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智能技术学报

智能技术学报

《CAAI Transactions on Intelligence Technology》是由中国人工智能学会和重庆理工大学共同创办的英文季刊。该刊为中国人工智能学会会刊，是中国人工智能界创办的第一本综合性AI国际学术期刊。编委会阵容强大，中国工程院何新贵院士任期刊顾问，李德毅院士任编委会主任，IEEE CIS副主席、意大利米兰理工大学Cesare Alippi教授任期刊海外主编。中国人工智能学会副理事长刘宏教授和王国胤教授分别担任期刊主编与编委会副主任。《CAAI Transactions on Intelligence Technology》（CAAI TIT）是发表人工智能领域技术创新成果的高水平研究型国际学术期刊，面向全球公开发行。重点刊登机器感知与人机交互、智能信息处理、网络智能与移动计算、智能控制与决策、机器人与智能系统等领域的原创性成果。
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  • 发文量：142
  • 被引量：16
  • H指数：0
  • 开始收录时间：2016,1(1)
  • CNKI综合因子：0.0
  • 维普期刊影响因子： 0.32
  • 万方期刊影响因子：0.0
  • 创刊时间：2016
  • 国内刊号/CN：-
    国际刊号/ISSN：2468-2322
  • 发行周期：季刊
    邮发代号：-
  • 主管单位：-
    主办单位：中国人工智能学会;重庆理工大学
智能技术学报-联系信息
  • 主编：刘宏
  • 电话：023-68820073 62561406
  • 邮箱：px@cqut.edu.cn
  • 地址：重庆市巴南区红光大道69号
  • 邮编：400054
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智能技术学报 >2023年第2期

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  • 摘要：The tolerance rough set is developed as one of the outstanding extensions of the Pawlak''s rough set model under incomplete information,and the limited tolerance relation is developed to overcome the problem that objects leniently satisfy the tolerance relation.However,the classification based on the limited tolerance relationship cannot reflect the matching degree of uncertain information of objects.In this article,we explore the influence of null values in an incomplete system,and propose the constrained tolerance relation based on the matching degree of uncertain information of objects.The proposed rough set based on the constrained tolerance relation can provide a more detailed structure of an object class through threshold.Proofs and example analyses further show the rationality and superiority of the proposed model.
  • 摘要：Safe operation of the power grid requires a complete and robust control network to ensure full observability;However,redundancy measurements can create problems in expense,management,and control.The optimal phasor measurement unit(PMU)positioning problem(OPPP)is proposed to limit the number of PMUs deployed in the power grid and ensure the whole grid''s observability in the meantime.A modified branch-and-bound algorithm(MBBA)to unravel the OPPP is presented.Original BBA,which uses a single search order to create a binary tree,gives only one solution to the OPPR although more than one optimal solution exists.The proposed MBBA method consists of two different stages:the vertexes in the search tree are investigated by depth-first search(DFS)in stage 1,and the search route continues as the breadth-first search.In stage 1,the LP relaxing problems are solved by dual simplex,and in stage 2,the basic viable solution from stage 1 is used to configure the primary simplex until the optimum solution is found.OPPP is formulated as a binary decision variable MBBA model,minimizing linear objective function subject to linear matrix observability constraints.The MBBA model is unravelled using a linear integer-based external approximation scheme.IEEE test systems are used to check the feasibility of the proposed approach.Matlab software performs simulation based on a number of graph theory-based methods such as DFS,graph-theoretical method,simulated annealing,and recursive N-algorithms.These algorithms are compared to the algorithmic perspective of the proposed MBBA method.IEEE test network results confirm the validity of the proposed methodology.
  • 摘要：In recent years,multi-view learning has attracted much attention in the fields of data mining,knowledge discovery and machine learning,and been widely used in classification,clustering and information retrieval,and so forth.A new supervised feature learning method for multi-view data,called low-rank constrained weighted discriminative regression(LWDR),is proposed.Different from previous methods handling each view separately,LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective.Based on least squares regression model,the highdimensional multi-view data is mapped into a common subspace,in which different views have different weights in ptojection.The weights are adaptively updated to estimate the roles of all views.To improve the intra-class similarity of learned features,a low-rank constraint is designed and imposed on the multi-view features of each class,which improves the feature discrimination.An iterative optimization algorithm is designed to solve the LWDR model efficiently;Experiments on four popular datasets,including Handwritten,CaltechlOl,PIE and AwA,demonstrate the effectiveness of the proposed method.
