bluuebunny/crossref_metadata_2025 huggingface dataset:
Text clustering algorithm based on deep representation learning
Authors: Wang Binyu , Liu Wenfen , Lin Zijie , Hu Xuexian , Wei Jianghong , Liu Chun
Published: August 2018

Text clustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, the text data to be clustered cannot be used to train the text representation model based on deep learning. To address the problem, an algorithm of text clustering based on deep representation learning is proposed using the transfer learning domain adaptation and the parameters update during cluster iteration. First, source domain data is used to perform the pre‐training of the deep learning classification model. This procedure acts as an initialisation of the model parameters. Then, the domain discriminator is added to the model, to domain‐divide the input sample. If the discriminator cannot distinguish which domain the data belongs to, the common feature space of two domains is obtained, so the domain adaptation problem is solved. Finally, the text feature vectors obtained by the model are clustered with MCSKM++ algorithm. The algorithm not only resolves the model pre‐training problem in unsupervised clustering, but also has a good clustering effect on the transfer problem caused by different numbers of domain labels. Experiments suggest that the clustering accuracy of the algorithm is superior to other similar algorithms.
Button recognition with texture feature based on spiking neural network
Authors: Zhang Zhenmin , Wu Qingxiang , Lai Xiaoyan , Lin Xiufang
Published: August 2018

Spiking neuron network is generally considered as the third generation of neural networks. This type of network is a class of machine learning techniques that exploit many layers of non‐linear information processing for supervised or unsupervised feature extraction, image classification, texture segmentation, and image recognition. On the other hand, the grey‐level co‐occurrence matrix algorithm is widely used in visual images for texture feature extraction and image structure characterisation analysis. For those buttons with the same size, same shape, similar colours, and analogous textures, they cannot be effectively identified by conventional methods. At this time, the spiking neural network trained with the improved GLCM algorithm can be used to achieve button image feature extraction, classification, and recognition. Experiments show that the method proposed here can effectively segment the button images with their texture features.
Dynamic target tracking based on corner enhancement with Markov decision process
Authors: Zuo Guoyu , Du Tingting , Ma Lei
Published: August 2018

The tracking–learning–detection (TLD) algorithm applied in the home environment can effectively improve the tracking robustness. However, it has the problems of single target tracking and poor selection of feature points. This study proposed a dynamic target tracking method based on corner enhancement with Markov decision process (MDP) model. The MDP target tracking method is adopted to change a multi‐target tracking problem into a strategy problem based on MDP model, in which one MDP model represents the life cycle of a target, and multiple targets are represented by multiple MDP models. In the tracking process, the strong corners generated by the Shi‐Tomasi corner method are used to replace the feature points generated by the traditional TLD algorithm at intermediate intervals, which makes the target feature points more stable during the tracking process. The similarity function learning for data association is equivalent to the learning of the MDP strategy, in which the reinforcement learning method is used and has double advantages of both online and offline learning. The tracking experiments with different data sets are performed, and the results show that dynamic target tracking algorithm based on the corner enhancement with MDP has both good tracking performance and good anti‐interference capability.
Indoor positioning technology based on map information perception
Authors: Gan Xingli , Huang Lu , Wang Boyuan , Li Shuang
Published: August 2018

With the increasing demand for location‐based services, indoor positioning technology has become one of the most attractive areas of research. Microelectromechanical systems sensors in smart terminals are used to realise a pedestrian dead reckoning algorithm. Owing to the accumulated error increased with time, the results of positioning will produce a large error, and an indoor positioning method based on the perception and constraint of map information is designed including straight path constraint and inflection point constraint. In the method of inflection point constraint, several common machine learning algorithms are compared through the experiments, and the secondary discriminant method is utilised to detect the inflection point with a detection accuracy of 97.62%. Finally, the performances of the improved algorithm and the traditional dead reckoning algorithm are compared in the experiments. The results show that the average positioning accuracy of the improved algorithm is 0.073 m, the positioning accuracy within 1 m reaches 100%, it is obviously higher than that of the traditional positioning algorithm and the effectiveness of the algorithm is verified.
Solution of the inverse problem of magnetic induction tomography based on Helmholtz coil
Authors: Luo Hai‐Jun , Wen Kai‐Xu , Yong Liao , Pan Haitao , Jing Xin
Published: August 2018

