50 research outputs found

    Identifying divergent design thinking through the observable behavior of service design novices

    Get PDF
    © 2018, Springer Nature B.V. Design thinking holds the key to innovation processes, but is often difficult to detect because of its implicit nature. We undertook a study of novice designers engaged in team-based design exercises in order to explore the correlation between design thinking and designers’ physical (observable) behavior and to identify new, objective, design thinking identification methods. Our study addresses the topic by using data collection method of “think aloud” and data analysis method of “protocol analysis” along with the unconstrained concept generation environment. Collected data from the participants without service design experience were analyzed by open and selective coding. Through the research, we found correlations between physical activity and divergent thinking, and also identified physical behaviors that predict a designer’s transition to divergent thinking. We conclude that there are significant relations between designers’ design thinking and the behavioral features of their body and face. This approach opens possible new ways to undertake design process research and also design capability evaluation

    Granulometry, chemistry and physical interactions of non-colloidal particulate matter transported by urban storm water

    Get PDF
    Urban rainfall-runoff is a major source of anthropogenic pollutions to the natural water bodies. Particulate matter generated from anthropogenic environments and activities is a constituent of environmental concern as well as a carrier substrate for reactive contaminants such as metals. Partitioning, transport and transformation of particulate-bound contaminants are determined by the granulometry, physical and geochemical properties of the particulate carriers. Previous research emphasized in the transport of colloidal and suspended particles in rainfall-runoff. The settleable and sediment material were ignored though they are a major granulometric fraction which may contain most of the sorbed or transported constituents such as metals, organics or inorganics. In this research the entire flow section of rainfall-runoff was captured. Particulate matters in the catchment were analyzed for solid fractions, metal partitioning and distribution, fractal nature, morphology, chemical composition, and settling characteristics. Unsteady hydrodynamic conditions and short residence time determine coagulation and flocculation is still a dynamic mechanism in urban rainfall-runoff. Natural coagulation and flocculation (C/F) as well as coagulants/flocculants assisted C/F was studied for particles in urban rainfall-runoff. A C/F model incorporating fractal geometry and sedimentation mechanism was applied to simulate the particle size distribution in a 2-m settling column test. The overarching objective is to facilitate decision-making with respect to urban runoff management, regulations, treatment and potential disposal of runoff sediment residuals

    Swarm intelligence for clustering dynamic data sets for web usage mining and personalization.

    Get PDF
    Swarm Intelligence (SI) techniques were inspired by bee swarms, ant colonies, and most recently, bird flocks. Flock-based Swarm Intelligence (FSI) has several unique features, namely decentralized control, collaborative learning, high exploration ability, and inspiration from dynamic social behavior. Thus FSI offers a natural choice for modeling dynamic social data and solving problems in such domains. One particular case of dynamic social data is online/web usage data which is rich in information about user activities, interests and choices. This natural analogy between SI and social behavior is the main motivation for the topic of investigation in this dissertation, with a focus on Flock based systems which have not been well investigated for this purpose. More specifically, we investigate the use of flock-based SI to solve two related and challenging problems by developing algorithms that form critical building blocks of intelligent personalized websites, namely, (i) providing a better understanding of the online users and their activities or interests, for example using clustering techniques that can discover the groups that are hidden within the data; and (ii) reducing information overload by providing guidance to the users on websites and services, typically by using web personalization techniques, such as recommender systems. Recommender systems aim to recommend items that will be potentially liked by a user. To support a better understanding of the online user activities, we developed clustering algorithms that address two challenges of mining online usage data: the need for scalability to large data and the need to adapt cluster sing to dynamic data sets. To address the scalability challenge, we developed new clustering algorithms using a hybridization of traditional Flock-based clustering with faster K-Means based partitional clustering algorithms. We tested our algorithms on synthetic data, real VCI Machine Learning repository benchmark data, and a data set consisting of real Web user sessions. Having linear complexity with respect to the number of data records, the resulting algorithms are considerably faster than traditional Flock-based clustering (which has quadratic complexity). Moreover, our experiments demonstrate that scalability was gained without sacrificing quality. To address the challenge of adapting to dynamic data, we developed a dynamic clustering algorithm that can handle the following dynamic properties of online usage data: (1) New data records can be added at any time (example: a new user is added on the site); (2) Existing data records can be removed at any time. For example, an existing user of the site, who no longer subscribes to a service, or who is terminated because of violating policies; (3) New parts of existing records can arrive at any time or old parts of the existing data record can change. The user\u27s record can change as a result of additional activity such as purchasing new products, returning a product, rating new products, or modifying the existing rating of a product. We tested our dynamic clustering algorithm on synthetic dynamic data, and on a data set consisting of real online user ratings for movies. Our algorithm was shown to handle the dynamic nature of data without sacrificing quality compared to a traditional Flock-based clustering algorithm that is re-run from scratch with each change in the data. To support reducing online information overload, we developed a Flock-based recommender system to predict the interests of users, in particular focusing on collaborative filtering or social recommender systems. Our Flock-based recommender algorithm (FlockRecom) iteratively adjusts the position and speed of dynamic flocks of agents, such that each agent represents a user, on a visualization panel. Then it generates the top-n recommendations for a user based on the ratings of the users that are represented by its neighboring agents. Our recommendation system was tested on a real data set consisting of online user ratings for a set of jokes, and compared to traditional user-based Collaborative Filtering (CF). Our results demonstrated that our recommender system starts performing at the same level of quality as traditional CF, and then, with more iterations for exploration, surpasses CF\u27s recommendation quality, in terms of precision and recall. Another unique advantage of our recommendation system compared to traditional CF is its ability to generate more variety or diversity in the set of recommended items. Our contributions advance the state of the art in Flock-based 81 for clustering and making predictions in dynamic Web usage data, and therefore have an impact on improving the quality of online services

