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International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures. We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client. This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.

To identify quality issues within the production and prevent defect products to be delivered to customers is critical for most manufacturing companies, and usually performed both within and at the end of each production section. In this paper we investigate the use of deep neural networks for performing automatic quality inspections based on image processing, with the aim of eliminating today’s manual inspection processes. A deep neural network is implemented on a real-world industrial case study and its performance is evaluated and analyzed when it comes to detecting quality problems in produced products. The results show that the network has an accuracy of 94.5% which is considered good in comparison to the 70-80% accuracy that a trained human inspector can achieve. CC BY-NC-ND 4.0Edited by Robert X. Gao, Kornel EhmannThe authors would like to thank Jonsac AB for their support in the study and for allowing us to work in their facility. The authors also want to thank Vinnova for financing the VISION project through the strategic innovation program Produktion2030, within which this work has been undertaken.

Sometimes the quality of a process is best expressed by a relationship between response variables and explanatory variables. Checking over the time the stability of such functional relationships using statistical methods is called “profile monitoring”. Since 2007, when a detailed review paper in the field of profile monitoring was presented, an increasing number of papers have been published in this area. In this paper, we present a conceptual classification scheme and classify the papers in this area since 2008 up to 2018. The relevant papers are categorized and analyzed under different metrics and directions for future studies are recommended.

In this work, we proposed a method to reduce text in technical documentation, aiming at Augmented Reality manuals, where text must be reduced as much as possible. In fact, most of technical information is conveyed through other means such as CAD models, graphic signs, images, etc.. The method classifies technical instructions into two categories: instructions that can be presented with graphic symbols and instructions that should be presented with text. It is based on the analysis of the action verbs used in the instruction, and makes use of ASD Simplified Technical English (STE) for remaining text instructions and let them easier to translate into other languages.

Robotic coordination is a crucial issue in the development of many applications in swarm robotics, ranging from mapping unknown and potentially dangerous areas to the synthesis of plans to achieve complex tasks such as moving goods between locations under resource constraints. In this context, stigmergy is a widely employed approach to robotic coordination based on the idea of interacting with the environment by means of markers called pheromones. Pheromones do not need to be "physical marks", and a number of works have investigated the use of digital, virtual pheromones. In this paper, we show how the concept of virtual pheromones can be implemented in Jason, a Java-based interpreter for an extended version of AgentSpeak, providing a high-level modelling and execution environment for multi-agent systems. We also exploit MQTT, a messaging infrastructure for the Internet-of-Things. This allows the implementation of stigmergic algorithms in a high-level declarative language, building on top of low-level infrastructures typically used only for controlling sensors and actuators.

International audience

Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradient-based resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush–Kuhn–Tucker conditions.

The purpose of this paper is to show how the knowledge of Process Mining techniques can provide a robust premise to build a Discrete Event Simulation (DES) model of a healthcare process. In order to analyze some specific processes of an ophthalmology ward of a large Italian hospital, ProM6 framework was implemented, which supports process mining techniques in form of plug-ins; the plug-ins process data, in form of Event Log, in order to extract information about the process. The DES model based on such information was run via a commercial tool, Simul8, which allows building sophisticated process models. A timely algorithm was then deployed to adapt the ProM6 information to the DES model. Following, a conformance analysis was conducted by comparing the original Event Log, and the Simulation tool data, taking into account the main case-related model attributes (routing probabilities profile, time perspective, and resources perspective). The paper aims at developing the line of inquiry for what concerns the deployment of approaches to set forth the link between Process mining and Simulation modeling.

GDPLL(k) grammars have been introduced as a tool for the construction of syntactic pattern recognition-based systems. The grammars have been successfully used in several different applications. The practical experience with the implementation of a syntactic pattern recognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.

In the context of Industry 4.0, (cyber) manufacturing systems enter in a world of services which is a convenient paradigm to match virtual and physical systems including Cloud computing, big data, Internet-of-Things (IoT) and mobility. The coordination and control of such complex system of actors or components requires methods and techniques to design, verify and deploy the services, possibly on the fly, making service engineering an unavoidable approach to develop new generations of cyber manufacturing systems. In this position paper, we advocate a service based component model that would be helpful to contribute to this goal.