Magnetic induction tomography is a contactless technique that can measure conductivity distribution in biological tissues. This study proposed that Helmholtz coils as the excitation coils will generate a set of uniform excitation fields. The system model consists of a circular background area, a circular disturbance object, a set of Helmholtz coils, and eight magnetic detection coils. The diameter of the Helmholtz coil is 200 mm. The detection coil is a square, and its side length is 12 mm. The excitation current of the system is 30 mA, and the frequency is 10 MHz. This study discusses the reconstructed images of different locations, different volumes, different distances, and different electrical conductivities of perturbation bodies. The anti‐noise ability of different noise signals is also analysed. Finally, the image differences are evaluated through structural similarity index (SSIM). The results show that the algorithm can distinguish the position and volume characteristics of the target object. However, when the volume of the target object is low, the position will be deviated. This algorithm can distinguish the position of the two, which is far away from each other.
Beetle swarm optimisation for solving investment portfolio problems
Authors: Chen Tingting , Zhu Yongjian , Teng Jun
Published: August 2018

A portfolio model is established after analysing the investment environment of the artificial intelligence concept stocks in China. To reduce the risk of investment, the beetle swarm optimisation (BSO) is proposed. BSO, based on the beetle antennae search (BAS) and the standard particle swarm optimisation (PSO), is derived from the standard PSO but the update rules of each particle originate from BAS. In global searching, BSO, making the model get a lower value at risk, is more capable than standard PSO, which is easily trapped in local optimal defects. This study tries to solve portfolio model by using BSO algorithm. The results prove that BSO can do better in dealing with optimisation problems of constrained multi‐dimensional functions.
Hysteretic chaotic neural network for crossbar switch problems
Authors: Zhang Wei , Le Tian , Wu Yuanhong , Jiang Youfu , Fan Ronggang
Published: August 2018

A hysteretic chaotic neural network is proposed to solve the crossbar switch problem effectively. The chaotic neural network structure with hysteresis and its set computation characteristics are carried out. The simulation results show that the theory is corrected by simulating the chaotic neural network with randomly generated neurons. The network architecture is applied to the crossbar switch problem, and the results of the computer simulation are given to illustrate the computational capability of the network architecture. The simulation results show that the chaotic neural network structure with hysteresis neurons is better than the previous network structure for the crossbar switch problem in terms of cost, time, and optimal solution rate.
Pitch tracking algorithm based on evolutionary computing with regularisation in very low SNR
Authors: Zhang Xiaoheng , Li Yongming
Published: August 2018

The authors present PTEAR_VLSNR (Pitch Tracking basing on Evolutionary Algorithm with Regularization at Very Low SNR), a pitch tracking algorithm for speech in strong noise. The algorithm builds a pitch enhancement and extraction model, which enhance the pitch by a matched filter, and to further deal with strong noise, the optimal factor was proposed, which can be optimised globally by the evolutionary computing. Specially, regularisation constraint of fitness function was applied to enhance the generalisation ability. Temporal dynamics constraints are used to improve the tracking rate and the voicing decision can be optimal by evolutionary computing similarly. In addition, the balance of optimisation accuracy and time cost were considered. In experiments, genetic algorithm and particle swarm optimisation with two‐norm term were represented as evolutionary algorithms with regularisation. At last, they compare the performance of the algorithm and other representative algorithms. The experimental results show that this proposed algorithm performs well in both high and low signal‐to‐noise ratios (SNRs).
Method for improving zero‐shot image classification
Authors: Chen Xiangfeng , Chen Wenbai , Zhang Chong , Lv Mengyao , Han Hu
Published: August 2018

In order to improve the robustness of the similarity metric method of image classification, and reduce the complexity of the measure function, the Pearson correlation coefficient is introduced to improve the zero‐shot image classification. Firstly, the mapping matrix from visual space to semantic space is learned by the training dataset, and the visual feature is aligned by it. Then in the semantic space, the similarity between features is calculated by the metric function, predicting the label of unseen class by the nearest neighbours. Experiments show that zero‐shot image classification based on Pearson correlation coefficient is better than Euclidean distance and cosine similarity.
Model compression of SDM‐based face alignment for mobile applications
Authors: Shen Yehu , Jiang Quansheng , Yang Yong , Wang Bangfu , Zhu Qixin
Published: August 2018

Face alignment could be widely used in face recognition, expression recognition, face‐based AR applications etc. Cascaded‐regression‐based face alignment algorithms have been popular in recent years for their low computational costs and impressive results in uncontrolled scenarios. Unfortunately, the size of the trained model is quite large for cascaded‐regression‐based methods which makes it unsuitable for commercial applications on mobile phones. In this study, the authors proposed a data compression method for the trained model of the supervised descent method (SDM). Firstly, the distribution of the model data was estimated using a non‐parametric method. Then an adaptive quantisation algorithm was proposed to quantise the model data. Finally, their adaptive quantisation algorithm was tightly coupled with the SDM training process to fine tune the results. The quantitative experimental results proved that their proposed method could compress the data model to <20% of its original size without hurting the performances. The proposed method has been integrated into a mobile AR application, subjective evaluations proved that the proposed compression method could provide similar visual effects compared with the uncompressed counterpart.
Application of quantisation‐based deep‐learning model compression in JPEG image steganalysis
Authors: Wu Xiancheng , Shao Zilong , Ou Pei , Tan Shunquan
Published: August 2018

