## Just Accepted

Just Accepted Papers are peer-reviewed and accepted for publication. They will soon (normally in 1–3 weeks) transform into Typeset Proofs when initial checks such as language editing and reference cross-validation are completed and typesettings of the papers are done. Note that for both types of papers under this directory, they are posted online prior to technical editing and author proofing. Please use with caution.
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, Available online  , doi: 10.1049/cje.2020.00.185
Abstract(18) HTML (9) PDF(3)
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Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine model to predict carotid artery stenosis, with the maximum geometric mean as the optimization target. The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.
, Available online  , doi: 10.1049/cje.2020.00.315
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A high-effciency waveguide slot array antenna with low sidelobe level (SLL) is investigated for W-band applications. The silicon micromachining technology is utilized to realize multilayer antenna architecture by three key steps of selective etching, gold plating and Au-Au bonding. However, the radiating slot based on this technique becomes thick with a minimum thickness of 0.2 mm and accompanies with the decrease of slot’s radiation ability. To overcome this weakness, a stepped radiation cavity is loaded on the slot. Next, the characteristic of cavity-loaded slot is investigated to synthesize the low-SLL array antenna. Then, the unequal hybrid corporate feeding network is constructed to achieve sidelobe suppression in the E-plane. Finally, a pair of 16×8 low-SLL and high-effciency slot arrays is fashioned and confirmed experimentally. The bandwidth for the radiation effciency higher than 80% is 92.3~96.3 GHz. The SLLs in both E- and H-planes are below −19 dB.
, Available online  , doi: 10.1049/cje.2020.00.217
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The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models, i.e. model I, model II, and model III are defined. For the model I type malware, its malicious behavior occurs in DexCode, so the API classes were used to characterize the behavior of the DexCode file. For the model II type and model III type malwares whose malicious behaviors occur in an external file, the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.
, Available online  , doi: 10.1049/cje.2020.00.168
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Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments (such as dense urban, valley), multipath interference is one of the main error sources deteriorating positioning accuracy, and it is difficult to eliminate via differential techniques due to its uncertainty of occurrence and irrelevance in different instants. To address this problem, we propose a positioning method for Global navigation satellite systems (GNSS) by adopting a modified Teaching-Learning based optimization (TLBO) algorithm after the positioning problem is formulated as an optimization problem. Experiments are conducted by using actual satellite data. The results show that the proposed positioning algorithm outperforms other algorithms, such as Particle swarm optimization (PSO) based positioning algorithm, Differential evolution (DE) based positioning algorithm, Variable projection (VP) method, and TLBO algorithm, in terms of accuracy and stability.
, Available online  , doi: 10.1049/cje.2020.00.130
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The theory of mechanical antenna is still in its infancy at present, and its radiation mechanism, field distribution, modulation methods and other basic theories need to be explored and improved. The radiation mechanism of a Rotating-magnet Based Mechanical Antenna (RMBMA) is explored. An equivalent radiation model of the mechanical antenna is established. The field formula of mechanical antenna is derived using this model and rotation matrix. The spatial direct modulation method of mechanical antenna is also investigated. Two prototype antennas are fabricated using DC/AC servo motors and NdFeB magnets, and experiments are carried out to verify the correctness of the derivation and analysis. The measured and simulated results are in consistent with each other. By precisely controlling the moving parameters of an AC servo motor, signal of Binary Amplitude Shift Keying (BASK) is generated, and the original code sequence is recovered by demodulation.
, Available online  , doi: 10.1049/cje.2020.00.125
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A family of binary sequences derived from Euler quotients $\psi(\cdot)$ with RSA modulus $pq$ is introduced. Here $p$ and $q$ are two distinct odd primes and satisfy $\gcd(pq, (p-1)(q-1))=1$. The minimal polynomials and linear complexities of the proposed sequences are determined. Besides, this kind of sequences is shown not to have correlation of order $four$, although there exists the following relation $\psi(t)-\psi(t+p^2q)-\psi(t+q^2p)+\psi(t+(p+q)pq)=$$0 \pmod {pq}$ for any integer $t$ by the properties of Euler quotients.