  • 摘要：Pixel value differencing(PVD)steganography techniques produce imperceptible stego-images.Most PVD-based techniques have a fkll-off boundary problem(FOBP).To avoid FOB£a steganographic scheme is presented that combines the quotient value difference(QVD)with pixel value correlation(PVC).It performs data embedding in two stages on 3 x 3 size pixel blocks.The first stage of the embedding procedure performs QVD and remainder substitution on five pixels.These five pixels are central pixel and it*s neighbours on the left,right,upper,and lower sides.Based on the new values of these five pixels,the PVC embedding procedure is applied on the remaining four corner pixels of the block.The experimental results are found to be improved.The hiding capacity is high that is 3.92 bits per byte with an acceptable peak signal-to-noise ratio value.A security check was performed using two tests:(i)a regular-singular(RS)test,(ii)a pixel difference histogram(PDH)test.The experimental outcomes demonstrate that PDH curves of the stego-images do not have a zigzag shape,that means the PDH test could not detect this steganography technique.Furthermore,the RS curves show that the RS test could not detect this steganography technique.
  • 摘要：The study of training hyperparameters optimisation problems remains underexplored in skin lesion research.This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem.The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics.In the authors'demonstration,pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters,namely epoch,mini-batch value,initial learning rate,and gradient threshold.The iterative search of the optimal global-local solution is by using the derivative-based method.The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters.The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability.The authors'results showed that a small fraction of combinations(5%)from constrained global search space,in contrarily to 82%at the local level,can converge with early stopping conditions.The mean(standard deviation,SD)validation accuracies increased from 78.4(4.44)%to 82.9(1.8)%using the authors'system.The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy,precision,sensitivity,and specificity of 85.3%,75.6%,64.4%,and 97.2%,respectively.The authors'system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors'dataset.The extended experiments also showed its superior performance consistency across different deep networks,where the overall classification accuracy increased by 5%with this technique.This approach reduces the risk of search being trapped in a suboptimal solution,and its use may be expanded to network architecture optimisation for enhanced diagnostic performance.
  • 摘要：Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.
  • 摘要：In order to deal with coarse-grained and multi-grained calculation problems,as well as granularity transformation problems in information system,quotient space theory is introduced in rough set theory.The main idea of this research is to try to maintain the important properties of the original space into the quotient space.Aimed to preserve the micro properties and the macro properties,two pairs of approximation operators on the quotient space are defined.When it comes to the composite of quotient spaces,the idea of these operators shows greater advantages.Examples are cited to illustrate possible applications of these operators,and their matrix representations are also given to make the calculations easy.Finally,all approximation operators on the quotient space involved so far are compared and their relationships are shown through a diagram.
  • 摘要：Shoulder disarticulation amputees account for a small portion of upper-limb amputees,thus little emphasis has been devoted to developing functional prosthesis for this cohort of amputees.In this study,shoulder girdle recognition was investigated with acquired data from electrophysiological(electromyography[EMG])and low frequency contraction(accelerometer[Acc])signals from both amputee and non-amputee participants.The contribution of this study is based around the contrast of the classification accuracy(CA)for different sensor configurations using a unique set of signal features.It was seen that the fusion of the EMG-Acc produced an enhancement in the CA in the range of 10%-20%,depending on which windowing parameters were considered.From this,it was seen that the best combination of a windowing scheme and classifier would likely be for the 350 ms and spectral regression discriminant analysis,with a fusion of the EMG-Acc information.The results have thus provided evidence that the two sensors can be combined and used in practice for prosthesis control.Taking a holistic view on the study,the authors conclude by providing a framework on how the shoulder motion recognition could be combined with neuromuscular reprogramming to contribute towards easing the cognitive burden of amputees during the prosthesis control process.
  • 摘要：Bloody Mahjong is a kind of mahjong.It is very popular in China in recent years.It not only has the characteristics of mahjong's conventional state space,huge hidden information,complicated rules,and large randomness of hand cards but also has special rules such as Change three,Hu must lack at least one suit,and Continue playing after Hu.These rules increase the difficulty of research.These special rules are used as the input of the deep learning DenseNet model.DenseNet is used to extract the Mahjong situation features.The learned features are used as the input of the classification algorithm XGBoost,and then the XGBoost algorithm is used to derive the card strategy.Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of highdimensional sparse features.In the case of fewer training rounds,accuracy of the model can still reach 83%.In the games against real people,it plays like human.
