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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

The main purpose of this paper is to present the methodology for calculating the electromagnetic behaviour of real integrated circuit (IC) parts using a no-mesh local Finite Differential Method (FDM). Furthermore, the comparison of computational results and measurements is presented. All considerations are based on typical long transmission lines (TLs) in modern ICs. The obtained results have been analysed in detail and compared with measured values. The measurement data are de-embedded using the test board model. This problem is illustrated in this paper based on a practical example of the Multi-Conductor Transmission Lines test structure whose electrical responses to various excitations are presented and analysed in detail.

Delivering confidential messages in wireless network is gaining continuous attraction as a fundamental building block for the Internet of things (IoT). This paper overviews the confidential message transmission techniques in broadcast channel (BC) where a transmitter serves multiple users by delivering confidential messages to their intended users. Each confidential message must be prevented from extracting its information by any unintended users. First, we consider a basic network consisting of two users. In the two-user BC, the secret dirty paper coding (S-DPC) achieves the secrecy capacity region. However, since the DPC requires a high computational complexity for characterizing the capacity region, two sub-optimal transmission techniques with low complexities are explained. Second, we extend our discussion to the network including more than two users. For $K$ -user BC with confidential messages, the optimal transmission scheme and the secrecy capacity region have not yet been derived. Instead of presenting the optimal scheme, we explain two sub-optimal transmission techniques and compare their features and performances. In conclusion, we discuss the open problems in the confidential message transmission for networks with more than two users.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

Random unitary beamforming (RUB) achieves multiuser diversity gain over multiple-input multiple-output broadcast channels with partial channel state information (CSI). RUB can asymptotically achieve the same growth of rate $M\log \log K$ as the optimal dirty paper coding or sub-optimal zero-forcing beamforming, which need full CSI at the transmitter. In this paper, we propose energy efficient RUB systems that select the optimal number of streams and transmission power to maximize energy efficiency (EE). In contrast to other beamforming schemes that use full CSI, optimizing RUB in terms of EE is a difficult task due to the CSI constraints. To solve this problem, we first select the EE-optimal number of streams and transmission power by using the statistical characteristics of RUB, which have been studied in previous works. Next, based on partial CSI feedback from users, we propose the method whereby the transmitter of RUB can adaptively control the transmission power. Simulation results demonstrate that our proposed systems can improve the EE performance of RUB and that the analytical results derived in this paper are accurate.

Citations play an important role in ranking of authors, journals, institutions, and organizations. Sometimes, citing documents cite a reference many times in their full-text, which is further used in many application scenarios, such as: 1) finding relationship between cited and citing papers; 2) identifying influential cited paper from set of references in citing paper; 3) identification of suitable citation functions; and 4) study of in-text citations in different logical sections of papers to conclude different findings. The accurate identification of in-text citations remained an open area of research. Recently, the complexities involving automatic identification of in-text citations have been reported with an accuracy rate of 58%. This is due to many issues as highlighted by the state-of-the-art research. This paper investigates such issues in further details: 1) by taking benefits from the previous research; 2) by analyzing different referencing formats; and 3) by experimenting on a comprehensive data set. Based on the investigation, this paper proposes a taxonomy and workable system, which utilizes a set of heuristics build from detailed study. The proposed model is then applied on unseen diversified data set taken from the Journal of Universal Computer Science and CiteSeer. The proposed model was able to achieve an average F-score of 0.97 as compared with the baseline 0.58.

Electrical power converters use semiconductor switching devices for larger loss reduction compared with non-switching power linear amplifier such as a class-B amplifier. However, it is well known that the switching operation produces harmonics and EMI noise, thus passive components are usually required for the suppression of these harmful sub-products. Use of recent fast power devices such as silicon carbide (SiC) and gallium nitride (GaN) devices produce larger dv/dt and di/dt than that of Si power devices and as a result above mentioned problem becomes more tangible. In this paper, the authors propose a concept of a family of multi-level linear amplifier (MLLA), which makes no switching power conversion possible. MLLA consist of series-connected switching devices and only one of them operates as a linear amplifier and the loss is much less than that of the class-B amplifier. The concept of the family is very useful when a proper MLLA topology might be selected for applications. In this paper, three kinds of MLLA—diode-clamped linear amplifier (DCLA), flying-capacitor linear amplifier (FCLA), and novel modular-cascaded linear amplifier (MCLA)—with four series connection of devices are investigated for the viewpoint of merits and demerits clarification and the efficiency over 82 % is demonstrated in the experiments.

