24 research outputs found
Evaluation of a case-based Reasoning Energy Prediction Tool for Commercial Buildings
This paper presents the results of an energy
predictor that predicts the energy demand of
commercial buildings using Case Based Reasoning
(CBR). The proposed approach is evaluated using
monitored data in a real office building located in
Varennes, Quebec. The energy demand is predicted
at every hour for the following three hours using
weather forecasts. The results show that during
occupancy, 7:00 to 17:00, the coefficient of
variance of the root-mean-square-error (CVRMSE)
is below 12.3%, the normalized mean bias
error (NMBE) is below 1.3% and the root-meansquare-
error (RMSE) is below 16.6 kW. When the
statistical criteria are calculated for all hours of the
day, the CV-RMSE is 13.9%, the NMBE is 2.7%
and the RMSE is 17.9 kW. The case study
demonstrates that CBR can be used for energy
demand prediction and could be implemented in
building operation systems
Synchronization for a class of generalized neural networks with interval time-varying delays and reaction-diffusion terms
In this paper, the synchronization problem for a class of generalized neural networks with interval time-varying delays and reaction-diffusion terms is investigated under Dirichlet boundary conditions and Neumann boundary conditions, respectively. Based on Lyapunov stability theory, both delay-derivative-dependent and delay-range-dependent conditions are derived in terms of linear matrix inequalities (LMIs), whose solvability heavily depends on the information of reaction-diffusion terms. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. The obtained synchronization results are easy to check and improve upon the existing ones. In our results, the assumptions for the differentiability and monotonicity on the activation functions are removed. It is assumed that the state delay belongs to a given interval, which means that the lower bound of delay is not restricted to be zero. Finally, the feasibility and effectiveness of the proposed methods is shown by simulation examples
A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data
Motivation: Identification of disease-related genes using high-throughput microarray data is more difficult for complex diseases as compared with monogenic ones. We hypothesized that an endophenotype derived from transcriptional data is associated with a set of genes corresponding to a pathway cluster. We assumed that a complex disease is associated with multiple endophenotypes and can be induced by their up/downregulated gene expression patterns. Thus, a neural network model was adopted to simulate the gene–endophenotype–disease relationship in which endophenotypes were represented by hidden nodes
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Predicting the state of charge and health of batteries using data-driven machine learning
Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future
Whole-Building Commercial HVAC System Simulation for Use in Energy Consumption Fault Detection
Numerous fault detection and diagnostic system techniques
have been developed for HVAC systems, but most focus
on detecting faults at the component level, for example, air handling
units or variable air volume boxes. This paper examines
the use of the ASHRAE simplified energy analysis procedure
(SEAP) for fault detection at the whole-building level. In
the procedure examined, an implementation of the SEAP is
“calibrated” to a period of measured heating and cooling data
from a building so the simulated data closely follow the
measured data. A small adjustment is added to the simulated
data so the total adjusted simulated heating and cooling
consumption values exactly match the measured heating and
cooling consumption totals for the same period. The adjusted
version of the calibrated SEAP simulation is then used to
predict future consumption, using future weather data. Visual
comparison with future measured data is used to diagnose
significant deviations from expected performance. The procedure
is applied retrospectively to three years of measured
consumption data as a test. It clearly identifies three significant
operational changes that occurred during the test period.
Three different presentation formats are tested for fault identification—
monthly deviations, daily percent deviations, and
cumulative deviation plots. All have value, and it is ultimately
a user preference as to which is the most informative
A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects
Equalization and channel decoding are “traditionally” two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited battery’s size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing
Проблеми і перспективи застосування нейронних мереж у задачах моніторингу технічного стану авіаційних двигунів вертольотів у польотних режимах
Владов С.І., Дєрябіна І.О., Подгорних Н.В., Грибанова С.А., Яніцький А.А. Проблеми і перспективи застосування нейронних мереж у задачах моніторингу технічного стану авіаційних двигунів вертольотів у польотних режимах. Вісник Херсонського національного технічного університету. 2021. № 4 (79). С. 64–73. https://doi.org/10.35546/kntu2078-4481.2021.4.7Для покращення показників якості систем автоматичного управління актуальною є задача
розробки нових алгоритмів ідентифікації та діагностики технічних об’єктів. Одним із шляхів
розв’язання задачі є застосування штучних нейронних мереж. З метою дослідження проблем
застосування штучних нейронних мереж для ідентифікації та діагностики технічних об’єктів, зокрема,
авіаційних двигунів вертольотів, проведено аналіз наукових праць з цієї тематики за останні роки.