, Available online  , doi: 10.1049/cje.2021.00.130
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Recently, many deep learning models have shown excellent performance in hyperspectral image classification. Among them, networks with multiple convolution kernels of different sizes have been proved to achieve richer receptive fields and extract more representative features than those with a single convolution kernel. However, in most networks, different-sized convolution kernels are usually used directly on multi-branch structures, and the image features extracted from them are fused directly and simply. In this paper, to fully and adaptively explore the multiscale information in both spectral and spatail domains of HSI, a novel multi-scale weighted kernel network (MSWKNet) based on an adaptive receptive field is proposed. First, the original HSI cubic patches are transformed to the input features by combining the principal component analysis and one-dimensional (1D) spectral convolution. Then, a three-branch network with different convolution kernels is designed to convolve the input features, and adaptively adjust the size of the receptive field through the attention mechanism of each branch. Finally, the features extracted from each branch are fused together for the task of classification. Experiments on three well-known hyperspectral data sets show that MSWKNet outperforms many deep learning networks in HSI classification.
, Available online  , doi: 10.1049/cje.2020.00.326
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A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontology-based semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for cross-domain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing (NLP) representation model Bidirectional Encoder Representations from Transformers (BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise Disease Ontology (DO) and Human Phenotype Ontology (HPO) terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.
, Available online  , doi: 10.1049/cje.2021.00.289
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Compared with cloud computing environment, edge computing has many choices of service providers due to different deployment environments. The flexibility of edge computing makes the environment more complex. The current edge computing architecture has the problems of scattered computing resources and limited resources of single computing node. When the edge node carries too many task requests, the makespan of the task will be delayed. Therefore, we propose a load balancing algorithm based on weighted bipartite graph for edge computing (LBA-EC), which makes full use of network edge resources, reduces user delay, and improves user service experience. The algorithm is divided into two phases for task scheduling. In the first phase, the tasks are matched to different edge servers. In the second phase, the tasks are optimally allocated to different containers in the edge server to execute according to the two indicators of energy consumption and completion time. The simulations and experimental results show that our algorithm can effectively map all tasks to available resources with a shorter completion time.
, Available online  , doi: 10.1049/cje.2021.00.058
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Type-I Generalized Feistel Networks (GFN) are widely used frameworks in symmetric-key primitive designs such as CAST-256 and Lesamnta. Different from the extensive studies focusing on specific block cipher instances, the analysis against Type-I GFN structures gives generic security evaluation of the basic frameworks and concentrates more on the effect of linear transformation. Currently, works in this field mainly evaluate the security against impossible differential attack, zero-correlation linear attack, meet-in-the-middle attack and yoyo game attack, while its security evaluation against rectangle attack is still missing. In this paper, we filled this gap and gave the first structural analytical results of Type-I GFN against rectangle attack. We proved there exists a $(b^2-b)$ round boomerang switch for the first time, which is independent of the round functions when the GFN has $b$ branches of $m$ bits. Then we proposed a new rectangle attack model and turned the boomerang switch into chosen plaintext setting. By appending 1 more round in the beginning of the boomerang switch, we constructed a $(b^2-b+1)$ round rectangle distinguisher with probability $2^{-2(b-1)m}$, whose advantage over random permutation is $2^{m}$. Using this distinguisher, a $b^2$ round key recovery attack is performed with $2^{\frac{bm}{2}+1}$ chosen plaintexts and $(2b^{-2})2^{bm}$ encryptions.
, Available online  , doi: 10.1049/cje.2021.00.059
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Adopt Software-Definition technology to decouple the functional components of the Industrial Control System in a service-oriented and distributed form is an important way for the Industrial Internet of Things to integrate Information Technology, Communication Technology, and Operation Technology. Therefore, this paper presents the concept of Software-Defined Control Architecture and describes the time consistency requirements under the paradigm shift of Industrial Control System architecture. By analyzing the physical clock and virtual clock mechanism models, the global clock synchronization space is logically divided into the physical and virtual clock synchronization domains, and a formal description of the global clock synchronization space is proposed. According to the fundamental analysis of the clock state model, the physical clock linear filtering synchronization model is derived, and a distributed observation fusion filtering model is constructed by considering the two observation modes of the virtual clock to realize the time synchronization of the global clock space by way of timestamp layer-by-layer transfer and fusion estimation. Finally, the simulation results show that the proposed model can significantly improve the accuracy and stability of clock synchronization.
