-
1 physical data model
= physical schema; = PDMописывает конкретные физические механизмы размещения данных (БД) в запоминающей средеАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > physical data model
-
2 physical data model
Вычислительная техника: физическая модель данных -
3 physical data model
fysiek gegevensmodel -
4 data model
Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > data model
-
5 conceptual data model
= conceptual schema; = CDMописывает семантику организации БД безотносительно к её физической моделиАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > conceptual data model
-
6 logical data model
= LDMAnt:Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > logical data model
-
7 logical data model
Программирование: логическая модель данных (генерируется полностью или частично из словаря данных. Ant: physical data model) -
8 physical schema
Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > physical schema
-
9 physical database
физическая база данных, физический уровень БДсовокупность структур хранения данных на внешних носителях, например на дискахАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > physical database
-
10 model
1) модель (1. упрощённое представление объекта, процесса или явления; структурная аналогия 2. макет 3. образец; эталон; шаблон 4. пример; тип 5. стиль; дизайн) || моделировать (1. создавать упрощённое представление объекта, процесса или явления; пользоваться структурной аналогией 2. макетировать 3. создавать образец, эталон или шаблон 4. пользоваться примером; относить к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)2) служить моделью; выполнять функции модели3) создавать по образцу, эталону или шаблону4) придерживаться определённого стиля; следовать выбранному дизайну•- 2-D model
- adaptive expectations model
- additive model of neural network
- analog model
- antenna scale model
- application domain model
- AR model
- ARCH model
- ARDL model
- ARIMA model
- ARMA model
- atmospheric density model
- autoregressive conditional heteroscedastic model
- autoregressive distributed lags model
- autoregressive integrated moving average model
- autoregressive moving average model
- band model
- behavioral model
- Benetton model
- Berkeley short-channel IGFET model
- binary model
- binary choice model
- Bohr-Sommerfeld model
- Bohr-Sommerfeld model of atom
- Box-Jenkins model
- Bradley-Terry-Luce model
- brain-state-in-a-box model
- breadboard model
- Brookings models
- BSB model
- business model
- CAD model
- capability maturity model
- carrier-storage model
- causal model
- censored model
- centralized model
- charge-control model
- Chen model
- classical normal linear regression model
- classical regression model
- client-server model
- CMY model
- CMYK model
- cobweb model
- collective-electron model
- color model
- compact model
- component object model
- computer model
- computer-aided-design model
- conceptual model of hypercompetition
- conceptual data model
- conductor impedance model
- congruent model
- connectionist model
- continuum model
- Cox proportional hazards regression model
- data model
- Davidson-Hendry-Srba-Yeo model
- descriptive model
- design model
- deterministic model
- DHSY model
- discrete choice model
- distributed component object model
- distributed computing model
- distributed lags model
- distributed system object model
- distribution-free model
- document object model
- domain model
- domain architecture model
- duration model
- dynamic model
- EER-model
- energy-gap model
- entity-relationship model
- ER-model
- error correction model
- errors-in-variables model
- experimental model
- extended entity-relationship model
- extended relational model
- extended relational data model
- extensional model
- ferromagnetic Fermi-liquid model
- file level model
- financial model
- finite-population model
- fixed-effects model
- flat Earth model
- flat free model of advertising
- formalized model
- fractal model
- frame model
- fuzzy model
- GARCH model
- generalized autoregressive conditional heteroscedastic model
- generalized linear model
- geometric model
- geometrical lags model
- gross-level model
- ground-environment model
- Haken-Kelso-Bunz model
- Heisenberg model
- heuristic model
- hierarchical data model
- HLS model
- holographic model
- HSB model
- HSV model
- Hubbard model
- huge model
- hybrid-pi model
- hypothesis model
- ideal model
- imaging model
- indexed colors model
- information model
- information-logical model
- intensional model
- intercept-only model
- ionospheric model
- irreversible growth model
- Ising model
- ISO/OSI reference model
- Klein model
- Kronig-Penney model
- L*a*b* model
- large model
- large-signal device model
- LCH model
- learning, induction and schema abstraction model