Steganography can hide secret information in an innocent cover medium. Its opponent is steganalysis, which is used to discriminate whether a suspicious carrier contains a hidden message or not. With the rapid development of deep‐learning frameworks, deep‐learning‐based steganalytic models have hold the dominant position in the field of steganalysis. In recent years, some scholars have successfully utilised model compression methods in the field of image classification. However, as far as the authors know, no prior works are devoted to the application of model compression methods in the field of deep‐learning‐based steganalysis. In this study, the authors explore the effect of two quantisation schemes, namely 8‐bit calculation and floating‐point calculation, on the performance of XuNet, a state‐of‐the‐art deep‐learning steganalytic model. The experimental results show that the two deep‐learning model quantisation schemes are applicable to steganalysis. It is even possible to compress the network size while retaining satisfactory performance.
Three‐way recommendation integrating global and local information
Authors: Ma Yuan‐Yuan , Zhang Heng‐Ru , Xu Yuan‐Yuan , Min Fan , Gao Lei
Published: August 2018

The matrix factorisation approach computes a low‐rank approximation of the incomplete user‐item rating matrix. Existing approaches suffer from under‐fitting due to the use of global information for all users and items. In this study, the authors propose a three‐way recommendation model that integrates global and local information. This new model has a number of main aspects. The first is rating prediction with global and local information. A clustering and two matrix factorisation algorithms are employed for this purpose. The second is the computation of recommendation thresholds based on the decision‐theoretic rough set model. Misclassification and promotion costs are considered simultaneously to build the cost matrix. The last is the determination of the recommender actions based on the prediction and thresholds. Experimental results on the well‐known datasets show that authors’ proposed model improves recommendation quality in terms of average cost.
Multi‐feature consultation model for human action recognition in depth video sequence
Authors: Liu Xueping , Li Yibo , Li Xiaoming , Tian Can , Yang Yueqi
Published: August 2018

In the field of computer vision research, the research on human action recognition of depth video sequence is an important research direction. Herein, considering the characteristics of temporal and spatial depth video sequence, the authors propose a framework of the consultation model of several action sequence features to solve the classification problem in‐depth video sequence. According to the characteristics of the 3D human action space, a variety of action sequence feature data is obtained, and then these data is projected to three coordinate planes, the acquired fusion features are used to train the consultation model, and finally the model is validated through the data. The authors have achieved good results by comparing the two publicly available datasets with the other methods. Experimental results demonstrate that the model performs well in existing identification methods.
Invert‐U‐Net DNN segmentation model for MRI cardiac left ventricle segmentation
Authors: Cong Chao , Zhang Hongmin
Published: August 2018

In this study, a deeply supervised end‐to‐end model is presented for fully automated segmentation for cardiac magnetic resonance imaging (MRI) images. Firstly, the mechanism of deep neural network (DNN)‐based segmentation is discussed with the relationship of channels and their distribution in network in depth. Following this idea, a U‐Net‐based model, namely an invert‐U‐Net model is presented with an innovative filter number structure. Based on the invert‐U‐Net model, the experiment is carefully designed, and the hyper‐parameters are considerately arranged. Finally, the model is applied and evaluated using Sunnybrook MR datasets from the MICCAI 2009 LV segmentation challenge and the experimental result shows that it outperforms the state‐of‐the‐art methods.
Bi‐directional CRC algorithm using CNN‐based features for face classification
Authors: Wang Yanan , Na Tian , Song Xiaoning , Hu Guosheng
Published: August 2018

Collaborative representation‐based classification (CRC) has become a breakthrough in face classification due to its distinguished collaborative capacity. Nevertheless, insufficient observations of per subject are usually offered by few or even a single gallery image for face classification, which lead to a sensitive response to the variations from the original data set. In this study, the authors present a bi‐directional CRC algorithm using convolutional neural network‐based features for face classification. They first employ a deep convolutional neural network to extract facial features from the original gallery and query sets, and then develop a fast reverse representation model to obtain the auxiliary residual information between each training sample and the reconstructed one that is achieved from the test sample. Secondly, they offer a new solution to the bi‐directional optimisation problem by which the input sample is well represented by the forward linear combination and the reverse one, respectively. The last contribution is to utilise a competitive fusion method for robust face recognition, which weighted reconstructed residuals from the bi‐directional representation model. Experimental results obtained from a set of well‐known face databases including AR, FERET, and ORL verify the validity of the proposed method, especially in the robustness to small sample size problem.
Optimisation algorithm for decision trees and the prediction of horizon displacement of landslides monitoring
Authors: Zhao Jiubin , Liu Yuanxue , Hu Ming
Published: August 2018