There has been an increased use of soft-sensors in process industry in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to hardware sensors. Since process variables, which are concerned with final product quality, cannot always be measured by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a soft-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or replacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are generally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plant data, taken from the process database, are available. In this paper the most popular methods for plant data-based modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a short survey of the most important papers, tips about choosing the appropriate methodology for process model building are also provided.

Dealing with data from the field of medicine is nowadays very current and difficult. On a global scale, a large amount of medical data is produced on an everyday basis. For the purpose of our research, we understand medical data as data about patients like results from laboratory analysis, results from screening examinations (CT, ECHO) and clinical parameters. This data is usually in a raw format, difficult to understand, non-standard and not suitable for further processing or analysis. This paper aims to describe the possible method of data preparation and preprocessing of such raw medical data into a form, where further analysis algorithms can be applied.

[EN]The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.

Availability of data and materials: The dataset used for supporting the conclusions of this paper is available from the public data repository at https://archive.ics.uci.edu/ml/index.php. The dataset is available at: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients. Copyright © The Author(s) 2021. With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring. Office of Research, Zayed University under Grant Number R20053.

International audience; In this paper, we consider the problem of symbolic model design for the class of incrementally stable switched systems. Contrarily to the existing results in the literature where switching is considered as periodically controlled, in this paper, we consider aperiodic time sampling resulting either from uncertain or event-based sampling mechanisms. Firstly, we establish sufficient conditions ensuring that usual symbolic models computed using periodic time-sampling remain approximately bisimilar to a switched system when the sampling period is uncertain and belongs to a given interval; estimates on the bounds of the interval are provided. Secondly, we propose a new method to compute symbolic models related by feedback refinement relations to incrementally stable switched systems, using an event-based approximation scheme. For a given precision, these event-based models are guaranteed to enable transitions of shorter duration and are likely to allow for more reactiveness in controller design. Finally, an example is proposed in order to illustrate the proposed results and simulations are performed for a Boost dc-dc converter structure.

A corpus is a massive body of structured textual data that are stored and operated electronically. It usually combines with statistics, machine learning algorithms, or artificial intelligence (AI) technologies to explore the semantic relationship between lexical units, and beneficial when applied to language learning, information processing, translation, and so forth. In the face of a novel disease, like, COVID‐19, establishing medical‐specific corpus will enhance frontline medical personnel's information acquisition efficiency, guiding them on the right approaches to respond to and prevent the novel disease. To effectively retrieve critical messages from the corpus, appropriately handling word‐ranking issues is quite crucial. However, traditional frequency‐based approaches may cause bias in handling word‐ranking issues because they neither optimize the corpus nor integrally take words' frequency dispersion and concentration criteria into consideration. Thus, this paper develops a novel corpus‐based approach that combines a corpus software and Hirsch index (H‐index) algorithm to handle the aforementioned issues simultaneously, making word‐ranking processes more accurate. This paper compiled 100 COVID‐19‐related research articles as an empirical example of the target corpus. To verify the proposed approach, this study compared the results of two traditional frequency‐based approaches and the proposed approach. The results indicate that the proposed approach can refine corpus and simultaneously compute words' frequency dispersion and concentration criteria in handling word‐ranking issues.

Accident causal scenario can describe the process logic of the accident clearly and concretely from the perspective of the control mechanism. Only by improving the quality of the causal scenario can the effective control measures be taken. Combining the technical characteristics of the fully automatic operation (FAO) system, the paper proposes an automated accident causal scenario identification method for FAO system based on the System-Theoretic Process Analysis (STPA) method. Aiming at the problem that there are too many layers in the hierarchical control structure diagram of STPA method, which makes it impossible to effectively trace the cause and the problem that the basic control structure model only contains the control structural information and lacks the cause information, a new basic control structure model is defined to model multiple control processes in time sequence, and then the paper extends it from four aspects: control action, input variables, external disturbance, and synchronous timing to add more system cause information. For the lack of a unified standard description problem for the causal scenario, a four-stage causal scenario description method is defined, this paper has developed the first timing, non-first timing, synchronous timing, and external disturbance causal scenario search rules to ensure the automatic identification of the causal scenarios. Applying the automated safety analysis method to the case study of the operational scenarios of parking in a station of Beijing Yanfang Line, the automated identification of related causal scenarios is successfully completed through the Auto-STPA platform, and corresponding safety requirements are added. The feasibility of the method and the applicability to the analysis of operational scenarios are verified.

