In this presentation, we describe and demonstrate this approach-which can be seen as an extension of popular current methods, such as the Neighbourhood Algorithm, and bridges the gap between prior- and posterior-sampling frameworks.

  2. Collaborative machining solution extends the operating life of a nuclear power plant

    International Nuclear Information System (INIS)

    Gilmore, Geoff; Becker, Andrew; Vandenberg, James

    2007-01-01

    Examination of a CANDU 6 nuclear power plant's steam generators during a scheduled maintenance outage revealed that the manway ports, part of the ASME Section III, Class 1 pressure boundary, needed repair. The port's inner cover gasket was not seating properly. Integrity was at risk. It was determined that this operation would required a specialized machine to successfully repair the manway port. The solution included the modification of a standard portable boring machine with a custom mounting option to enlarge the counterbore in the primary head shell from a round shape to an obround shape (76 mm of shell thickness, 16 mm radially). The shape change was needed to accommodate the new obround cover and gasket seal design. Once the new major shape was machined, the repair was finished with a Computer Numerically Controlled (CNC) machine developed by the service team to achieve the necessary gasket face location and sizing. The final result met all of the plant's expectations and was completed well within the time allotted during the maintenance shut down. This success was due to the positive partnership and collaboration of the service team and the machine tool manufacture working together to successfully extend the operating life of the nuclear power plant. (author)

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

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

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

  4. Optimizing block-based maintenance under random machine usage

    NARCIS (Netherlands)

    de Jonge, Bram; Jakobsons, Edgars

    Existing studies on maintenance optimization generally assume that machines are either used continuously, or that times until failure do not depend on the actual usage. In practice, however, these assumptions are often not realistic. In this paper, we consider block-based maintenance optimization

  5. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  6. Machine learning for network-based malware detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija

    and based on different, mutually complementary, principles of traffic analysis. The proposed approaches rely on machine learning algorithms (MLAs) for automated and resource-efficient identification of the patterns of malicious network traffic. We evaluated the proposed methods through extensive evaluations...

  7. Machine Learning Based Localization and Classification with Atomic Magnetometers

    Science.gov (United States)

    Deans, Cameron; Griffin, Lewis D.; Marmugi, Luca; Renzoni, Ferruccio

    2018-01-01

    We demonstrate identification of position, material, orientation, and shape of objects imaged by a Rb 85 atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97% are obtained. This circumvents the need of solving the inverse problem and demonstrates the extension of machine learning to diffusive systems, such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security.

  8. Status of the Single Stage AMS machine at Lund University after 4 years of operation

    International Nuclear Information System (INIS)

    Skog, Goeran; Rundgren, Mats; Skoeld, Pia

    2010-01-01

    The Lund SSAMS machine has been in routine operation since 2004. We present results from the last year of operation of the facility. The reference sample IAEA C7 and the 'old' oxalic acid, OxI, were used as secondary standards. As primary standard for calculations the OxII was used. The background and long term stability of the facility are discussed. We also report on the quality of the 13 C/ 12 C ratio from the SSAMS system.

  9. Orthogonal Operation of Constitutional Dynamic Networks Consisting of DNA-Tweezer Machines.

    Science.gov (United States)

    Yue, Liang; Wang, Shan; Cecconello, Alessandro; Lehn, Jean-Marie; Willner, Itamar

    2017-12-26

    Overexpression or down-regulation of cellular processes are often controlled by dynamic chemical networks. Bioinspired by nature, we introduce constitutional dynamic networks (CDNs) as systems that emulate the principle of the nature processes. The CDNs comprise dynamically interconvertible equilibrated constituents that respond to external triggers by adapting the composition of the dynamic mixture to the energetic stabilization of the constituents. We introduce a nucleic acid-based CDN that includes four interconvertible and mechanically triggered tweezers, AA', BB', AB' and BA', existing in closed, closed, open, and open configurations, respectively. By subjecting the CDN to auxiliary triggers, the guided stabilization of one of the network constituents dictates the dynamic reconfiguration of the structures of the tweezers constituents. The orthogonal and reversible operations of the CDN DNA tweezers are demonstrated, using T-A·T triplex or K + -stabilized G-quadruplex as structural motifs that control the stabilities of the constituents. The implications of the study rest on the possible applications of input-guided CDN assemblies for sensing, logic gate operations, and programmed activation of molecular machines.

  10. Computational Principle and Performance Evaluation of Coherent Ising Machine Based on Degenerate Optical Parametric Oscillator Network

    Directory of Open Access Journals (Sweden)

    Yoshitaka Haribara

    2016-04-01

    Full Text Available We present the operational principle of a coherent Ising machine (CIM based on a degenerate optical parametric oscillator (DOPO network. A quantum theory of CIM is formulated, and the computational ability of CIM is evaluated by numerical simulation based on c-number stochastic differential equations. We also discuss the advanced CIM with quantum measurement-feedback control and various problems which can be solved by CIM.

  11. An Android malware detection system based on machine learning

    Science.gov (United States)

    Wen, Long; Yu, Haiyang

    2017-08-01

    The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

  12. 29 CFR 570.63 - Occupations involved in the operation of paper-products machines, scrap paper balers, and paper...

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 3 2010-07-01 2010-07-01 false Occupations involved in the operation of paper-products machines, scrap paper balers, and paper box compactors (Order 12). 570.63 Section 570.63 Labor Regulations... involved in the operation of paper-products machines, scrap paper balers, and paper box compactors (Order...

  13. Energy Analysis of Contention Tree-Based Access Protocols in Dense Machine-to-Machine Area Networks

    Directory of Open Access Journals (Sweden)

    Francisco Vázquez-Gallego

    2015-01-01

    Full Text Available Machine-to-Machine (M2M area networks aim at connecting an M2M gateway with a large number of energy-constrained devices that must operate autonomously for years. Therefore, attaining high energy efficiency is essential in the deployment of M2M networks. In this paper, we consider a dense M2M area network composed of hundreds or thousands of devices that periodically transmit data upon request from a gateway or coordinator. We theoretically analyse the devices’ energy consumption using two Medium Access Control (MAC protocols which are based on a tree-splitting algorithm to resolve collisions among devices: the Contention Tree Algorithm (CTA and the Distributed Queuing (DQ access. We have carried out computer-based simulations to validate the accuracy of the theoretical models and to compare the energy performance using DQ, CTA, and Frame Slotted-ALOHA (FSA in M2M area networks with devices in compliance with the IEEE 802.15.4 physical layer. Results show that the performance of DQ is totally independent of the number of contending devices, and it can reduce the energy consumed per device in more than 35% with respect to CTA and in more than 80% with respect to FSA.

  14. Executable Architecture of Net Enabled Operations: State Machine of Federated Nodes

    Science.gov (United States)

    2009-11-01

    verbal descriptions from operators) of the current Command and Control (C2) practices into model form. In theory these should be Standard Operating...faudra une grande quantité de données pour faire en sorte que le modèle reflète les processus véritables, les auteurs recommandent que la machine à...descriptions from operators) of the current C2 practices into model form. In theory these should be SOPs that execute as a thread from start to finish. The

  15. Application of Elements of TPM Strategy for Operation Analysis of Mining Machine

    Science.gov (United States)

    Brodny, Jaroslaw; Tutak, Magdalena

    2017-12-01

    Total Productive Maintenance (TPM) strategy includes group of activities and actions in order to maintenance machines in failure-free state and without breakdowns thanks to tending limitation of failures, non-planned shutdowns, lacks and non-planned service of machines. These actions are ordered to increase effectiveness of utilization of possessed devices and machines in company. Very significant element of this strategy is connection of technical actions with changes in their perception by employees. Whereas fundamental aim of introduction this strategy is improvement of economic efficiency of enterprise. Increasing competition and necessity of reduction of production costs causes that also mining enterprises are forced to introduce this strategy. In the paper examples of use of OEE model for quantitative evaluation of selected mining devices were presented. OEE model is quantitative tool of TPM strategy and can be the base for further works connected with its introduction. OEE indicator is the product of three components which include availability and performance of the studied machine and the quality of the obtained product. The paper presents the results of the effectiveness analysis of the use of a set of mining machines included in the longwall system, which is the first and most important link in the technological line of coal production. The set of analyzed machines included the longwall shearer, armored face conveyor and cruscher. From a reliability point of view, the analyzed set of machines is a system that is characterized by the serial structure. The analysis was based on data recorded by the industrial automation system used in the mines. This method of data acquisition ensured their high credibility and a full time synchronization. Conclusions from the research and analyses should be used to reduce breakdowns, failures and unplanned downtime, increase performance and improve production quality.

  16. English to Sanskrit Machine Translation Using Transfer Based approach

    Science.gov (United States)

    Pathak, Ganesh R.; Godse, Sachin P.

    2010-11-01

    Translation is one of the needs of global society for communicating thoughts and ideas of one country with other country. Translation is the process of interpretation of text meaning and subsequent production of equivalent text, also called as communicating same meaning (message) in another language. In this paper we gave detail information on how to convert source language text in to target language text using Transfer Based Approach for machine translation. Here we implemented English to Sanskrit machine translator using transfer based approach. English is global language used for business and communication but large amount of population in India is not using and understand the English. Sanskrit is ancient language of India most of the languages in India are derived from Sanskrit. Sanskrit can be act as an intermediate language for multilingual translation.

  17. Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

    Science.gov (United States)

    Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

    2014-01-01

    Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. PMID:24737962

  18. Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

    Directory of Open Access Journals (Sweden)

    Supriya Kinger

    2014-01-01

    Full Text Available Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

  19. Prediction based proactive thermal virtual machine scheduling in green clouds.

    Science.gov (United States)

    Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

    2014-01-01

    Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

  20. Machine learning-based dual-energy CT parametric mapping.

    Science.gov (United States)

    Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

    2018-05-22

    The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρe), mean excitation energy (Ix), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 seconds. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. . © 2018 Institute of Physics and Engineering in

  1. Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

    Science.gov (United States)

    Abellán-Nebot, J. V.; Liu, J.; Romero, F.

    2009-11-01

    The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

  2. Memory Based Machine Intelligence Techniques in VLSI hardware

    OpenAIRE

    James, Alex Pappachen

    2012-01-01

    We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

  3. Successful attack on permutation-parity-machine-based neural cryptography.

    Science.gov (United States)

    Seoane, Luís F; Ruttor, Andreas

    2012-02-01

    An algorithm is presented which implements a probabilistic attack on the key-exchange protocol based on permutation parity machines. Instead of imitating the synchronization of the communicating partners, the strategy consists of a Monte Carlo method to sample the space of possible weights during inner rounds and an analytic approach to convey the extracted information from one outer round to the next one. The results show that the protocol under attack fails to synchronize faster than an eavesdropper using this algorithm.

  4. An expert system for vibration based diagnostics of rotating machines

    International Nuclear Information System (INIS)

    Korteniemi, A.

    1990-01-01

    Very often changes in the mechanical condition of the rotating machinery can be observed as changes in its vibration. This paper presents an expert system for vibration-based diagnosis of rotating machines by describing the architecture of the developed prototype system. The importance of modelling the problem solving knowledge as well as the domain knowledge is emphasized by presenting the knowledge in several levels

  5. Aging Detection of Electrical Point Machines Based on Support Vector Data Description

    Directory of Open Access Journals (Sweden)

    Jaewon Sa

    2017-11-01

    Full Text Available Electrical point machines (EPM must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

  6. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  7. Evolution of PHWR fuel transfer system based on operating experience

    International Nuclear Information System (INIS)

    Parvatikar, R.S.; Singh, Jaipal; Chaturvedi, P.C.; Bhambra, H.S.

    2006-01-01

    Fuel Transfer System facilitates loading of new fuel into Fuelling Machine, receipt of spent fuel from Fuelling Machine and its further transportation to Storage Bay. To overcome the limitations of transferring a pair of bundles in the single tube Airlock and Transfer Arm in RAPS-1 and 2/MAPS, a new concept of six tube Transfer Magazine was introduced in NAPS. This resulted in simultaneous loading of new fuel from Transfer Magazine into the Fuelling Machine and unloading of spent fuel from the Fuelling Machine through the exchange mode. It further facilitated the parallel/simultaneous operation of refuelling by Fuelling Machines on the reactor and transferring of spent fuel bundles from the Transfer Magazine to the bay. This new design of Fuel Transfer System was adopted for all standardised 220 MWe PHWRs. Based on the experience gained in 220 MWe PHWRs in the area of operation and maintenance, a number of improvements have been carried out over the years. These aspects have been further strengthened and refined in the Fuel Transfer System of 540 MWe units. The operating experience of the system indicates that the presence of heavy water in the Transfer Magazine poses limitations in its maintenance in the Fuel Transfer room. Further, Surveillance and maintenance of large number of under water equipment and associated valves, rams and underwater sensors is putting extra burden on the O and M efforts. A new concept of mobile light water filled Transfer Machine has been evolved for proposed 700 MWe PHWR units to simplify Fuel Transfer System. This has been made possible by adopting snout level control in the Fuelling Machine, elimination of Shuttle Transport System and locating the Storage Bay adjacent to the Reactor Building. This paper describes the evolution of Fuel Transfer System concepts and various improvements based on the experience gained in the operation and maintenance of the system. (author)

  8. Methods, systems and apparatus for controlling operation of two alternating current (AC) machines

    Science.gov (United States)

    Gallegos-Lopez, Gabriel [Torrance, CA; Nagashima, James M [Cerritos, CA; Perisic, Milun [Torrance, CA; Hiti, Silva [Redondo Beach, CA

    2012-02-14

    A system is provided for controlling two AC machines. The system comprises a DC input voltage source that provides a DC input voltage, a voltage boost command control module (VBCCM), a five-phase PWM inverter module coupled to the two AC machines, and a boost converter coupled to the inverter module and the DC input voltage source. The boost converter is designed to supply a new DC input voltage to the inverter module having a value that is greater than or equal to a value of the DC input voltage. The VBCCM generates a boost command signal (BCS) based on modulation indexes from the two AC machines. The BCS controls the boost converter such that the boost converter generates the new DC input voltage in response to the BCS. When the two AC machines require additional voltage that exceeds the DC input voltage required to meet a combined target mechanical power required by the two AC machines, the BCS controls the boost converter to drive the new DC input voltage generated by the boost converter to a value greater than the DC input voltage.

  9. Machine-vision-based identification of broken inserts in edge profile milling heads

    NARCIS (Netherlands)

    Fernandez Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai

    This paper presents a reliable machine vision system to automatically detect inserts and determine if they are broken. Unlike the machining operations studied in the literature, we are dealing with edge milling head tools for aggressive machining of thick plates (up to 12 centimetres) in a single

  10. Machine Translation Using Constraint-Based Synchronous Grammar

    Institute of Scientific and Technical Information of China (English)

    WONG Fai; DONG Mingchui; HU Dongcheng

    2006-01-01

    A synchronous grammar based on the formalism of context-free grammar was developed by generalizing the first component of production that models the source text. Unlike other synchronous grammars,the grammar allows multiple target productions to be associated to a single production rule which can be used to guide a parser to infer different possible translational equivalences for a recognized input string according to the feature constraints of symbols in the pattern. An extended generalized LR algorithm was adapted to the parsing of the proposed formalism to analyze the syntactic structure of a language. The grammar was used as the basis for building a machine translation system for Portuguese to Chinese translation. The empirical results show that the grammar is more expressive when modeling the translational equivalences of parallel texts for machine translation and grammar rewriting applications.

  11. Support vector machine based battery model for electric vehicles

    International Nuclear Information System (INIS)

    Wang Junping; Chen Quanshi; Cao Binggang

    2006-01-01

    The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

  12. Machine learning based Intelligent cognitive network using fog computing

    Science.gov (United States)

    Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

    2017-05-01

    In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

  13. Model for Investigation of Operational Wind Power Plant Regimes with Doubly–Fed Asynchronous Machine in Power System

    Directory of Open Access Journals (Sweden)

    R. I. Mustafayev

    2012-01-01

    Full Text Available The paper presents methodology for mathematical modeling of power system (its part when jointly operated with wind power plants (stations that contain asynchronous doubly-fed machines used as generators. The essence and advantage of the methodology is that it allows efficiently to mate equations of doubly-fed asynchronous machines, written in the axes that rotate with the machine rotor speed with the equations of external electric power system, written in synchronously rotating axes.

  14. EPICS IOC module development and implementation for the ISTTOK machine subsystem operation and control

    Energy Technology Data Exchange (ETDEWEB)

    Carvalho, Paulo, E-mail: pricardofc@ipfn.ist.utl.pt [Associacao EURATOM/IST, Instituto de Plasmas e Fusao Nuclear-Laboratorio Associado, Instituto Superior Tecnico, P-1049-001 Lisboa (Portugal); Duarte, Andre; Pereira, Tiago; Carvalho, Bernardo; Sousa, Jorge; Fernandes, Horacio [Associacao EURATOM/IST, Instituto de Plasmas e Fusao Nuclear-Laboratorio Associado, Instituto Superior Tecnico, P-1049-001 Lisboa (Portugal); Correia, Carlos [Grupo de Electronica e Instrumentacao-Centro de Instrumentacao, Departamento de Fisica, Universidade de Coimbra, P-3004-516 Coimbra (Portugal); Goncalves, Bruno; Varandas, Carlos [Associacao EURATOM/IST, Instituto de Plasmas e Fusao Nuclear-Laboratorio Associado, Instituto Superior Tecnico, P-1049-001 Lisboa (Portugal)

    2011-10-15

    This paper presents a developed, tested and integrated EPICS IOC (I/O controller) module solution for the ISTTOK tokamak machine operation and control for the vacuum and gas injection systems. The work is organized in two software layers which communicate through a serial RS-232 communication protocol. The first software layer is an EPICS IOC module running as a computer server application capable of receiving requests from remote or local clients providing driver interface to the system by forwarding requested commands and receiving system and control operation status. The second software layer is the firmware running in Microchip dsPIC microcontroller modules which performs the interface from RS-232 optical fiber serial protocol to EPICS IOC module. The dsPIC module communicates to the ISTTOK tokamak sensors and actuators via RS-485 and is programmed with a new protocol developed for this purpose that allows EPICS IOC module command sending/receiving, machine operation control and monitoring and system status information. Communication between EPICS IOC module and clients is achieved via a TCP/IP and UDP protocol referred as Channel Access. In addition, the EPICS IOC module provides user client applications access allowing operators to perform remote or local monitoring, operation and control.

  15. Analysis of the steady-state operation of vacuum systems for fusion machines

    International Nuclear Information System (INIS)

    Roose, T.R.; Hoffman, M.A.; Carlson, G.A.

    1975-01-01

    A computer code named GASBAL was written to calculate the steady-state vacuum system performance of multi-chamber mirror machines as well as rather complex conventional multichamber vacuum systems. Application of the code, with some modifications, to the quasi-steady tokamak operating period should also be possible. Basically, GASBAL analyzes free molecular gas flow in a system consisting of a central chamber (the plasma chamber) connected by conductances to an arbitrary number of one- or two-chamber peripheral tanks. Each of the peripheral tanks may have vacuum pumping capability (pumping speed), sources of cold gas, and sources of energetic atoms. The central chamber may have actual vacuum pumping capability, as well as a plasma capable of ionizing injected atoms and impinging gas molecules and ''pumping'' them to a peripheral chamber. The GASBAL code was used in the preliminary design of a large mirror machine experiment--LLL's MX

  16. Extending the features of RBMK refuelling machine simulator with a training tool based on virtual reality

    International Nuclear Information System (INIS)

    Khoudiakov, M.; Slonimsky, V.; Mitrofanov, S.

    2004-01-01

    The paper describes a continuation of efforts of an international Russian - Norwegian joint team to improve operational safety during the refuelling process of an RBMK-type reactor by implementing a training simulator based on an innovative Virtual Reality (VR) approach. During the preceding 1st stage of the project a display-based simulator was extended with VR models of the real Refuelling Machine (RM) and its environment in order to improve both the learning process and operation's effectiveness. The simulator's challenge is to support the performance (operational activity) of RM operational staff firstly by helping them to develop basic knowledge and skills as well as to keep skilled staff in close touch with the complex machinery of the Refuelling Machine. During the 2nd stage of the joint project the functional scope of the VR-simulator was greatly enhanced - firstly, by connecting to the RBMK-unit full-scope simulator, and, secondly, by including a training program and simulator model upgrade. The present 3rd stage of the Project is primarily oriented towards the improvement of the training process for maintenance and operational personnel by means of a development of the Training Support Methodology and Courses (TSMC) to be based on Virtual Reality and enlarged functionality of 3D and process modelling. The TMSC development is based on Russian and International Regulatory Bodies requirements and recommendations. Design, development and creation of a specialised VR-based Training System for RM Maintenance Personnel are very important for the Russian RBMK plants. The main goal is to create a powerful, autonomous VR-based simulator for training technical maintenance personnel on the Refuelling Machine. VR based training is expected to improve the effect of training compared to the current training based on traditional methods using printed documentation. The LNPP management and the Regulatory Bodies supported this goal. The VR-based Training System should

  17. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

    National Research Council Canada - National Science Library

    Qi, Yuan

    2000-01-01

    In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...

  18. Microscopic machining mechanism of polishing based on vibrations of liquid

    International Nuclear Information System (INIS)

    Huang, Z G; Guo, Z N; Chen, X; Yu, Z Q; Yu, T M; Lee, W B

    2007-01-01

    A molecular dynamics method has been applied to study the mechanism of polishing based on vibrations of liquid. Movements of polishing particles and formations of impact dents are simulated and discussed. The abrasive effect between particle and machined substrate is evaluated empirically. Polishing qualities, including roughness and fractal character under multiple impacts, are obtained by numerical methods. Results show that the particle will vibrate and roll viscously on the substrate. Press, tear and self-organization effects will be responsible for the formation of impact dents. Simulation results are compared with experimental data to verify the conclusions

  19. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

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

  20. Improved Extreme Learning Machine based on the Sensitivity Analysis

    Science.gov (United States)

    Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

    2018-03-01

    Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

  1. Machine Learning-based Intelligent Formal Reasoning and Proving System

    Science.gov (United States)

    Chen, Shengqing; Huang, Xiaojian; Fang, Jiaze; Liang, Jia

    2018-03-01

    The reasoning system can be used in many fields. How to improve reasoning efficiency is the core of the design of system. Through the formal description of formal proof and the regular matching algorithm, after introducing the machine learning algorithm, the system of intelligent formal reasoning and verification has high efficiency. The experimental results show that the system can verify the correctness of propositional logic reasoning and reuse the propositional logical reasoning results, so as to obtain the implicit knowledge in the knowledge base and provide the basic reasoning model for the construction of intelligent system.

  2. Summary of vulnerability related technologies based on machine learning

    Science.gov (United States)

    Zhao, Lei; Chen, Zhihao; Jia, Qiong

    2018-04-01

    As the scale of information system increases by an order of magnitude, the complexity of system software is getting higher. The vulnerability interaction from design, development and deployment to implementation stages greatly increases the risk of the entire information system being attacked successfully. Considering the limitations and lags of the existing mainstream security vulnerability detection techniques, this paper summarizes the development and current status of related technologies based on the machine learning methods applied to deal with massive and irregular data, and handling security vulnerabilities.

