Computer agents are frequently anthropomorphized, giving them appearances and responses similar to humans. Research has demonstrated that users tend to apply social norms and expectations to such ...computer agents, and that people interact with computer agents in a similar fashion as they would another human. Perceived expertise has been shown to affect trust in human-human relationships, but the literature investigating how this influences trust in computer agents is limited. The current study investigated the effect of computer agent perceived level of expertise and recommendation reliability on subjective (rated) and objective (compliance) trust during a pattern recognition task. Reliability of agent recommendations had a strong effect on both subjective and objective trust. Expert agents started with higher subjective trust, but showed less trust repair. Agent expertise had little impact on objective trust resiliency or repair.
In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature ...properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN’s further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.
The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes ...were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance.
Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge.
Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions.
Об’єктами дослідження є безпілотні транспортні засоби та розгалуження мосту міста Києва (Україна), який сполучає між собою Велику Окружну дорогу, Житомирське шосе та проспект Перемоги. Були проаналізовані побудовані маршрути з використанням технології розпізнання автодорожніх знаків, людей та автотранспортних засобів. Важливою проблемою даного дослідження є проведення аналізу можливостей з виявлення перешкод безпілотним транспортним засобом за допомогою розпізнавання образів, що поєднує в собі методи машинного зв’язку, навігацію та відеоспостереження в режимі реального часу.
На основі проведеного дослідження були отримані результати виявлення та уникнення перешкод на автодорожньому шляху, де проводилося дослідження з вивченням основних причин, які можуть спричинити затримки у часі (затори, погодні умови, ДТП). Отримано результати планування та навігації для визначення доцільного автодорожнього маршруту, що дозволяє виявляти та усувати перешкоди на дорожньому шляху, а також завчасно побудувати картографічний план маршруту за допомогою використання онлайн-сервісів з картами (Google Maps). Показано, що розпізнавання автодорожніх знаків (на основі класифікації з використанням карти дорожніх знаків, яка складається з 7 категорій), людей та автотранспортних засобів, зводить до мінімуму виникнення дорожньо-транспортних пригод, заторів та затримки у часі. Для розпізнавання образів дорожніх знаків, людей та автотранспортних засобів досліджувалися автодорожні ділянки, які сполучаються з розгалуженим мостом.
Таким чином, авторами було розглянуто та проаналізовано цифрові можливості ідентифікації та розпізнавання образів за допомогою методів машинного навчання, систем навігації та відеоспостереження, де вагоме значення відіграє безпека руху автомобільних засобів з виявленням на шляху автодорожніх знаків та перешкод. Отримані результати можуть доповнити можливості використання безпілотних транспортних засобів з метою уникнення перешкод та дорожньо-транспортних пригод на основі навченої системи для розпізнавання образів. Ця система за допомогою згорткових нейронних мереж та систем навігації з відеоспостереженням зможе забезпечити водія та оточуючих людей навколо безпечними умовами під час дорожнього руху.
•A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the ...planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common diagnostic models in classification precision.
Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples.
•A multi-mode energy management is devised for fuel cell hybrid electric vehicles.•A Markov Chain based driving pattern identification approach is developed.•Driving pattern recognition accuracy ...during pattern shifting phases is improved.•Multiple sets of control parameters are optimized based on dynamic programming.•Validation results denote the improved fuel economy and fuel cell durability.
Considering the changeable driving conditions in reality, energy management strategies for fuel cell hybrid electric vehicles should be able to effectively distribute power demands under multiple driving patterns. In this paper, the development of an adaptive energy management strategy is presented, including a driving pattern recognizer and a multi-mode model predictive controller. In the supervisory level, the Markov pattern recognizer can classify the real-time driving segment into one of three predefined patterns. Based on the periodically updated pattern identification results, one set of pre-optimized control parameters is selected to formulate the multi-objective cost function. Afterwards, the desirable control policies can be obtained by solving a constrained optimization problem within each prediction horizon. Validation results demonstrate the effectiveness of the Markov pattern recognizer, where at least 94.94% identification accuracy can be reached. Additionally, compared to a single-mode benchmark strategy, the proposed multi-mode strategy can reduce the average fuel cell power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen consumption, indicating the improved fuel cell system durability and the enhanced fuel economy.
•A one-dimensional convolutional neural network model is proposed to Diagnostic fault with the original vibration signal.•The parameter optimization selection of the 1-DCNN model is ...analyzed.•Compared to three traditional methods, both tests of 1-DCNN achieved an accuracy of about 99.3%.
Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of modern industrial systems. The traditional fault diagnosis methods usually need manually extracting the features from raw sensor data before classifying them with pattern recognition models. This requires much professional knowledge and complex feature extraction, only to cause results in a poor flexibility of the model, which only applies to the diagnosis of a fault in particular equipment. In recent years, deep learning has developed rapidly, and great achievements have been made in image analysis, speech recognition and natural language processing. However, its application in fault diagnosis of rotating machinery is still at the initial stage. In order to solve the problem of end-to-end fault diagnosis, this paper focuses on developing a convolutional neural network to learn features directly from the original vibration signals and then diagnose faults. The effectiveness of the proposed method is validated through PHM (Prognostics and Health Management) 2009 gearbox challenge data and a planetary gearbox test rig. Compared with the other three traditional methods, the results show that the one-dimensional convolutional neural network (1-DCNN) model has higher accuracy for fixed-shaft gearbox and planetary gearbox fault diagnosis than that of the traditional diagnostic ones.
The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and ...memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10
μm
devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.
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•Deep learning hybrid model is proposed for solar radiation prediction.•Convolutional network is used to extract features for solar radiation.•Long Short-Term Memory is used for ...prediction of solar radiation.•Deep learning hybrid model outperforms all comparative models.•The model can be adopted as a decision-support tool in solar energy simulation.
This paper designs a hybridized deep learning framework that integrates the Convolutional Neural Network for pattern recognition with the Long Short-Term Memory Network for half-hourly global solar radiation (GSR) forecasting. The Convolution network is applied to robustly extract data input features from predictive variables (i.e., statistically significant antecedent inputs) while Long Short-Term Memory absorbs them for prediction. Half-hourly GSR for Alice Springs (Australia: 01 January 2006 to 31 August 2018) are extracted with stationarity checks applied via unit-root and mutual information test to capture antecedent GSR values required to forecast future GSR. The proposed hybrid model is benchmarked with standalone models as well as other Deep Learning, Single Hidden Layer and Tree based models. The results show that the benchmarked models are not able to generate satisfactory GSR predictions and the proposed hybrid model outperforms all other counterparts. The hybrid model registers superior results with over 70% of predictive errors lying below ±10 Wm−2 and outperforms the benchmark model for 1-Day half-hourly GSR prediction with low Relative Root Mean Square Error (≈1.515%), Mean Absolute Percentage Error (≈4.672%) and Absolute Percentage Bias (≈1.233%). This study ascertains that a proposed hybrid model based on a convolution network framework can accurately predict GSR and enable energy availability to be regularly monitored over multi-step horizons when coupled with a low latency Long Short-Term Memory network. Furthermore, it also concludes that the proposed model can have practical implications in forecasting GSR, capitalizing its versatility as a stratagem in monitoring solar powered systems by integrating freely available solar radiation into a real power grid system.
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•Smartphone videos and pattern recognition were used for food authentication.•Visible spectral information was captured from samples under coloured illuminations.•Data obtained under ...variable conditions was handled by a locally weighted classification method.•The proposed sensor system was tested on olive oil and milk samples.
A novel sensor system for food authentication is presented, which is based on computer vision and pattern recognition. The sensor system uses a smartphone to generate a sequence of light with varying colours to illuminate a food sample, and uses the smartphone camera to receive reflected light by way of recording a video. The video is processed using computer vision techniques and transformed into sensor data in the form of a data vector. The sensor data is analysed using pattern recognition techniques. The locally weighted partial least squares regression method is extended for classification to improve the modelling effectiveness and robustness. The sensor system is evaluated on the task of authentication of olive oil and milk – to verify how they are labelled. Large quantities of olive oil and milk were purchased from supermarkets, and sensor videos were created using the sensor system. Test accuracies of 96.2% and 100% were achieved for olive oil and milk authentication respectively. These results suggest the proposed sensor system is effective. Since the sensor system is built in a smartphone, it has the potential to serve as a low-cost and effective solution for food authentication and to empower consumers in food fraud detection.
There is a growing interest in developing high-performance sensors monitoring organophosphate pesticides, primarily due to their broad usage and harmful effects on mammals. In the present study, a ...colorimetric sensor array consisting of citrate-capped 13 nm gold nanoparticles (AuNPs) has been proposed for the detection and discrimination of several organophosphate pesticides (OPs). The aggregation-induced spectral changes of AuNPs upon OP addition has been analyzed with pattern recognition techniques, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). In addition, the proposed sensor array has the capability to identify individual OPs or mixtures of them in real samples.
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