Fixed-beam, high-gain antenna systems can be used for a finer partitioning of the currently used cell-sectoring. This partitioning has the benefit of reducing the number of users seen per antenna beam, which reduces interference. Furthermore, the high antenna gain allows for a high effective isotropic radiated power while keeping the transmit power low. In this paper, we study the performance of such a fixed-beam, high gain antenna system design for millimeter-wave mobile communications. The antenna system is designed to keep the inter-sector interference in a multi-site scenario low. The performance is analyzed for single- and multi-user environments. In single-input single-output mode, the 50th percentile of the signal-to-interference-plus-noise ratio lies between 12.5 dB to 39.7 dB if 3 to 0 interferers are present, respectively. For multiple-input multiple-output transmission using zero-forcing, the signal-to-interference-plus-noise ratio increases and the 50th percentile ranges from 36.1 dB to 43.3 dB if 3 to 0 interferes are present, respectively. By using maximum ratio transmission, the best performance is achieved with no interferers present, while a plunge in performance is observed with interferers. Furthermore, the study revealed that the narrow beam antenna system can also provide a clear signal separation for small spatial separations. In the given example, the signal-to-interference-plus-noise ratio is larger than 32.1 dB with 11 active antenna elements, where 2.8 meters separate the users. Hence, the paper shows that the cell-partitioning antenna systems provide coverage in the desired area while keeping the inter-sector interference low, and the considered transmission techniques can be used for situation optimized mobile communication links.

For the dynamic spectrum sharing (DSS), this paper proposes a spectrum sensing scheme that measures packet lengths of interfering systems. DSS requires spectrum sensing techniques to measure a length of time for which the interfering systems use a channel during observation time. A ratio of the length of time to the observation time is referred to as the channel occupation ratio (COR). When the observation time is limited, a conventional estimation scheme suffers from large errors of the measured COR. To cope with this problem, another conventional scheme divides the observation time into multiple short slots so as to estimate mean squared estimation errors of COR. However, this conventional scheme cannot accurately estimate errors, because it does not consider the time length of packets. Therefore, this paper proposes a scheme to estimate the variance of measured COR by measuring how long packets from the interfering systems can be observed, which can estimate errors much more accurately than the conventional scheme. Computer simulations evaluate how the observation time affects the standard deviation of the measured COR. It is also demonstrated that the theoretical results of the proposed scheme agree with those of the simulations when the true value of COR is less than 0.4.

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

Distress image retrieval for infrastructure maintenance via self-trained deep metric learning using experts’ knowledge is proposed in this paper. Since engineers take multiple images of a single distress part for inspection of road structures, it is necessary to construct a similar distress image retrieval method considering the input of multiple images to support determination of the level of deterioration. Thus, the construction of an image retrieval method while selecting an effective input from multiple images is described in this paper. The proposed method performs deep metric learning by using a small number of effective images labeled by experts’ knowledge with information about their effectiveness and a large number of unlabeled images via a self-training approach. Specifically, an end-to-end learning approach that performs retraining of the model by assigning pseudo-labels to these unlabeled images according to the output confidence of the model is achieved. Thus, the proposed method can select an effective image from multiple images that are input at the retrieval as a query image. This is the main contribution of this paper. As a result, the proposed method realizes highly accurate retrieval of similar distress images considering the actual situation of inspection in which multiple images of a distress part are input.

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.

In this paper, we designed a registration framework that can be used to develop augmented reality environments, where all the real (including the users) and virtual elements are co-localized and registered in a common reference frame. The software is provided together with this paper, to contribute to the research community. The developed framework allows us to perform a quantitative assessment of interaction and egocentric perception in Augmented Reality (AR) environments. We assess perception and interaction in the peripersonal space through a 3D blind reaching task in a simple scenario and an interaction task in a kitchen scenario using both video (VST) and optical see-through (OST) head-worn technologies. Moreover, we carry out the same 3D blind reaching task in real condition (without head-mounted display and reaching real targets). This provides a baseline performance with which to compare the two augmented reality technologies. The blind reaching task results show an underestimation of distances with OST devices and smaller estimation errors in frontal spatial positions when the depth does not change. This happens with both OST and VST devices, compared with the real-world baseline. Such errors are compensated in the interaction kitchen scenario task. Thanks to the egocentric viewing geometry and the specific required task, which constrain the position perception on a table, both VST and OST have comparable and effective performance. Thus, our results show that such technologies have issues, though they can be effectively used in specific real tasks. This does not allow us to choose between VST and OST devices. Still, it provides a baseline and a registration framework for further studies and emphasizes the specificity of perception in interactive AR.