  3. Optimal Operation of Wind Turbines Based on Support Vector Machine and Differential Evolution Algorithm%基于支持向量机和微分进化算法的风电机优化运行

    Institute of Scientific and Technical Information of China (English)

    彭春华; 相龙阳; 刘刚; 易洪京

    2012-01-01

    Output control of wind turbines is the key item in the operation of wind farm. In view of complicated relations among wind turbine output, wind speed and blade pitch angle, it is hard to establish a versatile and accurate mathematical model. For this reason, a new mode to optimize wind turbine output is proposed: firstly a model for nonlinear fitting between wind turbine output and operational parameters is built; then based on the built model and the variation of wind speed and adopting the high-efficient differential evolution algorithm, the blade pitch angle of wind turbine is optimized fast and dynamically. Using the proposed method, the dynamic relation between wind speed and optimal blade pitch angle can be established. Simulation results of the proposed method show that the output of wind turbine can be effectively uprated, thus the feasibility of the proposed method is verified.%风电机出力控制是风电场运行过程中的一个关键问题。针对风电机出力与风速和桨距角之间存在非常复杂的关系,很难建立通用准确的数学模型,提出了一种新的风电机出力优化模式,即首先通过支持向量机算法建立风电机出力与运行参数之间的非线性拟合模型,并基于此模型和风速的变化,采用高效的微分进化算法对风力机桨距角进行快速动态优化,从而实现风电机出力最大化。以鄱阳湖长岭风电场风电机组实际运行数据进行了仿真应用与分析。结果表明通过优化风力机桨距角可有效地提高风电机出力,验证了文中方法的可行性和优越性。采用文中方法可准确建立风速与最优桨距角的动态对应关系,为风电机的优化运行提供了科学的指导。

  4. Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

    Directory of Open Access Journals (Sweden)

    Kaya Eren

    2017-12-01

    Full Text Available Nickel based superalloys offer high strength, corrosion resistance, thermal stability and superb thermal fatigue properties. However, they have been one of the most difficult materials to machine due to these properties. Although we are witnessing improved machining strategies with the developing machining, tooling and inspection technologies, machining of nickel based superalloys is still a challenging task due to in-process strains and post process part quality demands.

  5. Sample-Based Extreme Learning Machine with Missing Data

    Directory of Open Access Journals (Sweden)

    Hang Gao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information. However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

  6. Operational dynamics of the cutting head of the AM-50 heading machine

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, W; Bak, K; Klich, R [Politechnika Slaska, Gliwice (Poland). Instytut Mechanizacji Gornictwa

    1987-01-01

    Operation of the cutter head of an AM-50 heading machine is influenced by a large number of factors, many of them of a random character. Forces acting on each of the cutting tools participating in coal or rock cutting are determined and summed up. The total cutting force is then calculated and on that basis the turning moment is derived. Cutting tool operation also is analyzed as a stochastic process. Cutting forces of each cutting tool change from 0 to maximum. However these forces are distributed in cutting time and the total cutting force is not the sum of the average cutting forces, nor is it the sum of maximum cutting forces. Using calculus of probability, the probable force distribution was determined. This distribution is compared to force distribution calculated on the basis of power consumption of the cutter motors. The differences between the two force distributions are, among others, caused by insufficient investigation into operation of conic cutters. 10 refs.

  7. Development of a finite state machine for the automates operation of the LLRF control at FLASH

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, A.

    2007-07-15

    The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical

  8. Development of a finite state machine for the automated operation of the LLRF control at FLASH

    International Nuclear Information System (INIS)

    Brandt, A.

    2007-07-01

    The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2001-08-01

    A prototype of a computer simulation system named JACOS (JAERI COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of a nuclear power plant. The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also taken into account. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. This report is prepared as User's Manual for JACOS. The first chapter of this report describes both operator and plant models in detail. The second chapter includes instructive descriptions for program installation, building of a knowledge base for operator model, execution of simulation and analysis of simulation results. The examples of simulation with JACOS are shown in the third chapter. (author)

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

    International Nuclear Information System (INIS)

    Yoshida, Kazuo; Yokobayashi, Masao; Tanabe, Fumiya; Komiya, Akitoshi

    2001-08-01

    A prototype of a computer simulation system named JACOS (JAERI COgnitive Simulation system) has been developed at JAERI (Japan Atomic Energy Research Institute) to simulate the man-machine system behavior in which both the cognitive behavior of a human operator and the plant behavior affect each other. The objectives of this system development is to provide man-machine system analysts with detailed information on the cognitive process of an operator and the plant behavior affected by operator's actions in accidental situations of a nuclear power plant. The simulation system consists of an operator model and a plant model which are coupled dynamically. The operator model simulates an operator's cognitive behavior in accidental situations based on the decision ladder model of Rasmussen, and is implemented using the AI-techniques of the distributed cooperative inference method with the so-called blackboard architecture. Rule-based behavior is simulated using knowledge representation with If-Then type of rules. Knowledge-based behavior is simulated using knowledge representation with MFM (Multilevel Flow Modeling) and qualitative reasoning method. Cognitive characteristics of attentional narrowing, limitation of short-term memory, and knowledge recalling from long-term memory are also taken into account. The plant model of a 3-loop PWR is also developed using a best estimate thermal-hydraulic analysis code RELAP5/MOD2. This report is prepared as User's Manual for JACOS. The first chapter of this report describes both operator and plant models in detail. The second chapter includes instructive descriptions for program installation, building of a knowledge base for operator model, execution of simulation and analysis of simulation results. The examples of simulation with JACOS are shown in the third chapter. (author)

  11. Correction of engineering servicing regularity of transporttechnological machines in operational process

    Science.gov (United States)

    Makarova, A. N.; Makarov, E. I.; Zakharov, N. S.

    2018-03-01

    In the article, the issue of correcting engineering servicing regularity on the basis of actual dependability data of cars in operation is considered. The purpose of the conducted research is to increase dependability of transport-technological machines by correcting engineering servicing regularity. The subject of the research is the mechanism of engineering servicing regularity influence on reliability measure. On the basis of the analysis of researches carried out before, a method of nonparametric estimation of car failure measure according to actual time-to-failure data was chosen. A possibility of describing the failure measure dependence on engineering servicing regularity by various mathematical models is considered. It is proven that the exponential model is the most appropriate for that purpose. The obtained results can be used as a separate method of engineering servicing regularity correction with certain operational conditions taken into account, as well as for the technical-economical and economical-stochastic methods improvement. Thus, on the basis of the conducted researches, a method of engineering servicing regularity correction of transport-technological machines in the operational process was developed. The use of that method will allow decreasing the number of failures.

  12. Dosimetry measurements of X-Ray machine operating at ordinary radiology and fluoroscopic examinations

    International Nuclear Information System (INIS)

    Ayad, M.; Bakazi, A.; Elharby, H.

    2002-01-01

    An assessment of radiation dose levels inside diagnostic radiology rooms at King Khalid University Hospital was made. The measurements were taken using lithium Flouride detectors Also, an assessment of doses received by patients during some radiographic examinations especially at fluoroscopy has been measured. It has been noted that when rare-earth image intensifying screens were used the radiation dose received by the patient was reduced by 60%. It has been shown that a lead glass viewer caused a reduction of the radiation intensity by more than 50%. The variation of dose rate with the operating conditions of the X-ray tube has been studied, as well as the machine factor (P)

  13. Performance Improvement of Servo Machine Low Speed Operation Using RBFN Disturbance Observer

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Blaabjerg, Frede

    2004-01-01

    A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The typical speed estimation scheme in most servo system for low speed operation is sensitive to the variation of machine parameters, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Networks (RBFN) is applied. The effectiveness of the proposed inertia estimation method is verified by experiments. It is concluded that the speed control performance in the low speed region is improved with the proposed...

  14. A Cooperative Approach to Virtual Machine Based Fault Injection

    Energy Technology Data Exchange (ETDEWEB)

    Naughton III, Thomas J [ORNL; Engelmann, Christian [ORNL; Vallee, Geoffroy R [ORNL; Aderholdt, William Ferrol [ORNL; Scott, Stephen L [Tennessee Technological University (TTU)

    2017-01-01

    Resilience investigations often employ fault injection (FI) tools to study the effects of simulated errors on a target system. It is important to keep the target system under test (SUT) isolated from the controlling environment in order to maintain control of the experiement. Virtual machines (VMs) have been used to aid these investigations due to the strong isolation properties of system-level virtualization. A key challenge in fault injection tools is to gain proper insight and context about the SUT. In VM-based FI tools, this challenge of target con- text is increased due to the separation between host and guest (VM). We discuss an approach to VM-based FI that leverages virtual machine introspection (VMI) methods to gain insight into the target s context running within the VM. The key to this environment is the ability to provide basic information to the FI system that can be used to create a map of the target environment. We describe a proof- of-concept implementation and a demonstration of its use to introduce simulated soft errors into an iterative solver benchmark running in user-space of a guest VM.

  15. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

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

  16. Duality-based algorithms for scheduling on unrelated parallel machines

    NARCIS (Netherlands)

    van de Velde, S.L.; van de Velde, S.L.

    1993-01-01

    We consider the following parallel machine scheduling problem. Each of n independent jobs has to be scheduled on one of m unrelated parallel machines. The processing of job J[sub l] on machine Mi requires an uninterrupted period of positive length p[sub lj]. The objective is to find an assignment of

  17. Ergonomic risk factor identification for sewing machine operators through supervised occupational therapy fieldwork in Bangladesh: A case study.

    Science.gov (United States)

    Habib, Md Monjurul

    2015-01-01

    Many sewing machine operators are working with high risk factors for musculoskeletal health in the garments industries in Bangladesh. To identify the physical risk factors among sewing machine operators in a Bangladeshi garments factory. Sewing machine operators (327, 83% female), were evaluated. The mean age of the participants was 25.25 years. Six ergonomic risk factors were determined using the Musculoskeletal Disorders risk assessment. Data collection included measurements of sewing machine table and chair heights; this data was combined with information from informal interviews. Significant ergonomic risk factors found included the combination of awkward postures of the neck and back, repetitive hand and arm movements, poor ergonomic workstations and prolonged working hours without adequate breaks; these risk factors resulted in musculoskeletal complaints, sick leave, and switching jobs. One aspect of improving worker health in garment factories includes addressing musculoskeletal risk factors through ergonomic interventions.

  18. Work-organisational and personal factors associated with upper body musculoskeletal disorders among sewing machine operators.

    Science.gov (United States)

    Wang, P-C; Rempel, D M; Harrison, R J; Chan, J; Ritz, B R

    2007-12-01

    To assess the contribution of work-organisational and personal factors to the prevalence of work-related musculoskeletal disorders (WMSDs) among garment workers in Los Angeles. This is a cross-sectional study of self-reported musculoskeletal symptoms among 520 sewing machine operators from 13 garment industry sewing shops. Detailed information on work-organisational factors, personal factors, and musculoskeletal symptoms were obtained in face-to-face interviews. The outcome of interest, upper body WMSD, was defined as a worker experiencing moderate or severe musculoskeletal pain. Unconditional logistic regression models were adopted to assess the association between both work-organisational factors and personal factors and the prevalence of musculoskeletal pain. The prevalence of moderate or severe musculoskeletal pain in the neck/shoulder region was 24% and for distal upper extremity it was 16%. Elevated prevalence of upper body pain was associated with age less than 30 years, female gender, Hispanic ethnicity, being single, having a diagnosis of a MSD or a systemic illness, working more than 10 years as a sewing machine operator, using a single sewing machine, work in large shops, higher work-rest ratios, high physical exertion, high physical isometric loads, high job demand, and low job satisfaction. Work-organisational and personal factors were associated with increased prevalence of moderate or severe upper body musculoskeletal pain among garment workers. Owners of sewing companies may be able to reduce or prevent WMSDs among employees by adopting rotations between different types of workstations thus increasing task variety; by either shortening work periods or increasing rest periods to reduce the work-rest ratio; and by improving the work-organisation to control psychosocial stressors. The findings may guide prevention efforts in the garment sector and have important public health implications for this workforce of largely immigrant labourers.

  19. Gradient Evolution-based Support Vector Machine Algorithm for Classification

    Science.gov (United States)

    Zulvia, Ferani E.; Kuo, R. J.

    2018-03-01

    This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

  20. Coal demand prediction based on a support vector machine model

    Energy Technology Data Exchange (ETDEWEB)

    Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

    2007-01-15

    A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

  1. Analysis of doubly-fed induction machine operating at motoring mode subjected to voltage sag

    Directory of Open Access Journals (Sweden)

    Navneet Kumar

    2016-09-01

    Full Text Available Variable Speed (VS Pumped Storage Plants (PSP equipped with large asynchronous (Doubly-Fed Induction machines are emerging now in hydropower applications. Motoring mode of operation of Doubly-Fed Induction Machine (DFIM is essential and techno-economical in this application due to: (1 its uniqueness in active power controllability, (2 bulk power handing capability with less rated power converters in rotor circuit, and (3 integrating Renewable Energy Sources (RES. This paper investigates the performance of two DFIMs at different power ratings (2.2 kW and 2 MW under voltage sag with different attribute. The test results are analyzed in terms of the peaks in torque, speed, power taken and transient currents in rotor and stator circuits. During sag, stable region for DFIM operation along with speed and stator side reactive power input control is also illustrated. The negative effects of voltage sag are briefly discussed. MATLAB simulation is validated with experimentation. The various observations during simulation and experimental analysis are also supported by the theoretical explanations.

  2. Professional risk of developing diseases of the peripheral nervous system in tractor drivers – machine operators of agricultural production

    Directory of Open Access Journals (Sweden)

    G.A. Bezrukova

    2015-09-01

    Full Text Available Based on the results of the hygienic assessment of working conditions in the domestic agricultural machinery of old and new models when performing the main types of seasonal agricultural work during the annual production cycle and analysis of accumulated occupational diseases’ nosology structure in agricultural workers of the Saratov region over the period from 2004 to 2014, the estimation of professional risk diseases of the peripheral nervous system in tractor drivers – machine operators of agricultural production is given. Professional risk assessment carried out under the procedure set forth in P2.2.1766-03 has shown that the category of a priori risk to their health during an annual production cycle ranged from high to very high (unbearable. It was revealed that the most important factors shaping the harmful working conditions when working on agricultural machinery that can act as a trigger in the formation of vertebral diseases of the peripheral nervous system, are general and local vibration, adverse micro-climatic conditions, long uncomfortable static working posture and physical stress. The risk of diseases in the peripheral uneven system in machine operators of agriculture was attributed to the high risk category with an index of professional diseases (IPD equal to 0,5 %.

  3. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

    Science.gov (United States)

    Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

    2017-09-01

    The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

  4. DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Ahmed Almusawi

    2018-01-01

    Full Text Available DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f-measure of 0.80.

  5. NMF-Based Image Quality Assessment Using Extreme Learning Machine.

    Science.gov (United States)

    Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

    2017-01-01

    Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

  6. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  7. Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

    Directory of Open Access Journals (Sweden)

    M. Frutos

    2013-01-01

    Full Text Available Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

  8. Study and implementation high speed operating of induced magnetization machines; Etude et mise en oeuvre de machines a aimantation induite fonctionnant a haute vitesse

    Energy Technology Data Exchange (ETDEWEB)

    Alhassoun, Y.

    2005-05-15

    Actually, electromechanical machines are characterized by their low cost and reduced maintenance. Therefore, new types of magnetic materials such as soft magnetic composites (SMC), have to be considered not only for multiple applications (small motors for automotive) for cost reduction, but also when considering other special requirements such as high speed drive (aircraft and space applications). Our report of thesis is articulated around four chapters: The first chapter show the various types of magnetic interactions used in the electromagnetic actuators. The second chapter is devoted to the modelling of the induced magnetic machines by analytical resolution of equations of the field in two dimensions. The third chapter presents the four configurations prototypes of switched reluctance machine which mix the exploitation of laminated materials and the soft magnetic powders. The fourth chapter discusses the critical conditions of this machines operating at high speed. We conclude, insisting on the efforts carried out in term of analytical modelling of the induced magnetization machines for their dimensions and exploited in this same structure, the soft magnetic composite materials. The results show the potential of soft magnetic powders when considering in particular the high frequency losses and their ability to favour the heat dissipation in this structure. (author)

  9. The application of state machine based on labview for solid target transfer control system at BATAN’s cyclotron

    International Nuclear Information System (INIS)

    Heranudin; Rajiman; Parwanto; Edy Slamet R

    2015-01-01

    Software programming for the new solid target transfer control system referred to the working principle of the whole each sub system. System modeling with state machine diagram was chosen because this simplified a complex design of the control system. State machine implementation of this system was performed by creating basic state drawn from the working system of each sub system. All states with their described inputs, outputs and algorithms were compiled in the sequential state machine diagram. In order to ease the operation, three modes namely automatic, major states and micro states were created. Testing of the system has been conducted and as a result, the system worked properly. The implementation of State machine based on LabView has several advantages such as faster, easier programming and the capability for further developments. (author)

  10. Study of cutting speed on surface roughness and chip formation when machining nickel-based alloy

    International Nuclear Information System (INIS)

    Khidhir, Basim A.; Mohamed, Bashir

    2010-01-01

    Nickel- based alloy is difficult-to-machine because of its low thermal diffusive property and high strength at higher temperature. The machinability of nickel- based Hastelloy C-276 in turning operations has been carried out using different types of inserts under dry conditions on a computer numerical control (CNC) turning machine at different stages of cutting speed. The effects of cutting speed on surface roughness have been investigated. This study explores the types of wear caused by the effect of cutting speed on coated and uncoated carbide inserts. In addition, the effect of burr formation is investigated. The chip burr is found to have different shapes at lower speeds. Triangles and squares have been noticed for both coated and uncoated tips as well. The conclusion from this study is that the transition from thick continuous chip to wider discontinuous chip is caused by different types of inserts. The chip burr has a significant effect on tool damage starting in the line of depth-of-cut. For the coated insert tips, the burr disappears when the speed increases to above 150 m/min with the improvement of surface roughness; increasing the speed above the same limit for uncoated insert tips increases the chip burr size. The results of this study showed that the surface finish of nickel-based alloy is highly affected by the insert type with respect to cutting speed changes and its effect on chip burr formation and tool failure

  11. A data-based technique for monitoring of wound rotor induction machines: A simulation study

    KAUST Repository

    Harrou, Fouzi

    2016-05-09

    Detecting faults induction machines is crucial for a safe operation of these machines. The aim of this paper is to present a statistical fault detection methodology for the detection of faults in three-phase wound rotor induction machines (WRIM). The proposed fault detection approach is based on the use of principal components analysis (PCA). However, conventional PCA-based detection indices, such as the T2T2 and the Q statistics, are not well suited to detect small faults because these indices only use information from the most recent available samples. Detection of small faults is one of the most crucial and challenging tasks in the area of fault detection and diagnosis. In this paper, a new statistical system monitoring strategy is proposed for detecting changes resulting from small shifts in several variables associated with WRIM. The proposed approach combines modeling using PCA modeling with the exponentially weighted moving average (EWMA) control scheme. In the proposed approach, EWMA control scheme is applied on the ignored principal components to detect the presence of faults. The performance of the proposed method is compared with those of the traditional PCA-based fault detection indices. The simulation results clearly show the effectiveness of the proposed method over the conventional ones, especially in the presence of faults with small magnitudes.

  12. Noninvasive extraction of fetal electrocardiogram based on Support Vector Machine

    Science.gov (United States)

    Fu, Yumei; Xiang, Shihan; Chen, Tianyi; Zhou, Ping; Huang, Weiyan

    2015-10-01

    The fetal electrocardiogram (FECG) signal has important clinical value for diagnosing the fetal heart diseases and choosing suitable therapeutics schemes to doctors. So, the noninvasive extraction of FECG from electrocardiogram (ECG) signals becomes a hot research point. A new method, the Support Vector Machine (SVM) is utilized for the extraction of FECG with limited size of data. Firstly, the theory of the SVM and the principle of the extraction based on the SVM are studied. Secondly, the transformation of maternal electrocardiogram (MECG) component in abdominal composite signal is verified to be nonlinear and fitted with the SVM. Then, the SVM is trained, and the training results are compared with the real data to ensure the effect of the training. Meanwhile, the parameters of the SVM are optimized to achieve the best performance so that the learning machine can be utilized to fit the unknown samples. Finally, the FECG is extracted by removing the optimal estimation of MECG component from the abdominal composite signal. In order to evaluate the performance of FECG extraction based on the SVM, the Signal-to-Noise Ratio (SNR) and the visual test are used. The experimental results show that the FECG with good quality can be extracted, its SNR ratio is significantly increased as high as 9.2349 dB and the time cost is significantly decreased as short as 0.802 seconds. Compared with the traditional method, the noninvasive extraction method based on the SVM has a simple realization, the shorter treatment time and the better extraction quality under the same conditions.

  13. ACR-1000: Operator - based development

    International Nuclear Information System (INIS)

    Shalaby, B.; Alizadeh, A.

    2007-01-01

    Atomic Energy of Canada Limited (AECL) has adapted the successful features of CANDU * reactors to establish Generation III + Advanced CANDU Reactor T M (ACR T M) technology. The ACR-1000 T M nuclear power plant is an evolutionary product, starting with the strong base of CANDU reactor technology, coupled with thoroughly-demonstrated innovative features to enhance economics, safety, operability and maintainability. The ACR-1000 benefits from AECL's continuous-improvement approach to design, that enabled the traditional CANDU 6 product to compile an exceptional track record of on-time, on budget product delivery, and also reliable, high capacity-factor operation. The ACR-1000 engineering program has completed the basic plant design and has entered detailed pre-project engineering and formal safety analysis to prepare the preliminary (non-project-specific) safety case. The engineering program is strongly operator-based, and encompasses much more than traditional pre-project design elements. A team of utility-experienced operations and maintenance experts is embedded in the engineering team, to ensure that all design decisions, at the system and the component level, are taken with the owner-operator interest in mind. The design program emphasizes formal review of operating feedback, along with extensive operator participation in program management and execution. Design attention is paid to layout and access of equipment, to component and material selection, and to ensuring maximum ability for on-line maintenance. This enables the ACR-1000 to offer a three-year interval between scheduled maintenance outages, with a standard 21-day outage duration. SMART CANDU T M technology allows on-line monitoring and diagnostics to further enhance plant operation. Modules of the Advanced CANDU SMART technologies are already being back-fitted to current CANDU plants. As well as reviewing the ACR-1000 design features and their supporting background, the paper describes the status of

  14. New generation of human machine interfaces for controlling UAV through depth-based gesture recognition

    Science.gov (United States)

    Mantecón, Tomás.; del Blanco, Carlos Roberto; Jaureguizar, Fernando; García, Narciso

    2014-06-01

    New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

  15. Cosmic String Detection with Tree-Based Machine Learning

    Science.gov (United States)

    Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

    2018-05-01

    We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

  16. SAM: Support Vector Machine Based Active Queue Management

    International Nuclear Information System (INIS)

    Shah, M.S.

    2014-01-01

    Recent years have seen an increasing interest in the design of AQM (Active Queue Management) controllers. The purpose of these controllers is to manage the network congestion under varying loads, link delays and bandwidth. In this paper, a new AQM controller is proposed which is trained by using the SVM (Support Vector Machine) with the RBF (Radial Basis Function) kernal. The proposed controller is called the support vector based AQM (SAM) controller. The performance of the proposed controller has been compared with three conventional AQM controllers, namely the Random Early Detection, Blue and Proportional Plus Integral Controller. The preliminary simulation studies show that the performance of the proposed controller is comparable to the conventional controllers. However, the proposed controller is more efficient in controlling the queue size than the conventional controllers. (author)

  17. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    V. Dheepa

    2012-07-01

    Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.