  • 摘要：Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task.A deep learning-based model for live predictions of stock values is aimed to be developed here.The authors'have proposed two models for different applications.The first one is based on Fast Recurrent Neural Networks(Fast RNNs).This model is used for stock price predictions for the first time in this work.The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs,Convolutional Neural Networks,and Bi-Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company.The 1-min time interval stock data of four companies for a period of one and three days is considered.Along with the lower Root Mean Squared Error(RMSE),the proposed models have low computational complexity as well,so that they can also be used for live predictions.The models'performance is measured by the RMSE along with computation time.The model outperforms Auto Regressive Integrated Moving Average,FBProphet,LSTM,and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.
  • 摘要：Data hiding technologies aim to hide the existence of secret information within digital covers such as images by causing unnoticeable degradation to their quality.Reducing the image distortion and increasing the embedding capacity are the main points that the data hiding techniques revolved around.This article proposes two high payload embedding methods with high stego image quality using the Hue-Saturation-Value(HSV)colour model.The first method is hue-based embedding(HBE)that employs the H plane for hiding one or two bits in non-grey pixels.The second method uses the three HSV components,so it is called three-planes embedding(TPE).In TPE,one bit is hidden in the least significant bit(LSB)of V of the grey pixels,one or two bits in H of the pixels having low saturation or low brightness and one bit in the LSB of S otherwise.The experiments were conducted on 25 images and the results show that HBE hides more data on average than TPE with its quality reaching 60 dB.TPE achieves quality up to 61 dB and capacity reaches 364 Kb.TPE scores the highest capacity among six state-of-the-art techniques in Red-Green-Blue,HSV,Hue-Saturation-Intensity and YCbCr spaces with the highest average peak signal to noise ratio midst five of them.By embedding 60,90,and 120 Kb,this TPE attains the best average quality amid all the methods.
  • 摘要：The traditional K-singular value decomposition(K-SVD)algorithm has poor imagedenoising performance under strong noise.An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization.First,a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary.The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary.Then,non-local regularity is incorporated into the denoising model to further improve image-denoising performance.Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.
  • 摘要：Various time-frequency(T-F)masks are being applied to sound source localization tasks.Moreover,deep learning has dramatically advanced T-F mask estimation.However,existing masks are usually designed for speech separation tasks and are suitable only for single-channel signals.A novel complex-valued T-F mask is proposed that reserves the head-related transfer function(HRTF),customized for binaural sound source localization.In addition,because the convolutional neural network that is exploited to estimate the proposed mask takes binaural spectral information as the input and output,accurate binaural cues can be preserved.Compared with conventional T-F masks that emphasize single speech source–dominated T-F units,HRTFreserved masks eliminate the speech component while keeping the direct propagation path.Thus,the estimated HRTF is capable of extracting more reliable localization features for the final direction of arrival estimation.Hence,binaural sound source localization guided by the proposed T-F mask is robust under noisy and reverberant acoustic environments.The experimental results demonstrate that the new T-F mask is superior to conventional T-F masks and lead to the better performance of sound source localization in adverse environments.
  • 摘要：Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.
  • 摘要：Partial discharge(PD)signals are an important index to evaluate the operation state of intelligent substations.The correct distinction of PD pulse and interference pulse has become a challenging task.Because of the noise and the low signal-to-noise ratio,the stable signals become non-stationary.The selection of a wavelet basis,the selection rule of thresholdλand the design of the threshold function are the key factors affecting the final denoising effect.Therefore,an enhanced ant colony optimisition wavelet(ACOW)algorithm was applied to find the global optimal threshold through the continuous derivative threshold function and the ant colony optimisation(ACO)algo-rithm.At the same time the efficiency of adaptive search calculation,was also significantly improved.The method of the ACOW algorithm was compared with the soft wavelet method,gradient-based wavelet method and the genetic optimisation wavelet(GOW)method.Using these four methods to denoise four typical signals,different mean square errors(MSE),magnitude errors(ME)and time costs were obtained.Interestingly,the results show that the ACOW method can achieve the minimum MSE and has less time cost.It generates significantly smaller waveform distortion than the other three threshold estimation methods.In addition,the high efficiency and good quality of the output signals are beneficial to the diagnosis of local discharge signals in intelligent substations.