    Interactive Evolutionary Algorithms for Image Enhancement and Creation

    Get PDF
    Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited. Previous work has concentrated on the development of various aspects of the interactive evolutionary algorithms and their application to various image enhancement and creation problems. Robust evaluation of algorithmic design options in interactive evolutionary algorithms and the comparison of interactive evolutionary algorithms to alternative approaches to achieving the same goals is generally less well addressed. The work presented in this thesis is primarily concerned with different interactive evolutionary algorithms, search spaces, and operators for setting the input values required by image processing and image creation tasks. A secondary concern is determining when the use of the interactive evolutionary algorithm approach to image enhancement problems is warranted and how it compares with alternative approaches. Various interactive evolutionary algorithms were implemented and compared in a number of specifically devised experiments using tasks of varying complexity. A novel aspect of this thesis, with regards to other work in the study of interactive evolutionary algorithms, was that statistical analysis of the data gathered from the experiments was performed. This analysis demonstrated, contrary to popular assumption, that the choice of algorithm parameters, operators, search spaces, and even the underlying evolutionary algorithm has little effect on the quality of the resulting images or the time it takes to develop them. It was found that the interaction methods chosen when implementing the user interface of the interactive evolutionary algorithms had a greater influence on the performances of the algorithms

    Моделювання, керування та інформаційні технології

    Get PDF
    Aniksuhyn A., Zhyvolovych O. Generalized solvability and optimal control for an integro-differential equation of a hyperbolic type 8 Babudzhan R., Isaienkov K., Krasii D., Melkonian R., Vodka O., Zadorozhniy I. Collection and processing of bearing vibration data for their technical condition classification by machine learning methods 10 Bardan A., Bihun Y. Computer modeling of differential games . 16 Beridze Z., Shavadze Ju., Imnaishvili G., Geladze M. Concept and functions of building a private network (VPN) 19 Bomba A., Klymiuk Y. Computer prediction of technological modes of rapid cone shaped adsorption filters with automated discharge of part of heat from separation surfaces in filtering model 21 Boyko N., Dypko O. Analysis of machine learning methods using spam filtering 25 Boyko N., Kulchytska O. Analysis of tumor classification algorithms for breast cancer prediction by machine learning methods 29 Denysov S., Semenov V., Vedel Ya. A novel adaptive method for operator inclusions 33 Didmanidze M., Chachanidze G., Didmanidze T. Modern trends in unemployment . 36 Bagrationi I., Zaslavski V., Didmanidze I., Yamkova O. Ethics of information technology in the context of a global worldview . 38 Didmanidze D., Zoidze K., Akhvlediani N., Tsitskishvili G., Samnidze N., Diasamidze M. Use of computer teaching systems in the learning process . 42 Dobrydnyk Yu., Khrystyuk A. Analysis of the elevator as an object of automation 44 Gamzayev R., Shkoda B. Development and investigation of adaptive micro-service architecture for messaging software systems . 46 Gayev Ye. Student' own discoveries in information theory curriculum 50 Didmanidze I., Geladze D., Motskobili Ia, Akhvlediani D., Koridze L. Follow digitally by using a blog . 52 Kirpichnikov A., Khrystyuk A. Automatic apiary care system 54 Kunytskyi S., Ivanchuk N. Mathematical modeling of water purification in a bioplato filter 56 Kyrylych V., Milchenko O. Optimal control of a hyperbolic system that describes Slutsky demand . 58 6 Makaradze N., Nakashidze-Makharadze T., Zaslavski V., Gurgenidze M., Samnidze N., Diasamidze M. Challenges of using computer-based educational technologies in higher education 60 Mamenko P., Zinchenko S., Nosov P., Kyrychenko K., Popovych I., Nahrybelnyi Ya., Kobets V. Research of divergence trajectory with a given risk of ships collisions . 64 Mateichuk V., Zinchenko S., Tovstokoryi O., Nosov P., Nahrybelnyi Ya., Popovych I., Kobets V. Automatic vessel control in stormy conditions 68 Petrivskyi Ya., Petrivskyi V., Bychkov O., Pyzh O. Some features of creating a computer vision system 72 Poliakov V. Calculation of organic substrate decomposition in biofilm and bioreactor-filter taking into account its limitation and inhibition 75 Poliakov V. Mathematical modeling of suspension filtration on a rapid filter at an unregulated rate 78 Prokip V. On the semi-scalar equivalence of polynomial matrices 80 Pysarchuk O., Mironov Y. A proposal of algorithm for automated chromosomal abnormality detection . 83 Rybak O., Tarasenko S. Sperner’s Theorem . 87 Sandrakov G., Hulianytskyi A., Semenov V. Modeling of filtration processes in periodic porous media 90 Stepanets O., Mariiash Yu. Optimal control of the blowing mode parameters during basic oxygen furnace steelmaking process . 