  18. Machine-Learning-Based No Show Prediction in Outpatient Visits

    Directory of Open Access Journals (Sweden)

    Carlos Elvira

    2018-03-01

    Full Text Available A recurring problem in healthcare is the high percentage of patients who miss their appointment, be it a consultation or a hospital test. The present study seeks patient’s behavioural patterns that allow predicting the probability of no- shows. We explore the convenience of using Big Data Machine Learning models to accomplish this task. To begin with, a predictive model based only on variables associated with the target appointment is built. Then the model is improved by considering the patient’s history of appointments. In both cases, the Gradient Boosting algorithm was the predictor of choice. Our numerical results are considered promising given the small amount of information available. However, there seems to be plenty of room to improve the model if we manage to collect additional data for both patients and appointments.

  19. Control volume based modelling of compressible flow in reciprocating machines

    DEFF Research Database (Denmark)

    Andersen, Stig Kildegård; Thomsen, Per Grove; Carlsen, Henrik

    2004-01-01

    , and multidimensional effects must be calculated using empirical correlations; correlations for steady state flow can be used as an approximation. A transformation that assumes ideal gas is presented for transforming equations for masses and energies in control volumes into the corresponding pressures and temperatures......An approach to modelling unsteady compressible flow that is primarily one dimensional is presented. The approach was developed for creating distributed models of machines with reciprocating pistons but it is not limited to this application. The approach is based on the integral form of the unsteady...... conservation laws for mass, energy, and momentum applied to a staggered mesh consisting of two overlapping strings of control volumes. Loss mechanisms can be included directly in the governing equations of models by including them as terms in the conservation laws. Heat transfer, flow friction...

  20. A Machine Learning Based Intrusion Impact Analysis Scheme for Clouds

    Directory of Open Access Journals (Sweden)

    Junaid Arshad

    2012-01-01

    Full Text Available Clouds represent a major paradigm shift, inspiring the contemporary approach to computing. They present fascinating opportunities to address dynamic user requirements with the provision of on demand expandable computing infrastructures. However, Clouds introduce novel security challenges which need to be addressed to facilitate widespread adoption. This paper is focused on one such challenge - intrusion impact analysis. In particular, we highlight the significance of intrusion impact analysis for the overall security of Clouds. Additionally, we present a machine learning based scheme to address this challenge in accordance with the specific requirements of Clouds for intrusion impact analysis. We also present rigorous evaluation performed to assess the effectiveness and feasibility of the proposed method to address this challenge for Clouds. The evaluation results demonstrate high degree of effectiveness to correctly determine the impact of an intrusion along with significant reduction with respect to the intrusion response time.

  1. METAPHOR: Probability density estimation for machine learning based photometric redshifts

    Science.gov (United States)

    Amaro, V.; Cavuoti, S.; Brescia, M.; Vellucci, C.; Tortora, C.; Longo, G.

    2017-06-01

    We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).

  2. Machine learning based global particle indentification algorithms at LHCb experiment

    CERN Multimedia

    Derkach, Denis; Likhomanenko, Tatiana; Rogozhnikov, Aleksei; Ratnikov, Fedor

    2017-01-01

    One of the most important aspects of data processing at LHC experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging CHerenkov (RICH) detector, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, several neural networks including a deep architecture and gradient boosting have been applied to data. These new approaches provide higher identification efficiencies than existing implementations for all charged particle types. It is also necessary to achieve a flat dependency between efficiencies and spectator variables such as particle momentum, in order to reduce systematic uncertainties during later stages of data analysis. For this purpose, "flat” algorithms that guarantee the flatness property for efficiencies have also been developed. This talk presents this new approach based on machine learning and its performance.

  3. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  4. Explaining Support Vector Machines: A Color Based Nomogram.

    Directory of Open Access Journals (Sweden)

    Vanya Van Belle

    Full Text Available Support vector machines (SVMs are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models.In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables.Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant. When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable.This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method.

  5. Determination operation Time Risk of Box Spinning Components-oe Spinning Machine

    OpenAIRE

    Slobodan Stefanovic

    2013-01-01

    Based on the constructed dependency diagram reliability of the exploitation operation time of each constituent components of the analyzed frame in the case of selected statistical distributions, areas of the operation exploitation and repair intervals are determined. This is done by determining the first inflection points. Based on these points analysis to determine the time of safety operation of frame components with allowable risk to the segmental linear function of the intensity of failur...

  6. Dynamoelectric machine with a superconductive field winding that can operate in either a synchronous or an asynchronous mode

    International Nuclear Information System (INIS)

    Mole, C.J.; Haller, H.E. III.

    1977-01-01

    Two parallel magnetic flux paths are provided in a dynamoelectric machine having a superconductive field winding. A first, or main, magnetic flux path includes at least one area of nonferromagnetic or diamagnetic material. A second, or shunt, magnetic flux path prevents the relatively low frequency ac flux present during starting or asynchronous operation of the machine, when used as an ac motor, from penetrating the superconductive winding

  7. Human machine interface to manually drive rhombic like vehicles in remote handling operations

    Energy Technology Data Exchange (ETDEWEB)

    Lopes, Pedro; Vale, Alberto [Instituto de Plasmas e Fusao Nuclear, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Ventura, Rodrigo [Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal)

    2015-07-01

    In the thermonuclear experimental reactor ITER, a vehicle named CTS is designed to transport a container with activated components inside the buildings. In nominal operations, the CTS is autonomously guided under supervision. However, in some unexpected situations, such as in rescue and recovery operations, the autonomous mode must be overridden and the CTS must be remotely guided by an operator. The CTS is a rhombic-like vehicle, with two drivable and steerable wheels along its longitudinal axis, providing omni-directional capabilities. The rhombic kinematics correspond to four control variables, which are difficult to manage in manual mode operation. This paper proposes a Human Machine Interface (HMI) to remotely guide the vehicle in manual mode. The proposed solution is implemented using a HMI with an encoder connected to a micro-controller and an analog 2-axis joystick. Experimental results were obtained comparing the proposed solution with other controller devices in different scenarios and using a software platform that simulates the kinematics and dynamics of the vehicle. (authors)

  8. Human machine interface to manually drive rhombic like vehicles in remote handling operations

    International Nuclear Information System (INIS)

    Lopes, Pedro; Vale, Alberto; Ventura, Rodrigo

    2015-01-01

    In the thermonuclear experimental reactor ITER, a vehicle named CTS is designed to transport a container with activated components inside the buildings. In nominal operations, the CTS is autonomously guided under supervision. However, in some unexpected situations, such as in rescue and recovery operations, the autonomous mode must be overridden and the CTS must be remotely guided by an operator. The CTS is a rhombic-like vehicle, with two drivable and steerable wheels along its longitudinal axis, providing omni-directional capabilities. The rhombic kinematics correspond to four control variables, which are difficult to manage in manual mode operation. This paper proposes a Human Machine Interface (HMI) to remotely guide the vehicle in manual mode. The proposed solution is implemented using a HMI with an encoder connected to a micro-controller and an analog 2-axis joystick. Experimental results were obtained comparing the proposed solution with other controller devices in different scenarios and using a software platform that simulates the kinematics and dynamics of the vehicle. (authors)

  9. Man-machine interface systems and operator training program for ABWR in Japan

    International Nuclear Information System (INIS)

    Kunito, Susumu

    2004-01-01

    The Tokyo Electric Power Company (TEPCO) has developed a new Main Control Room design for the Advanced Boiling Water Reactor (ABWR) to improve man-machine interface. New configuration of panels and enhanced automation are some of the features of the ABWR type Main Control Room design. Various technologies such as Cathode Ray Tubes (CRTs) and Flat Displays (FDs) with touch-sensitive operations are contributed to the development of the ABWR type control room design. This design will be first applied to Kashiwazaki-Kariwa Nuclear Power Station unit 6 (K-6). To train the operators sufficiently, TEPCO reviewed the operator training program. Compared with the conventional training, new training menu will be added and the training of ABWR operators will be started 6 months earlier. An ABWR simulator is under construction and training using this simulator is scheduled to be started in August 1994, which is 18 months before fuel loading of K-6. We are reviewing malfunction modes on the simulator. (author)

  10. Graphic Arts--Offset Press Operator/Duplicating Machine. TI-622. Instructor's Manual and Student Learning Activity Guide.

    Science.gov (United States)

    Michelsen, Robert F.

    This instructor's manual and student learning activity guide comprise a kit for a graphic arts activity on offset press operator/duplicating machine. Purpose stated for the activity is to provide the student with an understanding of the basic operation involved in the production of printed matter in the graphic communications industry through the…

  11. Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

    Science.gov (United States)

    Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

    2018-01-01

    Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie

  12. Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

    Directory of Open Access Journals (Sweden)

    Zilong Zhou

    2018-01-01

    Full Text Available The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM, naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

  13. Control of soft machines using actuators operated by a Braille display.

    Science.gov (United States)

    Mosadegh, Bobak; Mazzeo, Aaron D; Shepherd, Robert F; Morin, Stephen A; Gupta, Unmukt; Sani, Idin Zhalehdoust; Lai, David; Takayama, Shuichi; Whitesides, George M

    2014-01-07

    One strategy for actuating soft machines (e.g., tentacles, grippers, and simple walkers) uses pneumatic inflation of networks of small channels in an elastomeric material. Although the management of a few pneumatic inputs and valves to control pressurized gas is straightforward, the fabrication and operation of manifolds containing many (>50) independent valves is an unsolved problem. Complex pneumatic manifolds-often built for a single purpose-are not easily reconfigured to accommodate the specific inputs (i.e., multiplexing of many fluids, ranges of pressures, and changes in flow rates) required by pneumatic systems. This paper describes a pneumatic manifold comprising a computer-controlled Braille display and a micropneumatic device. The Braille display provides a compact array of 64 piezoelectric actuators that actively close and open elastomeric valves of a micropneumatic device to route pressurized gas within the manifold. The positioning and geometries of the valves and channels in the micropneumatic device dictate the functionality of the pneumatic manifold, and the use of multi-layer soft lithography permits the fabrication of networks in a wide range of configurations with many possible functions. Simply exchanging micropneumatic devices of different designs enables rapid reconfiguration of the pneumatic manifold. As a proof of principle, a pneumatic manifold controlled a soft machine containing 32 independent actuators to move a ball above a flat surface.

  14. Quality assurance (QA) for operations of fusion machines as applied to the tandem mirror experiment upgrade (TMX-U)

    International Nuclear Information System (INIS)

    Chargin, A.K.; Damm, C.C.; Turner, W.C.

    1983-01-01

    Even the best QA plan and its successful execution during construction of a typical fusion machine will produce hardware that is inoperative for some fraction of time. Operating a machine with its hardware out of tolerance, with respect to the specifications, does produce data which is the goal of the experiment. However, a majority of such data are difficult to interpret and may not contribute to understanding the behavior of the experiment. In addition, few fusion machines just operate. The majority of the machines are in the process of being rebuilt and/or added to as they operate. These modifications can keep an otherwise operational machine from running. To insure quality in operation of TMX-U, the authors employ a series of QA procedures. They start with technical milestones, schedules, and budgets that are all negotiated with DOE. Within that framework they implement a total management scheme which, in addition to normal schedule and budget controls, includes: detailed experimental run plans, definition of machine configuration required to accomplish the run plan, subsystem work-ups, instrument calibration, verification of subsystem operation, and repetition of standard physics plasma parameters. All of these activities must be completed before taking data for the experimental run plan. If a subsystem is found out of tolerance, a decision must be made either to delay operation and fix the problem or to continue on a contingency-run plan which should still produce the data relevant to the project milestones. In this presentation those QA procedures for TMX-U operations that are applied to minimize the cost and time required to achieve the technical objectives are discussed

  15. Product Quality Modelling Based on Incremental Support Vector Machine

    International Nuclear Information System (INIS)

    Wang, J; Zhang, W; Qin, B; Shi, W

    2012-01-01

    Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

  16. Motor proteins and molecular motors: how to operate machines at the nanoscale

    International Nuclear Information System (INIS)

    Kolomeisky, Anatoly B

    2013-01-01

    Several classes of biological molecules that transform chemical energy into mechanical work are known as motor proteins or molecular motors. These nanometer-sized machines operate in noisy stochastic isothermal environments, strongly supporting fundamental cellular processes such as the transfer of genetic information, transport, organization and functioning. In the past two decades motor proteins have become a subject of intense research efforts, aimed at uncovering the fundamental principles and mechanisms of molecular motor dynamics. In this review, we critically discuss recent progress in experimental and theoretical studies on motor proteins. Our focus is on analyzing fundamental concepts and ideas that have been utilized to explain the non-equilibrium nature and mechanisms of molecular motors. (topical review)

  17. Efficient implementations of block sparse matrix operations on shared memory vector machines

    International Nuclear Information System (INIS)

    Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

    2000-01-01

    In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

  18. Realization of universal optimal quantum machines by projective operators and stochastic maps

    International Nuclear Information System (INIS)

    Sciarrino, F.; Sias, C.; Ricci, M.; De Martini, F.

    2004-01-01

    Optimal quantum machines can be implemented by linear projective operations. In the present work a general qubit symmetrization theory is presented by investigating the close links to the qubit purification process and to the programmable teleportation of any generic optimal antiunitary map. In addition, the contextual realization of the N→M cloning map and of the teleportation of the N→(M-N) universal-NOT (UNOT) gate is analyzed by a very general angular momentum theory. An extended set of experimental realizations by state symmetrization linear optical procedures is reported. These include the 1→2 cloning process, the UNOT gate and the quantum tomographic characterization of the optimal partial transpose map of polarization encoded qubits

  19. Component Pin Recognition Using Algorithms Based on Machine Learning

    Science.gov (United States)

    Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

    2018-04-01

    The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

  20. Large A.C. machines theory and investigation methods of currents and losses in stator and rotor meshes including operation with nonlinear loads

    CERN Document Server

    Boguslawsky, Iliya; Hayakawa, Masashi

    2017-01-01

    In this monograph the authors solve the modern scientific problems connected with A.C. motors and generators, based first on the detailed consideration of their physical phenomena. The authors describe the theory and investigative methods they developed and applied in practice, which are considered to be of essential interest for specialists in the field of the electrical engineering industry in European countries, the USA, Argentina, and Brazil, as well as in such countries as India, China, and Iran. This book will be of interest to engineers specialized in the field of the manufacture, operation, and repair of A.C. machines (motors and generators) as well as electric drives; to professors, lecturers, and post-graduate students of technical universities, who are specializing in the field of electric machine engineering and electric drives; and to students who are engaged in the field of high current techniques, electric drives, and electric machine engineering.

  1. Towards Measuring the Abstractness of State Machines based on Mutation Testing

    Directory of Open Access Journals (Sweden)

    Thomas Baar

    2017-01-01

    Full Text Available Abstract. The notation of state machines is widely adopted as a formalism to describe the behaviour of systems. Usually, multiple state machine models can be developed for the very same software system. Some of these models might turn out to be equivalent, but, in many cases, different state machines describing the same system also differ in their level of abstraction. In this paper, we present an approach to actually measure the abstractness level of state machines w.r.t. a given implemented software system. A state machine is considered to be less abstract when it is conceptionally closer to the implemented system. In our approach, this distance between state machine and implementation is measured by applying coverage criteria known from software mutation testing. Abstractness of state machines can be considered as a new metric. As for other metrics as well, a known value for the abstractness of a given state machine allows to assess its quality in terms of a simple number. In model-based software development projects, the abstract metric can help to prevent model degradation since it can actually measure the semantic distance from the behavioural specification of a system in form of a state machine to the current implementation of the system. In contrast to other metrics for state machines, the abstractness cannot be statically computed based on the state machine’s structure, but requires to execute both state machine and corresponding system implementation. The article is published in the author’s wording. 

  2. Accuracy Enhancement with Processing Error Prediction and Compensation of a CNC Flame Cutting Machine Used in Spatial Surface Operating Conditions

    Directory of Open Access Journals (Sweden)

    Shenghai Hu

    2017-04-01

    Full Text Available This study deals with the precision performance of the CNC flame-cutting machine used in spatial surface operating conditions and presents an accuracy enhancement method based on processing error modeling prediction and real-time compensation. Machining coordinate systems and transformation matrix models were established for the CNC flame processing system considering both geometric errors and thermal deformation effects. Meanwhile, prediction and compensation models were constructed related to the actual cutting situation. Focusing on the thermal deformation elements, finite element analysis was used to measure the testing data of thermal errors, the grey system theory was applied to optimize the key thermal points, and related thermal dynamics models were carried out to achieve high-precision prediction values. Comparison experiments between the proposed method and the teaching method were conducted on the processing system after performing calibration. The results showed that the proposed method is valid and the cutting quality could be improved by more than 30% relative to the teaching method. Furthermore, the proposed method can be used under any working condition by making a few adjustments to the prediction and compensation models.

  3. Numerical investigation of refrigeration machine compressor operation considering single-phase electric motor dynamic characteristics

    Science.gov (United States)

    Baidak, Y.; Smyk, V.

    2017-08-01

    Using as the base the differential equations system which was presented in relative units for generalized electric motor of hermetic refrigeration compressor, mathematical model of the software for dynamic performance calculation of refrigeration machine compressors drive low-power asynchronous motors was developed. Performed on its ground calculations of the basic model of two-phase electric motor drive of hermetic compressor and the proposed newly developed model of the motor with single-phase stator winding, which is an alternative to the industrial motor winding, have confirmed the benefits of the motor with innovative stator winding over the base engine. Given calculations of the dynamic characteristics of compressor drive motor have permitted to determine the value of electromagnetic torque swinging for coordinating compressor and motor mechanical characteristics, and for taking them into consideration in choosing compressor elements construction materials. Developed and used in the process of investigation of refrigeration compressor drive asynchronous single-phase motor mathematical and software can be considered as an element of computer-aided design system for design of the aggregate of refrigeration compression unit refrigerating machine.

  4. An Interactive Web-based Learning System for Assisting Machining Technology Education

    Directory of Open Access Journals (Sweden)

    Min Jou

    2008-05-01

    Full Text Available The key technique of manufacturing methods is machining. The degree of technique of machining directly affects the quality of the product. Therefore, the machining technique is of primary importance in promoting student practice ability during the training process. Currently, practical training is applied in shop floor to discipline student’s practice ability. Much time and cost are used to teach these techniques. Particularly, computerized machines are continuously increasing in use. The development of educating engineers on computerized machines becomes much more difficult than with traditional machines. This is because of the limitation of the extremely expensive cost of teaching. The quality and quantity of teaching cannot always be promoted in this respect. The traditional teaching methods can not respond well to the needs of the future. Therefore, this research aims to the following topics; (1.Propose the teaching strategies for the students to learning machining processing planning through web-based learning system. (2.Establish on-line teaching material for the computer-aided manufacturing courses including CNC coding method, CNC simulation. (3.Develop the virtual machining laboratory to bring the machining practical training to web-based learning system. (4.Integrate multi-media and virtual laboratory in the developed e-learning web-based system to enhance the effectiveness of machining education through web-based system.

  5. Adaptive Training and Collective Decision Support Based on Man-Machine Interface

    Science.gov (United States)

    2016-03-02

    Based on Man -machine Interface The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an...ADDRESS (ES) U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 adaptive training, EEG, man -machine interface...non peer-reviewed journals: Final Report: Adaptive Training and Collective Decision Support Based on Man -machine Interface Report Title The existence of

  6. Neck-Shoulder Pain and Work Status among Former Sewing Machine Operators: A 14-year Follow-up Study.

    Science.gov (United States)

    Jakobsen, Emma Lise Thorlund; Biering, Karin; Kærgaard, Anette; Andersen, Johan Hviid

    2018-03-01

    Purpose A total of 243 Danish female sewing machine operators lost their jobs in 1996 because of outsourcing. The aim was to investigate the employment status during follow-up from 1996 to 2008, and to estimate to what extent former neck-shoulder pain had an impact on later work participation. Methods Assessment of neck-shoulder pain was based on questionnaires completed in 1994. The Danish Register-Based Evaluation of Marginalization (DREAM) register was used to describe employment status during the follow-up period. Register data were explored by sequence analyses and graphics, and the association between neck-shoulder pain and work participation was analyzed by logistic regression analysis. Results In all, 987 working years were lost during follow-up, and a sequence index plot revealed interrupted and heterogeneous courses of incomes. The odds ratio between neck and shoulder pain and a work participation score less than 75% was 1.49 (95% CI 0.84-2.67). Conclusions After outsourcing of the textile industry, the former sewing machine operators had decreased work participation and frequent transitions between different income types. Previous neck-shoulder pain tended to be associated with poor work participation. The results suggest that increased attention should be to given to dismissed workers from other industries that become outsourced, especially unskilled workers with similar work-related health limitations. Additionally, we concluded that time-to-event measures in research involving employment status are insufficient because of the many transitions that take place in working life.

  7. Environmental noise forecasting based on support vector machine

    Science.gov (United States)

    Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

    2018-01-01

    As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

  8. Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

    Science.gov (United States)

    Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

    2017-12-01

    Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

  9. Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

    Directory of Open Access Journals (Sweden)

    Vicente García-Díaz

    2015-12-01

    Full Text Available Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.

  10. Optimization of fuel exchange machine operation for boiling water reactors using an artificial intelligence technique

    International Nuclear Information System (INIS)

    Sekimizu, K.; Araki, T.; Tatemichi, S.I.

    1987-01-01

    Optimization of fuel assembly exchange machine movements during periodic refueling outage is discussed. The fuel assembly movements during a fuel shuffling were examined, and it was found that the fuel assembly movements consist of two different movement sequences;one is the ''PATH,'' which begins at a discharged fuel assembly and terminates at a fresh fuel assembly, and the other is the ''LOOP,'' where fuel assemblies circulate in the core. It is also shown that fuel-loading patterns during the fuel shuffling can be expressed by the state of each PATH, which is the number of elements already accomplished in the PATH actions. Based on this fact, a scheme to determine a fuel assembly movement sequence within the constraint was formulated using the artificial intelligence language PROLOG. An additional merit to the scheme is that it can simultaneously evaluate fuel assembly movement, due to the control rods and local power range monitor exchange, in addition to normal fuel shuffling. Fuel assembly movements, for fuel shuffling in a 540-MW(electric) boiling water reactor power plant, were calculated by this scheme. It is also shown that the true optimization to minimize the fuel exchange machine movements would be costly to obtain due to the number of alternatives that would need to be evaluated. However, a method to obtain a quasi-optimum solution is suggested

  11. Multidisciplinary Investigations Regarding the Wear of Machine Tools Operating Into the Soil

    Science.gov (United States)

    Cardei, P.; Vladutoiu, L. C.; Gheorghe, G.; Fechete, T. L. V.; Chisiu, G.

    2018-01-01

    The paper presents the results obtained by the authors in investigating the problem of wear of work organs of machines working in continuous interaction with the soil. The phenomenon of the interaction of the tools of agricultural machinery for ploughing, and the soil, is a complex of phenomena, one of the most difficult to model. Among the phenomena involved in this interaction, friction and wear (of many types) are the most important. We did not take into account the chemical wear, and by the wear caused by weather conditions. Research has focused on formulating a theory that has more than a descriptive character, for it be used for application purposes. For this we used classical theoretical models, mathematical models based on the theory of continuous bodies, theory of flow of fluids around the profiles, as well as other theories, approached or not, in an attempt to solve as satisfactorily the issue of the wear, for the tools of the agricultural machines for the tillage. We also sought to highlight the fact that wear is a phenomenon on a micro and macro-scale scale, and its generating causes must ultimately be related to observable effects, on the macro-structural scale.

  12. DNA regulatory motif selection based on support vector machine ...

    African Journals Online (AJOL)

    ... machine (SVM) and its application in microarray experiment of Kashin-Beck disease. ... speed and amount of the corresponding mRNA in gene replication process. ... and revealed that some motifs may be related to the immune reactions.