  • 摘要：Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions are mainly three folds:first,a frame-work combining imitation learning with deep reinforcement learning is presented,which enables a robot to learn a stable navigation policy faster in the target-driven navigation task.Second,the surrounding images is taken as the observation instead of sequential images,which can improve the navigation performance for more information.Moreover,a simple yet efficient template matching method is adopted to determine the stop action,making the system more practical.Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches,which demonstrate the effectiveness and efficiency of our approach.
  • 摘要：Sentiment word embedding has been extensively studied and used in sentiment analysis tasks.However,most existing models have failed to differentiate high-frequency and low-frequency words.Accordingly,the sentiment information of low-frequency words is insufficiently captured,thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis.A Bayesian estimation-based sentiment word embedding(BESWE)model,which aims to precisely extract the sentiment information of low-frequency words,has been proposed.In the model,a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment proba-bilities of words,and a novel loss function is defined for sentiment word embedding learning.The experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods,for example,C&W,CBOW,GloVe,SE-HyRank and DLJT1,in sentiment analysis tasks,which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss function.In addition,replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.
  • 摘要：Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.
  • 摘要：Sentence simplification is an essential task in natural language processing and aims to simplify complex sentences while retaining their primary meanings.To date,the main research works on sentence simplification models have been based on sequence-to-sequence(Seq2Seq)models.However,these Seq2Seq models are incapable of analysing the hierarchical structure of sentences,which is of great significance for sentence simplification.The problem can be addressed with an ON-MULTI-STAGE model constructed based on the improved MULTI-STAGE encoder model.In this model,an ordered neurons network is introduced and can provide sentence-level structural infor-mation for the encoder and decoder.A weak attention connection method is then employed to make the decoder use the sentence-level structural details.Experimental results on two open data sets demonstrated that the constructed model outperforms the state-of-the-art baseline models in sentence simplification.
  • 摘要：Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras.The stereo vision system is generally composed of two cameras to mimic the human binocular vision.In this paper,a Python-based algorithm is pro-posed to find the parameters of each camera,rectify the images,create the disparity maps and finally use these maps for distance measurements.Experiments using real-time im-ages,which were captured from our stereo vision system,of different obstacles posi-tioned at multiple distances(60-200 cm)prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate.The accuracy of distance measurement is up to 99.83%.The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.
  • 摘要：Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.
  • 摘要：Current Chinese event detection methods commonly use word embedding to capture semantic representation,but these methods find it difficult to capture the dependence relationship between the trigger words and other words in the same sentence.Based on the simple evaluation,it is known that a dependency parser can effectively capture dependency relationships and improve the accuracy of event categorisation.This study proposes a novel architecture that models a hybrid representation to summarise semantic and structural information from both characters and words.This model can capture rich semantic features for the event detection task by incorporating the semantic representation generated from the dependency parser.The authors evaluate different models on kbp 2017 corpus.The experimental results show that the proposed method can significantly improve performance in Chinese event detection.
  • 摘要：Convolutional neural networks(CNNs)are the specific architecture of feed-forward artificial neural networks.It is the de-facto standard for various operations in ma-chine learning and computer vision.To transform this performance towards the task of network anomaly detection in cyber-security,this study proposes a model using one-dimensional CNN architecture.The authors'approach divides network traffic data into transmission control protocol(TCP),user datagram protocol(UDP),and OTHER protocol categories in the first phase,then each category is treated inde-pendently.Before training the model,feature selection is performed using the Chi-square technique,and then,over-sampling is conducted using the synthetic minority over-sampling technique to tackle a class imbalance problem.The authors'method yields the weighted average f-score 0.85,0.97,0.86,and 0.78 for TCP,UDP,OTHER,and ALL categories,respectively.The model is tested on the UNSW-NB15 dataset.
  • 摘要：Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data.There-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of computations.Accordingly,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments.This research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing capabilities.The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment.The proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit situations.The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting rate.The benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.
  • 摘要：The effective use of wind energy is an essential part of the sustainable development of human society,in particular,at the recent unprecedented pressure in shaping a low carbon energy environment.Accurate wind resource and power forecasting play a key role in improving the wind penetration.However,it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy.In recent years,the application boost of deep learning methods provides new effective tools in wind forecasting.This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy.Featured approaches include timeseries-based recurrent neural networks,restricted Boltzmann machines,convolutional neural networks as well as auto-encoder-based approaches.In addition,future development directions of deep-learning-based wind energy forecasting have also been discussed.