- life cycle model
- limited dependent variable model
- linear model
- linear probability model
- LISA model
- logical model
- logical-linguistic model
- logistic model
- logit model
- loglinear model
- Londons' model of superconductivity
- lookup-table model
- Lorentz model
- low-signal device model
- machine model
- macrolevel model
- magnetic hysteresis model
- magnetohydrodynamic plasma model
- mathematical model
- matrix-memory model
- medium model
- memory model
- MHD plasma model
- microlevel model
- Minsky model
- Minsky frame model
- mixed model
- molecular-field model
- moving average model
- multiple regression model
- multiplicative model
- nested model
- network model
- network data model
- non-nested model
- non-parametric model
- N-state Potts model
- N-tier model
- null model
- object model
- object data model
- one-dimensional model
- one-fluid plasma model
- operations model
- optimizing model
- parabolic-ionosphere model
- parametric model
- parsimonious model
- partial adjustment model
- phenomenological model
- physical model
- pilot model
- Pippard nonlocal model
- plant model
- Poisson model
- polar model
- polynomial lags model
- postrelational model
- postrelational data model
- Potts model
- predictive model
- Preisach model
- preproduction model
- price model of advertising
- probabilistic model
- probit model
- proportional hazard model
- proportional-odds model
- prototype model
- quadratic model
- qualitative dependent variable model
- quantum mechanical model of superconductivity
- quasi-equilibrium model
- quasi-linear model
- random coefficients model
- random-effects model
- register model
- relational model
- relational data model
- relative model
- representative model
- response-surface model
- RGB model
- Ridley-Watkins-Hilsum model
- rival models
- Rössler model
- RWH model
- saturated model
- scalar model
- SCSI architecture model
- semantic model
- semiotic model
- sharply bounded ionosphere model
- simulation model
- single-ion model
- Skyrme model
- small model
- small-signal device model
- solid model
- spherical Earth model
- state-space model
- statistical model
- stochastic model
- Stoner-Wohlfart model
- structural model
- stuck-at-fault model
- surface model
- symbolic model
- symbolic-form model
- synergetic model
- system model
- system object model
- test model
- thermodynamical model
- three-tier model
- tobit model
- transistor model
- translog model
- tropospheric model
- true model
- truncated model
- two-dimensional model
- two-dimensional regression model
- two-fluid model of superconductivity
- two-fluid plasma model
- two-tier model
- Van der Ziel's noise model
- variable parameter model
- vector model
- wire-frame model
- working model -
11 model
1) модель (1. упрощённое представление объекта, процесса или явления; структурная аналогия 2. макет 3. образец; эталон; шаблон 4. пример; тип 5. стиль; дизайн) || моделировать (1. создавать упрощённое представление объекта, процесса или явления; пользоваться структурной аналогией 2. макетировать 3. создавать образец, эталон или шаблон 4. пользоваться примером; относить к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)2) служить моделью; выполнять функции модели3) создавать по образцу, эталону или шаблону4) придерживаться определённого стиля; следовать выбранному дизайну•- 2-D model
- adaptive expectations model
- additive model of neural network
- analog model
- antenna scale model
- application domain model
- AR model
- ARCH model
- ARDL model
- ARIMA model
- ARMA model
- atmospheric density model
- autoregressive conditional heteroscedastic model
- autoregressive distributed lags model
- autoregressive integrated moving average model
- autoregressive model
- autoregressive moving average model
- band model
- behavioral model
- Benetton model
- Berkeley short-channel IGFET model
- binary choice model
- binary model
- Bohr-Sommerfeld model of atom
- Bohr-Sommerfeld model
- Box-Jenkins model
- Bradley-Terry-Luce model
- brain-state-in-a-box model
- breadboard model
- Brookings models
- BSB model
- business model
- CAD model
- capability maturity model
- carrier-storage model
- causal model
- censored model
- centralized model
- charge-control model
- Chen model
- classical normal linear regression model
- classical regression model
- client-server model
- CMY model
- CMYK model
- cobweb model
- collective-electron model
- color model
- compact model
- component object model
- computer model
- computer-aided-design model
- conceptual data model
- conceptual model of hypercompetition
- conductor impedance model
- congruent model
- connectionist model
- continuum model
- Cox proportional hazards regression model
- data model
- Davidson-Hendry-Srba-Yeo model
- descriptive model
- design model