Розглянуто існуючі підходи до побудови систем діагностики несправностей та систем автоматичного
управління на основі штучних нейронних мереж. Результати наведеного аналізу можуть бути
використані при розробці нових методів та алгоритмів ідентифікації та діагностики технічних
об’єктів на основі нейромережевих аналізаторів. У цій роботі як приклад наведена узагальнена
нейромережева модель авіаційних двигунів вертольотів, що застосовується для моніторингу їх
технічного стану в режимі польотів вертольотів. З даною моделлю у роботі сформульовано задача
ідентифікації технічного стану авіаційних двигунів вертольотів. У цій роботі наведено приклад
застосування нейромережевих технологій у задачах управління авіаційними двигунами вертольотів у
польотних режимах. Розроблено структуру моделі управління авіаційними двигунами вертольотів у
польотних режимах із застосуванням нейронної мережі архітектури персептрон. Отримані
результати свідчать про переваги застосування нейронних мереж у задачах управління авіаційними
двигунами вертольотів у польотних режимах перед іншими методами, наприклад, методом управління
із застосуванням ПІД-регуляторів. З боку сучасних програмно-технічних засобів відсутні будь-які
обмеження на складність використовуваних алгоритмів, проте для реалізації тих значних потенційних
можливостей, які мають системи управління на основі штучних нейронних мереж, потрібна розробка
концептуально нових підходів до побудови таких систем.
Для улучшения показателей качества систем автоматического управления актуальной является задача разработки новых алгоритмов идентификации и диагностики технических объектов.
Одним из путей решения задачи является применение искусственных нейронных сетей. В целях
исследования проблем использования искусственных нейронных сетей для идентификации и
диагностики технических объектов, в частности, авиационных двигателей вертолетов, проведен
анализ научных работ по данной тематике за последние годы. Рассмотрены существующие подходы к
построению систем диагностики неисправностей, а также систем автоматического управления на
основе искусственных нейронных сетей. Результаты приведенного анализа могут быть использованы
при разработке новых методов и алгоритмов идентификации и диагностики технических объектов на
основе нейросетевых анализаторов. В данной работе в качестве примера приведена обобщенная
нейросетевая модель авиационных двигателей вертолетов, которая применяется для мониторинга их
технического состояния в режиме полетов вертолетов. На основании данной модели в работе
сформулирована задача идентификации технического состояния авиационных двигателей вертолетов.
В данной работе приведен пример применения нейросетевых технологий в задачах управления
авиационными двигателями вертолетов в полетных режимах. Разработана структура модели
управления авиационными двигателями вертолетов в полетных режимах с применением нейронной сети
архитектуры персептрон. Полученные результаты свидетельствуют о преимуществах применения
нейронных сетей в задачах управления авиационными двигателями вертолетов в полетных режимах
перед другими методами, например, методом управления с применением ПИД-регуляторов. Со стороны
современных программно-технических средств отсутствуют какие-либо ограничения на сложность
используемых алгоритмов, однако для реализации тех значительных потенциальных возможностей,
которые имеют системы управления на основе искусственных нейронных сетей, требуется разработка
концептуально новых подходов к построению таких систем.
To improve the quality indicators of automatic control systems, it is urgent to develop new algorithms
for the identification and diagnostics of technical objects. One of the ways to solve the problem is the use of
artificial neural networks. In order to study the problems of using artificial neural networks for the identification
and diagnostics of technical objects, in particular, helicopters aircraft engines, an analysis of scientific works on
this topic in recent years has been carried out. The existing approaches to the construction of fault diagnostics
systems, as well as automatic control systems based on artificial neural networks, are considered. The results of
the above analysis can be used in the development of new methods and algorithms for identification and
diagnostics of technical objects based on neural network analyzers. In this paper, as an example, a generalized
neural network model of helicopter aircraft engines is presented, which is used to monitoring their technical
state in the helicopter flight mode. Based on this model, the paper formulates the problem of identifying the
technical condition of helicopter aircraft engines. This paper provides an example of the use of neural network
technologies in the control problems of helicopter aircraft engines in flight modes. The structure of the model for controlling helicopters aircraft engines in flight modes using a neural network of the perceptron architecture
has been developed. The results obtained indicate the advantages of using neural networks in the problems of
controlling helicopters aircraft engines in flight modes over other methods, for example, a control method using
PID controllers. On the part of modern software and hardware, there are no restrictions on the complexity of the
algorithms used, however, to realize the significant potential capabilities that control systems based on artificial
neural networks have, it is necessary to develop conceptually new approaches to the construction of such
systems
Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations
This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components of cutting forces and average shear plane temperature for a given set of machining parameters broadly categorized into three groups viz. (i) cutting tool geometrical parameters (ii) cutting parameters and (iii) workpiece material properties. In the present work, for representing overall machining condition, 15 machining parameters having major impact on the cutting forces and cutting temperature were chosen. The feed-forward back-propagated ANN architecture has been incorporated, which was initially trained with analytical data before incorporating it as part of an integrated system. Results obtained from the proposed model show good agreement with the experimental/numerical (FEM based) results available in the literature