  • 摘要：It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions.The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision-making problems from multi-levels and multi-angles,and the neighbourhood rough set model(NRS)can facilitate the analysis and processing of numerical or mixed type data,and can address the limitation of multigranulation decision-theoretic rough sets(MG-DTRS),which is not easy to cope with complex data.Based on the in-depth study of hybrid-valued decision systems and MG-DTRS models,this study analysed neigh-bourhood MG-DTRS(NMG-DTRS)deeply by fusing MG-DTRS and NRS;a matrix-based approach for approximation sets of NMG-DTRS model was proposed on the basis of the matrix representations of concepts;the positive,boundary and negative domains were constructed from the matrix perspective,and the concept of positive decision recognition rate was introduced.Furthermore,the authors explored the related properties of NMG-DTRS model,and designed and described the corresponding solving algorithms in detail.Finally,some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm,but also showed the relationship between the decision recognition rate and the granularity and threshold.
  • 摘要：Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.
  • 摘要：In this study,we examine the cross-view gait recognition problem.Many existing methods establish global feature representation based on the whole human body shape.However,they ignore some important details of different parts of the human body.In the latest literature,positioning partial regions to learn fine-grained features has been verified to be effective in human identification.But they only consider coarse fine-grained fea-tures and ignore the relationship between neighboring regions.Taken the above insights together,we propose a novel model called GaitGP,which learns both important details through fine-grained features and the relationship between neighboring regions through global features.Our GaitGP model mainly consists of the following two aspects.First,we propose a Channel-Attention Feature Extractor(CAFE)to extract the global features,which aggregates the channel-level attention to enhance the spatial information in a novel convolutional component.Second,we present the Global and Partial Feature Combiner(GPFC)to learn different fine-grained features,and combine them with the global fea-tures extracted by the CAFE to obtain the relevant information between neighboring regions.Experimental results on the CASIA gait recognition dataset B(CASIA-B),The OU-ISIR gait database,multi-view large population dataset,and The OU-ISIR gait database gait datasets show that our method is superior to the state-of-the-art cross-view gait recognition methods.
  • 摘要：In content-based image retrieval(CBIR),primitive image signatures are critical because they represent the visual characteristics.Image signatures,which are algorithmically descriptive and accurately recognized visual components,are used to appropriately index and retrieve comparable results.To differentiate an image in the category of qualifying contender,feature vectors must have image information's like colour,objects,shape,spatial viewpoints.Previous methods such as sketch-based image retrieval by salient contour(SBIR)and greedy learning of deep Boltzmann machine(GDBM)used spatial information to distinguish between image categories.This requires interest points and also feature analysis emerged image detection problems.Thus,a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary.In this study,a technique called CBIR-similarity measure via artificial neural network interpolation(CBIR-SMANN)has been presented.By collecting datasets,the images are resized then subject to Gaussian filtering in the pre-processing stage,then by permitting them to the Hessian detector,the interesting points are gathered.Based on Skewness,mean,kurtosis and standard deviation features were extracted then given to ANN for interpolation.Interpolated results are stored in a database for retrieval.In the testing stage,the query image was inputted that is subjected to pre-processing,and feature extraction was then fed to the similarity measurement function.Thus,ANN helps to get similar images from the database.CBIR-SMANN have been implemented in the python tool and then evaluated for its performance.Results show that CBIR-SMANN exhibited a high recall value of 78%with a minimum retrieval time of 980 ms.This showed the supremacy of the proposed model was comparatively greater than the previous ones.
  • 摘要：With the development of high-performance computing,it is possible to solve large-scale computing problems.However,the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation.Improving the performance of a single core makes it challenging to maintain Moore's law,and multi-core processors emerge.A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor.In high-performance computing,the granularity of computing tasks leads to the complexity of scheduling strategies.Satisfying high system performance,load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field,especially in specific multi-core hardware architecture.In this study,global real-time task scheduling is implemented in a high-performance multi-core system.The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy.In the cluster scheduling policy,tasks are allowed to preempt the core with low priority,and the priority of tasks that access memory is dynamically improved,higher than that of all the tasks without memory access.An intelligent fitting method is also proposed.When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold,the priority of the task is promoted to the highest priority,pre-empting the core without the access memory task.The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.

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    • 创刊时间：2016
    • 地区：CN
    • 语言：中文
    • 热门主题：智能技术 人工智能 计算机技术 BASED_ON BASED VALUED LEARNING NETWORK APPLICATION ALGORITHM
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