- deterministic model
- DHSY model
- discrete choice model
- distributed component object model
- distributed computing model
- distributed lags model
- distributed system object model
- distribution-free model
- document object model
- domain architecture model
- domain model
- duration model
- dynamic model
- EER-model
- energy-gap model
- entity-relationship model
- ER-model
- error correction model
- errors-in-variables model
- experimental model
- extended entity-relationship model
- extended relational data model
- extended relational model
- extensional model
- ferromagnetic Fermi-liquid model
- file level model
- financial model
- finite-population model
- fixed-effects model
- flat Earth model
- flat free model of advertising
- formalized model
- fractal model
- frame model
- fuzzy model
- GARCH model
- generalized autoregressive conditional heteroscedastic model
- generalized linear model
- geometric model
- geometrical lags model
- gross-level model
- ground-environment model
- Haken-Kelso-Bunz model
- Heisenberg model
- heuristic model
- hierarchical data model
- HLS model
- holographic model
- HSB model
- HSV model
- Hubbard model
- huge model
- hybrid-pi model
- hypothesis model
- ideal model
- imaging model
- indexed colors model
- information model
- information-logical model
- intensional model
- intercept-only model
- ionospheric model
- irreversible growth model
- Ising model
- ISO/OSI reference model
- Klein model
- Kronig-Penney model
- L*a*b* model
- large model
- large-signal device model
- LCH model
- learning, induction and schema abstraction model
- life cycle model
- limited dependent variable model
- linear model
- linear probability model
- LISA model
- logical model
- logical-linguistic model
- logistic model
- logit model
- loglinear model
- Londons' model of superconductivity
- lookup-table model
- Lorentz model
- low-signal device model
- machine model
- macrolevel model
- magnetic hysteresis model
- magnetohydrodynamic plasma model
- mathematical model
- matrix-memory model
- medium model
- memory model
- MHD plasma model
- microlevel model
- Minsky frame model
- Minsky model
- mixed model
- molecular-field model
- moving average model
- multiple regression model
- multiplicative model
- nested model
- network data model
- network model
- non-nested model
- non-parametric model
- N-state Potts model
- N-tier model
- null model
- object data model
- object model
- one-dimensional model
- one-fluid plasma model
- operations model
- optimizing model
- parabolic-ionosphere model
- parametric model
- parsimonious model
- partial adjustment model
- phenomenological model
- physical model
- pilot model
- Pippard nonlocal model
- plant model
- Poisson model
- polar model
- polynomial lags model
- postrelational data model
- postrelational model
- Potts model
- predictive model
- Preisach model
- preproduction model
- price model of advertising
- probabilistic model
- probit model
- proportional hazard model
- proportional-odds model
- prototype model
- quadratic model
- qualitative dependent variable model
- quantum mechanical model of superconductivity
- quasi-equilibrium model
- quasi-linear model
- random coefficients model
- random-effects model
- register model
- relational data model
- relational model
- relative model
- representative model
- response-surface model
- RGB model
- Ridley-Watkins-Hilsum model
- rival models
- Rössler model
- RWH model
- saturated model
- scalar model
- SCSI architecture model
- semantic model
- semiotic model
- sharply bounded ionosphere model
- simulation model
- single-ion model
- Skyrme model
- small model
- small-signal device model
- solid model
- spherical Earth model
- state-space model
- statistical model
- stochastic model
- Stoner-Wohlfart model
- structural model
- stuck-at-fault model
- surface model
- symbolic model
- symbolic-form model
- synergetic model
- system model
- system object model
- test model
- thermodynamical model
- three-tier model
- tobit model
- transistor model
- translog model
- tropospheric model
- true model
- truncated model
- two-dimensional model
- two-dimensional regression model
- two-fluid model of superconductivity
- two-fluid plasma model
- two-tier model
- Van der Ziel's noise model
- variable parameter model
- vector model
- wire-frame model
- working modelThe New English-Russian Dictionary of Radio-electronics > model
-
12 data link layer
- уровень канала передачи (звена) данных
- уровень канала данных
- уровень звена данных
- канальный уровень стека связи (сети и системы связи)
- канальный уровень
канальный уровень
Второй уровень эталонной модели ISO/OSI, обеспечивающий базовые коммуникационные сервисы. Канальный уровень CAN определяет кадры данных, удаленного запроса, ошибки и перегрузки.
