Computational Thinking - Учебное пособие, страница 3

However, it is only when computingconcepts and methods are combined with the power ofautomation afforded by contemporary computing technologiesand tools that the full potential of CT is unleashed. Drawingdeeply on computational concepts, methods, technologies andtools, CT serves as a powerful strategy to more effectively13design, understand and solve problems associated withcomplex systems in many aspects of modern life.Notes:Carnegie Mellon University – a private research university inPittsburgh, Pennsylvania (USA) recognized for world-classarts and technology programsA heuristic – an experience-based technique for problemsolving that gives a solution which is not guaranteed to beoptimalMoore's Law – an observation that the number of transistorson integrated circuits doubles approximately every two yearsNSF – National Science Foundation, a US governmentagency that supports fundamental research and education inall non-medical fields of science and engineeringA rule of thumb – a principle that is not intended to bestrictly accurateAn automaton – a self-operating machineA divide and conquer algorithm – works by breaking aproblem into sub-problemsExercises1.

Give Russian equivalents to the following words andphrases and explain them in your own words:To augment the scientific method;in a nutshell;multidisciplinary thinking; problem decomposition; patternrecognition; pattern generalization; data visualization; to filterout information; to develop step-by-step strategy; algorithm iswritten abstractly; utilizing variables; draw on math as afoundation; to be constrained by the underlying machine;integral to human endeavors.2. Translate the sentences into Russian paying attention to theprefixes and suffixes:1.

Computational thinking is thinking in terms ofabstractions, invariably multiple layers of abstraction at once.142. Abstraction is used in defining patterns, generalizing frominstances, and parametrization.3. Software computing makes possible building virtual worldsthat are unconstrained by physical reality.4. One heuristic for solving a complex problem is to break theproblem into smaller, more manageable problems.5. Doing arithmetic, solving mathematical equations by sheerbulldozing power, is not the most significant of the machines'accomplishments.6. Computers are thinking aids of enormous potentialities.7. Computers are too important to overrate or underrate.8.

There is no real point in sensationalizing or exaggeratingactivities which are striking enough without embellishment.9. Initially, many computer scientists were interdisciplinaryscholars, studying both CIS and deep applications of this newdiscipline in other disciplines.10. In education, undergraduate computer science curriculumand outreach programs teach students how to think like acomputer scientist.11. A computer system can provide users with a threedimensional structured walk-through of a planned buildingbefore physical work begins.12. In an unimaginably complex future, the digitallyunenhanced person, however wise, will not be able to accessthe tools of wisdom that will be available to even the leastwise digitally enhanced human.”3.

Give English equivalents to the following words andphrases:Различныеуровниабстракции;уловитьсуть;возможности компьютера; эффективно использовать;фундаментальные понятия; термин был придуман …; сбольшойскоростью;несущественныеотличия;соответствовать желаемой цели; конечное количествошагов; мозговой штурм; дополнительные возможности;грамотность;подчинятьсячьим-тотребованиям;15проблемы, поддающиеся вычислительной стратегии;преуменьшать; нет смысла...; выполнять инструкции4. Answer the following questions:1. What methods does computational thinking employ toreveal realities hidden within data?2.

How do simulation, visualization, data analysis andabstraction serve the scientific method search for mechanisms,relationships and the truths and realities hidden within data?3. Why is it important for a practitioner in computer scienceto have confidence to look inside the computing ‘black box’and to have the courage to be non-expert on some parts ofthe problem?4. Why may a scientifically correct answer containuncertainties? How simplicity may be present in complexity,once we expand the way we look at objects?5. Why is it more important for a computational scientist tohave an accurate and reliable answer to a particular problemthan the fastest one, and why is this surprisingly hard?6.

How can one understand multiple diciplines needed tosolve a problem more easily?7. What are the integral parts of the computational thinkingprocess?5. Decide whether the statements are true or false :1. Computational thinking is a process involved in mentalactivity of a person to facilitate his or her personal decisionmaking.2.

Computational thinking is a way of solving problems,designing systems, and understanding human behaviour thatdraws the concepts fundamental to computer science.3. To flourish in today’s world, computational thinking hasto be a fundamental part of the way people think andunderstand the world.4. Users of the Internet, without skills of computationalthinking, will demistify privacy technologies to surf the Web16safely.5. Computational thinking means creating and making use ofdifferent levels of abstraction, to understand and solveproblems more effectively.6. Computational thinking means thinking algorithmically andwithout the ability to apply mathematical concepts such asinduction to develop more efficient, fair, and secure solutions.7.