, Available online  , doi: 10.1049/cje.2021.00.125
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Because a large number of algorithms in computational science include search problems and a large number of algorithms that can be transformed into search problems have attracted a lot of attention, especially the time rate of search and the accuracy of search, a quantum walk search algorithm on hypergraphes is proposed in this paper, whose aim is to reduce time consumption and increase the readiness and controllability of search. Firstly, the data points are divided into groups and then isomorphic to the permutation set. Secondly, the element coordinates in the permutation set are used to mark the position of the data points. Finally, search the target data by the controllable quantum walk with multiparticle on the ring. By controlling the coin operator of quantum walk, it is found that search algorithm can increase the accuracy and controllability of search. It is found that search algorithm can reduce time consumption by increasing the number of search particles. It also provides a new direction for design of quantum walk algorithms, which may eventually lead to entirely new algorithms.
, Available online  , doi: 10.1049/cje.2021.00.214
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Since the basic probability of an interval-valued belief structure (IBS) is assigned as interval number, its combination becomes difficult. Especially, when dealing with highly conflicting IBSs, most of the existing combination methods may cause counter-intuitive results, which can bring extra heavy computational burden due to nonlinear optimization model, and lose the good property of associativity and commutativity in Dempster-Shafer theory (DST). To address these problems, a novel conflicting IBSs combination method named CSUI (conflict, similarity, uncertainty, intuitionistic fuzzy sets)-DST method is proposed by introducing a similarity measurement to measure the degree of conflict among IBSs, and an uncertainty measurement to measure the degree of discord, non-specificity and fuzziness of IBSs. Considering these two measures at the same time, the weight of each IBS is determined according to the modified reliability degree. From the perspective of intuitionistic fuzzy sets, we propose the weighted average IBSs combination rule by the addition and number multiplication operators. The effectiveness and rationality of this combination method are validated with two numerical examples and its application in target recognition.
, Available online  , doi: 10.1049/cje.2020.00.299
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Orthogonal frequency-division multiplexing (OFDM) has been developed into a popular modulation scheme for wireless communication systems, used in applications such as LTE and 5G. In wireless communication systems, nonlinearity caused by RF amplifiers will generate distortions to both passband and adjacent channels such that the transmission quality is degraded. The study of this article aims to predict the power spectrum for OFDM based signals at the output of a RF amplifier due to the nonlinearity. In this article, based on Taylor polynomial coefficients, a power spectrum expression for amplified OFDM signals in terms of intercept points (up to nth-order) is derived. This model is useful to RF engineers in choosing and testing RF amplifiers with appropriate specifications, such as intercept points and gain, to meet the requirements of wireless standards. Measurements are carried out to confirm the results of the proposed model.
, Available online  , doi: 10.1049/cje.2021.00.021
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Identifying and determining behaviors of attack gangs is not only an advanced stage of the network security event tracing and analysis, but also a core step of large-scale combat and punishment of cyber attacks. Most of the work in the field of DDoS attack analysis has focused on DDoS attack detection, and a part of the work involves the research of DDoS attack sourcing. We find that very little work has been done on the mining and analysis of DDoS attack gangs. DDoS attack gangs naturally have the attributes of human community relations. We propose a framework named HiAtGang, in which we define the concept of the gang detection in DDoS attacks and introduce the community analysis technology is to the DDoS attack gang Analysis. Different attacker clustering algorithms are compared and analyzed. Based on analysis results of massive DDoS attack events recorded by CNCERT/CC, the effective gang mining and attribute calibration have been achieved. More than 250 DDoS attack gangs have been successfully tracked. Using our proposed method, we have further solved the problem of some difficult DDoS attacks sourcing. Our research fills the gaps in the field of the DDoS attack gang detection and has supported CNCERT/CC in publishing “Analysis Report on DDoS Attack Resources” for three consecutive years and achieved a good practical effect on combating DDoS attack crimes.
, Available online  , doi: 10.1049/cje.2019.00.102
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We consider the general problem of the approximate joint diagonalization of a set of non-Hermitian matrices. This problem mainly arises in the data model of the joint blind source separation for two datasets. Based on a special parameterization of the two diagonalizing matrices and on adapted approximations of the classical cost function, we establish two Jacobi-like algorithms. They may serve for the canonical polyadic decomposition (CPD) of a third-order tensor, and in some scenarios they can outperform traditional CPD methods. Simulation results demonstrate the competitive performance of the proposed algorithms.
, Available online  , doi: 10.1049/cje.2020.00.417
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With the recent increase in the number of Internet of Things (IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning (RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffc transmission path and provide qualified IoT services.