  13. Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics

    Directory of Open Access Journals (Sweden)

    Haejoon Jung

    2018-01-01

    Full Text Available As an intrinsic part of the Internet of Things (IoT ecosystem, machine-to-machine (M2M communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

  14. Performance Analysis of Millimeter-Wave Multi-hop Machine-to-Machine Networks Based on Hop Distance Statistics.

    Science.gov (United States)

    Jung, Haejoon; Lee, In-Ho

    2018-01-12

    As an intrinsic part of the Internet of Things (IoT) ecosystem, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines. Millimeter-wave (mmWave) communication is another promising technology for the future communication systems to alleviate the pressure of scarce spectrum resources. For this reason, in this paper, we consider multi-hop M2M communications, where a machine-type communication (MTC) device with the limited transmit power relays to help other devices using mmWave. To be specific, we focus on hop distance statistics and their impacts on system performances in multi-hop wireless networks (MWNs) with directional antenna arrays in mmWave for M2M communications. Different from microwave systems, in mmWave communications, wireless channel suffers from blockage by obstacles that heavily attenuate line-of-sight signals, which may result in limited per-hop progress in MWNs. We consider two routing strategies aiming at different types of applications and derive the probability distributions of their hop distances. Moreover, we provide their baseline statistics assuming the blockage-free scenario to quantify the impact of blockages. Based on the hop distance analysis, we propose a method to estimate the end-to-end performances (e.g., outage probability, hop count, and transmit energy) of the mmWave MWNs, which provides important insights into mmWave MWN design without time-consuming and repetitive end-to-end simulation.

  15. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

    Science.gov (United States)

    Khandelwal, Manoj; Monjezi, M.

    2013-03-01

    Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.

  16. International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines

    CERN Document Server

    Belyaev, Alexander; Krommer, Michael

    2017-01-01

    The papers in this volume present and discuss the frontiers in the mechanics of controlled machines and structures. They are based on papers presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines held in Vienna in September 2015. The workshop continues a series of international workshops held in Linz (2008) and St. Petersburg (2010).

  17. Installation and evaluation of a nuclear power plant operator advisor based on artificial intelligence technology

    International Nuclear Information System (INIS)

    Hajek, B.K.; Miller, D.W.

    1989-01-01

    This report discusses the following topics on a Nuclear Power Plant operator advisor based on artificial Intelligence Technology; Workstation conversion; Software Conversion; V ampersand V Program Development Development; Simulator Interface Development; Knowledge Base Expansion; Dynamic Testing; Database Conversion; Installation at the Perry Simulator; Evaluation of Operator Interaction; Design of Man-Machine Interface; and Design of Maintenance Facility

  18. Design Methodology of a Brushless IPM Machine for a Zero Speed Injection Based Sensorless Control

    OpenAIRE

    Godbehere, Jonathan; Wrobel, Rafal; Drury, David; Mellor, Phil

    2015-01-01

    In this paper a design approach for a sensorless controlled, brushless, interior permanent magnet machine is attained. An initial study based on established electrical machine formulas provides the machine’s basic geometrical sizing. The next design stage combines a particle swarm optimisation (PSO) search routine with a magneto-static finite element (FE) solver to provide a more in depth optimisation. The optimisation system has been formulated to derive alternative machine design variants, ...

  19. Crack identification for rotating machines based on a nonlinear approach

    Science.gov (United States)

    Cavalini, A. A., Jr.; Sanches, L.; Bachschmid, N.; Steffen, V., Jr.

    2016-10-01

    In a previous contribution, a crack identification methodology based on a nonlinear approach was proposed. The technique uses external applied diagnostic forces at certain frequencies attaining combinational resonances, together with a pseudo-random optimization code, known as Differential Evolution, in order to characterize the signatures of the crack in the spectral responses of the flexible rotor. The conditions under which combinational resonances appear were determined by using the method of multiple scales. In real conditions, the breathing phenomenon arises from the stress and strain distribution on the cross-sectional area of the crack. This mechanism behavior follows the static and dynamic loads acting on the rotor. Therefore, the breathing crack can be simulated according to the Mayes' model, in which the crack transition from fully opened to fully closed is described by a cosine function. However, many contributions try to represent the crack behavior by machining a small notch on the shaft instead of the fatigue process. In this paper, the open and breathing crack models are compared regarding their dynamic behavior and the efficiency of the proposed identification technique. The additional flexibility introduced by the crack is calculated by using the linear fracture mechanics theory (LFM). The open crack model is based on LFM and the breathing crack model corresponds to the Mayes' model, which combines LFM with a given breathing mechanism. For illustration purposes, a rotor composed by a horizontal flexible shaft, two rigid discs, and two self-aligning ball bearings is used to compose a finite element model of the system. Then, numerical simulation is performed to determine the dynamic behavior of the rotor. Finally, the results of the inverse problem conveyed show that the methodology is a reliable tool that is able to estimate satisfactorily the location and depth of the crack.

  20. Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    Satoshi Suzuki

    2012-01-01

    Full Text Available We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trained SOM. This method enables to omit the troublesome process to specify types of information which should be used to build the estimator. Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed. As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts. On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes. Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate. And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered.

  1. Numerical simulation of the manual operation of the charging/discharging machine (MID) control desk

    International Nuclear Information System (INIS)

    Doca, C; Dobre, A

    2004-01-01

    Since the year 2000 at 7th Division TAR of Institute for Nuclear Research - Pitesti continuous efforts were made to implement a software product package devoted to numerical simulation of operations at the test bench of charging/discharging machine (MID). Till now there were specified, designed, worked out and implemented on a computer the PUPITRU code, the present version fulfilling the following requirements: - graphical output specific for the computer/human operator interface: - design at a 1 : 4 scale for each of the 25 drawers of the control desk; - graphical and functional simulation of all the physical objects mounted in these drawers, namely: 12 measuring analog instruments with linear and non-linear dials (ampermeters), 21 measuring digital instruments (voltmeters), 24 two up/down settings switches, 13 switches with three up/down settings, 23 switches with two left/right hand settings, one switch with three left/right hand settings, one switch with four left/right hand settings, 2 switches with five left/right hand settings, 68, 16, 23, 53, 81 signaling lamps of white, yellow, orange, red and green light, respectively; implementation in the frame of PUPITRU code of the main notations used in the automation schemes in the execution design of the control desk, in view of a quick identification of the physical objects: switches, lamps, instruments, etc. ; - implementation in the frame of PUPITRU code of the full database (mnemonics and numerical values) used in the frame of MID tests; - implementation of over 1000 equations of numerical simulation appropriate to the situations characteristic for test bench and MID operation. At the moment, the final functional simulation for all the control desk components is finalized. In this paper a description and a demonstration run of the PUPITRU code is presented. (authors)

  2. Evaluation of musculoskeletal disorders in sewing machine operators of a shoe manufacturing factory in Iran.

    Science.gov (United States)

    Aghili, Mir Masih Moslemi; Asilian, Hasan; Poursafa, Parinaz

    2012-03-01

    A 15-year research conducted in USA showed that compensation expenses paid to workers for musculoskeletal disorders (MSDs) of back exceeded 128 million Dollars calculated on the basis of 0.97 Dollars per hour of work. In addition, according to the latest studies carried out in relation with disease burdens with risk factors in Iran, DALYs indices for low back pain, knee arthrosis and other musculoskeletal disorders have been reported to be 307772, 291305 and 872633 respectively, which have caused the work related diseases to occupy the second position in the country, after cardiovascular diseases. On the other hand, in accordance with occupational health indices of Iranian health ministry, 37% of all working population had had poor work postures with 15% of all working population had been working with inappropriate working tools in the year 2009. This was a case study comparing exposed workers with control group using Standard Nordic Questionnair in sewing machine operators of a shoe manufacturing factory in Iran. In this study, the mentioned questionnaires were filled out for the exposed group (25 sewing machine operators with average age of 43.5 years with work records of 16.8 years) and control group (15 employees from administrative department with average age of 39.8 years with work records of 13.4 years) which both were selected through simple random method. There were statistically significant differences in age between musculoskeletal disorders of right elbow (p = 0.033), thigh (p = 0.044), both knees (p = 0.019) and ankles (p = 0.039). There were also statistically significant association between gender and musculoskeletal disorders of right elbow (p = 0.028), thigh (p = 0.026) both knees (p = 0.011); right shoulder disorders (p = 0.018) and work records; disorders of both knees (p = 0.031) and number of cigarettes smoked. In general, prevalence of disorders of cervical area, shoulders with hands, vertebral column, back, knees, thigh with feet were higher

  3. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  4. Repeatability and Reproducibility of Fibre-Based Nanogenerator Synthesized by Electrospinning Machine

    International Nuclear Information System (INIS)

    Suyitno; Huda, Sholiehul; Arifin, Zainal; Hadi, Syamsul; Lambang, Raymundus Lullus

    2014-01-01

    Zinc oxide fibres-based nanogenerators synthesized easily by electrospinning machine are promising to harvest electricity from mechanical energy. However, the repeatability and reproducibility were two major factors needed to be investigated to minimize product failure and to determine the feasibility of mass production of nanogenerators. The green fibres of zinc oxide were produced by electrospinning machine of zinc acetate and polyvinyl alcohol solution at a flow rate of 4 μL/min followed by sintering at temperature 550°C with heating rate 240°C/h. Each 10 nanogenerators was tested by three trained operators with three times of repetition at compressive load 0.5 kg. The nanogenerators revealed the maximum output voltage ranging from 203 to 217 mV. The value of repeatability and reproducibility of nanogenerators was approximately 24.29% showing that nanogenerators were still acceptable to be mass-produced. The relatively low reproducibility was mainly due to the operators, so that the checklist needed to be made easier and simpler for all the variables affecting to the quality of the fibres. Reducing the value of the repeatability and reproducibility is interesting to study further by creating a rotating collector so that the thickness and orientation of fibres can be arranged better

  5. The research on construction and application of machining process knowledge base

    Science.gov (United States)

    Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

    2018-03-01

    In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

  6. Machine intelligence and signal processing

    CERN Document Server

    Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay

    2016-01-01

    This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...

  7. Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

    Directory of Open Access Journals (Sweden)

    Li Deng

    2016-01-01

    Full Text Available In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

  8. Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

    Science.gov (United States)

    Deng, Li; Wang, Guohua; Yu, Suihuai

    2016-01-01

    In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

  9. Anodic solubility and electrochemical machining of hard alloys on the base of chromium and titanium carbides

    Energy Technology Data Exchange (ETDEWEB)

    Davydov, A D; Klepikov, A N; Malofeeva, A N; Moroz, I I

    1985-01-01

    The regularities of anodic behaviour and electrochemical machining (ECM) of the samples of three materials with the following compositions: 25% of Cr/sub 3/C/sub 2/, 15% of Ni, 70% of TiC, 25% of Ni, 5% of Cr, 70% of TiC, 15% of Ni, 15% of Mo are investigated. It is shown that the electrochemical method is applicable to hard alloys machining on the base of chromium and titanium carbides, the machining of which mechanically meets serious difficulties. The alloys machining rate by a mobile cathode constitutes about 0.5 mm/min.

  10. Distributed Control System Design for Portable PC Based CNC Machine

    Directory of Open Access Journals (Sweden)

    Roni Permana Saputra

    2014-07-01

    Full Text Available The demand on automated machining has been increased and emerges improvement research to achieve many goals such as portability, low cost manufacturability, interoperability, and simplicity in machine usage. These improvements are conducted without ignoring the performance analysis and usability evaluation. This research has designed a distributed control system in purpose to control a portable CNC machine. The design consists of main processing unit, secondary processing unit, motor control, and motor driver. A preliminary simulation has been conducted for performance analysis including linear accuracy and circular accuracy. The results achieved in the simulation provide linear accuracy up to 2 μm with total cost for the whole processing unit is up to 5 million IDR.

  11. A new accurate curvature matching and optimal tool based five-axis machining algorithm

    International Nuclear Information System (INIS)

    Lin, Than; Lee, Jae Woo; Bohez, Erik L. J.

    2009-01-01

    Free-form surfaces are widely used in CAD systems to describe the part surface. Today, the most advanced machining of free from surfaces is done in five-axis machining using a flat end mill cutter. However, five-axis machining requires complex algorithms for gouging avoidance, collision detection and powerful computer-aided manufacturing (CAM) systems to support various operations. An accurate and efficient method is proposed for five-axis CNC machining of free-form surfaces. The proposed algorithm selects the best tool and plans the tool path autonomously using curvature matching and integrated inverse kinematics of the machine tool. The new algorithm uses the real cutter contact tool path generated by the inverse kinematics and not the linearized piecewise real cutter location tool path

  12. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  13. A Rational Deconstruction of Landin's SECD Machine with the J Operator

    DEFF Research Database (Denmark)

    Danvy, Olivier; Millikin, Kevin

    2008-01-01

    to this extension of the SECD machine, using a series of elementary transformations (transformation into continuation-passing style (CPS) and defunctionalization, chiefly) and their left inverses (transformation into direct style and refunctionalization). To this end, we modernize the SECD machine into a bisimilar...

  14. Operating Regions of Adjustable-Speed Units with Doubly Fed Machines

    Czech Academy of Sciences Publication Activity Database

    Schreier, Luděk; Chomát, Miroslav; Bendl, Jiří

    2004-01-01

    Roč. 49, č. 2 (2004), s. 119-136 ISSN 0001-7043 R&D Projects: GA AV ČR IAA2057102 Keywords : AC machines * adjustable-speed systems * doubly fed machine Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering

  15. 3D GIS spatial operation based on extended Euler operators

    Science.gov (United States)

    Xu, Hongbo; Lu, Guonian; Sheng, Yehua; Zhou, Liangchen; Guo, Fei; Shang, Zuoyan; Wang, Jing

    2008-10-01

    The implementation of 3 dimensions spatial operations, based on certain data structure, has a lack of universality and is not able to treat with non-manifold cases, at present. ISO/DIS 19107 standard just presents the definition of Boolean operators and set operators for topological relationship query, and OGC GeoXACML gives formal definitions for several set functions without implementation detail. Aiming at these problems, based mathematical foundation on cell complex theory, supported by non-manifold data structure and using relevant research in the field of non-manifold geometry modeling for reference, firstly, this paper according to non-manifold Euler-Poincaré formula constructs 6 extended Euler operators and inverse operators to carry out creating, updating and deleting 3D spatial elements, as well as several pairs of supplementary Euler operators to convenient for implementing advanced functions. Secondly, we change topological element operation sequence of Boolean operation and set operation as well as set functions defined in GeoXACML into combination of extended Euler operators, which separates the upper functions and lower data structure. Lastly, we develop underground 3D GIS prototype system, in which practicability and credibility of extended Euler operators faced to 3D GIS presented by this paper are validated.

  16. Constant Cutting Force Control for CNC Machining Using Dynamic Characteristic-Based Fuzzy Controller

    Directory of Open Access Journals (Sweden)

    Hengli Liu

    2015-01-01

    Full Text Available This paper presents a dynamic characteristic-based fuzzy adaptive control algorithm (DCbFACA to avoid the influence of cutting force changing rapidly on the machining stability and precision. The cutting force is indirectly obtained in real time by monitoring and extraction of the motorized spindle current, the feed speed is fuzzy adjusted online, and the current was used as a feedback to control cutting force and maintain the machining process stable. Different from the traditional fuzzy control methods using the experience-based control rules, and according to the complex nonlinear characteristics of CNC machining, the power bond graph method is implemented to describe the dynamic characteristics of process, and then the appropriate variation relations are achieved between current and feed speed, and the control rules are optimized and established based on it. The numerical results indicated that DCbFACA can make the CNC machining process more stable and improve the machining precision.

  17. Simulation and Community-Based Instruction of Vending Machines with Time Delay.

    Science.gov (United States)

    Browder, Diane M.; And Others

    1988-01-01

    The study evaluated the use of simulated instruction on vending machine use as an adjunct to community-based instruction with two moderately retarded children. Results showed concurrent acquisition of the vending machine skills across trained and untrained sites. (Author/DB)

  18. Global detection of live virtual machine migration based on cellular neural networks.

    Science.gov (United States)

    Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

    2014-01-01

    In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

  19. Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2014-01-01

    Full Text Available In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM migration detection algorithm based on the cellular neural networks (CNNs, is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation allowing the VM migration detection to be performed better.

  20. Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

    Energy Technology Data Exchange (ETDEWEB)

    Klymenko, M. V. [Department of Chemistry, University of Liège, B4000 Liège (Belgium); Klein, M. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Levine, R. D. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Crump Institute for Molecular Imaging and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine and Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095 (United States); Remacle, F., E-mail: fremacle@ulg.ac.be [Department of Chemistry, University of Liège, B4000 Liège (Belgium); The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel)

    2016-07-14

    A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.

  1. Operation of a quantum dot in the finite-state machine mode: Single-electron dynamic memory

    International Nuclear Information System (INIS)

    Klymenko, M. V.; Klein, M.; Levine, R. D.; Remacle, F.

    2016-01-01

    A single electron dynamic memory is designed based on the non-equilibrium dynamics of charge states in electrostatically defined metallic quantum dots. Using the orthodox theory for computing the transfer rates and a master equation, we model the dynamical response of devices consisting of a charge sensor coupled to either a single and or a double quantum dot subjected to a pulsed gate voltage. We show that transition rates between charge states in metallic quantum dots are characterized by an asymmetry that can be controlled by the gate voltage. This effect is more pronounced when the switching between charge states corresponds to a Markovian process involving electron transport through a chain of several quantum dots. By simulating the dynamics of electron transport we demonstrate that the quantum box operates as a finite-state machine that can be addressed by choosing suitable shapes and switching rates of the gate pulses. We further show that writing times in the ns range and retention memory times six orders of magnitude longer, in the ms range, can be achieved on the double quantum dot system using experimentally feasible parameters, thereby demonstrating that the device can operate as a dynamic single electron memory.

  2. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

    Science.gov (United States)

    Zhang, Jianhua; Yin, Zhong; Wang, Rubin

    2017-01-01

    This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

  3. Open Source Powder based Rapid Prototyping Machine for Ceramics

    NARCIS (Netherlands)

    Budding, A.; Vaneker, Thomas H.J.; Winnubst, Aloysius J.A.

    2013-01-01

    3DP (Three Dimensional Printing) technology is one of the SFF (Solid Freeform Fabrication) technologies which have recently come into the spotlight due to its adaptability to various applications. However, commercial 3DP machines are limited as to the use of building material, without voiding the

  4. The systematic development of a machine vision based milking robot

    NARCIS (Netherlands)

    Gouws, J.

    1993-01-01

    Agriculture involves unique interactions between man, machines, and various elements from nature. Therefore the implementation of advanced technology in agriculture holds different challenges than in other sectors of the economy. This dissertation stems from research into the application of

  5. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

    Science.gov (United States)

    2016-06-01

    monitoring. This analyzed payload is within the application layer of the OSI model . The analysis tries to establish whether or not the payload is...24 3.2.5 Model Drift Experiments...ADVERSARIAL ENVIRONMENTS (SPIE DSS 2014) .................................................. 58 APPENDIX C - EVALUATING MODEL DRIFT IN MACHINE LEARNING

  6. Application for vibration monitoring of aspheric surface machining based on wireless sensor networks

    Science.gov (United States)

    Han, Chun Guang; Guo, Yin Biao; Jiang, Chen

    2010-05-01

    Any kinds of tiny vibration of machine tool parts will have a great influence on surface quality of the workpiece at ultra-precise machining process of aspheric surface. At present the major way for decreasing influence of vibration is machining compensation technology. Therefore it is important for machining compensation control to acquire and transmit these vibration signals effectively. This paper presents a vibration monitoring system of aspheric surface machining machine tool based on wireless sensor networks (WSN). Some key issues of wireless sensor networks for vibration monitoring system of aspheric surface machining are discussed. The reliability of data transmission, network communication protocol and synchronization mechanism of wireless sensor networks are studied for the vibration monitoring system. The proposed system achieves multi-sensors vibration monitoring involving the grinding wheel, the workpiece and the workbench spindle. The wireless transmission of vibration signals is achieved by the combination with vibration sensor nodes and wireless network. In this paper, these vibration sensor nodes are developed. An experimental platform is structured which employs wireless sensor networks to the vibration monitoring system in order to test acquisition and wireless transmission of vibration signal. The test results show that the proposed system can achieve vibration data transmission effectively and reliability and meet the monitoring requirements of aspheric surface machining machine tool.

  7. Influence of Chatter of VMC Arising During End Milling Operation and Cutting Conditions on Quality of Machined Surface

    Directory of Open Access Journals (Sweden)

    A.K.M.N. Amin, M.A. Rizal, and M. Razman

    2012-08-01

    Full Text Available Machine tool chatter is a dynamic instability of the cutting process. Chatter results in poor part surface finish, damaged cutting tool, and an irritating and unacceptable noise. Exten¬sive research has been undertaken to study the mechanisms of chatter formation. Efforts have been also made to prevent the occurrence of chatter vibration. Even though some progress have been made, fundamental studies on the mechanics of metal cutting are necessary to achieve chatter free operation of CNC machine tools to maintain their smooth operating cycle. The same is also true for Vertical Machining Centres (VMC, which operate at high cutting speeds and are capable of offering high metal removal rates. The present work deals with the effect of work materials, cutting conditions and diameter of end mill cutters on the frequency-amplitude characteristics of chatter and on machined surface roughness. Vibration data were recorded using an experimental rig consisting of KISTLER 3-component dynamometer model 9257B, amplifier, scope meters and a PC.  Three different types of vibrations were observed. The first type was a low frequency vibration, associated with the interrupted nature of end mill operation. The second type of vibration was associated with the instability of the chip formation process and the third type was due to chatter. The frequency of the last type remained practically unchanged over a wide range of cutting speed.  It was further observed that chip-tool contact processes had considerable effect on the roughness of the machined surface.Key Words: Chatter, Cutting Conditions, Stable Cutting, Surface Roughness.

  8. Prediction Model of Machining Failure Trend Based on Large Data Analysis

    Science.gov (United States)

    Li, Jirong

    2017-12-01

    The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.

  9. Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications

    OpenAIRE

    Okamoto, Hironao; Nishio, Takayuki; Nakashima, Kota; Koda, Yusuke; Yamamoto, Koji; Morikura, Masahiro; Asai, Yusuke; Miyatake, Ryo

    2018-01-01

    This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks. The scheme provides the future RSS prediction of any mmWave links within the camera's view, including links where nodes are not transmitting frames. This enables network controllers to conduct network operations before line-of-sight path blockages degrade the RSS. Using the ML techniques, the prediction scheme automatically...

  10. Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel based hastelloy - 276

    International Nuclear Information System (INIS)

    Khidhir, Basim A; Mohamed, Bashir

    2011-01-01

    Machining parameters has an important factor on tool wear and surface finish, for that the manufacturers need to obtain optimal operating parameters with a minimum set of experiments as well as minimizing the simulations in order to reduce machining set up costs. The cutting speed is one of the most important cutting parameter to evaluate, it clearly most influences on one hand, tool life, tool stability, and cutting process quality, and on the other hand controls production flow. Due to more demanding manufacturing systems, the requirements for reliable technological information have increased. For a reliable analysis in cutting, the cutting zone (tip insert-workpiece-chip system) as the mechanics of cutting in this area are very complicated, the chip is formed in the shear plane (entrance the shear zone) and is shape in the sliding plane. The temperature contributed in the primary shear, chamfer and sticking, sliding zones are expressed as a function of unknown shear angle on the rake face and temperature modified flow stress in each zone. The experiments were carried out on a CNC lathe and surface finish and tool tip wear are measured in process. Machining experiments are conducted. Reasonable agreement is observed under turning with high depth of cut. Results of this research help to guide the design of new cutting tool materials and the studies on evaluation of machining parameters to further advance the productivity of nickel based alloy Hastelloy - 276 machining.