In the era of big data, the data scale of landslide monitoring could reach above terabyte level, the traditional database and data mining technology could no longer meet the requirements of intelligent monitoring and early warning. To obtain early warning information with high reliability and real time by applying big data theory, mechanisms, models and methods as well as machine learning methods are the inevitable trends in the future. This study aimed to realise a real time and precise mid‐long prediction of landslide displacement, proposed two distributed landslide displacement prediction models: DLDP‐GBTs (distributed landslide displacement prediction with Gradient Boosted Trees algorithm) and DLDP‐RF (distributed landslide displacement prediction with Random algorithm); the cross‐validation method was also adopted to evaluate and adjust parameters to reduce the root mean squared error of the model predicted results. In addition, this study proposed the rapid selection of features by using XGboost model in distributed situations can improve the Model training efficiency under distributed condition. By comparing different regression algorithms models, it was found that the DLDP‐GBTs model based on the gradient optimisation decision tree was better than the other two models in terms of accuracy and real‐time performance, which meets the requirements under the big data background.
Linear representation of intra‐class discriminant features for small‐sample face recognition
Authors: Shao Changbin , Gao Shang , Song Xiaoning , Yang Xibei , Xu Gang
Published: August 2018

The authors argue that the mean of discriminant features calculated across the samples of a class (intra‐class samples) cannot perform well for the classification task. The main reason is that the mean feature ignores intra‐class membership's different responses to their own class for a query sample. Meanwhile, they present that the discriminant features of a test sample can also be well‐linear approximated by the discriminant features of intra‐class memberships. The adaptive weighted intra‐class features will be more suitable for the identification ability of a class than original samples via a regression algorithm. To verify this, a new linear representation‐based classification method using Fisher discriminant features (LRFC) is suggested. To be more specific, they first extract Fisher discriminant features of all face samples. Second linear regression (LR) algorithm is exploited to obtain weight coefficients of intra‐class feature information for the feature representation of a query sample. At last, the weighted intra‐class features are re‐combined as an agent of each class and the test sample is identified as the class with the maximum similarity. The method is simple but particularly effective. Experimental results on benchmark face databases verify improvements of LRFC over its original methods.
Research on the Opening Book of the computer game of draughts
Authors: Liu Xueping , Zhao Kai , Tian Can , Yang Yueqi , Yu Shi
Published: August 2018

Opening Book is a kind of assistive technologies to enhance the performance of computer games. The opening stages of the game method generally used to query the database generated. This method improving search efficiency and avoids the missing strategic of traditional evaluation systems. This paper studies the technical problems of using the Opening Book of draughts, and introduces the generation and usage of the Opening Book. The authors also discussed the detail of statistics in Opening Book. Besides, this paper presents a new idea of introducing the information of the Opening Book into the traditional Alpha‐Beta valuation system. The experiment proved that the method proposed in this paper can effectively solve the issues of the start of the draughts and improve the game level of the draughts.
Combining HWEBING and HOG‐MLBP features for pedestrian detection
Authors: Xiao Ling , Zhang Yongjun , Zhang Jian , Wang Qian , Li Yuewei
Published: August 2018

Pedestrian detection has vital value in many areas such as driver assistance systems, driverless cars, intelligent tourism systems etc., but there are some difficulties that need to be solved. The algorithm with high detection rate is complex and requires substantial time. Therefore, how to improve the detection accuracy and speed has become the key of pedestrian detection. For these reasons, firstly, an improved algorithm, called hash and window enhancement of binarised normed gradients (HWEBING), based on binarised normed gradients feature is proposed. Subsequently, the authors present an improved local texture feature, namely mean of local binary pattern (MLBP), based on uniform pattern local binary pattern (ULBP) for increasing the detection rate. Finally, after using the HWEBING algorithm to get the candidate windows, the combination of MLBP feature and histograms of oriented gradients feature is extracted from these windows to further enhance the detection accuracy. Experimental results reveal that speed of using the HWEBING algorithm for pre‐detection is 5.5 times faster than the traditional method of pedestrian detection. Furthermore, the detection rate of MLBP feature is 3.5 and 2.1% higher than those of ULBP and basic pattern local binary pattern (Basic‐LBP), respectively.
Multi‐objective social spider optimisation algorithm
Authors: Wang Yanjiao , Sun Xiaonan , Wang Chao , Tao Huanhuan
Published: August 2018