Computational thinking means understanding theconsequences of the scale, not only for reasons of efficiencybut also for economic and social reasons.6. Translate from Russian into English:1. С появлением кибернетики, компьютеров икомпьютерныхсистем,которыесталиназыватьинтеллектуальными системами, с развитием направления«Искусственный Интеллект» мышление, интеллект сталипредметом интереса математических и инженернотехнических дисциплин.2. Компьютерное моделирование дало мощный толчокисследованиям механизмов познавательной деятельностив рамках такого направления как психология.3.Компьютерноемоделированиемышления,использование методов математических и техническихнаук в его исследовании породило надежды на созданиестрогих теорий мышления.4.

В последние два десятилетия в компьютерной наукезаметное вниманиеуделяется такому предмету какзнание, которое стало использоваться в названияхнаправлений и компьютерных систем, основанных назнаниях.5. Теория искусственного интеллекта стала иногдахарактеризоваться как наука о знаниях, о том, как ихдобывать, представлять в искусственных системах,обрабатывать внутри системы и использовать длярешения задач.177. Summarize the text and express your own opinion. Hereare some possible statements to support:1. Computational thinking is the thought process involved informulating problems and their solutions to be carried out byan information-processing agent.2. Computational thinking incorporates logical thinking andsystem thinking.3.

Computational thinking enables you to bend computationsto your needs.4. Computational thinking gives you the ability to askquestions that were not dared to ask because of scale.5. Computational thinking helps to recognize an opportunityto use computation in a new way.8. Develop the following ideas in writing an essay (120 -150words).1. It is nearly impossible to do research in any scientific orengineering discipline without an ability to thinkcomputationally.2. The impact of computing extends far beyond scienceaffecting all aspects of our lives, and to flourish in today’sworld, everyone needs to master computational thinking.18Model Predictive Control Using Neural NetworksWords and phrases:Modelpredictivecontrol - (MPC)Constrained problemsMultivariable systemsIntrinsicDebilitatePredictorControllerDynamic matrixSpecial causesViscosityDerivation of a modelOpen-loopexperimentsExcitationTopologyFeedforward network- система управления с использованиеммодели прогнозирования(экстраполяции)- задачи с ограничениями- многосвязная система- свойственный, присущий- ослаблять- предсказывающее устройство,прогнозирующий параметр- управляющее устройство, конроллер- динамическая матрица- особые причины- вязкость- деривация модели- эксперименты с разомкнутым циклом- возмущение- топология (сети)- сеть с механизмом прогнозированияArbitraryConvergenceSigmoid functionBackpropagation-Mapping capability ofthe networkHigh gain areaPrediction horizonDisturbance rejectionGradient-basedmethodTraining runs-событийпроизвольныйсходимостьсигмоидная функцияобратная связь ( при обучениинейронной сети)свойство преобразования данных-область высокого приростагоризонт прогнозированияподавление возмущенияградиентный метод- прогоны обучения19Prediction horizonDisturbance rejection- горизонт прогнозирования- подавление возмущенияModel predictive control (MPC) is widely adopted in theprocess industry as an effective means to deal with largemultivariable constrained control problems.

The main idea ofMPC is to choose the control action by repeatedly solving online an optimal control problem. This aims at minimizing aperformance criterion over a future horizon, possibly subjectto constraints on the manipulated inputs and outputs, wherethe future behavior is computed according to a model of theplant. MPC has been used in industry for more than 30 years,and has become an industry standard (mainly in thepetrochemical industry) due to its intrinsic capability fordealing with constraints and with multivariable systems.

Mostcommercially available MPC technologies are based on alinear model of the process. For processes that are highlynonlinear, the performance of an MPC based on a linearmodel can be poor. This has motivated the development ofNonlinear Model Predictive Control (NMPC), where a moreaccurate (nonlinear) model of the plant is used for predictionand optimization. Predictive Constrained ControlPID type controllers do not perform well when applied tosystems with significant time-delay. Perhaps the best knowntechnique for controlling systems with large time-delays is theSmith Predictor. It overcomes the debilitating problems ofdelayed feedback by using predicted future states of the outputfor control.