Live work is a dangerous and complex operation that endangers the lives of operators. This paper presents an autonomous live-line maintenance robot (ALMR), which is applied to 10 kV overhead lines. The ALMR can complete most of the operations automatically, requiring only a small amount of correction and operation. The paper briefly introduces the overall design of the ALMR. The main part of the paper describes the work carried out to realize automatic operation: (1) Propose a dynamic hierarchical task planning method that can decompose complex operations into a sequence of instructions that the robot can directly execute and can effectively deal with unexpected situations during operation; (2) solve the problem of recognition and positioning of cables under outdoor sunlight interference and complex backgrounds; (3) realize the robot’s motion planning in the live-line scene; (4) build a virtual environment to solve the telepresence problem of remote operation. Field experiments show that the ALMR can replace manual operations such as connecting and disconnecting lead-flow lines, and the operating efficiency is close to that of manual operations.

Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

This paper attempts to analyze the Korean sentence classification system. Sentence classification is the task of classifying an input sentence based on predefined categories. However, spelling or space error contained in the input sentence causes problems in morphological analysis and tokenization. This paper proposes a novel approach of Integrated Eojeol (Korean syntactic word separated by space) Embedding to reduce the effect of poorly analyzed morphemes on sentence classification. The paper also proposes two noise insertion methods that further improve classification performance. Our evaluation results indicate that by applying the proposed methods on the existing sentence classifiers, the sentence classification accuracy on erroneous sentences is increased by 8% to 15%.

Introduction In the near future, automated vehicles will drive on public roads together with traditional vehicles. Even though almost the whole academia agrees on that statement, the possible interferences between the two different kinds of driver are still to be analyzed and the real impacts on the traffic flow to be under-stood. Objectives Aim of this paper is to study one of the most likely L3 automated system to be deployed on public roads in the short term: Highway Chauffeur. The analysis of this system is carried out on a roadwork scenario to assess the positive impacts arising from a joint implementation of the automated system and the C-ITS Use Case signaling the closure of a lane. In fact, the main contribution of this paper is the assessment of the possible benefits in travel times and driving regime arising from the joint implementation of the Highway Chauffeur system and of C-ITS messages, both for the vehicles equipped with both technologies and for the surrounding traffic. Methods The assessment is achieved through traffic simulations carried out with the VISSIM software and a Python script developed by the authors. The overall process is described and the obtained results are provided, commented and compared to define the implementation of the C-ITS Use Case that could maximize the benefits of L3 driving. Results These results showed how triggering the take-over maneuver in ad-vance fosters the bottleneck efficiency (the same speed values reached between 80 and 100% Market Penetration for around 700 m range of the C-ITS message are reached at 50% Market Penetration with a 1500 m range). Besides, an in-creased speed up to 30 km/h at the bottleneck is recorded, depending on the mar-ket penetration and the message range. Finally, the delay upstream the roadworks entrance is reduced by 6% and arises at around 700 m, without the need to deploy the message up to 1500 m. Conclusions The paper investigates the impacts of take-over maneuvers and of automated driving while considering different operational parameters such as the message range. The results suggest all the potentialities of the Use Case while providing interesting figures that frame the trends related to the different imple-mentations. Finally, the tool developed to carry out the presented analysis is re-ported and made available so that hopefully the Use Case may be explored further and a precise impact assessment may be carried out with different prototypes of AVs and on different infrastructures.

