The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the prop...
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The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.
The paper applies a two-stage modelling concept to a gas-gas heat exchanger located in a tyre production plant. Modelling relies on combination of prior knowledge and recorded data. During the identification procedure...
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Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adver...
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Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.
The problem of detecting actuator faults in the presence of modelling errors is addressed. A likelihood-ratio test is derived such that the effects of undermodelling are properly taken into account via a stochastic em...
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The problem of robust fault detection in command inputs in the presence of modelling errors in linear time-invariant systems is addressed. The problem leads to a hypothesis test with a test statistic taking form of a ...
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The main motivation of the presented work is to realise a prototype of the CAD tool that supports the whole life cycle of the diagnostic system development process. In the proposed approach, the fault trees are automa...
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This paper is a presentation of the ongoing research project being under development at the institute of automation and control processes (Vladivostok, Russia). The main purpose of the project is to develop a prototyp...
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Online interactions,especially user generated contents on social events,reveal a variety of communicative purposes ranging from expressing feelings to proposing *** intents in users' online interactive behavior fr...
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ISBN:
(纸本)9781509036202
Online interactions,especially user generated contents on social events,reveal a variety of communicative purposes ranging from expressing feelings to proposing *** intents in users' online interactive behavior from massive social media data can effectively identify users' motives and intents behind communication and provide important information to aid monitoring,analysis and decision making for a variety of ***,user intents recognition from online communication is inherently challenging due to the ambiguity in semantic processing and diversity of syntax ***,the massive online data are usually unlabeled,which greatly hinders the usage of typical machine learning based methods that can automate the recognition *** this paper,we tackle this problem by proposing a Speech Act Theory guided classification scheme,which regards online communication as performative actions of users and classifies user utterances according to their pragmatic *** the basis of this,we construct a dictionary of performative words,expand it using external knowledge sources and refine it by word embedding and similarity *** then use this dictionary to automatically label the online textual data with *** a large amount of the labeled data,we train feature based classifiers to identify user intents in their online *** experimental study using a microblog dataset on social events from Sina Weibo shows the effectiveness of our proposed method.
Motion detection plays a crucial role in most video based applications.A particular background subtraction technique called ViBe(Visual Background Extractor) is commonly used to obtain foreground objects from the back...
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ISBN:
(纸本)9781509009107
Motion detection plays a crucial role in most video based applications.A particular background subtraction technique called ViBe(Visual Background Extractor) is commonly used to obtain foreground objects from the background due to its high detection rate and low computational ***,the performance is not very ***,this paper presents an improved ViBe algorithm to increase the accuracy and robustness of motion ***,a foreground feature map is created by optimizing the result of ViBe *** the edge detection of the original video frames is achieved after pre-sharpening using improved Sobel operator and Otsu ***,by feature fusion(of the foreground and background feature maps) and contour filling,the motion detection results can be *** experiments demonstrate the improvements of the proposed modifications at a limited additional cost.
With the continuous development of social networking sites,the volume of social media data has exploded and the user-generated content is becoming more and more *** a result,the modality of massive social media data i...
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ISBN:
(纸本)9781509036202
With the continuous development of social networking sites,the volume of social media data has exploded and the user-generated content is becoming more and more *** a result,the modality of massive social media data is no longer confined to the single text *** brings new challenges to social media analytics in general and its examplar field such as sentiment analysis in *** sentiment analysis has become an increasingly important research topic in recent years,especially in the context of social media big *** of the previous work only focuses on single modality content such as text,image or ***,as the traditional sentiment analysis methods often lack the support of scalable deep models,this hinders their usage in processing large amount of online *** overcome the limitations in the previous work,in this paper,we propose an end-to-end framework for multimodal sentiment analysis based on deep neural *** propose a Merged Neural Network(MNN) model that utilizes CNNs to extract representations of text and image *** fuse the multimodal features,we introduce the residual model and propose two combined merged strategies,namely the EarlyRMNN(*** Residual MNN) and Late-RMNN(*** Residual MNN),to get deeper and more discriminative features than the previous *** experiments on two public available datasets demonstrate the effectiveness of our models for multimodal sentiment analysis in comparison with the related methods.
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