[ http://can-cia.com/fileadmin/cia/pdfs/CANdictionary-v2_ru.pdf]Тематики
EN
канальный уровень стека связи (сети и системы связи)
Уровень канала передачи данных.
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]EN
data link layer
layer 2 of the OSI reference model for Open Systems Interconnection, responsible for the transmission of data over a physical medium. After establishment of a link, layer 2 performs data rate control, error detection, contention/collision detection, quality of service monitoring and error recovery
[IEC 61850-2, ed. 1.0 (2003-08)]Тематики
EN
уровень звена данных
Ндп. канальный уровень
Уровень взаимосвязи открытых систем, обеспечивающий услуги по обмену данными между логическими объектами сетевого уровня, протокол управления звеном данных, формирование и передачу кадров данных
[ ГОСТ 24402-88]Недопустимые, нерекомендуемые
Тематики
EN
уровень канала данных
Уровень 2 в модели OSI. Этот уровень обеспечивает организацию, поддержку и разрыв связи на уровне передачи данных между элементами сети. Основной функцией уровня 2 является передача модулей информации или кадров и связанный с этим контроль ошибок. См. также LLC, MAC.
[ http://www.lexikon.ru/dict/net/index.html]Тематики
EN
уровень канала передачи (звена) данных
Уровень 2 в архитектуре взаимосвязи открытых систем (BОC); это уровень, предоставляющий услуги для передачи данных по звену передачи данных между открытыми системами. (МСЭ-R F.1499).
[ http://www.iks-media.ru/glossary/index.html?glossid=2400324]Тематики
- электросвязь, основные понятия
EN
Ндп. Канальный уровень
Data link layer
Уровень взаимосвязи открытых систем, обеспечивающий услуги по обмену данными между логическими объектами сетевого уровня, протокол управления звеном данных, формирование и передачу кадров данных
Источник: ГОСТ 24402-88: Телеобработка данных и вычислительные сети. Термины и определения оригинал документа
Англо-русский словарь нормативно-технической терминологии > data link layer
-
13 data-link layer
"Layer two of the OSI model. A layer that packages raw bits from the physical layer into frames (logical, structured packets for data). This layer is responsible for transferring frames from one computer to another, without errors. After sending a frame, the data-link layer waits for an acknowledgment from the receiving computer." -
14 PDM
1) Компьютерная техника: Physical Data Member, pit depth modulation2) Военный термин: Pay Duties Manual, Power Distribution Module, Product Descriptive Matter, Programmed Decision Making, payload deployment mechanism, period-delay mechanism, point defense missile, program decision memorandum, programmed depot maintenance, project data manual, projectile-delivered mine, propellant dispersion munition, publications distribution manager, pursuit deterrent munitions3) Техника: portable differential magnetometer, pulse-delay modulation, pulse-duration modulation, Plastic Design and Moulding4) Сельское хозяйство: Protected Difference Milk5) Автомобильный термин: passenger door module6) Биржевой термин: possible duplication message7) Телекоммуникации: Physical Medium Dependent (FDDI)8) Сокращение: Periodic Depot Maintenance, Point Defence Missile, Program Decision Memorandum (USA), Pursuit Deterrent Mine, Pursuit Deterrent Munition (See also ADAM), physical distribution management9) Электроника: Product Data Manager10) Вычислительная техника: Program Development Manager (IBM, ADT), physical medium-dependent (layer), управление данными о продуктах (product data management), physical data model11) Нефть: photoclinometer dip meter, positive displacement meter, positive displacement motor, preliminary design memorandum, product development memorandum12) Космонавтика: широтно-импульсная модуляция13) Деловая лексика: Performance Data Manufacturing, Product Definition Management, Product Differentiation Method14) Бурение: ГЗД, гидравлический забойный двигатель15) Глоссарий компании Сахалин Энерджи: precedence diagraming method, used in project controls, Project Director - Marketing (Project execution sub-team)16) Менеджмент: precedence diagram method, precedence diagramming method17) Сетевые технологии: physical medium-dependent layer, pulse duration modulation18) Автоматика: product data management19) Полупроводники: phase detection microscopy20) Химическое оружие: projectile disassembly machine21) Нефть и газ: PDM motor, забойный двигатель двигатель объёмного типа для КГТ [винтовой или аксиально-поршневой], mud motor22) Программное обеспечение: Parallel Development Manager, Php Dump Maker -
15 PDM