  11. Effects Based Operations (EDO) Endstate

    National Research Council Canada - National Science Library

    James, Ron; Daniels, Troy

    2005-01-01

    .... An analysis of the combination of physical (e.g. petroleum, electric power) and behavioral (e.g. leadership) COGs at the operational level models was performed and the results were incorporated in the EndState Tool...

  12. Validation of a Numerical Model for the Prediction of the Annoyance Condition at the Operator Station of Construction Machines

    Directory of Open Access Journals (Sweden)

    Eleonora Carletti

    2016-11-01

    Full Text Available It is well-known that the reduction of noise levels is not strictly linked to the reduction of noise annoyance. Even earthmoving machine manufacturers are facing the problem of customer complaints concerning the noise quality of their machines with increasing frequency. Unfortunately, all the studies geared to the understanding of the relationship between multidimensional characteristics of noise signals and the auditory perception of annoyance require repeated sessions of jury listening tests, which are time-consuming. In this respect, an annoyance prediction model was developed for compact loaders to assess the annoyance sensation perceived by operators at their workplaces without repeating the full sound quality assessment but using objective parameters only. This paper aims at verifying the feasibility of the developed annoyance prediction model when applied to other kinds of earthmoving machines. For this purpose, an experimental investigation was performed on five earthmoving machines, different in type, dimension, and engine mechanical power, and the annoyance predicted by the numerical model was compared to the annoyance given by subjective listening tests. The results were evaluated by means of the squared value of the correlation coefficient, R2, and they confirm the possible applicability of the model to other kinds of machines.

  13. The Milling Assistant, Case-Based Reasoning, and machining strategy: A report on the development of automated numerical control programming systems at New Mexico State University

    Energy Technology Data Exchange (ETDEWEB)

    Burd, W. [Sandia National Labs., Albuquerque, NM (United States); Culler, D.; Eskridge, T.; Cox, L.; Slater, T. [New Mexico State Univ., Las Cruces, NM (United States)

    1993-08-01

    The Milling Assistant (MA) programming system demonstrates the automated development of tool paths for Numerical Control (NC) machine tools. By integrating a Case-Based Reasoning decision processor with a commercial CAD/CAM software, intelligent tool path files for milled and point-to-point features can be created. The operational system is capable of reducing the time required to program a variety of parts and improving product quality by collecting and utilizing ``best of practice`` machining strategies.

  14. Steering a Tractor by Means of an EMG-Based Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Sergio Alonso-Garcia

    2011-07-01

    Full Text Available An electromiographic (EMG-based human-machine interface (HMI is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.

  15. A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

    OpenAIRE

    Hamed Hassanzadeh; MohammadReza Keyvanpour

    2011-01-01

    The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as ...

  16. Support Vector Machine Based Tool for Plant Species Taxonomic Classification

    OpenAIRE

    Manimekalai .K; Vijaya.MS

    2014-01-01

    Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...

  17. Housing Value Forecasting Based on Machine Learning Methods

    OpenAIRE

    Mu, Jingyi; Wu, Fang; Zhang, Aihua

    2014-01-01

    In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...

  18. ANN Based Tool Condition Monitoring System for CNC Milling Machines

    Directory of Open Access Journals (Sweden)

    Mota-Valtierra G.C.

    2011-10-01

    Full Text Available Most of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutting tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to attach the system sensors. This paper presents an intelligent classification system which determines the status of cutt ers in a Computer Numerical Control (CNC milling machine. This tool state is mainly detected through the analysis of the cutting forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classification is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital filter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the filtered signal in order to compress the data amount and to optimize the classifier structure. Then a multilayer perceptron- type neural network is responsible for carrying out the classification of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutter.

  19. Housing Value Forecasting Based on Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Jingyi Mu

    2014-01-01

    Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

  20. Temperature estimation of induction machines based on wireless sensor networks

    Directory of Open Access Journals (Sweden)

    Y. Huang

    2018-04-01

    Full Text Available In this paper, a fourth-order Kalman filter (KF algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.

  1. Grid-connected in-stream hydroelectric generation based on the doubly fed induction machine

    Science.gov (United States)

    Lenberg, Timothy J.

    Within the United States, there is a growing demand for new environmentally friendly power generation. This has led to a surge in wind turbine development. Unfortunately, wind is not a stable prime mover, but water is. Why not apply the advances made for wind to in-stream hydroelectric generation? One important advancement is the creation of the Doubly Fed Induction Machine (DFIM). This thesis covers the application of a gearless DFIM topology for hydrokinetic generation. After providing background, this thesis presents many of the options available for the mechanical portion of the design. A mechanical turbine is then specified. Next, a method is presented for designing a DFIM including the actual design for this application. In Chapter 4, a simulation model of the system is presented, complete with a control system that maximizes power generation based on water speed. This section then goes on to present simulation results demonstrating proper operation.

  2. Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

    Directory of Open Access Journals (Sweden)

    Héctor Herrero

    2017-05-01

    Full Text Available This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

  3. PARAMETER DETERMINATION FOR ADDITIONAL OPERATING FORCE MECHANISM IN DEVICE FOR PNEUMO-CENTRIFUGAL MACHINING OF BALL-SHAPED WORKPIECES

    Directory of Open Access Journals (Sweden)

    A. A. Sukhotsky

    2014-01-01

    Full Text Available The paper describes development of the methodology for optimization of parameters for an additional operating force mechanism in a device for pneumo-centrifugal machining of glass balls. Specific feature in manufacturing glass balls for micro-optics in accordance with technological process for obtaining ball-shaped workpieces is grinding and polishing of spherical surface in a free state. In this case component billets of future balls are made in the form of cubes and the billets are given preliminary a form of ball with the help of rough grinding. An advanced method for obtaining ball-shaped work-pieces from brittle materials is a pneumocentrifugal machining. This method presupposes an application of two conic rings with abrasive working surfaces which are set coaxially with large diameters to each other and the billets are rolled along these rings. Rotation of the billets is conveyed by means of pressure medium.The present devices for pneumo-centrifugal machining are suitable for obtaining balls up to 6 mm. Machining of the work-pieces with full spherical surfaces and large diameter is non-productive due to impossibility to ensure a sufficient force on the billet in the working zone. For this reason the paper proposes a modified device where an additional force on the machined billet is created by upper working disc that is making a reciprocating motion along an axis of abrasive conic rings. The motion is realized with the help of a cylindrical camshaft mechanism in the form of a ring with a profile working end face and the purpose of present paper is to optimize parameters of the proposed device.The paper presents expressions for calculation of constitutive parameters of the additional operating force mechanism including parameters of loading element motion, main dimensions of the additional operating force mechanism and parameters of a profile element in the additional operating force mechanism.Investigation method is a mathematical

  4. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

    Science.gov (United States)

    Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

    2009-05-01

    Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

  5. A cost prediction model for machine operation in multi-field production systems

    Directory of Open Access Journals (Sweden)

    Alessandro Sopegno

    Full Text Available ABSTRACT Capacity planning in agricultural field operations needs to give consideration to the operational system design which involves the selection and dimensioning of production components, such as machinery and equipment. Capacity planning models currently onstream are generally based on average norm data and not on specific farm data which may vary from year to year. In this paper a model is presented for predicting the cost of in-field and transport operations for multiple-field and multiple-crop production systems. A case study from a real production system is presented in order to demonstrate the model’s functionalities and its sensitivity to parameters known to be somewhat imprecise. It was shown that the proposed model can provide operation cost predictions for complex cropping systems where labor and machinery are shared between the various operations which can be individually formulated for each individual crop. By so doing, the model can be used as a decision support system at the strategic level of management of agricultural production systems and specifically for the mid-term design process of systems in terms of labor/machinery and crop selection conforming to the criterion of profitability.

  6. Development of advanced human-machine system for plant operation and maintenance

    International Nuclear Information System (INIS)

    Wu, Wei; Ohi, Tadashi; Yoshikawa, Hidekazu; Sawaragi, Tetsuo; Kitamura, Masaharu; Furuta, Kazuo; Gofuku, Akio; Ito, Koji

    2004-01-01

    With the worldwide deregulation of the power industry, and the aging of the nuclear power plants (NPPs), concerns are growing over the reliability and safety of the NPPs, because the regulation of man power may lower the current high level of reliability and safety. In this paper, a concept of overall integrated plant management mechanism is proposed, in order to meet the requirements of cutting costs of NPPs and the requirements of maintaining or increasing safety and reliability. The concept is called as satellite operation maintenance center (SOMC). SOMC integrates the operation and maintenance activities of several NPP units by utilizing advanced information technologies to support cooperation activities between workers allocated at SOMC and the field workers. As for the operation activities, a framework called as Advanced Operation System (AOS) is proposed in this paper. AOS consists of three support sub-systems: dynamic operation permission system(DyOPS), supervisor information presentation system using interface agent, and crew performance evaluation system. As for the maintenance activities, a framework called as Ubiquitous-Computing-based Maintenance support System (UCMS) is proposed next. Two case studies are described, in order to show the way of how UCMS support field workers to do maintenance tasks efficiently, safely, and infallibly as well. Finally, a prospect of SOMC is shown in order to explain the way of how the technology elements developed in this project could be integrated as a whole one system to support maintenance activities of NPPs in the future. (author)

  7. Operator-based metric for nuclear operations automation assessment

    Energy Technology Data Exchange (ETDEWEB)

    Zacharias, G.L.; Miao, A.X.; Kalkan, A. [Charles River Analytics Inc., Cambridge, MA (United States)] [and others

    1995-04-01

    Continuing advances in real-time computational capabilities will support enhanced levels of smart automation and AI-based decision-aiding systems in the nuclear power plant (NPP) control room of the future. To support development of these aids, we describe in this paper a research tool, and more specifically, a quantitative metric, to assess the impact of proposed automation/aiding concepts in a manner that can account for a number of interlinked factors in the control room environment. In particular, we describe a cognitive operator/plant model that serves as a framework for integrating the operator`s information-processing capabilities with his procedural knowledge, to provide insight as to how situations are assessed by the operator, decisions made, procedures executed, and communications conducted. Our focus is on the situation assessment (SA) behavior of the operator, the development of a quantitative metric reflecting overall operator awareness, and the use of this metric in evaluating automation/aiding options. We describe the results of a model-based simulation of a selected emergency scenario, and metric-based evaluation of a range of contemplated NPP control room automation/aiding options. The results demonstrate the feasibility of model-based analysis of contemplated control room enhancements, and highlight the need for empirical validation.

  8. Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

    Science.gov (United States)

    Genuardi, Michael T.

    1993-01-01

    One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

  9. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

    OpenAIRE

    Weng, Wei-Hung; Wagholikar, Kavishwar B.; McCray, Alexa T.; Szolovits, Peter; Chueh, Henry C.

    2017-01-01

    Background The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. Methods We constructed the pipeline using the clinical ...

  10. Application of wire electrodes in electric discharge machining of metal samples of reactor blocks of the operative atomic power station

    International Nuclear Information System (INIS)

    Gozhenko, S.V.

    2007-01-01

    Features of application of electroerosive methods are considered during the process of direct definition of properties of metal of the equipment of power units of the atomic power station. Results of development of a complex of the equipment for wire electric discharge machining of metal templet and its use are presented at the control of the basic metal of the main circulating pipelines over blocks of the atomic power station of Ukraine over long terms of operation

  11. Logic synthesis for FPGA-based finite state machines

    CERN Document Server

    Barkalov, Alexander; Kolopienczyk, Malgorzata; Mielcarek, Kamil; Bazydlo, Grzegorz

    2016-01-01

    This book discusses control units represented by the model of a finite state machine (FSM). It contains various original methods and takes into account the peculiarities of field-programmable gate arrays (FPGA) chips and a FSM model. It shows that one of the peculiarities of FPGA chips is the existence of embedded memory blocks (EMB). The book is devoted to the solution of problems of logic synthesis and reduction of hardware amount in control units. The book will be interesting and useful for researchers and PhD students in the area of Electrical Engineering and Computer Science, as well as for designers of modern digital systems.

  12. Modal analysis of spindle of grinder machine based on ANSYS

    Directory of Open Access Journals (Sweden)

    HE Chaocong

    2015-10-01

    Full Text Available The object of research is to a certain type grinding wheel spindle for which a 3D model of the spindle is established by SolidWorks software and ANSYS software is imported for model analysis.Natural frequency,vibration type and critical speed of the spindle model are obtained and the resulting data are scientifically analyzed.The results show that the spindle structure is reasonable,the machining accuracy can be ensured and the position where the most severe deformation and the main shaft fatigue fracture may occur can be found out,which also provide the theoretical basis for further optimization design and precision control.

  13. Modal analysis of spindle of grinder machine based on ANSYS

    OpenAIRE

    HE Chaocong; LIU Peipei; YAN Chunfei; WANG Muhuan; LIN Jun

    2015-01-01

    The object of research is to a certain type grinding wheel spindle for which a 3D model of the spindle is established by SolidWorks software and ANSYS software is imported for model analysis.Natural frequency,vibration type and critical speed of the spindle model are obtained and the resulting data are scientifically analyzed.The results show that the spindle structure is reasonable,the machining accuracy can be ensured and the position where the most severe deformation and the main shaft fat...

  14. On Intelligent Design and Planning Method of Process Route Based on Gun Breech Machining Process

    Science.gov (United States)

    Hongzhi, Zhao; Jian, Zhang

    2018-03-01

    The paper states an approach of intelligent design and planning of process route based on gun breech machining process, against several problems, such as complex machining process of gun breech, tedious route design and long period of its traditional unmanageable process route. Based on gun breech machining process, intelligent design and planning system of process route are developed by virtue of DEST and VC++. The system includes two functional modules--process route intelligent design and its planning. The process route intelligent design module, through the analysis of gun breech machining process, summarizes breech process knowledge so as to complete the design of knowledge base and inference engine. And then gun breech process route intelligently output. On the basis of intelligent route design module, the final process route is made, edited and managed in the process route planning module.

  15. Evolution of Machine Reliability and Life and Economics of Operational Use

    Directory of Open Access Journals (Sweden)

    Młynarski Stanisław

    2016-12-01

    Full Text Available The article presents new assumptions for reliability and life of machines, resulting from the development of technology. The innovative approach to reliability and life design as well as warranty duration planning is presented on an example of vehicle reliability characteristics. A new algorithm is proposed for the replacement of repairable objects costs by the price of life and reliability of new unrepairable ones. For the planning of the life of innovative machines, an effective method of technical progress rate determination is proposed. In conclusion, necessary modifications of machine and vehicle use systems, resulting from technology evolution and technical progress, are indicated. Finally, recommendations and directions of indispensable research in engineering and management of technical means of production are formulated.

  16. The LHC machine: from beam commissioning to operation and future upgrades

    CERN Document Server

    Giovannozzi, Massimo

    2015-01-01

    This chapter describes the current status of the LHC. General machine parameters are reviewed and the beam commissioning process is presented, showing the evolution of the machine’s performance over recent years. The highlights of the powerful complex of injectors are described, in order to provide a global picture of the impressive performance of CERN’s flagship machine, which relies on both the astonishing quality of the LHC itself and the incredible flexibility of the injectors. The focus is on proton physics performance, with emphasis on the different possible scenarios leading to an upgrade of the LHC performance. Finally, the prospects for the development of the machine into the far future are briefly discussed.

  17. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

    Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.

  18. GNSS-based operational monitoring devices for forest logging operation chains

    Directory of Open Access Journals (Sweden)

    Raimondo Gallo

    2013-09-01

    Full Text Available The first results of a new approach for implementing operational monitoring tool to control the performance of forest mechanisation chains are proposed and discussed. The solution is based on Global Navigation Satellite System (GNSS tools that are the core of a datalogging system that, in combination with a specific inference-engine, is able to analyse process times, work distances, forward speeds, vehicle tracking and number of working cycles in forest operations. As a consequence the operational monitoring control methods could provide an evaluation of the efficiency of the investigated forest operations. The study has monitored the performance of a tower yarder with crane and processor-head, during logging operations. The field surveys consisted on the installation of the GNSS device directly on the forest equipment for monitoring its movements. Simultaneously the field survey considered the integration of the GNSS information with a time study of work elements based on the continuous time methods supported by a time study board. Additionally, where possible, the onboard computer of the forest machine was also used in order to obtain additional information to be integrated to the GNSS data and the time study. All the recorded GNSS data integrated with the work elements study were thus post-processed through GIS analysis. The preliminary overview about the application of this approach on harvesting operations has permitted to assess a good feasibility of the use of GNSS in the relief of operative times in high mechanised forest chains. Results showed an easy and complete identification of the different operative cycles and elementary operations phases, with a maximum difference between the two methodologies of 10.32%. The use of GNSS installed on forest equipment, integrated with the inferenceengine and also with an interface for data communication or data storage, will permit an automatic or semi-automatic operational monitoring, improving

  19. Discussion paper for a highly parallel array processor-based machine

    International Nuclear Information System (INIS)

    Hagstrom, R.; Bolotin, G.; Dawson, J.

    1984-01-01

    The architectural plant for a quickly realizable implementation of a highly parallel special-purpose computer system with peak performance in the range of 6 billion floating point operations per second is discussed. The architecture is suitable to Lattice Gauge theoretical computations of fundamental physics interest and may be applicable to a range of other problems which deal with numerically intensive computational problems. The plan is quickly realizable because it employs a maximum of commercially available hardware subsystems and because the architecture is software-transparent to the individual processors, allowing straightforward re-use of whatever commercially available operating-systems and support software that is suitable to run on the commercially-produced processors. A tiny prototype instrument, designed along this architecture has already operated. A few elementary examples of programs which can run efficiently are presented. The large machine which the authors would propose to build would be based upon a highly competent array-processor, the ST-100 Array Processor, and specific design possibilities are discussed. The first step toward realizing this plan practically is to install a single ST-100 to allow algorithm development to proceed while a demonstration unit is built using two of the ST-100 Array Processors

  20. Magnetic Vortex Based Transistor Operations

    Science.gov (United States)

    Kumar, D.; Barman, S.; Barman, A.

    2014-01-01

    Transistors constitute the backbone of modern day electronics. Since their advent, researchers have been seeking ways to make smaller and more efficient transistors. Here, we demonstrate a sustained amplification of magnetic vortex core gyration in coupled two and three vortices by controlling their relative core polarities. This amplification is mediated by a cascade of antivortex solitons travelling through the dynamic stray field. We further demonstrated that the amplification can be controlled by switching the polarity of the middle vortex in a three vortex sequence and the gain can be controlled by the input signal amplitude. An attempt to show fan–out operation yielded gain for one of the symmetrically placed branches which can be reversed by switching the core polarity of all the vortices in the network. The above observations promote the magnetic vortices as suitable candidates to work as stable bipolar junction transistors (BJT). PMID:24531235

  1. Ultra-precision machining induced phase decomposition at surface of Zn-Al based alloy

    International Nuclear Information System (INIS)

    To, S.; Zhu, Y.H.; Lee, W.B.

    2006-01-01

    The microstructural changes and phase transformation of an ultra-precision machined Zn-Al based alloy were examined using X-ray diffraction and back-scattered electron microscopy techniques. Decomposition of the Zn-rich η phase and the related changes in crystal orientation was detected at the surface of the ultra-precision machined alloy specimen. The effects of the machining parameters, such as cutting speed and depth of cut, on the phase decomposition were discussed in comparison with the tensile and rolling induced microstrucutural changes and phase decomposition

  2. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  3. Towards Massive Machine Type Cellular Communications

    OpenAIRE

    Dawy, Zaher; Saad, Walid; Ghosh, Arunabha; Andrews, Jeffrey G.; Yaacoub, Elias

    2015-01-01

    Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters - often referred to as machine-to-machine (M2M) or machine-type communications (MTC) - introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently support...

  4. A Multistage Control Mechanism for Group-Based Machine-Type Communications in an LTE System

    Directory of Open Access Journals (Sweden)

    Wen-Chien Hung

    2013-01-01

    Full Text Available When machine-type communication (MTC devices perform the long-term evolution (LTE attach procedure without bit rate limitations, they may produce congestion in the core network. To prevent this congestion, the LTE standard suggests using group-based policing to regulate the maximum bit rate of all traffic generated by a group of MTC devices. However, previous studies on the access point name-aggregate maximum bit rate based on group-based policing are relatively limited. This study proposes a multistage control (MSC mechanism to process the operations of maximum bit rate allocation based on resource-use information. For performance evaluation, this study uses a Markov chain with to analyze MTC application in a 3GPP network. Traffic flow simulations in an LTE system indicate that the MSC mechanism is an effective bandwidth allocation method in an LTE system with MTC devices. Experimental results show that the MSC mechanism achieves a throughput 22.5% higher than that of the LTE standard model using the group-based policing, and it achieves a lower delay time and greater long-term fairness as well.

  5. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  6. Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

    Science.gov (United States)

    Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

    2017-09-01

    This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

  7. Twin target self-amplification-based DNA machine for highly sensitive detection of cancer-related gene.

    Science.gov (United States)

    Xu, Huo; Jiang, Yifan; Liu, Dengyou; Liu, Kai; Zhang, Yafeng; Yu, Suhong; Shen, Zhifa; Wu, Zai-Sheng

    2018-06-29

    The sensitive detection of cancer-related genes is of great significance for early diagnosis and treatment of human cancers, and previous isothermal amplification sensing systems were often based on the reuse of target DNA, the amplification of enzymatic products and the accumulation of reporting probes. However, no reporting probes are able to be transformed into target species and in turn initiate the signal of other probes. Herein we reported a simple, isothermal and highly sensitive homogeneous assay system for tumor suppressor p53 gene detection based on a new autonomous DNA machine, where the signaling probe, molecular beacon (MB), was able to execute the function similar to target DNA besides providing the common signal. In the presence of target p53 gene, the operation of DNA machine can be initiated, and cyclical nucleic acid strand-displacement polymerization (CNDP) and nicking/polymerization cyclical amplification (NPCA) occur, during which the MB was opened by target species and cleaved by restriction endonuclease. In turn, the cleaved fragments could activate the next signaling process as target DNA did. According to the functional similarity, the cleaved fragment was called twin target, and the corresponding fashion to amplify the signal was named twin target self-amplification. Utilizing this newly-proposed DNA machine, the target DNA could be detected down to 0.1 pM with a wide dynamic range (6 orders of magnitude) and single-base mismatched targets were discriminated, indicating a very high assay sensitivity and good specificity. In addition, the DNA machine was not only used to screen the p53 gene in complex biological matrix but also was capable of practically detecting genomic DNA p53 extracted from A549 cell line. This indicates that the proposed DNA machine holds the potential application in biomedical research and early clinical diagnosis. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Combining discrepancy analysis with sensorless signal resampling for condition monitoring of rotating machines under fluctuating operations

    CSIR Research Space (South Africa)

    Heyns, T

    2012-12-01

    Full Text Available This paper proposes a novel framework for monitoring the condition of a rotating machine (for example a gearbox or a bearing) that may be subject to load and speed fluctuations. The methodology is especially relevant in situations where no (or only...

  9. Tunnelling in Soft Soil : Tunnel Boring Machine Operation and Soil Response

    NARCIS (Netherlands)

    Festa, D.; Broere, W.; Bosch, J.W.

    2013-01-01

    Constructing tunnels in soft soil with the use of Tunnel Boring Machines may induce settlements including soil movements ahead of the face, soil relaxation into the tail void, possible heave due to grouting, long lasting consolidation processes, and potentially several other mechanisms. A

  10. A theory of operation of a hydraulic drilling machine with a distributor

    Energy Technology Data Exchange (ETDEWEB)

    Totev, S Kh; Tsvetkov, Kh K

    1982-01-01

    The moment of impact of a striker piston against a drilling instrument is studied. The basic parameters of the drilling machine are identified, including cycle length, piston travel, impact velocity and impact energy. Formulas are acquired for rating the cited parameters.