The existing studies are generally focused on the pixel intensity or noise variance, and not much attention is given to describe the actual distribution. Some modifications have been proposed over the years in conventional parametric estimation techniques such as maximum likelihood estimation (MLE) and Bayesian estimation (BE). These methods have better properties to provide visually pleasing results for any imagery. In this paper, we use the likelihood estimate from the perspective that as the distribution order of likelihood increases, the overall effect of parameters under consideration improves within the defined confidence interval. In this paper, we use two parameters of interest, i.e., contrast and edge information. The proposed idea uses prior information w.r.t. particular parameter of interest, which is derived from likelihood estimate. The prior estimation has also been used to compute the reliability of the point estimations under a confidence interval. The proposed idea has desirable properties such that its optimization improves the overall image reconstruction.

Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their dp/dt limitation, the installed energy storages cannot cover the challenge of high df/dt. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers’ values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6% and 32%, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum df/dt is well compatible with different standards in the presence of load perturbations and different wind speeds.

Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance Rds,on is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using Rds,on, to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analytical tool to implement a long-term mission profile that requires reliability assessment.

Made available in DSpace on 2022-04-29T08:29:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration. Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira Department of Electrical Power Engineering Tishreen University School of Electrical and Computer Engineering National Technical University of Athens Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

Ultrasonic water meters have current widespread problems of poor site adaptability, low measurement accuracy, and poor stability. A high-precision intelligent ultrasonic water meter with self-diagnosis function and adaptive technology was proposed in this paper, which is focus on improving the measurement accuracy and repeatability of the small pipe diameters. The hardware circuit design of the ultrasonic water meter studied in this paper adopts the low-power STM32L053 microcontroller with Cortex-M0 core architecture and the high-precision 11 ps time resolution timing chip TDC-GP30 to complete the metering function. The software combines pulse width ratio and amplitude voltage measurement technology to make the water meter have self-diagnosis function, avoid measurement errors caused by accidental factors, improve adaptability and measurement accuracy, also the application of adaptive measurement period method is used to improve measurement repeatability. The software adopts the moving average filtering algorithm, which effectively reduces the fluctuation of the time-of-flight(ToF) difference and improve the measurement accuracy of the flow point in the low zone (between the minimum flow and the boundary flow). The experimental verification results show that the accuracy of the small-caliber ultrasonic water meter can reach within ±1.5 %, and the repeatability is less than 0.5 %. In the face of fluid disturbances, adaptive technology is used in this water meter to adjust the measurement period and carry out self-diagnostic function research, and automatically respond to deal with abnormal faults, thereby realizing the demand for intelligence.

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

Predictive control has emerged as a promising control method in a variety of technological fields. Model predictive control, as one of the subdivisions of this control method, has found a growing number of applications in power electronics and motor drives. In practical implementations, model predictive control faces performance degradation of the controlled plant due to its dependency on a model. There are considerable numbers of review papers that are devoted to the different points of view of predictive control. However, the existing literature lacks a review study that addresses the solutions for parameter dependency of the model predictive control method. Recently, model-free predictive control has been used in drives and power electronics as a solution for dealing with the model-dependency of the model predictive control method. There are many papers that have used such methods. In this paper, a classification is proposed for the different implementation types of model-free predictive control or similar methods that address model parameter uncertainties. Additionally, a comparison between the methods is also presented.