2) pulse duration modulation - широтно-импульсная модуляция, ШИМSyn:см. тж. pulse modulation3) см. physical data modelАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > PDM
-
16 PDM
сокр. от Physical Data ModelEnglish-Russian dictionary of acronyms found in the documentation for the Alcatel 1000 System > PDM
-
17 PDM
abbr. for Physical Data Model -
18 control
1) управление; регулирование || управлять; регулировать2) контроль || контролировать3) управляющее устройство; устройство управления; регулятор4) профессиональное мастерство, квалификация, техническая квалификация5) pl органы управления•"in control" — "в поле допуска" ( о результатах измерения)
to control closed loop — управлять в замкнутой системе; регулировать в замкнутой системе
- 2-handed controlsto control open loop — управлять в разомкнутой системе; регулировать в разомкнутой системе
- 32-bit CPU control
- acceptance control
- access control
- acknowledge control
- active process control
- adaptable control
- adaptive constraint control
- adaptive control for optimization
- adaptive control
- adaptive feed rate control
- adaptive quality control
- adjustable feed control
- adjustable rotary control
- adjustable speed control
- adjusting control
- adjustment control
- AI control
- air logic control
- analog data distribution and control
- analogical control
- analytical control
- application control
- arrows-on-curves control
- autodepth control
- autofeed control
- automated control of a document management system
- automated technical control
- automatic backlash control
- automatic control
- automatic editing control
- automatic gain control
- automatic gripper control
- automatic level control
- automatic process closed loop control
- automatic remote control
- automatic sensitivity control
- automatic sequence control
- automatic speed control
- automatic stability controls
- auxiliaries control
- balanced controls
- band width control
- bang-bang control
- bang-bang-off control
- basic CNC control
- batch control
- bibliographic control
- bin level control
- boost control
- built-in control
- button control
- cam control
- cam throttle control
- camshaft control
- carriage control
- Cartesian path control
- Cartesian space control
- cascade control
- C-axis spindle control
- cell control
- center control
- central control
- central supervisory control
- centralized control
- centralized electronic control
- central-station control
- changeover control
- chip control
- circumferential register control
- close control
- closed cycle control
- closed loop control
- closed loop machine control
- closed loop manual control
- closed loop numerical control
- closed loop position control
- clutch control
- CNC control
- CNC indexer control
- CNC programmable control
- CNC symbolic conversational control
- CNC/CRT control
- CNC/MDI control
- coarse control
- coded current control
- coded current remote control
- color control
- combination control
- command-line control
- compensatory control
- composition control
- compound control
- computed-current control
- computed-torque control
- computer control
- computer numerical control
- computer process control
- computer-aided measurement and control
- computer-integrated manufacturing control
- computerized control
- computerized numerical control
- computerized process control
- constant surface speed control
- constant value control
- contactless control
- contact-sensing control
- contamination control
- continuous control
- continuous path control
- continuous process control
- contour profile control
- contouring control
- conventional hardware control
- conventional numerical control
- conventional tape control
- convergent control
- conversational control
- conversational MDI control
- coordinate positioning control
- coordinate programmable control
- copymill control
- counter control
- crossed controls
- current control
- cycle control
- dash control
- data link control
- data storage control
- deadman's handle controls
- depth control
- derivative control
- dial-in control
- differential control
- differential gaging control
- differential gain control
- differential temperature control
- digital brushless servo control
- digital control
- digital position control
- digital readout controls
- dimensional control
- direct computer control
- direct control
- direct digital control
- direct numerical control
- direction control
- directional control
- dirt control
- discontinuous control
- discrete control
- discrete event control
- discrete logic controls
- dispatching control
- displacement control
- distance control
- distant control
- distributed control
- distributed numerical control
- distributed zone control