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario&mdash, an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

Terminology is the most basic information that researchers and literature analysis systems need to understand. Mining terms and revealing the semantic relationships between terms can help biomedical researchers find solutions to some major health problems and motivate researchers to explore innovative biomedical research issues. However, how to mine terms from biomedical literature remains a challenge. At present, the research on text segmentation in natural language processing (NLP) technology has not been well applied in the biomedical field. Named entity recognition models usually require a large amount of training corpus, and the types of entities that the model can recognize are limited. Besides, dictionary-based methods mainly use pre-established vocabularies to match the text. However, this method can only match terms in a specific field, and the process of collecting terms is time-consuming and labour-intensive. Many scenarios faced in the field of biomedical research are unsupervised, i.e. unlabelled corpora, and the system may not have much prior knowledge. This paper proposes the TermInformer project, which aims to mine the meaning of terms in an open fashion by calculating terms and find solutions to some of the significant problems in our society. We propose an unsupervised method that can automatically mine terms in the text without relying on external resources. Our method can generally be applied to any document data. Combined with the word vector training algorithm, we can obtain reusable term embeddings, which can be used in any NLP downstream application. This paper compares term embeddings with existing word embeddings. The results show that our method can better reflect the semantic relationship between terms. Finally, we use the proposed method to find potential factors and treatments for lung cancer, breast cancer, and coronavirus.

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.

In this paper we consider Model Predictive Control (MPC) design for roll angle control for a Fixed-Wing Unmanned Aerial Vehicle (UAV) with Multiple Segmented Control Surfaces. The challenge of roll angle control for a Fixed-Wing UAV consists of switching between inner and outer aileron-pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid-control problem as a switched linear constrained MPC-QP problem and switched state observer design for Fixed-Wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation target, which is to stay below the sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good choice for real-time roll angle control for Fixed-Wing UAVs., In this paper we consider model predictive control (MPC) design for roll angle control for a fixed-wing unmanned aerial vehicle (UAV) with multiple segmented control surfaces. The challenge of roll angle control for a fixed-wing UAV consists of switching between inner and outer aileron pairs with hard constraints due to safety, energy saving and switching actuators. The novelty consists of formulating a hybrid control problem as a switched linear constrained MPC-QP problem and switched state observer design for fixed-wing UAV. A fast novel QP-solver based on the active-set QP-solver Hildreth is developed to meet the real-time implementation sampling time of Ts = 10 ms. The designed MPC controllers are simulated using Matlab. Simulations and the CPU-time from the improved QP-solvers show MPC to be a very good solution for real-time roll angle control of fixed-wing UAVs.

Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and develops a multi-stage interactive consensus reaching algorithm to deal with multi-expert decision making problem with LPOs. Furthermore, some numerical examples are presented to illustrate the developed methods and models. Finally, some comparative analyses between the proposed methods and models and some existing methods have been made to show the advantages of the proposed methods and models. First published online 24 February 2020

In this paper, we proposed a new design scheme of real time electronic countermeasure simulation system. This paper mainly expounds the modeling and realization methods of each part of the whole simulation system, and the real-time property of system has been lucubrated. Electronic countermeasure simulation system is the key part of military training of individuals; it can also allow the realistic evaluation of the performance of modern equipments and techniques. As a proof, we have drawn up a series of simulation scenarios, such as radar electronic reconnaissance simulation scenario, to explain the feasibility and the superiority of our modeling scheme in this paper.

With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization - supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting.

Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.

This paper proposed a novel first-order single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) forecasting model. Our aim is to improve the previously proposed neutrosophic time series (NTS) model by incorporating the degree of the hesitancy using single-valued neutrosophic hesitant fuzzy set (SVNHFS) model instead of single-valued neutrosophic set (SVNS). Our paper's novelty is that we incorporate an algorithm that automatically converts the crisp dataset into the neutrosophic set that eliminates the need for experts' input or opinions in determining the membership in each of the partitioned neutrosophic set. We also incorporate Markov Chain algorithm in the de-neutrosophication process to include the weightage of the repeating neutrosophic logical relationships (NLRs). Our paper's significant contribution is to add to the existing body of knowledge related to fuzzy time series (FTS) by developing a new FTS model based on SVNHFS, one of the improved version of fuzzy sets, since this area of research is still relatively underdeveloped. To determine our proposed model's capability, we apply our proposed SVNHFTS model to three real datasets while also comparing the result to the other FTS models based on improved versions of fuzzy sets. Our datasets include benchmark enrollment data of University of Alabama, IDX Composite (Indonesian composite stock index), and MERVAL index (Argentinian composite stock index). The result shows that our proposed SVNHFTS model outperforms most of the other FTS models in terms of AFE and RMSE, especially the previously proposed NTS model.