Considering that the social spider algorithm is still unable to solve the multi‐objective optimisation problem, this study presents a multi‐objective social spider optimisation algorithm. Firstly, a new normalised fitness value formula is proposed based on the multi‐objective optimisation purposes, which is able to trade off the non‐dominated rankings and crowded distances and evaluate individual strengths and weaknesses effectively; secondly, the gravitational factor is used in order to balance the impact of individual fitness and distance to individual performance, which improves the vibration perception ability of the calculation method as well; once again, the renewal pattern of the female and male population is improved in order to balance the convergence rate and population diversity of the algorithm; lastly, the environmental selection strategy which is based on cosine distance is proposed for female and male population renewal. Testing on the ZDT test set, experimental results show that, compared with the six representative multi‐objective evolutionary algorithms, the proposed algorithm in this study has better distribution and better convergence performance.
Attribute reduction in interval‐valued fuzzy ordered decision tables via evidence theory
Authors: Zhang Jia , Zhang Xiaoyan , Xu Weihua
Published: August 2018

There are two different theory methods that are rough set theory and evidence theory, but these two theories can both handle some incomplete and uncertain information. In this study, these two models are combined in the interval‐valued fuzzy ordered information system (IVFOIS). Belief functions and plausibility functions are proposed based on dominance relations in IVFOISs. The belief and plausibility reducts are defined in interval‐valued fuzzy ordered decision tables (IVFODTs) and the attribute reduction of IVFODTs based on evidence theory is established. Finally, the authors use an instance to verify the above argument.
Equilibrium point smooth escape algorithm for local route of unmanned surface vehicles
Authors: Chen Jinxin , Liu Zhong , Zhu Xufang
Published: August 2018

For the problem of local equilibrium point in local path planning, a local equilibrium point smooth escape algorithm is proposed. In this method, a confirmation rule for obstructing obstacles is established to confirm obstacles obstructing routes of an unmanned surface vehicle and causing local equilibrium point from multiple detecting targets. A model for tracking point confirmation is constructed to guide the unmanned surface vehicle to bypass obstructing obstacles safely while tracking points. A tracking point smooth switching model is constructed to ensure smooth change of heading planned by the algorithm in tracking point switching. The idea of ‘wall‐following’ algorithm and curve smoothing idea are integrated in the model and then a heading planning method escaping local equilibrium point is given. Simulation results show that the above algorithm can guide the unmanned surface vehicle out of the area where it is trapped when it falls into a local equilibrium point. The algorithm can improve the traceability of planned paths with a heading change rate much lower than that of conventional algorithms.
Deep imitation reinforcement learning with expert demonstration data
Authors: Yi Menglong , Xu Xin , Zeng Yujun , Jung Seul
Published: August 2018

In recent years, deep reinforcement learning (DRL) has made impressive achievements in many fields. However, existing DRL algorithms usually require a large amount of exploration to obtain a good action policy. In addition, in many complex situations, the reward function cannot be well designed to meet task requirements. These two problems will make it difficult for DRL to learn a good action policy within a relatively short period. The use of expert data can provide effective guidance and avoid unnecessary exploration. This study proposes a deep imitation reinforcement learning (DIRL) algorithm that uses a certain amount of expert demonstration data to speed up the training of DRL. In the proposed method, the learning agent imitates the expert's action policy by learning from demonstration data. After imitation learning, DRL is used to optimise the action policy in a self‐learning way. By experimental comparison on a video game called the Mario racing game, it is shown that the proposed DIRL algorithm with expert demonstration data can obtain much better performance than previous DRL algorithms without expert guidance.
Information measure of absolute and relative quantification in double‐quantitative decision‐theoretic rough set model
Authors: Li Wentao , Pedrycz Witold , Xue Xiaoping , Zhang Xiaoyan , Fan Bingjiao , Long Binghan
Published: August 2018

The absolute and relative quantifications between the equivalence class and the target concept are the two important research endeavours in rough set theory. Double‐quantitative decision‐theoretic rough set (Dq‐DTRS) models utilise both absolute quantification and relative quantification in their upper and lower approximations to reflect the distinctive degrees of quantitative information. Herein, the authors apply the information theory to Dq‐DTRS model to characterise and measure these two types of quantitative information. The expressions of the information entropy with regard to the two quantifications and their corresponding information co‐entropy are presented in DqI‐DTRS model and DqII‐DTRSmodel, respectively. This work makes a further study of Dq‐DTRS models by discussing the information measures with respect to absolute and relative quantification.
Pedestrian trajectory prediction via the Social‐Grid LSTM model
Authors: Cheng Bang , Xu Xin , Zeng Yujun , Ren Junkai , Jung Seul
Published: August 2018