- distribution control
- dog control
- drum control
- dual control
- dual-mode control
- duplex control
- dust control
- dynamic control
- eccentric control
- edge position control
- EDP control
- electrical control
- electrofluidic control
- electromagnetic control
- electronic control
- electronic level control
- electronic speed control
- electronic swivel control
- elevating control
- emergency control
- end-point control
- engineering change control
- engineering control
- entity control
- environmental control
- error control
- error plus error-rate control
- error-free control
- external beam control
- factory-floor control
- false control
- feed control
- feed drive controls
- feedback control
- feed-forward control
- field control
- fine control
- finger-tip control
- firm-wired numerical control
- fixed control
- fixed-feature control
- fixture-and-tool control
- flexible-body control
- floating control
- flow control
- fluid flow control
- follow-up control
- foot pedal control
- force adaptive control
- forecasting compensatory control
- fork control
- four quadrant control
- freely programmable CNC control
- frequency control
- FROG control
- full computer control
- full order control
- full spindle control
- gage measurement control
- gain control
- ganged control
- gap control
- gear control
- generative numerical control
- generic path control
- geometric adaptive control
- graphic numerical control
- group control
- grouped control
- guidance control
- hairbreath control
- hand control
- hand feed control
- hand wheel control
- hand-held controls
- handle-type control
- hand-operated controls
- hardened computer control
- hardwared control
- hardwared numerical control
- heating control
- heterarchical control
- hierarchical control
- high-integrity control
- high-level robot control
- high-low control
- high-low level control
- high-technology control
- horizontal directional control
- humidity control
- hybrid control
- hydraulic control
- I/O control
- immediate postprocess control
- inching control
- in-cycle control
- independent control
- indexer control
- indirect control
- individual control
- industrial processing control
- industrial-style controls
- infinite control
- infinite speed control
- in-process control
- in-process size control
- in-process size diameters control
- input/output control
- integral CNC control
- integral control
- integrated control
- intelligent control
- interacting control
- interconnected controls
- interlinking control
- inventory control
- job control
- jogging control
- joint control
- joystick control
- just-in-time control
- language-based control
- laser health hazards control
- latching control
- lead control
- learning control
- lever control
- lever-operated control
- line motion control
- linear control
- linear path control
- linearity control
- load control
- load-frequency control
- local control
- local-area control
- logic control
- lubricating oil level control
- machine control
- machine programming control
- machine shop control
- macro control
- magnetic control
- magnetic tape control
- main computer control
- malfunction control
- management control
- manual control
- manual data input control
- manual stop control
- manually actuatable controls
- manufacturing change control
- manufacturing control
- master control
- material flow control
- MDI control
- measured response control
- mechanical control
- memory NC control
- memory-type control
- metering control
- metrological control of production field
- microbased control
- microcomputer CNC control
- microcomputer numerical control
- microcomputer-based sequence control
- microprocessor control
- microprocessor numerical control
- microprogrammed control
- microprogramming control
- milling control
- model reference adaptive control
- model-based control
- moisture control
- motion control
- motor control
- motor speed control
- mouse-driven control
- movable control
- multicircuit control
- multidiameter control
- multilevel control
- multimachine tool control
- multiple control
- multiple-processor control
- multiposition control
- multistep control
- multivariable control
- narrow-band proportional control
- navigation control
- NC control
- neural network adaptive control
- noise control
- noncorresponding control
- noninteracting control
- noninterfacing control
- nonreversable control
- nonsimultaneous control
- numerical contouring control
- numerical control
- numerical program control
- odd control
- off-line control
- oligarchical control
- on-board control
- one-axis point-to-point control