In this paper, a model for integrating production scheduling and maintenance planning is proposed for flow shop production system. The suggested model in this paper is based on the optimal jobs sequence for jobs that will be processed in multiple machines connected in series. The objective of this study is to find the optimal sequence for jobs while reducing the total production and maintenance costs. The model works by generating an initial solution using longest processing time (LPT) dispatching rule. Then, tabu search algorithm is established to obtain the optimal sequence for jobs. Computational experiments are performed on problems with five serially machines which are assigned to process eight diverse jobs from the same product family. The result is compared with the genetic algorithm optimization technique under individual PM scheme for obtaining superior solutions that has been proved in the literature to be one of the best approach. The computational results show that the recommended approach is qualified over the simulation based genetic algorithm optimization technique.

This paper investigates the leader-following consensus tracking problems via iterative learning control for singular fraction-order multi-agent systems in the presence of iteration-varying switching topologies and initial state errors. First, in order to eliminate the impulsive effect of singular systems and handle iteration-varying topologies, the closed-loop Dα-type iterative learning control protocol is proposed. To deal with initial state errors, the initial state learning laws are introduced in light of the initial output errors of each follower agent. The developed Dα-type learning protocols based on initial state learning laws can guarantee each follower track perfectly the leader agent in the fixed time interval. Next, the sufficient convergent conditions of consensus tracking errors are provided. Moreover, the Dα-type learning protocols are extended to nonlinear singular fraction-order multi-agent systems with iteration-varying topologies and initial state errors. Finally, two numerical examples are presented to verify the validity of the proposed Dα type learning scheme in this paper.

The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis. Compared with other classification and prediction problems, fault diagnosis often faces the problem of data scarcity. To overcome the lack of fault data, the transfer learning based on different working condition is gradually introduced into fault diagnosis by scholars. This paper discusses the current mainstream AI-based fault diagnosis methods, and analyzes the advantage of transfer learning for fault diagnosis problem. Then, a transfer component analysis (TCA) based method is proposed to transfer data features between different working conditions. Through the TCA-based method, the fault diagnosis model under the working condition can be established with the help of historical working condition. It effectively alleviates the problem of data scarcity under the condition to be predicted. Different from other fault diagnosis studies, this paper considers the online maintenance process based on TCA. A fault diagnosis framework including online maintenance process is proposed. Finally, a case study of bearing diagnosis from Case Western Reserve University proves the feasibility and effectiveness of the proposed TCA-based method and our fault diagnosis framework.

To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several prediction methods. In particular, much research has been done on the selection of experimental data because representative time series data can better test the effectiveness and practicability of the prediction method. Based on the idea of divide and conquer of complex problems and the strategy of continuous optimization of machine learning, we proposed the prediction methods of LSTM-TFE, LR-TFE, and BR-TFE combined the EEMD, LSTM, LR, and BR methods in this paper. These methods use EEMD to decompose complex time series into several relatively milder, more regular and stable subsequences. Then the prediction model of each subsequence based on machine learning is carried out by using the LSTM, LR, or BR methods. We use these prediction models to predict the value of each subsequence. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series. To verify the proposed method comprehensively, we select three representative time series data to test this paper. From the experimental results, we found that the proposed method has a good effect.