In the design of intelligent driving systems, reliable and accurate trajectory prediction of pedestrians is necessary. With the prediction of pedestrians’ trajectory, the possible collisions can be avoided or warned as early as possible by changing the behaviour of intelligent vehicles. The trajectory prediction problem can be considered as a sequence learning problem, in which one of the recurrent neural network (RNN) models called long short term memory (LSTM) has been regarded as a promising method. The authors present a new method for predicting the pedestrian's trajectory, which is called Social‐Grid LSTM based on RNN architecture. The proposed method combines the human–human interaction model called social pooling and the Grid LSTM network model. The performance of the proposed method is demonstrated on two available public datasets, and compared with two baseline methods (LSTM and Social LSTM). The experimental results indicate that the authors’ proposed method outperforms previous prediction approaches.
Vector‐based approaches for computing approximations in multigranulation rough set
Authors: Yu Peiqiu , Li Jinjin , Lin Guoping
Published: August 2018

Approximation computation is a significant issue when the rough set model is applied. However, few authors focus on how to calculate approximations of multigranulation rough set (MGRS). Herein, the authors clarify a fact that only a part of elements in the universe need to be judged whether they belong to approximations of MGRS. If X is a target concept which is approximated by approximations in MGRS, then the element whose equivalence class does not intersect with X is of no need to be judged. Based on the fact, the authors clarify that they proposed a vector‐based algorithm to compute approximations in MGRS. Time complexity of the proposed algorithm is .
Entropy‐based feature extraction algorithm for stone carving character detection
Authors: Qin Xiao , Chu Xutao , Yuan Changan , Wang Ruili
Published: August 2018

Text detection is the premise of semantic recognition of natural scenes. At present, many studies only focus on print fonts or hand‐written fonts. There are many stone texts in China that reflect historical and cultural values but are difficult to identify. Detecting these stone carving texts in natural scenes is more difficult than detecting them in handwritten or printed texts. In this study, the entropy‐based feature extraction algorithm is proposed to extract stone texts in natural scenes. Compared with other excellent machine learning algorithms, it has achieved better results.
Fusion‐based holistic road scene understanding
Authors: Huang Wenqi , Zhang Fuzheng , Xu Aidong , Chen Huajun , Li Peng
Published: August 2018

This study addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, the authors propose an approach that jointly tackles object‐level image segmentation and semantic region labelling within a conditional random field (CRF) framework. Specifically, the authors first generate semantic object hypotheses by clustering 3D points, learning their prior appearance models, and using a deep learning method for reasoning their semantic categories. The learned priors, together with spatial and geometric contexts, are incorporated in CRF. With this formulation, visual and range data are fused thoroughly, and moreover, the coupled segmentation and semantic labelling problem can be inferred via graph cuts. The authors’ approach is validated on the challenging KITTI dataset that contains diverse complicated road scenarios. Both quantitative and qualitative evaluations demonstrate its effectiveness.
Intelligent decision method for supplier selection
Authors: Zhou Kangqu , Xun Huiyu
Published: August 2018

In the process of supply chain risk management, selecting the right supplier quickly and accurately is one of the most critical issues for the company. Due to the influence of various risks, improper selection or time‐consuming evaluation may lead to the company losing the best time, cost and market share. For the multi‐criteria decision‐making problem of supplier selection, this study proposes an intelligent method combining the combination of triangular fuzzy number and network analysis method, evaluates the alternative solutions, obtains the supplier ranking result and proves the effectiveness of the method by case studies of the electronics industry. A scientific reference is provided by this method for the development of supplier‐selected intelligent systems.
Harvesting sequence planning of a harvesting robot based on the principle of minimum energy consumption
Authors: Wang Yi , Ma Jitong , Zhang Zhe , Zhao Hongmei , Teng Juyuan , He Yu , Xu Hongbin
Published: August 2018

According to the operational requirements of citrus harvesting robot, a new harvesting sequence planning method and its intelligent optimization algorithm are proposed based on the existing harvesting sequence planning methods, which realized dynamic planning for inverse kinematics solution selection and sequence planning. The simulation results show that the energy consumption is reduced by the proposed method. By means of harvesting robot, the harvesting sequence planning based on the principle of ‘minimum energy consumption’ and principle of ‘shortest path’ are carried out respectively, and the energy consumption is counted. The results show, compared with the multi‐citrus harvesting algorithm based on the principle of ‘shortest path’, the proposed method based on the principle of ‘minimum energy consumption’ and dual intelligent optimization algorithm can realize the continuous multi‐citrus harvesting, and reduce the energy and time consumption as a whole. The energy consumed by the proposed algorithm is reduced by about 12.5% when the number of harvesting points is 3–8, and about 23.0% when the number of harvesting points is 9–12. The average time consumed by the proposed algorithm is reduced by about 13.0% when the number of harvesting points is 3–8, and about 22.0% when the number of harvesting points is 9–12.
Handover detection approach based on trajectory data mining techniques
Authors: Han Nan , Qiao Shaojie , Liu Dunhu , Ding Peng , Zhang Yongqing , Xiong Xi , Wang Mengran , Gutierrez Louis Alberto
Published: August 2018