Switch Allocation (SA) holds a critical stage in Network-on-Chip (NoC) routers, its performance gets affected adversely due to Head-of-Line (HoL) blocking. In traditionally used Input-Queued Routers (IQR), packets are arranged in a particular order in each Virtual Channel (VC). This implementation is vulnerable to HoL blocking, as the switch allocator can allocate only those packets which are available at the head in a VC. In this paper, Swapped Buffer (SB) Router architecture is proposed to schedule packets in input buffers by using SB registers. The VCs are designed as SBs, this allows the packets stored in SB registers along with the head packet of VC to participate in SA. The concept of the SB register minimizes the conflicts in SA and thus reduces HoL blocking, therefore improves the performance of NoC. This paper proposes a priority mechanism to prioritize the non-head packets as compared to head packets in case of conflict between them. Two methods have been proposed in this paper, to enhance the performance of the NoC router. First, a VC allocation technique is proposed to optimize the order of packets in the input buffer. Next, SB-Router is combined with the Fill VC allocation technique to further enhance the performance of NoC routers. The performance of the proposed router is evaluated and the experimental results indicate that our design achieves latency improvement of 68.75% over (Time-Series) TS-Router for uniform traffic at the injection rate of 0.42 flits/cycle for a 64 node mesh network with moderate power consumption and area usage. The performance improvement in packet latency for traces from Princeton Application Repository for Shared-Memory Computers (PARSEC) has also been evaluated. With the achieved reduction in latency, the proposed method has the potential to serve high-speed operations while mapping different applications on multiple core architectures.

This paper discusses the development and implementation of the architecture of a Framework for Aerospace Vehicle Reasoning, ‘FAVER’. Integrated Vehicle Health Management systems require a holistic view of the aircraft to isolate faults cascading between aircraft systems. FAVER is a system-agnostic framework developed to isolate such propagating faults by incorporating Digital Twins (DTs) and reasoning techniques. The flexibility of FAVER to work with different types and scales of DTs and diagnostics, and its ability to adapt and expand for previously unknown faults and new systems are demonstrated in this paper. The paper also shows the novel combination of relationship matrix and fault attributes database used to structure the knowledge of FAVER’s expert system. The paper provides the working mechanism of FAVER’s reasoning and its ability to isolate faults at the system level, identify their root causes, and predict the cascading effects at the vehicle level. Four aircraft systems are used for demonstration purposes: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, and the use case scenarios are adapted from real aircraft incidents. The paper also discusses the pros and cons of FAVER’s reasoning via demonstrations and evaluates the performance of FAVER’s reasoning through a comparative study with a supervised neural network model.

Aiming at the low-voltage DC distribution network system and combining the characteristics of F2F structure and ISOP-DAB structure DC transformer, this paper proposes a DC transformer structure based on modular multilevel converter (MMC) and cascaded H-bridge. First of all, this paper analyzes several different cases. Case 1 proposes a type of equal carrier-phase-shift pulse-width modulation (CPS-PWM) of MMC, which can produce an equal five-level voltage waveform. In the meanwhile, another unequal CPS-PWM control is proposed to produce an unequal five-level voltage waveform. According to the descriptions of case 2, the quasi-square-wave-modulation method with a wide gain range can greatly reduce the insulation design difficulty of the intermediate AC side high-frequency transformer and the voltage stress on the AC side. Case 3 demonstrates the extended-phase-shift (EPS) control of three-port DC-DC converter. Combining the voltage sorting algorithm of voltage sequencing and cyclic switching, a strategy of voltage equalization for grouped capacitors is proposed. Considering the various application scenarios of actual working conditions, the soft switching characteristics and multi-port power characteristics of the DC transformer are analyzed. In order to verify the feasibility of the DC transformer structure and its control strategy, a simulation model was built in Matlab/Simulink and the results were verified. The results are in accordance with the theoretical analysis.

This paper focuses on the disturbance attenuation control of the quadrotor suspended payload system with unknown payload mass. Since the quadrotor transportation system is an eight-degree-of-freedom system with only four actuated degrees, it is difficult to achieve rapid stabilization of the quadrotor under varying payload and external disturbances. In this paper, a nonlinear controller based on rotation matrix is designed to rapid stabilization of attitude. By analyzing the effect of load mass on the trajectory tracking control, a parameter adaptive controller is proposed for position control. A mass estimation algorithm is established to estimate the payload mass in the air, which improves the robustness of the system. This approach can maximize load-bearing capacity within the thrust range of the quadrotor. The Lyapunov-based analysis proves the exponential convergence of the attitude error and the stability of the whole system. The contrast simulations demonstrate that the controller is superior to the sliding-mode controller incorporating input shaping on mass estimation, trajectory tracking, and disturbance resistance.