  11. Automated Bug Assignment: Ensemble-based Machine Learning in Large Scale Industrial Contexts

    OpenAIRE

    Jonsson, Leif; Borg, Markus; Broman, David; Sandahl, Kristian; Eldh, Sigrid; Runeson, Per

    2016-01-01

    Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learni...

  12. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.

    Science.gov (United States)

    Weng, Wei-Hung; Wagholikar, Kavishwar B; McCray, Alexa T; Szolovits, Peter; Chueh, Henry C

    2017-12-01

    The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note. We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets. The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied. Our study shows that a supervised

  13. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Wind generator based on cascade connection of two asynchronized synchronous machines

    International Nuclear Information System (INIS)

    Dzhagarov, N.; Dzhagarova, Yu.

    2000-01-01

    A model of a wind generator with two asynchronized synchronous machines presented and different regimes are investigated. The analysis shows that the suggested scheme of a brushless generator works and has more advantages (reliability, easy for operation) in comparison with the known ones

  15. A deviation based assessment methodology for multiple machine health patterns classification and fault detection

    Science.gov (United States)

    Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

    2018-01-01

    Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

  16. A new workstation based man/machine interface system for the JT-60 Upgrade

    International Nuclear Information System (INIS)

    Yonekawa, I.; Shimono, M.; Totsuka, T.; Yamagishi, K.

    1992-01-01

    Development of a new man/machine interface system was stimulated by the requirements of making the JT-60 operator interface more 'friendly' on the basis of the past five-year operational experience. Eleven Sun/3 workstations and their supervisory mini-computer HIDIC V90/45 are connected through the standard network; Ethernet. The network is also connected to the existing 'ZENKEI' mini-computer system through the shared memory on the HIDIC V90/45 mini-computer. Improved software, such as automatic setting of the discharge conditions, consistency check among the related parameters and easy operation for discharge result data display, offered the 'user-friendly' environments. This new man/machine interface system leads to the efficient operation of the JT-60. (author)

  17. Machine technology: a survey

    International Nuclear Information System (INIS)

    Barbier, M.M.

    1981-01-01

    An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

  18. Development of Estimating Equation of Machine Operational Skill by Utilizing Eye Movement Measurement and Analysis of Stress and Fatigue

    Directory of Open Access Journals (Sweden)

    Satoshi Suzuki

    2013-01-01

    Full Text Available For an establishment of a skill evaluation method for human support systems, development of an estimating equation of the machine operational skill is presented. Factors of the eye movement such as frequency, velocity, and moving distance of saccade were computed using the developed eye gaze measurement system, and the eye movement features were determined from these factors. The estimating equation was derived through an outlier test (to eliminate nonstandard data and a principal component analysis (to find dominant components. Using a cooperative carrying task (cc-task simulator, the eye movement and operational data of the machine operators were recorded, and effectiveness of the derived estimating equation was investigated. As a result, it was confirmed that the estimating equation was effective strongly against actual simple skill levels (r=0.56–0.84. In addition, effects of internal condition such as fatigue and stress on the estimating equation were analyzed. Using heart rate (HR and coefficient of variation of R-R interval (Cvrri. Correlation analysis between these biosignal indexes and the estimating equation of operational skill found that the equation reflected effects of stress and fatigue, although the equation could estimate the skill level adequately.

  19. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  20. Identification and non-integer order modelling of synchronous machines operating as generator

    Directory of Open Access Journals (Sweden)

    Szymon Racewicz

    2012-09-01

    Full Text Available This paper presents an original mathematical model of a synchronous generator using derivatives of fractional order. In contrast to classical models composed of a large number of R-L ladders, it comprises half-order impedances, which enable the accurate description of the electromagnetic induction phenomena in a wide frequency range, while minimizing the order and number of model parameters. The proposed model takes into account the skin eff ect in damper cage bars, the eff ects of eddy currents in rotor solid parts, and the saturation of the machine magnetic circuit. The half-order transfer functions used for modelling these phenomena were verifi ed by simulation of ferromagnetic sheet impedance using the fi nite elements method. The analysed machine’s parameters were identified on the basis of SSFR (StandStill Frequency Response characteristics measured on a gradually magnetised synchronous machine.

  1. A Flywheel Energy Storage System Based on a Doubly Fed Induction Machine and Battery for Microgrid Control

    Directory of Open Access Journals (Sweden)

    Thai-Thanh Nguyen

    2015-06-01

    Full Text Available Microgrids are eco-friendly power systems because they use renewable sources such as solar and wind power as the main power source. However, the stochastic nature of wind and solar power is a considerable challenge for the efficient operation of microgrids. Microgrid operations have to satisfy quality requirements in terms of the frequency and voltage. To overcome these problems, energy storage systems for short- and long-term storage are used with microgrids. Recently, the use of short-term energy storage systems such as flywheels has attracted significant interest as a potential solution to this problem. Conventional flywheel energy storage systems exhibit only one control mode during operation: either smoothing wind power control or frequency control. In this paper, we propose a new flywheel energy storage system based on a doubly fed induction machine and a battery for use with microgrids. The new flywheel energy storage system can be used not only to mitigate wind power fluctuations, but also to control the frequency as well as the voltage of the microgrid during islanded operation. The performance of the proposed flywheel energy storage system is investigated through various simulations using MATLAB/Simulink software. In addition, a conventional flywheel energy storage system based on a doubly fed induction machine is simulated and its performance compared with that of the proposed one.

  2. Influence of Voltage Dips on the Operation of Brushless Exciter System of Synchronous Machines

    Directory of Open Access Journals (Sweden)

    Fedotov A.

    2016-06-01

    Full Text Available This paper presents a mathematical model with continuous variables for brushless exciter system of synchronous machines, containing the thyristor elements. Discrete Laplace transform is used for transition from a mathematical model of a system with variable structure in continuous variables to equation finite difference with permanent structure. Then inverse transition is made to a mathematical model in continuous variables with permanent structure.

  3. A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions

    DEFF Research Database (Denmark)

    Petersen, Joan P; Winther, Ole; Jacobsen, Daniel J

    2012-01-01

    The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive...... settings. Performance results for the real-time models are shown. The presented models were successfully developed in a trim optimisation application onboard a product tanker....

  4. Investigation into the effect of fixturing systems on the design of condition monitoring for machining operations

    OpenAIRE

    Abbas, JK

    2013-01-01

    The global market competition has drawn the manufacturer’s attention on automated manufacturing processes using condition monitoring systems. These systems have been used for improving product quality, eliminating inspection, and enhancing manufacturing productivity. Fixtures are essential devices in machining processes to hold the tool or workpiece, hence they are influenced directly by the stability of the cutting tool. Therefore, tool and fixturing faults play an important part in the inac...

  5. New concept of electrical drives for paper and board machines based on energy efficiency principles

    Directory of Open Access Journals (Sweden)

    Jeftenić Borislav

    2006-01-01

    Full Text Available In this paper, it is described how the reconstruction of the facility of paper machine has been conducted, at the press and drying part of the machine in June 2001, as well as the expansion of the Paper Machine with the "third coating" introducing, that has been done in July 2002, in the board factory "Umka". The existing old drive of the press and the drive of drying groups were established as a Line Shaft Drive, 76 m long. The novel drive is developed on the basis of conventional squirrel cage induction motor application, with frequency converter. The system control is carried out with the programmable controller, and the communication between controllers, converters, and control boards is accomplished trough profibus. Reconstruction of the coating part of the machine, during technological reconstruction of this part of the machine, was being conducted with a purpose to improve performance of the machine by adding device for spreading "third coating". The demands for the power facility were to replace existing facility with the new one, based on energy efficiency principles and to provide adequate facility for new technological sections. Also, new part of the facility had to be connected with the remaining part of the machine, i.e. with the press and drying part, which have been reconstructed in 2001. It has to be stressed that energy efficiency principles means to realize new, modernized drive with better performances and greater capacity for the as small as possible amount of increased installed power of separate drives. In the paper are also, graphically presented achieved energy savings results, based on measurements performed on separate parts of paper machine, before and after reconstruction. .

  6. Estimation of pump operational state with model-based methods

    International Nuclear Information System (INIS)

    Ahonen, Tero; Tamminen, Jussi; Ahola, Jero; Viholainen, Juha; Aranto, Niina; Kestilae, Juha

    2010-01-01

    Pumps are widely used in industry, and they account for 20% of the industrial electricity consumption. Since the speed variation is often the most energy-efficient method to control the head and flow rate of a centrifugal pump, frequency converters are used with induction motor-driven pumps. Although a frequency converter can estimate the operational state of an induction motor without external measurements, the state of a centrifugal pump or other load machine is not typically considered. The pump is, however, usually controlled on the basis of the required flow rate or output pressure. As the pump operational state can be estimated with a general model having adjustable parameters, external flow rate or pressure measurements are not necessary to determine the pump flow rate or output pressure. Hence, external measurements could be replaced with an adjustable model for the pump that uses estimates of the motor operational state. Besides control purposes, modelling the pump operation can provide useful information for energy auditing and optimization purposes. In this paper, two model-based methods for pump operation estimation are presented. Factors affecting the accuracy of the estimation methods are analyzed. The applicability of the methods is verified by laboratory measurements and tests in two pilot installations. Test results indicate that the estimation methods can be applied to the analysis and control of pump operation. The accuracy of the methods is sufficient for auditing purposes, and the methods can inform the user if the pump is driven inefficiently.

  7. Study on HRA-based method for assessing digital man-machine interface

    International Nuclear Information System (INIS)

    Li Pengcheng; Dai Licao; Zhang Li; Zhao Ming; Hu Hong

    2014-01-01

    In order to identify the design flaws of digital man-machine interface (MMI) that may trigger human errors or weaken the performance of operators, a HRA-based method (namely HCR + CREAM + HEC) for assessing digital MMI was established. Firstly, the HCR method was used to identify the risk scenarios of high human error probability from the overall event as a whole perspective. Then, for the identified high-risk scenarios, the CREAM was adopted to determine the various error modes and its error probability, and the failure probability was ranked. Finally, the human factors engineering checklist of digital MMI was established according to the characteristics of digital MMI, it was used to check the digital MMI with high error probability in order to identify the design flaws of digital MMI, and the suggestions of optimization were provided. The results show that the provided assessment method can quickly and efficiently identify the design flaws of digital MMI which easily trigger human errors, and the safety of operation of the digital control system for nuclear power plants can be enhanced by optimization of design. (authors)

  8. Modeling and control of PEMFC based on least squares support vector machines

    International Nuclear Information System (INIS)

    Li Xi; Cao Guangyi; Zhu Xinjian

    2006-01-01

    The proton exchange membrane fuel cell (PEMFC) is one of the most important power supplies. The operating temperature of the stack is an important controlled variable, which impacts the performance of the PEMFC. In order to improve the generating performance of the PEMFC, prolong its life and guarantee safety, credibility and low cost of the PEMFC system, it must be controlled efficiently. A nonlinear predictive control algorithm based on a least squares support vector machine (LS-SVM) model is presented for a family of complex systems with severe nonlinearity, such as the PEMFC, in this paper. The nonlinear off line model of the PEMFC is built by a LS-SVM model with radial basis function (RBF) kernel so as to implement nonlinear predictive control of the plant. During PEMFC operation, the off line model is linearized at each sampling instant, and the generalized predictive control (GPC) algorithm is applied to the predictive control of the plant. Experimental results demonstrate the effectiveness and advantages of this approach

  9. Craniux: a LabVIEW-based modular software framework for brain-machine interface research.

    Science.gov (United States)

    Degenhart, Alan D; Kelly, John W; Ashmore, Robin C; Collinger, Jennifer L; Tyler-Kabara, Elizabeth C; Weber, Douglas J; Wang, Wei

    2011-01-01

    This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  10. Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

    Directory of Open Access Journals (Sweden)

    Alan D. Degenhart

    2011-01-01

    Full Text Available This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  11. Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

    Science.gov (United States)

    Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

    2015-01-01

    Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

  12. Spike sorting based upon machine learning algorithms (SOMA).

    Science.gov (United States)

    Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

    2007-02-15

    We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

  13. Performance of machine learning methods for ligand-based virtual screening.

    Science.gov (United States)

    Plewczynski, Dariusz; Spieser, Stéphane A H; Koch, Uwe

    2009-05-01

    Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

  14. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

    Science.gov (United States)

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

    2018-03-01

    Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

  16. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

    Science.gov (United States)

    Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

    2018-04-01

    A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

  17. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  18. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  19. A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

    Directory of Open Access Journals (Sweden)

    Yanbing Liu

    2014-01-01

    Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

  20. Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

    Science.gov (United States)

    Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

    2014-01-01

    In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

  1. Modelling rollover behaviour of exacavator-based forest machines

    Science.gov (United States)

    M.W. Veal; S.E. Taylor; Robert B. Rummer

    2003-01-01

    This poster presentation provides results from analytical and computer simulation models of rollover behaviour of hydraulic excavators. These results are being used as input to the operator protective structure standards development process. Results from rigid body mechanics and computer simulation methods agree well with field rollover test data. These results show...

  2. Lithium-ion battery remaining useful life prediction based on grey support vector machines

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2015-12-01

    Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

  3. Human-Machine Communication

    International Nuclear Information System (INIS)

    Farbrot, J.E.; Nihlwing, Ch.; Svengren, H.

    2005-01-01

    New requirements for enhanced safety and design changes in process systems often leads to a step-wise installation of new information and control equipment in the control room of older nuclear power plants, where nowadays modern digital I and C solutions with screen-based human-machine interfaces (HMI) most often are introduced. Human factors (HF) expertise is then required to assist in specifying a unified, integrated HMI, where the entire integration of information is addressed to ensure an optimal and effective interplay between human (operators) and machine (process). Following a controlled design process is the best insurance for ending up with good solutions. This paper addresses the approach taken when introducing modern human-machine communication in the Oskarshamn 1 NPP, the results, and the lessons learned from this work with high operator involvement seen from an HF point of view. Examples of possibilities modern technology might offer for the operators are also addressed. (orig.)

  4. [Card-based age control mechanisms at tobacco vending machines. Effect and consequences].

    Science.gov (United States)

    Schneider, S; Meyer, C; Löber, S; Röhrig, S; Solle, D

    2010-02-01

    Until recently, 700,000 tobacco vending machines provided uncontrolled access to cigarettes for children and adolescents in Germany. On January 1, 2007, a card-based electronic locking device was attached to all tobacco vending machines to prevent the purchase of cigarettes by children and adolescents under 16. Starting in 2009, only persons older than 18 are able to buy cigarettes from tobacco vending machines. The aim of the present investigation (SToP Study: "Sources of Tobacco for Pupils" Study) was to assess changes in the number of tobacco vending machines after the introduction of these new technical devices (supplier's reaction). In addition, the ways smoking adolescents make purchases were assessed (consumer's reaction). We registered and mapped the total number of tobacco points of sale (tobacco POS) before and after the introduction of the card-based electronic locking device in two selected districts of the city of Cologne. Furthermore, pupils from local schools (response rate: 83%) were asked about their tobacco consumption and ways of purchase using a questionnaire. Results indicated that in the area investigated the total number of tobacco POSs decreased from 315 in 2005 to 277 in 2007. The rates of decrease were 48% for outdoor vending machines and 8% for indoor vending machines. Adolescents reported circumventing the card-based electronic locking devices (e.g., by using cards from older friends) and using other tobacco POSs (especially newspaper kiosks) or relying on their social network (mainly friends). The decreasing number of tobacco vending machines has not had a significant impact on cigarette acquisition by adolescent smokers as they tend to circumvent the newly introduced security measures.

  5. A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation

    Directory of Open Access Journals (Sweden)

    Phuoc Tran

    2016-01-01

    Full Text Available Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English and vice versa. However, it is a matter for consideration that words may or may not be segmented when translating between two languages in which spaces are not used between words, such as Chinese and Vietnamese. Since Chinese-Vietnamese is a low-resource language pair, the sparse data problem is evident in the translation system of this language pair. Therefore, while translating, whether it should be segmented or not becomes more important. In this paper, we propose a new method for translating Chinese to Vietnamese based on a combination of the advantages of character level and word level translation. In addition, a hybrid approach that combines statistics and rules is used to translate on the word level. And at the character level, a statistical translation is used. The experimental results showed that our method improved the performance of machine translation over that of character or word level translation.

  6. A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.

    Science.gov (United States)

    Tran, Phuoc; Dinh, Dien; Nguyen, Hien T

    2016-01-01

    Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English) and vice versa. However, it is a matter for consideration that words may or may not be segmented when translating between two languages in which spaces are not used between words, such as Chinese and Vietnamese. Since Chinese-Vietnamese is a low-resource language pair, the sparse data problem is evident in the translation system of this language pair. Therefore, while translating, whether it should be segmented or not becomes more important. In this paper, we propose a new method for translating Chinese to Vietnamese based on a combination of the advantages of character level and word level translation. In addition, a hybrid approach that combines statistics and rules is used to translate on the word level. And at the character level, a statistical translation is used. The experimental results showed that our method improved the performance of machine translation over that of character or word level translation.

  7. Support vector machine-based open crop model (SBOCM: Case of rice production in China

    Directory of Open Access Journals (Sweden)

    Ying-xue Su

    2017-03-01

    Full Text Available Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

  8. Quality Evaluation for Appearance of Needle Green Tea Based on Machine Vision and Process Parameters

    DEFF Research Database (Denmark)

    Dong, Chunwang; Zhu, Hongkai; Zhou, Xiaofen

    2017-01-01

    ), extreme learning machine (ELM) and strong predictor integration algorithm (ELM-AdaBoost). The comparison of the results showed that the ELM-AdaBoost model based on image characteristics had the best performance (RPD was more than 2). Its predictive performance was superior to other models, with smaller......, and modeling faster (0.014~0.281 s). AdaBoost method, which was a hybrid integrated algorithm, can further promote the accuracy and generalization capability of the model. The above conclusions indicated that it was feasible to evaluate the quality of appearance of needle green tea based on machine vision...

  9. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  10. Modified Brokk Demolition Machine with Remote Operator Console. Innovative Technology Summary Report

    International Nuclear Information System (INIS)

    2001-01-01

    The Low-Cost D and D System modifies a commercially available BROKK demolition system for remote viewing and long tether remote operation that provides a portable facility camera pod and interfaces with the Compact Remote Operator Console (TMS Tech ID 2180) to extend the applicability of the BROKK system to projects that require removal of the operator from the work area due to exposure to radiological, chemical, or industrial hazards. The modified BROKK has been integrated with the Compact Remote Operator Console to provide a true remotely operated low-cost D and D system applicable to a wide range of small D and D demolition tasks across the DOE complex

  11. Prediction of drug synergy in cancer using ensemble-based machine learning techniques

    Science.gov (United States)

    Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

    2018-04-01

    Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

  12. The Effect of Operational Cutting Parameters on Nitinol-60 in Wire Electrodischarge Machining

    Directory of Open Access Journals (Sweden)

    Ali Akbar LotfiNeyestanak

    2013-01-01

    Full Text Available Shape memory alloys are a kind of active materials, which have significant characteristics in comparison with other alloys. Since these materials are applicable in different fields such as aerospace, automobile industry, medicine, and dentistry, the effects of wire electrodischarge machining on the properties of these alloys have been studied. In this paper, changes in the shape recovery ability and microhardness of the machined surface of Nitonol-60 shape memory alloy have been studied considering recasting and formation of resolidificated layer on the shape memory alloy surface. XRD and EDXA analyses of the surface layer of the sample besides a microscopic study of the shape memory alloy layer by SEM and a study of the changes in mechanical properties of the surface layer were done by performing microhardness and tension tests on the work piece surface. Considering the surface layer, reversible strain has been studied according to the shape recovery percentage of Nitinol-60 shape memory alloy. Results show that the surface layer formed on the surface of the samples has caused changes in both physical and mechanical properties of the cut surface because of the penetration of the separated materials in comparison with deeper layers of the piece.

  13. Mastering the non-equilibrium assembly and operation of molecular machines.

    Science.gov (United States)

    Pezzato, Cristian; Cheng, Chuyang; Stoddart, J Fraser; Astumian, R Dean

    2017-09-18

    In mechanically interlocked compounds, such as rotaxanes and catenanes, the molecules are held together by mechanical rather than chemical bonds. These compounds can be engineered to have several well-defined mechanical states by incorporating recognition sites between the different components. The rates of the transitions between the recognition sites can be controlled by introducing steric "speed bumps" or electrostatically switchable gates. A mechanism for the absorption of energy can also be included by adding photoactive, catalytically active, or redox-active recognition sites, or even charges and dipoles. At equilibrium, these Mechanically Interlocked Molecules (MIMs) undergo thermally activated transitions continuously between their different mechanical states where every transition is as likely as its microscopic reverse. External energy, for example, light, external modulation of the chemical and/or physical environment or catalysis of an exergonic reaction, drives the system away from equilibrium. The absorption of energy from these processes can be used to favour some, and suppress other, transitions so that completion of a mechanical cycle in a direction in which work is done on the environment - the requisite of a molecular machine - is more likely than completion in a direction in which work is absorbed from the environment. In this Tutorial Review, we discuss the different design principles by which molecular machines can be engineered to use different sources of energy to carry out self-organization and the performance of work in their environments.

  14. Study on the status of the working bodies grinding machines based on vibration analysis

    Directory of Open Access Journals (Sweden)

    S. T. Antipov

    2016-01-01

    Full Text Available Improvement of technology and engineering aimed at the use of secondary raw material is an important task. One of the most important operations in the preparation of raw materials for mixed feeds is fine grinding. In this regard, the article discusses the grinding equipment allowing to obtain raw materials of higher quality with the lower energy consumption. Methods and diagnostic tools were proposed, the principle of determining the locations (points of installation of vibration measurement sensors as well as the choice of the vibration signal analysis method were considered. Investigation of the state of the disintegrator working bodies was carried out in the workshop of LLC PСF "Luch 2000". The object of study is a disintegrator with rotors diameter of 350 mm, each of them having two rows of pins. The result of the experiment revealed that during the operation the working bodies of grinding machines are exposed to uneven wear and under the action of multicycle load micro-cracks and fatigue fractures occur. The method of spectral analysis revealed the appearance of harmonics with large vibration at a frequency of 126 Hz, as well as multiple frequencies, allowing a high degre e of probability to determine not only the actual state of the working bodies, but also to predict the defect development trend. Based on the analysis of the spectra, the decision on further time operation of the equipment is made, which significantly reduces the probability of an emergency stop of equipment and expensive repairs. The research data will be relevant when using vibration diagnostics tools in enterprises, as well as in the design, construction and choice of materials for grinding equipment.

  15. Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

    Science.gov (United States)

    Pasupa, Kitsuchart; Kudisthalert, Wasu

    2018-01-01

    Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets-Maximum Unbiased Validation Dataset-which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

  16. Predicting the Performance of Chain Saw Machines Based on Shore Scleroscope Hardness

    Science.gov (United States)

    Tumac, Deniz

    2014-03-01

    Shore hardness has been used to estimate several physical and mechanical properties of rocks over the last few decades. However, the number of researches correlating Shore hardness with rock cutting performance is quite limited. Also, rather limited researches have been carried out on predicting the performance of chain saw machines. This study differs from the previous investigations in the way that Shore hardness values (SH1, SH2, and deformation coefficient) are used to determine the field performance of chain saw machines. The measured Shore hardness values are correlated with the physical and mechanical properties of natural stone samples, cutting parameters (normal force, cutting force, and specific energy) obtained from linear cutting tests in unrelieved cutting mode, and areal net cutting rate of chain saw machines. Two empirical models developed previously are improved for the prediction of the areal net cutting rate of chain saw machines. The first model is based on a revised chain saw penetration index, which uses SH1, machine weight, and useful arm cutting depth as predictors. The second model is based on the power consumed for only cutting the stone, arm thickness, and specific energy as a function of the deformation coefficient. While cutting force has a strong relationship with Shore hardness values, the normal force has a weak or moderate correlation. Uniaxial compressive strength, Cerchar abrasivity index, and density can also be predicted by Shore hardness values.