In order to overcome the disadvantages of existing handover approaches in cellular communication networks, a new handover detection approach based on trajectory data mining techniques is presented. It discovers frequent trajectory patterns from massive historical trajectories of moving objects by applying the improved Apriori algorithm and determines whether or not to perform handover in a cellular network based on the discovered prediction results, according to the coverage area of cellular networks. Experiments were conducted on synthetic datasets and the results show that: the proposed frequent‐trajectory‐based handover detection model can successfully avoid the ping‐pong effect caused by unnecessary frequent handover operations, greatly reduce the error rate of handover, and the accuracy of handover detection is very high.
RGB‐D static gesture recognition based on convolutional neural network
Authors: Xie Bin , He Xiaoyu , Li Yi
Published: August 2018

In the area of human–computer interaction (HCI) and computer vision, gesture recognition has always been a research hotspot. With the appearance of depth camera, gesture recognition using RGB‐D camera has gradually become mainstream in this field. However, how to effectively use depth information to construct a robust gesture recognition system is still a problem. In this paper, an RGB‐D static gesture recognition method based on fine‐tuning Inception V3 is proposed, which can eliminate the steps of gesture segmentation and feature extraction in traditional algorithms. Compared with general CNN algorithms, the authors adopt a two‐stage training strategy to fine‐tune the model. This method sets a feature concatenate layer of RGB and depth images in the CNN structure, using depth information to promote the performance of gesture recognition. Finally, on the American Sign Language (ASL) Recognition dataset, the authors compared their method with other traditional machine learning methods, CNN algorithms, and the RGB input only method. Among three groups of comparative experiments, the authors’ method reached the highest accuracy of 91.35%, reaching the state‐of‐the‐art currently on ASL dataset.
Robust visual tracking via two‐stage binocular sparse learning
Authors: Ma Ziang , Lu Wei , Yin Jun , Zhang Xingming
Published: August 2018

Combining multiple features and enforcing joint sparsity have proven to be beneficial for robust tracking. In this study, a novel stereo vision and two‐stage sparse representation‐based method is presented. First, the colouring information‐based features are augmented with a depth view in the appearance modelling of a target object. Unreliable features are then dynamically removed for robust feature‐level fusion in the first stage of sparse optimisation. Next, the low rank constraint is imposed onto the objective function, which facilitates a more robust representation of the ensemble of particles over the pruned views. Finally, the authors propose to detect occlusion via depth‐based histogram analysis to guarantee the effectiveness of the template update. Experiments are performed on two large‐scale benchmark datasets: KITTI and Princeton. Authors’ approach achieves state‐of‐the‐art results in the aspect of robustness and accuracy.
Semantic model of ship behaviour based on ontology engineering
Authors: Zhang Yimeng , Wen Yuanqiao , Zhang Fan , Zhou Chunhui , Du Lei , Huang Liang , Xiao Changshi
Published: August 2018

Given the complex situation of traffic at sea and the large amount of data that grows with the information technology, the ship behaviour cannot be precise recognised or fully expressed based on the raw and noisy data itself. A semantic model of ship behaviour based on ontology engineering is developed in this study. First, an interrelated semantic network is created, which allows the easy annotation, expression, and acquisition of information about ship trajectory. Then, the time‐series compression techniques are used to recognise behaviour and event in the semantic model, which, respectively, indicate the states of track points and trajectory segments, and which also can be represented as a time series. Finally, the accuracy and practicality of the model is verified by automated identification system data and geographic data; the result shows that the semantic model solves the conflict between expressiveness of ship behaviour and complexity of the situation, and reduces the amount of data by building semantic representation in higher abstraction levels.
3D vision measurement for small devices based on consumer sensors
Authors: Zhang Qian , Tu Juanhui , Li Zhiyong , Liu Hong
Published: August 2018

High‐precision, low‐cost three‐dimensional (3D) space measurement and positioning technology is desperately needed in wide applications. This study analyses the key technologies in the recognition of the devices to achieve the requirement of device recognition and capture on the production line. A 3D measurement algorithm for the small devices based on the consumer‐level sensors is proposed in this study. Histogram of gradients feature is used to classify the devices, and structure light is used to get the depth data of the devices. Object extraction and Euclidean cluster segmentation are used to analyse the depth data, in order to determine their positions and orientations. In the database built on iPhone X, the accuracy of category identification reached 0.97, and the measurement error of angle is small. The results show that the proposed method is feasible and can be applied to the recognition and position of the devices.
Effect of demographic structure on resource utilisation using term frequency–inverse document frequency algorithm – evidence from China
Authors: Liu Yulin , Li Zhihui , Yin Xingmin , Lyu Leifei
Published: August 2018