, Available online  , doi: 10.1049/cje.2021.00.108
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With the development of deep learning, Graph Neural Networks(GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured data. In many real-world networks, the relations between objects are complex and go beyond pairwise. Hypergraph is a flexible modeling tool to describe intricate and higher-order correlations. Therefore, the researchers have been concerned how to develop hypergraph-based neural network model. The existing hypergraph neural networks show better performance in node classification tasks and so on, while they are shallow network because of over-smoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel Deep hypergraph neural network (DeepHGNN). We design DeepHGNN by using the technologies of residual connection, identity mapping and sampling hyperedge, residual connection and identity mapping bring from GCNs. We evaluate DeepHGNN on two visual object datasets. The experiments show the positive effects of DeepHGNN, and it works better in visual object classification tasks.

, Available online  , doi: 10.1049/cje.2020.00.411
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Chinese part-of-speech (POS) tagging is an essential task for Chinese downstream natural language processing (NLP) tasks. The accuracy of the Chinese POS task will drop dramatically by word-based methods because of the segmentation errors and the word sparsity. Also, there are several Chinese POS tagging sets with different criteria. Some of them only have a small-scale annotated corpus and are hard to train. To this end, we propose a modified word-based Transformer neural network architecture. Meanwhile, we utilize an adversarial transfer learning method that splits the architecture into shared and private parts. This work directly improves the ability of the word-based model, instead of adopting a joint character-based method. Extensive experiments show that: 1) our method achieves state-of-the-art performance on all datasets. 2) Importantly, our method improves performance effectively for the word-based Chinese sequence labeling task.
, Available online  , doi: 10.1049/cje.2020.00.007
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Vehicles or pedestrians tracking is an important task in intelligent transportation system (ITS). In this paper, we propose an online multi-object tracking (MOT) for intelligent traffic platform that employs improved sparse representation and structural constraint. We first build the spatial-temporal constraint via the geometric relations and appearance of tracked objects, then we construct a robust appearance model by incorporating the discriminative sparse representation (DSR) with weight constraint and local sparse appearance (LSR) with occlusion analysis. Finally, we complete data association by using maximum a posteriori (MAP) in a Bayesian framework in the pursuit for the optimal detection estimation. Experimental results in two challenging vehicle tracking benchmark datasets show that the proposed method has a good tracking performance.
, Available online  , doi: 10.1049/cje.2021.00.089
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Side-channel attack (SCA) and Fault attack (FA) are two classical physical attacks against cryptographic implementation. In order to resist them, we present a combined countermeasure scheme which can resist both SCA and FA. The scheme combines the Threshold implementation (TI) and duplication-based exchange technique. The exchange technique can confuse the fault propagation path and randomize the faulty values. The TI technique can ensure a provable security against SCA. Moreover, it can also help to resist the FA by its incomplete property and random numbers. Compared with other methods, the proposed scheme has simple structure, which can be easily implemented in hardware and result in a low implementation cost. Finally, we present a detailed design for the block cipher LED and implement it. The hardware cost evaluation shows our scheme has the minimum overhead factor.
, Available online  , doi: 10.1049/cje.2021.00.113
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To solve the problem of semantic loss in text representation, this paper proposes a new embedding method of word representation in semantic space called wt2svec based on SLDA(Supervised LDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. Meanwhile, it gets the local semantic embedding word vector based on the Word2vec. Therefore, the new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words, and improve the performance of text categorization compared with Word2vec.
, Available online  , doi: 10.1049/cje.2021.00.005
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The devices in the Internet of Things (IoT) gain capability of sustainable operation when they harvest energy from ambient sources. Fluctuation in the harvested energy may cause the energy-harvesting IoT devices to suffer from frequent energy shortage, which may bring in intolerable packet delay or packet discarding. It is important to design a low-delay packet delivery scheme that adapts to variation in the harvested energy. In this paper, we present the Timely Data Delivery (TDD) scheme for the IoT devices. Using Markov chain, we develop a probability model for the TDD scheme, which leads to the expected number of packets delivered in an operation cycle, the expected numbers of packets waiting in the data buffer in an operation cycle and an energy-harvesting cycle, and the expected packet delay. Additionally, we formulate the optimization problem that minimizes the packet delay in the TDD scheme, and the solution to the optimization problem yields the optimal parameters for the IoT devices to determine when to harvest energy and when to deliver data under the TDD scheme. The simulation results show that the proposed TDD scheme outperforms the existing schemes in terms of packet delay.