94 Stepanchenko O., Shostak L., Kozhushko O., Moshynskyi V., Martyniuk P. Modelling soil organic carbon turnover with assimilation of satellite soil moisture data 97 Vinnychenko D., Nazarova N., Vinnychenko I. The dependence of the deviation of the output stabilized current of the resonant power supply during frequency control in the systems of materials pulse processing 100 Voloshchuk V., Nekrashevych O., Gikalo P. Exergy analysis of a reversible chiller 105 Шарко О., Петрушенко Н., Мосін М., Шарко М., Василенко Н., Белоусов А. Інформаційно-керуючі системи та технології оцінки ступеня підготовленості підприємств до інноваційної діяльності за допомогою ланцюгів Маркова . 107 Барановський С., Бомба А., Прищепа О. Модифікація моделі інфекційного захворювання для урахування дифузійних збурень в умовах логістичної динаміки 110 Бомба А., Бойчура М., Мічута О. Ідентифікація параметрів структури ґрунтових криволінійних масивів числовими методами квазіконформних відображень . 112 Василець К. Метод оцінювання невизначеності вимірювання електроенергії вузлом комерційного обліку 114 Волощук В., Некрашевич О., Гікало П. Доцільність застосування критеріїв ексергетичного аналізу для оцінювання ефективності об'єктів теплоенергетики . 117 Гудь В. Математичне моделювання енергетичної ефективності постійних магнітів в циліндричних магнітних системах . 120 Демидюк М. Параметрична оптимізація циклічних транспортних операцій маніпуляторів з активними і пасивними приводами 122 Клепач М., Клепач М. Вейвлет аналіз температурних трендів днища скловарної печі 125 Козирєв С. Керування високовольтним імпульсним розрядом в екзотермічному середовищі . 127 Очко О., Аврука І. Безпечне збереження конфіденційної інформації на серверах . 131 Реут Д., Древецький В., Матус С. Застосування комп’ютерного зору для автоматичного вимірювання швидкості рідин з тонкодисперсними домішками 133 Сафоник А., Грицюк І. Розроблення інформаційної системи для спектрофотометричного аналізу . 135 Ткачук В. Квантовий генетичний алгоритм та його реалізація на квантовому компютері 137 Цвєткова Т. Комп’ютерна візуалізація гідродинамічного поля в області зкриволінійними межами 140 Шпортько О., Бомба А., Шпортько Л. Пристосування словникових методів компресії до прогресуючого ієрархічного стиснення зображень без втрат . 142 Сафоник А., Таргоній І. Розробка системи керування напруженістю магнітного поля для процесу знезалізнення технологічних вод . 14

    Natural landscape scenic preference: techniques for evaluation and simulation.

    Get PDF
    The aesthetic beauty of a landscape is a very subjective issue: every person has their own opinions and their own idea of what beauty is. However, all people have a common evolutionary history, and, according to the Biophilia hypothesis, a genetic predisposition to liking certain types of landscapes. It is possible that this common inheritance allows us to attempt to model scenic preference for natural landscapes. The ideal type of model for such predictions is the psychophysical preference model, integrating psychological responses to landscapes with objective measurements of quantitative and qualitative landscape variables. Such models commonly predict two thirds of the variance in the predications of the general public for natural landscapes. In order to create such a model three sets of data were required: landscape photographs (surrogates of the actual landscape), landscape preference data and landscape component variable measurements. The Internet was used to run a questionnaire survey; a novel, yet flexible, environmentally friendly and simple method of data gathering, resulting in one hundred and eighty responses. A geographic information system was used to digitise ninety landscape photographs and measure their landforms (based on elevation) in terms of areas and perimeters, their colours and proxies for their complexity and coherence. Landscape preference models were created by running multiple linear regressions using normalised preference data and the landscape component variables, including mathematical transformations of these variables. The eight models created predicted over sixty percent of variance in the responses and had moderate to high correlations with a second set of landscape preference data. A common base to the models were the variables of complexity, water and mountain landform, in particular the presence or absence of water and mountains was noted as being significant in determining landscape scenic preference. In order to fully establish the utility of these models, they were further tested against: changes in weather and season; the addition of cultural structures; different photographers; alternate film types; different focal lengths; and composition. Results showed that weather and season were not significant in determining landscape preference; cultural structures increased preferences for landscapes; and photographs taken by different people did not produce consistent results from the predictive models. It was also found that film type was not significant and that changes in focal length altered preferences for landscapes
    corecore