Search is an essential technology for rescue and other mobile robot applications. Many robotic search and rescue systems rely on teleoperation. One of the key problems in search tasks is how to cover the search space efficiently. Search is also central to humans’ daily activities. This paper analyzes and models human search behavior using data from actual teleoperation experiments. The analysis of the experimental data uses a novel technique to decompose search data, based on structure learning and K-means clustering. The analysis explores three hypotheses: (1) humans are able to solve a complex search task by breaking it up into smaller tasks, (2) humans consider both coverage and motion cost, and (3) robots can outperform humans in search problems. The enhanced understanding of human search strategies can then be applied to the design of human-robot interfaces and search algorithms. The paper describes a technique for augmenting human search. Since the objective functions in search problems are submodular, greedy algorithms can generate near-optimal subgoals. These subgoals then can be used to guide humans in searching. Experiments showed that the humans’ search performance is improved with the subgoals’ assistance.

Spatial change detection is a fundamental technique for finding the differences between two or more pieces of geometrical information. This technique is critical in some robotic applications, such as search and rescue, security, and surveillance. In these applications, it is desirable to find the differences quickly and robustly. The present paper proposes a fast and robust spatial change detection technique for a mobile robot using an on-board range sensors and a highly precise 3D map created by a 3D laser scanner. This technique first converts point clouds in a map and measured data to grid data (ND voxels) using normal distributions transform. The voxels in the map and the measured data are then compared according to the features of the ND voxels. Three techniques are introduced to make the proposed system robust for noise, that is, classification of point distribution, overlapping of voxels, and voting using consecutive sensing. The present paper shows the results of indoor and outdoor experiments using an RGB-D camera and an omni-directional laser scanner mounted on a mobile robot to confirm the performance of the proposed technique.

Conventional robotic surgery employs pinch and power grips for manipulating a master manipulator. As such, the manipulating methods exhibit limitations that are strongly related to the advantages and disadvantages of the gripping type. The context of this paper is focused on the introduction of a combined grip to compensate for such restraints. In particular, this paper proposed the combined‐grip‐handle scheme on a master manipulator, which employs both the pinch and power grips. A pointing experiment applying the master‐slave scheme was conducted with the master manipulator under several manipulating conditions of the combined grip and the conventional gripping types. Results of using the combined grip demonstrated that the positioning operation performed with fewer failures and required shorter time and trajectory. Compared with the conventional gripping types, the proposed combined grip showed better performance on the positioning operation and provided a possibility to perform precise work at lower scale factors.

Digital inspection twin (DIT) acting as a mirror between the physical and virtual measuring world. In this paper the measurement system with the CMMs ZEISS UMM 500 and DEA-IOTA-2203 was used as a physical twin, and a virtual machine, generated after modelling and configuring in PTC Creo software. Virtual measurement system is developed for inspection of the standard types of tolerances (concentricity, perpendicularity i.e.) and basic inspection features (IF) (plane, cylinder, truncated cone i.e.). The result of this paper is a new approach of the conceptual development of the digital inspection twin, based on CMMs - towards off-line DIT based on CMM by virtue of the control data list (CDL) and .ncl (DMIS) file.

Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.

The rapid development of technology is leading to the emergence of smart factories where the Artificial Intelligence paradigm of deep learning plays a significant role in processing data streams from machines. This paper presents the application of Augmented Attention Blocks embedded in a deep convolutional neural network for the purposes of estimating the state of remote machines using remotely collected acoustic data. An Android application was developed for the purposes of transferring audio data from a remote machine to a base station. At the base station, we propose and developed a deep convolutional neural network called MAABL (MobileNetv2 with Augmented Attention Block). The structure of the neural network is constructed by combining an inverted residual block of MobileNetv2 with an augmented attention mechanism block. Attention Mechanism is an attempt to selectively concentrate on a few relevant things, while ignoring others in deep neural networks. Due to the presence of audio frames containing silent features not relevant to the task at hand, an Attention Mechanism is particularly important when processing audio data. The MAABL network proposed in this paper obtains the state of the art results on the accuracy and parameters of three different acoustic data sets. On a relatively large-scale acoustic dataset regarding machine faults, the method proposed in this paper achieves 98% accuracy on the test set. Moreover, after using transfer learning, the model achieved the state of the art accuracy with less training time and fewer training samples.

