Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the service, we use Quantile Regression to estimate the marginal distribution of movement counts between each pair of serviced locations. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between the marginals. For supply optimization, we devise a linear programming model, which simultaneously determines the route structure and the service frequency according to the predicted demand. Importantly, our framework both preserves the uncertainty structure of future demand and leverages this for robust route optimization, while keeping both components decoupled. We evaluate our framework using a real-world case study of autonomous mobility in a university campus in Denmark. The results show that our framework often obtains the ground truth optimal solution, and can outperform conventional methods for route optimization, which do not leverage full predictive distributions.Comment: 34 pages, 12 figures, 5 table

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review

Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.

Digital system of quarry management as a SAAS solution: mineral deposit module

Purpose. Improving the efficiency of functioning the mining enterprises and aggregation of earlier obtained results into a unified digital system of designing and operative management by quarry operation. Methods. Both the traditional (analysis of scientific and patent literature, analytical methods of deposit parameters research, analysis of experience and exploitation of quarries, conducting the passive experiment and processing the statistical data) and new forms of scientific research - deposit modeling on the basis of classical and neural network methods of approximation – are used in the work. For the purpose of the software product realization on the basis of cloud technologies, there were used: for back-end implementation – server-based scripting language php; for the front-end – multi-paradigm programming language javascript, javascript framework jQuery and asynchronous data exchange technology Ajax. Findings. The target audience of the system has been identified, SWOT-analysis has been carried out, conceptual directions of 3D-quarry system development have been defined. The strategies of development and promotion of the software product, as well as the strategies of safety and reliability of the application both for the client and the owner of the system have been formulated. The modular structure of the application has been developed, and the system functions have been divided to implement both back-end and front-end applications. The Mineral Deposit Module has been developed: the geological structure of the deposit has been simulated and its block model has been constructed. It has been proved that the use of neural network algorithms does not give an essential increase in the accuracy of the block model for the deposits of 1 and 2 groups in terms of the geological structure complexity. The possibility and prospects of constructing the systems for subsoil users on the basis of cloud technologies and the concept of SaaS have been substantiated. Originality. For the first time, the modern software products for solving the problems of designing and operational management of mining operations have been successfully developed on the basis of the SaaS concept. Practical implications. The results are applicable for enterprises-subsoil users, working with deposits of 1 and 2 groups in terms of the geological structure complexity: design organizations, as well as mining and processing plants.Мета. Підвищення ефективності функціонування гірничорудних підприємств та агрегація раніше отриманих результатів в єдину цифрову систему проектування і оперативного управління роботою кар’єрів. Методика. У роботі використані як традиційні (аналіз науково-патентної літератури, аналітичні методи дослідження параметрів родовища, аналіз досвіду й експлуатації кар’єрів, проведення пасивного експерименту та статистичної обробки даних), так і нові форми наукового дослідження – моделювання родовища на основі класичних і нейромережевих методів апроксимації. Для реалізації програмного продукту на основі хмарних технологій використані: для реалізації back-end – серверна скриптова мова програмування php; для front-end – мультипарадігменна мова програмування javascript, javascript framework jQuery і технологія асинхронного обміну даними Ajax. Результати. Виявлено цільову аудиторію системи, проведено SWOT-аналіз, визначено концептуальні напрями розвитку системи 3D-кар’єр, розроблені стратегії розвитку та просування програмного продукту, розроблені стратегії безпеки й надійності додатки як для клієнта, так і власника системи. Розроблено модульну структуру програми, вироблено розподіл функцій системи для реалізації як back-end і front-end додатки. Розроблено модуль “Родовище”: проведено моделювання геологічної структури родовища та побудована його блокова модель. Доведено, що використання нейромережевих алгоритмів не дає принципового підвищення точності блокової моделі для родовищ 1 і 2 груп за складністю геологічної будови. Виявлено недоліки нейромережевих алгоритмів, такі як високі витрати обчислювальних ресурсів сервера і проблеми візуалізації великих масивів геоданих при використанні web-рішень, знайдені шляхи їх вирішення. Доведено можливість і перспективність побудови систем для надрокористувачів на основі хмарних технологій і концепції SaaS. Наукова новизна. Вперше на основі концепції ASP успішно побудовані сучасні програмні продукти для вирішення завдань проектування та оперативного керування гірничими роботами. Практична значимість. Результати корисні для підприємств-надрокористувачів, які працюють з родовищами 1 і 2 груп за складністю геологічної будови – проектних організацій і ГЗК.Цель. Повышение эффективности функционирования горнорудных предприятий и агрегация ранее полученных результатов в единую цифровую систему проектирования и оперативного управления работой карьеров. Методика. В работе использованы как традиционные (анализ научно-патентной литературы, аналитические методы исследования параметров месторождения, анализ опыта и эксплуатации карьеров, проведение пассивного эксперимента и статистической обработкой данных), так и новые формы научного исследования – моделирование месторождения на основе классических и нейросетевых методов аппроксимации. Для реализации программного продукта на основе облачных технологий использованы: для реализации back-end – серверный скриптовый язык программирования php; для front-end – мультипарадигменный язык программирования javascript, javascript framework jQuery и технология асинхронного обмена данными Ajax. Результаты. Выявлена целевая аудитория системы, проведен SWOT-анализ, определены концептуальные направления развития системы 3D-карьер, разработаны стратегии развития и продвижения программного продукта, разработаны стратегии безопасности и надежности приложения как для клиента, так и владельца системы. Разработана модульная структура приложения, произведено деление функций системы для реализации как back-end и front-end приложения. Разработан модуль “Месторождение”: проведено моделирование геологической структуры месторождения и построена его блочная модель. Доказано, что использование нейросетевых алгоритмов не дает принципиального повышения точности блочной модели для месторождений 1 и 2 групп по сложности геологического строения. Выявлены недостатки нейросетевых алгоритмов, такие как высокие затраты вычислительных ресурсов сервера и проблемы визуализации больших массивов геоданных при использовании web-решений, найдены пути их решения. Доказана возможность и перспективность построения систем для недропользователей на основе облачных технологий и концепции SaaS. Научная новизна. Впервые на основе концепции ASP успешно построены современные программные продукты для решения задач проектирования и оперативного управления горными работами. Практическая значимость. Результаты применимы для предприятий-недропользователей, работающих с месторождениями 1 и 2 групп по сложности геологического строения – проектных организаций и ГОКов.We express our profound gratitude to A.B. Naizabekov for his assistance in scientific research, to A.F. Tsekhovoy, P.A. Tsekhovoy, D.Sh. Akhmedov, V. V. Yankovenko and D.V. Nikitas for scientific advice in implementation of the program code. The research was carried out within the framework of the initiative research theme “Improving the Efficiency of Mining Enterprises” on the basis of the RSE at the Rudny Industrial Institute of the Ministry of Education and Science of the Republic of Kazakhstan

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201