  17. A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems

    Directory of Open Access Journals (Sweden)

    Yingni Zhai

    2014-10-01

    Full Text Available Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems (JSP is proposed.Design/methodology/approach: In the algorithm, a number of sub-problems are constructed by iteratively decomposing the large-scale JSP according to the process route of each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality, a detection method for multi-bottleneck machines based on critical path is proposed. Therewith the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck operations. According to the principle of “Bottleneck leads the performance of the whole manufacturing system” in TOC (Theory Of Constraints, the bottleneck operations are scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are scheduled by dispatching rules for the improvement of the solving efficiency.Findings: In the process of the sub-problems' construction, partial operations in the previous scheduled sub-problem are divided into the successive sub-problem for re-optimization. This strategy can improve the solution quality of the algorithm. In the process of solving the sub-problems, the strategy that evaluating the chromosome's fitness by predicting the global scheduling objective value can improve the solution quality.Research limitations/implications: In this research, there are some assumptions which reduce the complexity of the large-scale scheduling problem. They are as follows: The processing route of each job is predetermined, and the processing time of each operation is fixed. There is no machine breakdown, and no preemption of the operations is allowed. The assumptions should be considered if the algorithm is used in the actual job shop.Originality/value: The research provides an efficient scheduling method for the

  18. An information theory based approach for quantitative evaluation of man-machine interface complexity

    International Nuclear Information System (INIS)

    Kang, Hyun Gook

    1999-02-01

    In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

  19. An information theory based approach for quantitative evaluation of man-machine interface complexity

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Hyun Gook

    1999-02-15

    In complex and high-risk work conditions, especially such as in nuclear power plants, human understanding of the plant is highly cognitive and thus largely dependent on the effectiveness of the man-machine interface system. In order to provide more effective and reliable operating conditions for future nuclear power plants, developing more credible and easy to use evaluation methods will afford great help in designing interface systems in a more efficient manner. In this study, in order to analyze the human-machine interactions, I propose the Human-processor Communication(HPC) model which is based on the information flow concept. It identifies the information flow around a human-processor. Information flow has two aspects: appearance and content. Based on the HPC model, I propose two kinds of measures for evaluating a user interface from the viewpoint of these two aspects of information flow. They measure the communicative complexity of each aspect. In this study, for the evaluation of the aspect of appearance, I propose three complexity measures: Operation Complexity, Transition Complexity, and Screen Complexity. Each one of these measures has its own physical meaning. Two experiments carried out in this work support the utility of these measures. The result of the quiz game experiment shows that as the complexity of task context increases, the usage of the interface system becomes more complex. The experimental results of the three example systems(digital view, LDP style view and hierarchy view) show the utility of the proposed complexity measures. In this study, for the evaluation of the aspect of content, I propose the degree of informational coincidence, R (K, P) as a measure for the usefulness of an alarm-processing system. It is designed to perform user-oriented evaluation based on the informational entropy concept. It will be especially useful inearly design phase because designers can estimate the usefulness of an alarm system by short calculations instead

  20. The modelling of dynamic chemical state of paper machine unit operations; Dynaamisen kemiallisen tilan mallintaminen paperikoneen yksikkoeoperaatioissa - MPKT 04

    Energy Technology Data Exchange (ETDEWEB)

    Ylen, J P; Jutila, P [Helsinki Univ. of Technology, Otaniemi (Finland)

    1999-12-31

    The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)

  1. The modelling of dynamic chemical state of paper machine unit operations; Dynaamisen kemiallisen tilan mallintaminen paperikoneen yksikkoeoperaatioissa - MPKT 04

    Energy Technology Data Exchange (ETDEWEB)

    Ylen, J.P.; Jutila, P. [Helsinki Univ. of Technology, Otaniemi (Finland)

    1998-12-31

    The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)

  2. How the choice of Operating System can affect databases on a Virtual Machine

    OpenAIRE

    Karlsson, Jan; Eriksson, Patrik

    2014-01-01

    As databases grow in size, the need for optimizing databases is becoming a necessity. Choosing the right operating system to support your database becomes paramount to ensure that the database is fully utilized. Furthermore with the virtualization of operating systems becoming more commonplace, we find ourselves with more choices than we ever faced before. This paper demonstrates why the choice of operating system plays an integral part in deciding the right database for your system in a virt...

  3. Human factors engineering measures taken by nuclear power plant owners/operators for optimisation of the man-machine interface

    International Nuclear Information System (INIS)

    Eisgruber, H.

    1996-01-01

    Both operating results and human factors studies show that man is able to meet the requirements in this working environment. Hence the degree of human reliability required by the design basis of nuclear power plants is ensured. This means: - Nuclear technology for electricity generation is justifiable from the human factors point of view. - The chief opponent is not right in saying that man is not able to cope with the risks and challenges brought about by nuclear technology applications. The human factors concept for optimisation or configuration of the man-machine systems represents an additional endeavor on the part of nuclear power plant operators within the framework of their responsibilities. Human factors analyses meet with good response by the personnel, as analysis results and clarification of causes of accident scenarios contribute to relieve the personnel (exoneration) and find ways for remedial action. (orig./DG) [de

  4. Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators

    Directory of Open Access Journals (Sweden)

    R. Nawawi

    2015-12-01

    Full Text Available Effects of payment method on work control, work risk and work-related musculoskeletal health among sewing machine operators R. Nawawi1, B.M. Deros1*, D.D.I. Daruis2, A. Ramli3, R.M. Zein4 and L.H. Joseph3 1Dept. of Mechanical and Materials Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia, Malaysia *Email: hjbaba@ukm.edu.my 2Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Malaysia 3Department of Physiotherapy Faculty of Science, Lincoln University College, Malaysia 4Department of Consultation, Research & Development, National Institute of Occupational Safety and Health (NIOSH, Malaysia ABSTRACT This study aimed to identify payment method and its effects on work control, work risk and work-related musculoskeletal health among Malaysian sewing machine operators. The study sample comprised 337 sewing machine operators (male, n=122, female, n=215; aged between 18-54 years old; mean 30.74±8.44 from four different garment-making companies in Malaysia. They were being paid via time rate wages (n=246 and piece rate wages (n=91. Data was collected through Nordic Musculoskeletal Questionnaire and pen-and-paper assessment via Rapid Upper Limb Assessment (RULA. From the study, the piece rate wage group was found to take fewer breaks, had high work production demands, worked at a faster pace and experienced more exhaustion and pressure due to increasing work demands as compared to the time rate group. They were also observed working with higher physical exposure such as repetitive tasks, awkward static postures, awkward grips and hand movements, pulling, lifting and pushing as compared to those in the time rate wage group. The final RULA scores was also higher from the piece rate wage group (72.53% RULA score 7 which indicated higher work risks among them. The study found that the type of wage payment was significantly associated with work risks (p=0.036, df=1 and WRMSD at the shoulder, lower back

  5. DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

    Directory of Open Access Journals (Sweden)

    J. Rhee

    2016-06-01

    Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

  6. Machine learning-based methods for prediction of linear B-cell epitopes.

    Science.gov (United States)

    Wang, Hsin-Wei; Pai, Tun-Wen

    2014-01-01

    B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

  7. Evidence of end-effector based gait machines in gait rehabilitation after CNS lesion.

    Science.gov (United States)

    Hesse, S; Schattat, N; Mehrholz, J; Werner, C

    2013-01-01

    A task-specific repetitive approach in gait rehabilitation after CNS lesion is well accepted nowadays. To ease the therapists' and patients' physical effort, the past two decades have seen the introduction of gait machines to intensify the amount of gait practice. Two principles have emerged, an exoskeleton- and an endeffector-based approach. Both systems share the harness and the body weight support. With the end-effector-based devices, the patients' feet are positioned on two foot plates, whose movements simulate stance and swing phase. This article provides an overview on the end-effector based machine's effectiveness regarding the restoration of gait. For the electromechanical gait trainer GT I, a meta analysis identified nine controlled trials (RCT) in stroke subjects (n = 568) and were analyzed to detect differences between end-effector-based locomotion + physiotherapy and physiotherapy alone. Patients practising with the machine effected in a superior gait ability (210 out of 319 patients, 65.8% vs. 96 out of 249 patients, 38.6%, respectively, Z = 2.29, p = 0.020), due to a larger training intensity. Only single RCTs have been reported for other devices and etiologies. The introduction of end-effector based gait machines has opened a new succesful chapter in gait rehabilitation after CNS lesion.

  8. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  9. Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

    Science.gov (United States)

    Yu, Jianbo

    2015-12-01

    Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

  10. Diagnostics of the Technical State of Bearings of Mining Machines Base Assemblies

    Science.gov (United States)

    Gerike, Boris L.; Mokrushev, Andrey A.

    2017-10-01

    The article reviews the methods of technical diagnostics of equipment used during maintenance of mining machines in accordance with their actual technical state, and considers the basics of vibration parameters measuring. The classification of existing methods for diagnosing the technical condition of rolling bearings is given. The advantages and disadvantages of these methods are considered. The main defects of rolling bearings arising during manufacturing, transportation, storage, and operation are considered.

  11. Improved Fuzzy Logic based DTC of Induction machine for wide range of speed control using AI based controllers

    Directory of Open Access Journals (Sweden)

    H. Sudheer

    2016-06-01

    Full Text Available This paper presents improvements in Direct Torque control of induction motor using Fuzzy logic switching controller (FDTC. The conventional DTC (CDTC and FDTC drive performance is compared using Conventional PI, Fuzzy controller and Neural Network controllers. The major disadvantages of CDTC are high torque and flux ripples in steady state operation of the drive, inferior performance at low speed operation and variable switching frequency. The presence of hysteresis bands is the major reason for high torque and flux ripples in CDTC. In FDTC the hysteresis band and switching table are replaced by Fuzzy logic switching controller. Using fuzzy logic torque, stator flux space are divided into smaller subsections which results in precise and optimal selection of switching state to meet load torque. In high performance drives accurate tuning of PI speed controller is required. The conventional PI controller cannot adapt to the variation in model parameters. Artificial intelligence based fuzzy controller and neural network controller are compared with PI controller for both CDTC and FDTC of Induction machine. The proposed schemes are developed in Matlab/Simulink environment. Simulation results shows reduction in torque and flux ripples in FDTC and dynamic performance of the drive at low speeds and sudden change in load torque can be improved using Fuzzy logic controller compared to PI and neural network controller.

  12. Machinability assessment of commercially pure titanium (CP-Ti) during turning operation: Application potential of GRA method

    Science.gov (United States)

    Khan, Akhtar; Maity, Kalipada

    2018-03-01

    This paper explores some of the vital machinability characteristics of commercially pure titanium (CP-Ti) grade 2. Experiments were conducted based on Taguchi’s L9 orthogonal array. The selected material was machined on a heavy duty lathe (Model: HMT NH26) using uncoated carbide inserts in dry cutting environment. The selected inserts were designated by ISO as SNMG 120408 (Model: K313) and manufactured by Kennametal. These inserts were rigidly mounted on a right handed tool holder PSBNR 2020K12. Cutting speed, feed rate and depth of cut were selected as three input variables whereas tool wear (VBc) and surface roughness (Ra) were the major attentions. In order to confirm an appreciable machinability of the work part, an optimal parametric combination was attained with the help of grey relational analysis (GRA) approach. Finally, a mathematical model was developed to exhibit the accuracy and acceptability of the proposed methodology using multiple regression equations. The results indicated that, the suggested model is capable of predicting overall grey relational grade within acceptable range.

  13. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    Science.gov (United States)

    Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

    2013-07-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

  14. Support vector machine based fault detection approach for RFT-30 cyclotron

    Energy Technology Data Exchange (ETDEWEB)

    Kong, Young Bae, E-mail: ybkong@kaeri.re.kr; Lee, Eun Je; Hur, Min Goo; Park, Jeong Hoon; Park, Yong Dae; Yang, Seung Dae

    2016-10-21

    An RFT-30 is a 30 MeV cyclotron used for radioisotope applications and radiopharmaceutical researches. The RFT-30 cyclotron is highly complex and includes many signals for control and monitoring of the system. It is quite difficult to detect and monitor the system failure in real time. Moreover, continuous monitoring of the system is hard and time-consuming work for human operators. In this paper, we propose a support vector machine (SVM) based fault detection approach for the RFT-30 cyclotron. The proposed approach performs SVM learning with training samples to construct the classification model. To compensate the system complexity due to the large-scale accelerator, we utilize the principal component analysis (PCA) for transformation of the original data. After training procedure, the proposed approach detects the system faults in real time. We analyzed the performance of the proposed approach utilizing the experimental data of the RFT-30 cyclotron. The performance results show that the proposed SVM approach can provide an efficient way to control the cyclotron system.

  15. Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

    Science.gov (United States)

    Kase, Sue E.; Vanni, Michelle; Caylor, Justine; Hoye, Jeff

    2017-05-01

    The Human-Assisted Machine Information Exploitation (HAMIE) investigation utilizes large-scale online data collection for developing models of information-based problem solving (IBPS) behavior in a simulated time-critical operational environment. These types of environments are characteristic of intelligence workflow processes conducted during human-geo-political unrest situations when the ability to make the best decision at the right time ensures strategic overmatch. The project takes a systems approach to Human Information Interaction (HII) by harnessing the expertise of crowds to model the interaction of the information consumer and the information required to solve a problem at different levels of system restrictiveness and decisional guidance. The design variables derived from Decision Support Systems (DSS) research represent the experimental conditions in this online single-player against-the-clock game where the player, acting in the role of an intelligence analyst, is tasked with a Commander's Critical Information Requirement (CCIR) in an information overload scenario. The player performs a sequence of three information processing tasks (annotation, relation identification, and link diagram formation) with the assistance of `HAMIE the robot' who offers varying levels of information understanding dependent on question complexity. We provide preliminary results from a pilot study conducted with Amazon Mechanical Turk (AMT) participants on the Volunteer Science scientific research platform.

  16. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    International Nuclear Information System (INIS)

    Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E

    2013-01-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)

  17. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  18. Optics education for machine operators in the semiconductor industry: moving beyond button pushing

    Science.gov (United States)

    Karakekes, Meg; Currier, Deborah

    1995-10-01

    In the competitive semiconductor manufacturing industry, employees who operate equipment are able to make greater contributions if they understand how the equipment works. By understanding the 'why' behind the 'what', the equipment operators can better partner with other technical staff to produce quality integrated circuits efficiently and effectively. This additional knowledge also opens equipment operators to job enrichment and enlargement opportunities. Advanced Micro Devices (AMD) is in the process of upgrading the skills of its equipment operators. This paper is an overview of a pilot program that employs optics education to upgrade stepper operators' skills. The paper starts with stepper tasks that require optics knowledge, examines teaching methods, reports both end-of-course and three months post-training knowledge retention, and summarizes how the training has impacted the production floor.

  19. Predictive Models of Work-Related Musculoskeletal Disorders (WMSDs Among Sewing Machine Operators in the Garments Industry

    Directory of Open Access Journals (Sweden)

    Carlos Ignacio P. Lugay

    2015-02-01

    Full Text Available The Philippine garments industry has been a driving force in the country’s economy, with apparel manufacturing firms catering to the local and global markets and providing employment opportunities for skilled Filipinos. Tight competition from neighboring Asian countries however, has made the industry’s situation difficult to flourish, especially in the wake of the Association of Southeast Asian Nations (ASEAN 2015 Integration. To assist the industry, this research examined one of the more common problems among sewing machine operators, termed as Work-related Musculoskeletal Disorders (WMSDs. These disorders are reflective in the frequency and severity of the pain experienced by the sewers while accomplishing their tasks. The causes of these disorders were identified and were correlated with the frequency and severity of pain in various body areas of the operator. To forecast pain from WMSDs among the operators, mathematical models were developed to predict the combined frequency and severity of the pain from WMSDs. Loss time or “unofficial breaktimes” due to pain from WMSDs was likewise forecasted to determine its effects on the firm’s production capacity. Both these predictive models were developed in order to assist garment companies in anticipating better the effects of WMSDs and loss time in their operations. Moreover, ergonomic interventions were suggested to minimize pain from WMSDs, with the expectation of increased productivity of the operators and improved quality of their outputs.

  20. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    OpenAIRE

    Fu Yu; Mu Jiong; Duan Xu Liang

    2016-01-01

    By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research...

  1. Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

    OpenAIRE

    Thiruvenkadam, T; Karthikeyani, V

    2014-01-01

    Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

  2. A human-machine cooperation route planning method based on improved A* algorithm

    Science.gov (United States)

    Zhang, Zhengsheng; Cai, Chao

    2011-12-01

    To avoid the limitation of common route planning method to blindly pursue higher Machine Intelligence and autoimmunization, this paper presents a human-machine cooperation route planning method. The proposed method includes a new A* path searing strategy based on dynamic heuristic searching and a human cooperated decision strategy to prune searching area. It can overcome the shortage of A* algorithm to fall into a local long term searching. Experiments showed that this method can quickly plan a feasible route to meet the macro-policy thinking.

  3. Web based machine status display for INDUS-1 And INDUS-2

    International Nuclear Information System (INIS)

    Srivastava, B.S.K.; Fatnani, P.

    2003-01-01

    Web based machine status display for Indus-1 and Indus-2 is designated to provide on-line status of Indus-1 and Indus-2 to the clients located at various places of CAT premises. Presently, this system provides Indus-1 machine status (e.g. beam current, integrated current, beam life-time etc) to the users working in Indus-1 building, but using the web browsers the same information can be accessed throughout the CAT network. This system is basically a part of Indus-1 Control System Web Site which is under construction (partially constructed). (author)

  4. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2016-01-01

    Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

  5. The machinability of nickel-based alloys in high-pressure jet assisted (HPJA turning

    Directory of Open Access Journals (Sweden)

    D. Kramar

    2013-10-01

    Full Text Available Due to their mechanical, thermal and chemical properties, nickel-based alloys are generally included among materials that are hard to machine. An experimental study has been performed to investigate the capabilities of conventional and high-pressure jet assisted (HPJA turning of hard-to-machine materials, namely Inconel 718. The capabilities of different hard turning procedures are compared by means of chip breakability. The obtained results show that HPJA method offers a significant increase in chip breakability, under the same cutting conditions (cutting speed, feed rate, depth of cut.

  6. A rule-based approach to model checking of UML state machines

    Science.gov (United States)

    Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

    2016-12-01

    In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

  7. Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

    Science.gov (United States)

    Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

    2015-01-01

    Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

  8. Machine Shop Lathes.

    Science.gov (United States)

    Dunn, James

    This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

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

    DEFF Research Database (Denmark)

    Sabuncu, Mert R.; Van Leemput, Koen

    2011-01-01

    This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to ...

  10. The Integration of Project-Based Methodology into Teaching in Machine Translation

    Science.gov (United States)

    Madkour, Magda

    2016-01-01

    This quantitative-qualitative analytical research aimed at investigating the effect of integrating project-based teaching methodology into teaching machine translation on students' performance. Data was collected from the graduate students in the College of Languages and Translation, at Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi…

  11. Classification of HTTP traffic based on C5.0 Machine Learning Algorithm

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Riaz, Tahir; Pedersen, Jens Myrup

    2012-01-01

    streaming through third-party plugins, etc. This paper suggests and evaluates two approaches to distinguish various types of HTTP traffic based on the content: distributed among volunteers' machines and centralized running in the core of the network. We also assess the accuracy of the centralized classifier...

  12. Integrating source-language context into phrase-based statistical machine translation

    NARCIS (Netherlands)

    Haque, R.; Kumar Naskar, S.; Bosch, A.P.J. van den; Way, A.

    2011-01-01

    The translation features typically used in Phrase-Based Statistical Machine Translation (PB-SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling

  13. Machine vision-based high-resolution weed mapping and patch-sprayer performance simulation

    NARCIS (Netherlands)

    Tang, L.; Tian, L.F.; Steward, B.L.

    1999-01-01

    An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques

  14. Improving the quality of automated DVD subtitles via example-based machine translation

    DEFF Research Database (Denmark)

    Armstrong, Stephen; Caffrey, Colm; Flanagan, Marian

    Denoual (2005) discovered that, contrary to popular belief, an Example-Based Machine Translation system trained on heterogeneous data produced significantly better results than a system trained on homogeneous data. Using similar evaluation metrics and a few additional ones, in this paper we show...

  15. Improved method for SNR prediction in machine-learning-based test

    NARCIS (Netherlands)

    Sheng, Xiaoqin; Kerkhoff, Hans G.

    2010-01-01

    This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach,

  16. The Value Simulation-Based Learning Added to Machining Technology in Singapore

    Science.gov (United States)

    Fang, Linda; Tan, Hock Soon; Thwin, Mya Mya; Tan, Kim Cheng; Koh, Caroline

    2011-01-01

    This study seeks to understand the value simulation-based learning (SBL) added to the learning of Machining Technology in a 15-week core subject course offered to university students. The research questions were: (1) How did SBL enhance classroom learning? (2) How did SBL help participants in their test? (3) How did SBL prepare participants for…

  17. VATE: VAlidation of high TEchnology based on large database analysis by learning machine

    NARCIS (Netherlands)

    Meldolesi, E; Van Soest, J; Alitto, A R; Autorino, R; Dinapoli, N; Dekker, A; Gambacorta, M A; Gatta, R; Tagliaferri, L; Damiani, A; Valentini, V

    2014-01-01

    The interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine

  18. Design challenges for stepper motor actuated microvalve based on fine and micro-machining

    NARCIS (Netherlands)

    Fazal, I.; Elwenspoek, Michael Curt

    2007-01-01

    We present a normally open stepper motor actuated microvalve based on micro and fine-machining technique. In this paper, first we have described how the larger controllable flow range can be achieved with simple micromachining techniques and secondly we have presented the results which show how the

  19. Fuzzy-based multi-kernel spherical support vector machine for ...

    Indian Academy of Sciences (India)

    In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is ...

  20. Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest.

    Science.gov (United States)

    Blasco, José; Munera, Sandra; Aleixos, Nuria; Cubero, Sergio; Molto, Enrique

    Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

  1. Recycling Texts: Human evaluation of example-based machine translation subtitles for DVD

    DEFF Research Database (Denmark)

    Flanagan, Marian

    2009-01-01

    This project focuses on translation reusability in audiovisual contexts. Specifically, the project seeks to establish (1) whether target language subtitles produced by an Example-Based Machine Translation (EBMT) system are considered intelligible and acceptable by viewers of movies on DVD, and (2...