This paper presents the software Partial Pole Placement via Delay Action, or P3$\delta$ for short. P3$\delta$ is a Python software with a friendly user interface for the design of parametric stabilizing feedback laws with time-delays, thanks to two properties of the distribution of quasipolynomials' zeros, called multiplicity-induced-dominancy and coexisting real roots-induced-dominancy. After recalling recent theoretical results on these properties and their use for the feedback stabilization of control systems operating under time delays, the paper presents the main features of the current version of P3$\delta$. We detail, in particular, the assignable admissible region (the set of allowable dominant roots and the corresponding delay), which helps the user in the choice of input information, allowing a reliable stabilizing delayed feedback. We also present the newly set online version of P3$\delta$., Comment: 16th IFAC Workshop on Time Delay Systems (IFAC TDS 2021). arXiv admin note: substantial text overlap with arXiv:2007.05809

International audience; The Computer Aided Process Planning (CAPP) systems are recently developed in manufacturing engineering to provide links between Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) systems. The CAPP systems are developed by considering the different issues of computer applications in production engineering. Optimization techniques can be applied to the CAPP to increase efficiency in part production processes. The energy consumption of part production process can be analyzed and optimized using the CAPP systems in order to increase added value in the part manufacturing process. Also, artificial neural networks as well as cloud manufacturing systems can be applied to the CAPP systems to share advantages of the different CAPP systems in different industry applications. Flexible process planning systems are developed using dynamic CAPP in order to cope with product varieties in process of part production. To develop potential energy saving strategies during product design and process planning stages, the advanced CAPP systems can be used. In this paper, a review of Computer Process Planning systems (CAPP) is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers.

In the shipbuilding industry, each production process has a respective lead time, that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO–JTVAC-SVM model is feasible for predicting the lead time in shipbuilding.

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monitoring (SHM). The use of high-frequency waves such as these offers an advantage over low-frequency methods from their ability to detect damage on a smaller scale. However, in order to assess damage in a structure, and implement any NDE or SHM tool, knowledge of the behaviour of a guided wave throughout the material/structure is important (especially when designing sensor placement for SHM systems). Determining this behaviour is extremely diffcult in complex materials, such as fibre-matrix composites, where unique phenomena such as continuous mode conversion takes place. This paper introduces a novel method for modelling the feature-space of guided waves in a composite material. This technique is based on a data-driven model, where prior physical knowledge can be used to create structured machine learning tools; where constraints are applied to provide said structure. The method shown makes use of Gaussian processes, a full Bayesian analysis tool, and in this paper it is shown how physical knowledge of the guided waves can be utilised in modelling using an ML tool. This paper shows that through careful consideration when applying machine learning techniques, more robust models can be generated which offer advantages such as extrapolation ability and physical interpretation., Comment: 33 pages, 11 figures

This paper deals with the synchronization of Mobile Smart Objects (MSOs). Today, this scenario is becoming typical in Industrial IoT applications due to the plethora of MSOs available as robots, drones and wearables, equipped by sensors making them measurement instruments cooperating in distributed measurement systems. In this context, the synchronization accuracy is directly tied with the accuracy of the performed measurements. In hierarchical synchronization approaches, the presence of an MSO makes the network topology time varying, and this could prevent the synchronization of the whole network. Peer to peer approaches do not need node hierarchy to synchronize but could not converge to a common sense of time. To overcome these challenges, this paper proposes a consensus-based approach for which the convergence to a common sense of time is here demonstrated. The proposal deploys the MSO to bring the common sense of time from an SO to another, establishing new paths among SOs. The new paths are temporary and depend on the MSO’s route. In the paper, the influence of the MSO’s route on the synchronization accuracy σ and the time interval to synchronize all the SOs ∆TIS is investigated, also. The mathematical proof, the simulations and the experimental tests confirm that the MSO can reduce both the values of σ and ∆TIS, because the new connections introduced by the MSO can boost the exchange of information among SOs. Consequently, the criteria to a priori select the route ameliorating σ and ∆TIS values are proposed.