The fade-in and fade-out algorithm based on the Bernstein polynomial has certain limitations in image fusion. Therefore, this article proposes a new image fusion algorithm. First, the SIFT algorithm is used to register the images. Second, for the disjointed case of overlapping regions, a progressive image mosaic fusion algorithm in the form of a sine function is proposed. Finally, in order to make the progressive image mosaic fusion algorithm suitable for a variety of overlapping regions, this paper adds segmentation technology. The simulation experiment results show that the algorithm proposed in this paper is in good agreement with the spatial details and texture details of a high-resolution panchromatic image, and the time is shorter, which meets the real-time requirements. In addition, the algorithm proposed in this paper is effective in applications such as virtual reality and art image fusion.

In this study, a novel method about building Deep Knowledge Graph for the Plant Insect Pest and Disease, namely DKG-PIPD, was proposed. Specifically, the semi-automatic extraction of semi-structured and unstructured knowledge was carried out on the basis of domain ontology, and the knowledge graph was stored in the third-party knowledge database according to the corpus characteristic of the plant insect pest and disease, to realize the visual display of entity interactive relationship and knowledge inference. Furthermore, DKG-PIPD performed joint extraction about the entity and the relationship in unstructured knowledge in a corpus tagging method that is suitable for domain data. In this way, the entity and the relationship were annotated synchronically, and the triplet can be obtained directly through label matching and label mapping, which not only effectively improved the annotation efficiency, but also solved the problem of one-versus-many overlapping relation extraction. In addition, DKG-PIPD used a novel end-to-end model to train and predict on the crawled dataset. The experimental contrast results with other classical benchmark methods demonstrated the effectiveness of the proposed method. Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity relationship extraction and knowledge inference using deep learning are emphatically introduced. Finally, the improvement direction of this paper was also introduced in the discussion section.

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

In this paper, we propose an efficient attention module for visible and thermal infrared (TIR) matching deep learning networks. This method judges the right or wrong of heterogeneous sensor matching through the proposed deep learning model and increases the matching rate through the attention module using the edge-utilizing structure. This paper contributes to three aspects: 1) The first aspect is Convolutional Neural Network (CNN) structure comparisons for heterogeneous sensor registration. We consider the matching network as a classification problem when stacked heterogeneous sensor data become input of a single CNN network. Therefore, this paper shows result that is related with not only the network designed for heterogeneous sensor matching, but also various deep learning networks used for classification. 2) the second is a consideration for efficient attention module. The experiments show the module can replace lots of convolution blocks and the results achieve more better performance. The attention module uses a $1\times $ k filter and a k $\times $ 1 filter to extract horizontal and vertical edges and convolves two paths using them. 3) The third is suitable deep learning model for aerial complex visible and TIR data registration. To compare the various methods, we describe the calibration process of aerial visible and TIR data obtained directly from a drone. By using the calibrated data, this paper presents an AVIR attention block-based architecture that shows optimal matching results with minimal addition of parameters.

In the process of power transmission and distribution, non-technical losses are usually caused by users’ abnormal power consumption behavior. It will not only affect the dispatch and operation of the distribution network, bring hidden dangers to the security of the power grid, but also damage the operating costs of power companies and disrupt the operation of the power market. Aiming at users’ abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM). Firstly, according to the actual electricity theft behavior, six typical electricity theft modes are summarized, and 4 composite modes are obtained by combining them, so as to comprehensively test the detection performance of the model for various electricity theft behaviors. Secondly, a detection model based on PSO-Attention-LSTM is proposed, and the model is built using the TensorFlow framework. The model uses the attention mechanism to give different weights to the hidden state of LSTM, which reduces the loss of historical information, strengthens important information and suppresses useless information. Use PSO to solve the difficult problem of model parameter selection, and optimize the hyperparameters to improve the model performance. Finally, the data set of the University of Massachusetts was used for simulation and compared with convolutional neural network-long short term memory (CNN-LSTM), attention mechanism-based long short term memory (Attention-LSTM), LSTM, gated recurrent unit (GRU), support vector regression (SVR), random forest (RF) and linear regression (LR) to verify the effectiveness and accuracy of the method used in this article. In this paper, Matlab software is used to analyze and visualize the detection result data.

Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an understanding can be fabricated while differentiating the types of Indian Classical Music. Classical music is one of the essential characteristics of Indian Cultural Heritage. Indian Classical Music is divided into two major parts, i.e. Hindustani and Carnatic. Models have been sculptured and trained to classify between Hindustani and Carnatic Music. In this paper, two approaches are used to implement classification models. MFCCs are used as features and implemented models like DNN (1 Layer, 2 Layers, 3 Layers), CNN (1 Layer, 2 Layers, 3 Layers), RNN-LSTM, SVM (Sigmoid, Polynomial & Gaussian Kernel) as one approach. A 3 channels input is created by merging features like MFCC, Spectrogram and Scalogram and implemented models like VGG-16, CNN (1 Layer, 2 Layers, 3 Layers), ResNet-50 as another approach. 3 Layered CNN and RNN-LSTM model performed best among all the approaches.

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

This paper introduces an advanced space vector modulation technique for three-level neutral-point-clamped (3L-NPC) converters. The studied 3L-NPC converters within aircraft electric starter generator (ESG) systems normally operate at high modulation index, low pulse ratio and full power factor range for most of the operation time. Conventional modulation techniques exhibit large neutral point voltage ripple and possible voltage imbalance under such conditions. The increase of neutral point voltage ripple implies significant increase in converter capacitor size and decrease in capacitor reliability. The modulation technique introduced in this paper achieves improved neutral point voltage balancing within each switching period through enhanced applications of small vectors and restriction on medium vectors. The advanced implementation of small vectors is achieved using a sharing factor aided by a robust incremental current prediction procedure. With the predicted machine currents, the proposed method enables the optimum application of small vectors to balance the neutral point voltages. Simulation results based on a PLECS/Simulink model and experimental results obtained from a 45kVA, 32krpm aircraft electric starter generator prototype platform validate the reduction of the neutral point voltage ripple.

Parafoil systems are strongly nonlinear due to the flexibility of the canopy and suspension lines, and this characteristic cannot be simulated accurately by existing dynamic models. Hence, model-independent control methods are necessary for parafoil systems. This paper introduces a model-free adaptive control method based on the iterative feedback tuning (IFT-MFAC) method for parafoil systems that is a data-driven control method in which only input/output (I/O) data are needed during construction. In this paper, the MFAC construction process and stability analysis are explained, and then the IFT method is used to tune the two stepping factors of the MFAC method. A six-degree-of-freedom (DOF) dynamic parafoil system model is built to assess the performance of the method in a simulation with disturbances added to imitate real flights. A series of simulations and hardware-in-loop (HIL) tests are designed to verify the performance of the IFT-MFAC controller, and it is compared with the PID control method, the active disturbance rejection control (ADRC) method, and the MFAC method.

Aiming at the problem that the base coordinate system of industrial robot is not unified with the center coordinate system of tool, and it is impossible to input the action position in the robot teaching device, this paper adopts ROMER HEXAGON METROLOGY 7530SE three-coordinate measuring instrument as the robot measuring equipment and proposes a multi-objective normalization algorithm. In this paper, the reliability of the multi-objective normalization algorithm is verified by the experiment, and the measurement accuracy difference of the multi-objective normalization algorithm under different reference systems is studied. The results have shown that the accuracy of the system reaches 0.8 mm and the root mean square (RMS) value is maintained at 0.3-0.7 mm when the engine is taken as the reference coordinate system within the robot’s 0-50mm motion stroke. When the body-in-white is used as the reference coordinate system, the accuracy of the system can reach 2.2 mm, the RMS value can be maintained at 1.0-1.8mm. The reasons for good accuracy and stability of the measurement system established with the engine as the reference coordinate are analyzed. The multi-objective normalization algorithm proposed in this paper has high engineering universality, and the accuracy analysis of the algorithm under different reference system has guiding significance for the selection of reference coordinate system of measurement system.