- one-dimensional point-to-point control
- on-line control
- on-off control
- open loop control
- open loop manual control
- open loop numerical control
- open-architecture control
- operating control
- operational control
- operator control
- optical pattern tracing control
- optimal control
- optimalizing control
- optimizing control
- oral numerical control
- organoleptic control
- overall control
- overheat control
- override control
- p. b. control
- palm control
- parameter adaptive control
- parameter adjustment control
- partial d.o.f. control
- path control
- pattern control
- pattern tracing control
- PC control
- PC-based control
- peg board control
- pendant control
- pendant-actuated control
- pendant-mounted control
- performance control
- photoelectric control
- physical alignment control
- PIC control
- PID control
- plugboard control
- plug-in control
- pneumatic control
- point-to-point control
- pose-to-pose control
- position/contouring numerical control
- position/force control
- positional control
- positioning control
- positive control
- postprocess quality control
- power adaptive control
- power control
- power feed control
- power-assisted control
- powered control
- power-operated control
- precision control
- predictor control
- preselective control
- preset control
- presetting control
- pressbutton control
- pressure control
- preview control
- process control
- process quality control
- production activity control
- production control
- production result control
- programmable adaptive control
- programmable cam control
- programmable control
- programmable logic adaptive control
- programmable logic control
- programmable machine control
- programmable microprocessor control
- programmable numerical control
- programmable sequence control
- proportional plus derivative control
- proportional plus floating control
- proportional plus integral control
- prototype control
- pulse control
- pulse duration control
- punched-tape control
- purpose-built control
- pushbutton control
- quality control
- radio remote control
- radium control
- rail-elevating control
- ram stroke control
- ram-positioning control
- rapid-traverse controls for the heads
- rate control
- ratio control
- reactive control
- real-time control
- reduced-order control
- register control
- registration control
- relay control
- relay-contactor control
- remote control
- remote program control
- remote switching control
- remote valve control
- remote-dispatch control
- resistance control
- resolved motion rate control
- retarded control
- reversal control
- revolution control
- rigid-body control
- robot control
- robot perimeter control
- robot teach control
- rod control
- safety control
- sampled-data control
- sampling control
- schedule control
- SCR's control
- second derivative control
- selective control
- selectivity control
- self-acting control
- self-adaptive control
- self-adjusting control
- self-aligning control
- self-operated control
- self-optimizing control
- self-programming microprocessor control
- semi-automatic control
- sensitivity control
- sensor-based control
- sequence control
- sequence-type control
- sequential control
- series-parallel control
- servo control
- servo speed control
- servomotor control
- servo-operated control
- set value control
- shaft speed control
- shape control
- shift control
- shop control
- shower and high-pressure oil temperature control
- shut off control
- sight control
- sign control
- single variable control
- single-flank control
- single-lever control
- size control
- slide control
- smooth control
- software-based NC control
- softwared numerical control
- solid-state logic control
- space-follow-up control
- speed control
- stabilizing control
- stable control
- standalone control
- start controls
- static control
- station control
- statistical quality control
- steering control
- step-by-step control
- stepless control
- stepped control
- stick control
- stock control
- stop controls
- stop-point control
- storage assignment control
- straight cut control
- straight line control
- stroke control
- stroke length control
- supervisor production control
- supervisory control
- swarf control
- switch control
- symbolic control
- synchronous data link control
- table control
- tap-depth controls
- tape control
- tape loop control
- teach controls
- temperature control
- temperature-humidity air control
- template control
- tension control
- test control
- thermal control
- thermostatic control
- three-axis contouring control
- three-axis point-to-point control
- three-axis tape control
- three-mode control