China has stepped into middle‐income status, which is characterised by demographic imbalance and huge discrepancy of resource utilisation, resulting in an imbalance of regional development. Using term frequency–inverse document frequency algorithm, the authors extract the resource keywords and construct the resource utilisation index. The authors then explore the impact of demographic structure on resource utilisation. The main conclusions are as follows: (i) according to the Moran Index, there are strong spatial autocorrelations in resource utilisation at the provincial level in China; (ii) the empirical results demonstrate that the regression coefficients of spatial lags are significant. This provides compelling evidences that the neighbouring regions have obvious spatial spillover effects on local resource utilisation; (iii) the authors’ findings also reveal that the total dependency ratio which reflects the demographic structure has a negative and significant effect on resource utilisation. With other explanatory variables held constant, the child‐age dependency ratio and old‐age dependency ratio perform differently in the robustness testing. Ultimately, targeted and systematic policy suggestions are proposed to improve resource utilisation and adjust the demographic structure.
Morphology control and optical properties of Bi2O3 crystals prepared by low‐temperature liquid phase method
Authors: Li Jin , Wu Ben Ze , Zhou Zhen Xiang
Published: August 2018

γ‐Bi2O3 powders were prepared from [Bi(NO3)3 ·5H2 O] and NaOH through low‐temperature liquid phase method at <90°C. This process featured low cost, simplicity and a normal pressure. Bismuth oxides were synthesised in large quantities in water systems. Morphology, structure and optical properties were characterised by X‐ray diffraction, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and ultraviolet visible absorption spectrophotometry (UV–Vis). The SEM indicated that tetrahedral Bi2O3 with the edge length of 1–5 μm was formed. The FTIR spectra show a chemical bond of Bi–O existed. Bi2O3 presents the photo absorption properties from UV light region to visible light and the band gap of the Bi2O3 is 3.0 eV.
CNN‐RNN based method for license plate recognition
Authors: Shivakumara Palaiahnakote , Tang Dongqi , Asadzadehkaljahi Maryam , Lu Tong , Pal Umapada , Hossein Anisi Mohammad
Published: August 2018

Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle (e.g., cars) have numbers with dark background, while public vehicle (taxis/cabs) have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks namely, BLSTM (Bi‐Directional Long Short Term Memory), for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting (DCV), which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.
Systematic Review and Meta-Analysis of Randomised, Other-than-Placebo Controlled, Trials of Individualised Homeopathic Treatment
Authors: Ulbrich-Zürni Susanne , Viksveen Petter , Roberts E. , Baitson Elizabeth , Legg Lynn , Davidson Jonathan , Mathie Robert
Published: August 2018

Background This study focuses on randomised controlled trials (RCTs) of individualised homeopathic treatment (IHT) in which the control (comparator) group was other than placebo (OTP).

Aims To determine the comparative effectiveness of IHT on health-related outcomes in adults and children for any clinical condition that has been the subject of at least one OTP-controlled trial. For each study, to assess the risk of bias and to determine whether its study attitude was predominantly ‘pragmatic’ or ‘explanatory’.

Methods Systematic review. For each eligible trial, published in the peer-reviewed literature up to the end of 2015, we assessed its risk of bias (internal validity) using the seven-domain Cochrane tool, and its relative pragmatic or explanatory attitude (external validity) using the 10-domain PRECIS tool. We grouped RCTs by whether they examined IHT as an alternative treatment (study design Ia), adjunctively with another intervention (design Ib), or compared with a no-intervention group (design II). For each RCT, we identified a ‘main outcome measure’ to use in meta-analysis: ‘relative effect size’ was reported as odds ratio (OR; values >1 favouring homeopathy) or standardised mean difference (SMD; values < 0 favouring homeopathy).

Results Eleven RCTs, representing 11 different medical conditions, were eligible for study. Five of the RCTs (four of which in design Ib) were judged to have pragmatic study attitude, two were explanatory, and four were equally pragmatic and explanatory. Ten trials were rated ‘high risk of bias’ overall: one of these, a pragmatic study with design Ib, had high risk of bias solely regarding participant blinding (a bias that is intrinsic to such trials); the other trial was rated ‘uncertain risk of bias’ overall. Eight trials had data that were extractable for analysis: for four heterogeneous trials with design Ia, the pooled OR was statistically non-significant; collectively for three clinically heterogeneous trials with design Ib, there was a statistically significant SMD favouring adjunctive IHT; in the remaining trial of design 1a, IHT was non-inferior to fluoxetine in the treatment of depression.

Conclusions Due to the low quality, the small number and the heterogeneity of studies, the current data preclude a decisive conclusion about the comparative effectiveness of IHT. Generalisability of findings is limited by the variable external validity identified overall; the most pragmatic study attitude was associated with RCTs of adjunctive IHT. Future OTP-controlled trials in homeopathy should aim, as far as possible, to promote both internal validity and external validity.