Distribution network connected distributed energy resources (DER) are able to provide various flexibility services for distribution system operators (DSOs) and transmission system operators (TSOs). These local and system-wide flexibility services offered by DER can support the frequency ( $f$ ) and voltage ( $U$ ) management of a future power system with large amounts of weather-dependent renewable generation and electric vehicles. Depending on the magnitude of frequency deviation, other active network management-based frequency control services for TSOs could also be provided by DSOs in coordination with adaptive control of DER. This paper proposes utilisation of demand response based on frequency-dependent HV/MV transformer on-load tap-changer (OLTC) operation in case of larger frequency deviations. The main principle underlying the proposed scheme lies in the voltage dependency of the distribution network connected loads. In this paper, it is also proposed to, simultaneously with frequency-dependent OLTC control, utilise reverse reactive power -voltage (QU) - and adaptive active power -voltage (PU) -droops with distribution network connected DER units during these larger frequency deviations, in order to enable better frequency support service for TSOs from DSO networks. The effectivity and potential of the proposed schemes are shown through PSCAD simulations. In addition, this paper also presents a holistic and collaborative view of potential future frequency control services which are provided by DSO network-connected resources for TSOs at different frequency deviation levels.

The key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use them, especially in the robotics field. Some papers have successfully introduced single-modal or multi-modal methods. However, in practice, robots can be faced with extreme conditions; microphones do not work well in crowded scenes, and an RGB camera cannot capture terrains well in the dark. In this paper, we present a novel framework using the multi-modal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain type clustering by forcing the features to be closer together in the feature space. Our method enables the terrain type clustering even if one of the modalities (either image or audio) is missing at the test-time. We evaluated the clustering accuracy with a conventional multi-modal terrain type clustering method and we conducted ablation studies to show the effectiveness of our approach.

As a basic task in the field of computer vision, target detection has been concerned by many researchers. The performance of target detection method is also directly related to the research in many advanced semantic fields. Current general target detection methods are not effective in small target detection, so this paper studies the problem of small target detection and proposes a small target detection method based on deep learning with considerate feature and effectively expanded sample size. Firstly, according to the characteristics of convolutional neural network, we improve the current deep network architecture which performs well in target detection, and introduce considerate multi-feature and multi-scale detection. Then, we transform the high-resolution images obtained on the Internet by combining two groups of sampling method, so that the feature distribution of the high-resolution target is closer to that of the low-resolution target, thus effectively expanding the training data set, solving the problem that small target data is difficult to be labeled and effectively avoiding overfitting. The results show the effectiveness of the improved method in small target detection. In addition, in view of the shortage of small target detection review literature, this paper gives a comprehensive and detailed introduction to the field of small target detection in terms of related work and future work.

Deep convolutional neural networks (DCNNs) have attracted extensive attention in synthetic aperture radar (SAR) image target detection. However, there is usually a lack of labeled data in SAR images which results in difficulties to improve the detection performance with DCNN based methods. Transfer learning borrows generic knowledge from other tasks for the target task and can be an effective solution to the small sample problem, but the features’ generality and specificity across various models are not discussed in target detection. In addition, conventional DCNN based models have too many parameters related to the prior information of the targets which increase the difficulty of transfer learning between different tasks. In this paper, we mainly conduct research in three folds to address these mentioned issues. First, the generality and specificity of features in different network models are analyzed in the field of SAR target detection. Second, a transitive transfer framework is developed for detection under small sample condition. Third, an anchor free rotatable detector with flexible structure is designed for SAR images detection to suit the transfer detection framework proposed in this paper and a reasonable explanation is given for its upper hand with small samples. Experimental results demonstrate that: 1) transitive transfer framework is valid in SAR target detection; 2) the anchor free detector in this framework is superior to the anchor based one; 3) the proposed detector achieves better performance under small sample condition by comparison with state-of-the-art methods.

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

This paper focuses on how to represent the testing face images for multi-feature face recognition. The choice of feature is critical for face recognition. The different features of the sample contribute differently to face recognition. The joint similar and specific learning (JSSL) has been effectively applied in multi-feature face recognition. In the JSSL, although the representation coefficient is divided into the similar coefficient and the specific coefficient, there is the disadvantage that the training images cannot represent the testing images well, because there are probable expressions, illuminations and disguises in the testing images. We think that the intra-class variations of one person can be linearly represented by those of other people. In order to solve well the disadvantage of JSSL, in the paper, we extend JSSL and propose the extended joint similar and specific learning (EJSSL) for multi-feature face recognition. EJSSL constructs the intra-class variant dictionary to represent the probable variation between the training images and the testing images. EJSSL uses the training images and the intra-class variant dictionary to effectively represent the testing images. The proposed EJSSL method is perfectly experimented on some available face databases, and its performance is superior to many current face recognition methods.