- three-position control
- throttle control
- thumbwheel control
- time control
- time cycle control
- time optimal control
- time variable control
- time-critical control
- time-proportional control
- timing control
- token-passing access control
- tool life control
- tool run-time control
- torque control
- total quality control
- touch-panel NC control
- touch-screen control
- tracer control
- tracer numerical control
- trajectory control
- triac control
- trip-dog control
- TRS/rate control
- tuning control
- turnstile control
- two-axis contouring control
- two-axis point-to-point control
- two-dimension control
- two-hand controls
- two-position control
- two-position differential gap control
- two-step control
- undamped control
- user-adjustable override controls
- user-programmable NC control
- variable flow control
- variable speed control
- variety control
- varying voltage control
- velocity-based look-ahead control
- vise control
- vision responsive control
- visual control
- vocabulary control
- vocal CNC control
- vocal numerical control
- voltage control
- warehouse control
- washdown control
- water-supply control
- welding control
- wheel control
- wide-band control
- zero set control
- zoned track controlEnglish-Russian dictionary of mechanical engineering and automation > control
-
19 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
-
20 board
комитет; совет; комиссия; планшет; доска; пульт; борт; совершать посадку (на) ; садиться (напр. на корабль, машину) ; разг. «комиссовать, увольнять по состоянию здоровья; пропускать через комиссию; см. тж. committeeArmy (Central) Physical Evaluation board — (центральная) комиссия СВ по оценке уровня физической подготовки ЛС
Army Airborne, Electronics and Special Warfare board — комитет СВ по авиационным бортовым электронным системам и специальным методам ведения боевых действий
— on board— target status board
Страницы
- 1
- 2
См. также в других словарях:
Physical data model — A physical data model (a.k.a. database design) is a representation of a data design which takes into account the facilities and constraints of a given database management system. In the lifecycle of a project it is typically derived from a… … Wikipedia
Data model — Overview of data modeling context: A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional… … Wikipedia
Logical data model — A logical data model also referred to as LDM in computer science is a representation of an organization s data, organized in terms of a particular data management technology. When ANSI first laid out the idea of a logical schema (in 1975 [1] ),… … Wikipedia
Data model (GIS) — A data model in geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example, the vector data model represents geography as collections of points, lines, and polygons; the raster… … Wikipedia
Core Architecture Data Model — Example of a CADM Diagram for Overview and Summary Information (AV 1) of the DoDAF.[1] Core Architecture Data Model (CADM) in Enterprise Architecture is a logical data model of information used to describe and build architectures … Wikipedia
Semantic data model — A semantic data model in software engineering is a data modeling technique to define the meaning of data within the context of its interrelationships with other data. A semantic data model is an abstraction which defines how the stored symbols… … Wikipedia
Data architecture — in enterprise architecture is the design of data for use in defining the target state and the subsequent planning needed to achieve the target state. It is usually one of several architecture domains that form the pillars of an enterprise… … Wikipedia
Physical design — can refer to*Physical database design see also Physical data model *Physical design (electronics) … Wikipedia
Data modeling — The data modeling process. The figure illustrates the way data models are developed and used today. A conceptual data model is developed based on the data requirements for the application that is being developed, perhaps in the context of an… … Wikipedia
Data Format Description Language — (DFDL, often pronounced daff o dil) is a modeling language from the Open Grid Forum for describing general text and binary data. A DFDL model or schema allows any text or binary data to be read (or parsed ) from its native format and to be… … Wikipedia
Data flow diagram — example.[1] A data flow diagram (DFD) is a graphical representation of the flow of data through an information system, modelling its process aspects. Often they are a preliminary step used to create an overview of the system which can later be… … Wikipedia