  2. THE FUZZY LOGIC BASED POWER INJECTION INTO ROTOR CIRCUIT FOR INSTANTANEOUS HIGH TORQUE AND SPEED CONTROL IN INDUCTION MACHINES

    Directory of Open Access Journals (Sweden)

    Selami KESLER

    2009-01-01

    Full Text Available The power flow of the rotor circuit is controlled by different methods in induction machines used for producing high torque in applications involved great power and constant output power with constant frequency in wind turbines. The voltage with slip frequency can be applied on rotor windings to produce controlled high torque and obtain optimal power factor and speed control. In this study, firstly, the dynamic effects of the voltage applying on rotor windings through the rings in slip-ring induction machines are researched and undesirable aspects of the method are exposed with simulations supported by experiments. Afterwards, a fuzzy logic based inverter model on rotor side is proposed with a view to improving the dynamic effects, controlling high torque producing and adjusting machine speed in instantaneous forced conditions. For the simulation model of the system in which the stator side is directly connected to the grid in steady state operation, a C/C++ algorithm is developed and the results obtained for different load conditions are discussed.

  3. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  4. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    resources management in diverse wireless networks. The core operation of the proposed architecture is decision-making for resource allocation and system’s parameters adaptation. Thus, we develop the decision-making mechanism using different artificial intelligence techniques, evaluate the performance achieved and determine the tradeoff of using one technique over the others. The techniques include decision-trees, genetic algorithm, hybrid engine based on decision-trees and case based reasoning, and supervised engine with machine learning contribution to determine the ultimate technique that suits the current environment conditions. All the proposed techniques are evaluated using testbed implementation in different topologies and scenarios. LTE networks have been considered as a potential environment for demonstration of our proposed cognitive based resource allocation techniques as they lack of radio resource management. In addition, we explore the use of enhanced online learning to perform efficient resource allocation in the upcoming 5G networks to maximize energy efficiency and data rate. The considered 5G structures are heterogeneous multi-tier networks with device to device communication and heterogeneous cloud radio access networks. We propose power and resource blocks allocation schemes to maximize energy efficiency and data rate in heterogeneous 5G networks. Moreover, traffic offloading from large cells to small cells in 5G heterogeneous networks is investigated and an online learning based traffic offloading strategy is developed to enhance energy efficiency. Energy efficiency problem in heterogeneous cloud radio access networks is tackled using online learning in centralized and distributed fashions. The proposed online learning comprises improvement features that reduce the algorithms complexities and enhance the performance achieved.

  5. Programming and machining of complex parts based on CATIA solid modeling

    Science.gov (United States)

    Zhu, Xiurong

    2017-09-01

    The complex parts of the use of CATIA solid modeling programming and simulation processing design, elaborated in the field of CNC machining, programming and the importance of processing technology. In parts of the design process, first make a deep analysis on the principle, and then the size of the design, the size of each chain, connected to each other. After the use of backstepping and a variety of methods to calculate the final size of the parts. In the selection of parts materials, careful study, repeated testing, the final choice of 6061 aluminum alloy. According to the actual situation of the processing site, it is necessary to make a comprehensive consideration of various factors in the machining process. The simulation process should be based on the actual processing, not only pay attention to shape. It can be used as reference for machining.

  6. Investigation into the accuracy of a proposed laser diode based multilateration machine tool calibration system

    International Nuclear Information System (INIS)

    Fletcher, S; Longstaff, A P; Myers, A

    2005-01-01

    Geometric and thermal calibration of CNC machine tools is required in modern machine shops with volumetric accuracy assessment becoming the standard machine tool qualification in many industries. Laser interferometry is a popular method of measuring the errors but this, and other alternatives, tend to be expensive, time consuming or both. This paper investigates the feasibility of using a laser diode based system that capitalises on the low cost nature of the diode to provide multiple laser sources for fast error measurement using multilateration. Laser diode module technology enables improved wavelength stability and spectral linewidth which are important factors for laser interferometry. With more than three laser sources, the set-up process can be greatly simplified while providing flexibility in the location of the laser sources improving the accuracy of the system

  7. Static Object Detection Based on a Dual Background Model and a Finite-State Machine

    Directory of Open Access Journals (Sweden)

    Heras Evangelio Rubén

    2011-01-01

    Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

  8. Adsorption Machine & Desiccant Wheel based SOLAR COOLING in a Second Law perspective

    OpenAIRE

    Bivona, Santo

    2011-01-01

    This thesis work is intended to investigate energy and exergy performance of a low power prototype solar air conditioning system based on sorption materials. Its performance is analyzed in the light of both the First and Second Law of Thermodynamics and compared with conventional HVAC systems as well as with a further solar cooling technology based on desiccant wheels (Solar DEC). The adsorption machine based solar cooling plant was thoroughly designed and its thermal performance analysed ...

  9. Developing Probabilistic Operating Rules for Real-time Conjunctive Use of Surface and Groundwater Resources:Application of Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Bazargan-Lari

    2011-01-01

    Full Text Available Developing optimal operating policies for conjunctive use of surface and groundwater resources when different decision makers and stakeholders with conflicting objectives are involved is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this paper, a new methodology is developed for real time conjunctive use of surface and groundwater resources. In the proposed methodology, a well-known multi-objective genetic algorithm, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II is employed to develop a Pareto front among the objectives. The Young conflict resolution theory is also used for resolving the conflict of interests among decision makers. To develop the real time conjunctive use operating rules, the Probabilistic Support Vector Machines (PSVMs, which are capable of providing probability distribution functions of decision variables, are utilized. The proposed methodology is applied to Tehran Aquifer inTehran metropolitan area,Iran. Stakeholders in the study area have some conflicting interests including supplying water with acceptable quality, reducing pumping costs, improving groundwater quality and controlling the groundwater table fluctuations. In the proposed methodology, MODFLOW and MT3D groundwater quantity and quality simulation models are linked with NSGA-II optimization model to develop Pareto fronts among the objectives. The best solutions on the Pareto fronts are then selected using the Young conflict resolution theory. The selected solution (optimal monthly operating policies is used to train and verify a PSVM. The results show the significance of applying an integrated conflict resolution approach and the capability of support vector machines for the real time conjunctive use of surface and groundwater resources in the study area. It is also shown that the validation accuracy of the proposed operating rules is higher that 80

  10. Plant operator performance evaluation based on cognitive process analysis experiment

    International Nuclear Information System (INIS)

    Ujita, H.; Fukuda, M.

    1990-01-01

    This paper reports on an experiment to clarify plant operators' cognitive processes that has been performed, to improve the man-machine interface which supports their diagnoses and decisions. The cognitive processes under abnormal conditions were evaluated by protocol analyses interviews, etc. in the experiment using a plant training simulator. A cognitive process model is represented by a stochastic network, based on Rasmussen's decision making model. Each node of the network corresponds to an element of the cognitive process, such as observation, interpretation, execution, etc. Some observations were obtained as follows, by comparison of Monte Carlo simulation results with the experiment results: A process to reconfirm the plant parameters after execution of a task and feedback paths from this process to the observation and the task definition of next task were observed. The feedback probability average and standard deviation should be determined for each incident type to explain correctly the individual differences in the cognitive processes. The tendency for the operator's cognitive level to change from skill-based to knowledge-based via rule-based behavior was observed during the feedback process

  11. Development of Simulation-Based Evaluation System for Iterative Design of Human-Machine Interface in a Nuclear Power Plant - Application for Reducing Workload

    International Nuclear Information System (INIS)

    Fumizawa, Motoo; Kameda, Akiyuki; Nakagawa, Takashi; Wu Wei; Yoshikawa, Hidekazu

    2003-01-01

    Development of simulation-based evaluation and analysis support system for man-machine interface design (SEAMAID) has been conducted in the Nuclear Power Engineering Corporation to simulate the behavior of a few operators and the human-machine interface (HMI) in a commercialized pressurized water reactor plant. The workload is one of the key factors with respect to reducing the human error in the operation of nuclear power plants. In order to produce a high-quality design of HMI, the evaluation method was developed to simulate and analyze the operator's workload. Our method was adopted from the cognition model proposed by Reason. The workload such as the length of the visual point movement and the moving length of the operators was visualized in a monitor image during the simulation, and then recorded as a movie-file. As a consequence, the validation of SEAMAID was clarified

  12. Using example-based machine translation to translate DVD subtitles

    DEFF Research Database (Denmark)

    Flanagan, Marian

    between Swedish and Danish and Swedish and Norwegian subtitles, with the company already reporting a successful return on their investment. The hybrid EBMT/SMT system used in the current research, on the other hand, remains within the confines of academic research, and the real potential of the system...... allotted to produce the subtitles have both decreased. Therefore, this market is recognised as a potential real-world application of MT. Recent publications have introduced Corpus-Based MT approaches to translate subtitles. An SMT system has been implemented in a Swedish subtitling company to translate...

  13. Passivity-Based Control of a Class of Blondel-Park Transformable Electric Machines

    Directory of Open Access Journals (Sweden)

    Per J. Nicklasson

    1997-10-01

    Full Text Available In this paper we study the viability of extending, to the general rotating electric machine's model, the passivity-based controller method that we have developed for induction motors. In this approach the passivity (energy dissipation properties of the motor are taken advantage of at two different levels. First, we prove that the motor model can be decomposed as the feedback interconnection of two passive subsystems, which can essentially be identified with the electrical and mechanical dynamics. Then, we design a torque tracking controller that preserves passivity for the electrical subsystem, and leave the mechanical part as a "passive disturbance". In position or speed control applications this procedure naturally leads to the well known cascaded controller structure which is typically analyzed invoking time-scale separation assumptions. A key feature of the new cascaded control paradigm is that the latter arguments are obviated in the stability analysis. Our objective in this paper is to characterize a class of machines for which such a passivity-based controller solves the output feedback torque tracking problem. Roughly speaking, the class consists of machines whose nonactuated dynamics are well damped and whose electrical and mechanical dynamics can be suitably decoupled via a coordinate transformation. The first condition translates into the requirement of approximate knowledge of the rotor resistances to avoid the need of injecting high gain into the loop. The latter condition is known in the electric machines literature as Blondel-Park transformability, and in practical terms it requires that the air-gap magnetomotive force must be suitably approximated by the first harmonic in its Fourier expansion. These conditions, stemming from the construction of the machine, have a clear physical interpretation in terms of the couplings between its electrical, magnetic and mechanical dynamics, and are satisfied by a large number of practical

  14. Machine-learning & QMU for multi-fidelity analysis of scramjet operability, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Dual-mode scramjets have the potential to operate efficiently in a variety of flight conditions without requiring complicated variable configurations, thus providing...

  15. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

    2014-01-01

    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

  16. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  17. Investigation on Wire Electrochemical Micro Machining of Ni-based Metallic Glass

    International Nuclear Information System (INIS)

    Meng, Lingchao; Zeng, Yongbin; Zhu, Di

    2017-01-01

    Highlights: • WECMM with nanosecond pulses is proposed firstly for fabricating micro complex components based on metallic glasses. • Applicable electrolyte for WECMM of the Ni-based MG is discussed. • Significantly uniform machined surface is achieved in H_2SO_4 solution. • High machining efficiency and stability are obtained experimentally by modifying pulse waveforms and electrolyte compositions. • Complex microstructures of Ni-based MG are fabricated by WECMM with optimized parameters. - Abstract: Metallic glasses (MGs) have been recognized as promising materials for realizing high-performance micro devices in micro electromechanical systems (MEMS) due to their excellent functional and structural characteristics. However, the applications of MGs are currently limited because of the difficulty of shaping them on the microscale. Wire electrochemical micro machining (WECMM) is increasingly recognized as a flexible and effective method to fabricate complex-shaped micro metal components with many advantages relative to the thermomechanical processing, which appears to be well suitable for micro shaping of MGs. We consider the example of a Ni-based MG, Ni_7_2Cr_1_9Si_7B_2, which has a typical passivation characteristic in 0.1 M H_2SO_4 solution. The transpassive process can be used for localized material removal when combined with nanosecond pulsed WECMM technique. In present work, the applicable electrolyte for WECMM of the Ni-based MG was discussed firstly. Then the voltage pulse waveform and electrolyte composition were modified to improve machining efficiency and stability. Several complex microstructures such as micro curved cantilever beam, micro gear, and micro square helix were machined with different optimized parameters.

  18. Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

    Science.gov (United States)

    Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

    2018-05-01

    State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.

  19. A Symbiotic Brain-Machine Interface through Value-Based Decision Making

    Science.gov (United States)

    Mahmoudi, Babak; Sanchez, Justin C.

    2011-01-01

    Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward

  20. A symbiotic brain-machine interface through value-based decision making.

    Directory of Open Access Journals (Sweden)

    Babak Mahmoudi

    Full Text Available BACKGROUND: In the development of Brain Machine Interfaces (BMIs, there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC. METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1 and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and

  1. Human machine interface by using stereo-based depth extraction

    Science.gov (United States)

    Liao, Chao-Kang; Wu, Chi-Hao; Lin, Hsueh-Yi; Chang, Ting-Ting; Lin, Tung-Yang; Huang, Po-Kuan

    2014-03-01

    The ongoing success of three-dimensional (3D) cinema fuels increasing efforts to spread the commercial success of 3D to new markets. The possibilities of a convincing 3D experience at home, such as three-dimensional television (3DTV), has generated a great deal of interest within the research and standardization community. A central issue for 3DTV is the creation and representation of 3D content. Acquiring scene depth information is a fundamental task in computer vision, yet complex and error-prone. Dedicated range sensors, such as the Time­ of-Flight camera (ToF), can simplify the scene depth capture process and overcome shortcomings of traditional solutions, such as active or passive stereo analysis. Admittedly, currently available ToF sensors deliver only a limited spatial resolution. However, sophisticated depth upscaling approaches use texture information to match depth and video resolution. At Electronic Imaging 2012 we proposed an upscaling routine based on error energy minimization, weighted with edge information from an accompanying video source. In this article we develop our algorithm further. By adding temporal consistency constraints to the upscaling process, we reduce disturbing depth jumps and flickering artifacts in the final 3DTV content. Temporal consistency in depth maps enhances the 3D experience, leading to a wider acceptance of 3D media content. More content in better quality can boost the commercial success of 3DTV.

  2. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    Directory of Open Access Journals (Sweden)

    Fu Yu

    2016-01-01

    Full Text Available By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes

  3. Microcomputer-based instrument for the detection and analysis of precession motion in a gas centrifuge machine

    International Nuclear Information System (INIS)

    Paulus, S.S.

    1986-03-01

    The Centrifuge Precession Analyzer (CPA) is a microcomputer-based instrument which detects precession motion in a gas centrifuge machine and calculates the amplitude and frequency of precession. The CPA consists of a printed circuit board which contains signal-conditioning circuitry and a 24-bit counter and an INTEL iSBC 80-/24 single-board computer. Precession motion is detected by monitoring a signal generated by a variable reluctance pick-up coil in the top of the centrifuge machine. This signal is called a Fidler signal. The initial Fidler signal triggers a counter which is clocked by a high-precision, 20.000000-MHz, temperature-controlled, crystal oscillator. The contents of the counter are read by the computer, and the counter reset after every ten Fidler signals. The speed of the centrifuge machine and the amplitude and frequency of precession are calculated, and the results are displayed on a liquid crystal display on the front panel of the CPA. The thesis contains results from data generated by a Fidler signal simulator and data taken when the centrifuge was operated under three test conditions: (1) nitrogen gas during drive-up, steady state, and drive-down, (2) xenon gas during slip test, steady state, and the addition of gas, and (3) no gas during steady state. The qualitative results were consistent with experience with centrifuge machines UF 6 in that the amplitude of precession increased and the frequency of precession decreased during drive-up, drive-down and the slip check. The magnitude of the amplitude and frequency of precession were proportional to the molecular weight of the gases in steady state

  4. Microcomputer-based instrument for the detection and analysis of precession motion in a gas centrifuge machine. Revision 1

    International Nuclear Information System (INIS)

    Paulus, S.S.

    1986-03-01

    The Centrifuge Procession Analyzer (CPA) is a microcomputer-based instrument which detects precession motion in a gas centrifuge machine and calculates the amplitude and frequency of precession. The CPA consists of a printed circuit board which contains signal-conditioning circuitry and a 24-bit counter and an INTEL iSBC 80/24 single/board computer. Pression motion is detected by monitoring a signal generated by a variable reluctance pick-up coil in the top of the centrifuge machine. This signal is called a Fidler signal. The initial Fidler signal triggers a counter which is clocked by a high-precision, 20.000000-MHz, temperature-controlled, crystal oscillator. The contents of the counter are read by the computer and the counter reset after every ten Fidler signals. The speed of the centrifuge machine and the amplitude and frequency of precession are calculated and the results are displayed on a liquid crystal display on the front panel of the CPA. The report contains results from data generated by a Fidler signal simulator and data taken when the centrifuge was operated under three test conditions: (1) nitrogen gas during drive-up, steady state, and drive-down; (2) xenon gas during slip test, steady state, and the addition of gas; and (3) no gas during steady state. The qualitative results were consistent with experience with centrifuge machines using UF 6 in that the amplitude of precession increased and the frequency of precession decreased during drive-up, drive-down and the slip check. The magnitude of the amplitude and frequency of precession were proportional to the molecular weight of the gases in steady state

  5. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  6. Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

    Science.gov (United States)

    Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

    2016-09-01

    In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

  7. Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

    Science.gov (United States)

    Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

    2018-04-25

    Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

  8. Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

    International Nuclear Information System (INIS)

    Stern, R.E.; Song, J.; Work, D.B.

    2017-01-01

    The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.

  9. Analytical Model-Based Design Optimization of a Transverse Flux Machine

    Energy Technology Data Exchange (ETDEWEB)

    Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal; Muljadi, Eduard

    2017-02-16

    This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variables that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.

  10. Research on Key Technologies of Unit-Based CNC Machine Tool Assembly Design

    Directory of Open Access Journals (Sweden)

    Zhongqi Sheng

    2014-01-01

    Full Text Available Assembly is the part that produces the maximum workload and consumed time during product design and manufacturing process. CNC machine tool is the key basic equipment in manufacturing industry and research on assembly design technologies of CNC machine tool has theoretical significance and practical value. This study established a simplified ASRG for CNC machine tool. The connection between parts, semantic information of transmission, and geometric constraint information were quantified to assembly connection strength to depict the assembling difficulty level. The transmissibility based on trust relationship was applied on the assembly connection strength. Assembly unit partition based on assembly connection strength was conducted, and interferential assembly units were identified and revised. The assembly sequence planning and optimization of parts in each assembly unit and between assembly units was conducted using genetic algorithm. With certain type of high speed CNC turning center, as an example, this paper explored into the assembly modeling, assembly unit partition, and assembly sequence planning and optimization and realized the optimized assembly sequence of headstock of CNC machine tool.

  11. SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC)

    Science.gov (United States)

    Zhang, Xiang; Chen, Zhangwei

    2013-01-01

    This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users' configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels. PMID:23459385

  12. 14 CFR 119.47 - Maintaining a principal base of operations, main operations base, and main maintenance base...

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Maintaining a principal base of operations, main operations base, and main maintenance base; change of address. 119.47 Section 119.47 Aeronautics... Under Part 121 or Part 135 of This Chapter § 119.47 Maintaining a principal base of operations, main...

  13. Engine Lathe Operator. Instructor's Guide. Part of Single-Tool Skills Program Series. Machine Industries Occupations.

    Science.gov (United States)

    New York State Education Dept., Albany. Bureau of Secondary Curriculum Development.

    Expected to help meet the need for trained operators in metalworking and suitable for use in the adult education programs of school districts, in manpower development and training programs, and in secondary schools, this guide consists of four sections: Introduction, General Job Content, Shop Projects, and Drawings for the Projects. General Job…

  14. Machine-operated low temperature system for cooling a germanium detector at great depths of the sea

    International Nuclear Information System (INIS)

    Bruederle, F.; Hain, K.; Huebener, J.; Schloss, F.

    1978-07-01

    The report outlines the conceptual design and technical implementation phases of a very reliable low temperature system for long-time cooling of a germanium detector at great depths of the sea. The approach chosen as the solution involves the choise of a proven commercial small-scale refrigeration unit operation by the Gifford-Mc Mahon process, which is modified so as to suit special requirements. Testing for the severe conditions of use is carried out on a jarring table for the critical components and on a rolling test rig for the whole low temperature machine so as to simulate the stresses imposed by ships and high seas. The cooling system designed in this way has demonstrated its full functioning capability in a test conducted at sea. (orig.) 891 HP [de

  15. SU-E-T-255: Development of a Michigan Quality Assurance (MQA) Database for Clinical Machine Operations

    Energy Technology Data Exchange (ETDEWEB)

    Roberts, D [University of Michigan Hospital, Ann Arbor, MI (United States)

    2015-06-15

    Purpose: A unified database system was developed to allow accumulation, review and analysis of quality assurance (QA) data for measurement, treatment, imaging and simulation equipment in our department. Recording these data in a database allows a unified and structured approach to review and analysis of data gathered using commercial database tools. Methods: A clinical database was developed to track records of quality assurance operations on linear accelerators, a computed tomography (CT) scanner, high dose rate (HDR) afterloader and imaging systems such as on-board imaging (OBI) and Calypso in our department. The database was developed using Microsoft Access database and visual basic for applications (VBA) programming interface. Separate modules were written for accumulation, review and analysis of daily, monthly and annual QA data. All modules were designed to use structured query language (SQL) as the basis of data accumulation and review. The SQL strings are dynamically re-written at run time. The database also features embedded documentation, storage of documents produced during QA activities and the ability to annotate all data within the database. Tests are defined in a set of tables that define test type, specific value, and schedule. Results: Daily, Monthly and Annual QA data has been taken in parallel with established procedures to test MQA. The database has been used to aggregate data across machines to examine the consistency of machine parameters and operations within the clinic for several months. Conclusion: The MQA application has been developed as an interface to a commercially available SQL engine (JET 5.0) and a standard database back-end. The MQA system has been used for several months for routine data collection.. The system is robust, relatively simple to extend and can be migrated to a commercial SQL server.

  16. SU-E-T-255: Development of a Michigan Quality Assurance (MQA) Database for Clinical Machine Operations

    International Nuclear Information System (INIS)

    Roberts, D

    2015-01-01

    Purpose: A unified database system was developed to allow accumulation, review and analysis of quality assurance (QA) data for measurement, treatment, imaging and simulation equipment in our department. Recording these data in a database allows a unified and structured approach to review and analysis of data gathered using commercial database tools. Methods: A clinical database was developed to track records of quality assurance operations on linear accelerators, a computed tomography (CT) scanner, high dose rate (HDR) afterloader and imaging systems such as on-board imaging (OBI) and Calypso in our department. The database was developed using Microsoft Access database and visual basic for applications (VBA) programming interface. Separate modules were written for accumulation, review and analysis of daily, monthly and annual QA data. All modules were designed to use structured query language (SQL) as the basis of data accumulation and review. The SQL strings are dynamically re-written at run time. The database also features embedded documentation, storage of documents produced during QA activities and the ability to annotate all data within the database. Tests are defined in a set of tables that define test type, specific value, and schedule. Results: Daily, Monthly and Annual QA data has been taken in parallel with established procedures to test MQA. The database has been used to aggregate data across machines to examine the consistency of machine parameters and operations within the clinic for several months. Conclusion: The MQA application has been developed as an interface to a commercially available SQL engine (JET 5.0) and a standard database back-end. The MQA system has been used for several months for routine data collection.. The system is robust, relatively simple to extend and can be migrated to a commercial SQL server

  17. Novel nonlinear knowledge-based mean force potentials based on machine learning.

    Science.gov (United States)

    Dong, Qiwen; Zhou, Shuigeng

    2011-01-01

    The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based

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

    International Nuclear Information System (INIS)

    Sun Chuang; Zhang Zhousuo; He Zhengjia

    2011-01-01

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

  19. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

    International Nuclear Information System (INIS)

    Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

    2010-01-01

    Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

  20. Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM Based on Measured Data

    Directory of Open Access Journals (Sweden)

    Yu Huiling

    2016-01-01

    Full Text Available The suppor1t vector machine (SVM is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.