Problem Of Knowledge Transfer Research Articles

Research on the Key Paths of Enterprise Integration of Domestic and Foreign Trade from the Perspective of Knowledge Transfer

The issue of inefficient industry innovation caused by the segmentation of domestic and foreign trade markets has been prominent, leading to a long-standing "productivity paradox" between domestic and foreign trade. "Constructing a new pattern of integration of domestic and foreign trade with the domestic cycle as the main body and the mutual promotion of domestic and international cycles" is a strategic deployment of the Party Central Committee and the State Council. This paper investigates the problems and intrinsic mechanisms faced in the process of enterprise integration of domestic and foreign trade from the perspective of knowledge transfer. It clarifies that the primary task of integrating domestic and foreign trade is to solve the problem of inefficient knowledge transfer between domestic and foreign trade. At the same time, the study shows that: On the one hand, by constructing a horizontal system of knowledge transfer between domestic and foreign trade, it is possible to effectively enable foreign trade enterprises to enjoy the dividends of unified large market reforms, better integrate resources and standards with the domestic trade market, and promote the transfer of knowledge achievements of foreign trade enterprises to the domestic market. On the other hand, through vertical industrial ecological knowledge embedding, it can promote domestic enterprises to leverage the advantages of domestic consumption scale, integrate technology, channels, and platform resources, achieve knowledge embedding of enterprises in the vertical ecological chain of industries, and ultimately achieve the goal of integrated development of domestic and foreign trade.

Guided Exploration of Industrial Sensor Data

AbstractIn recent years, digitization in the industrial sector has increased steadily. Digital data not only allows us to monitor the underlying production process using machine learning methods (anomaly detection, behaviour analysis) but also to understand the underlying production process. Insights from Exploratory Data Analysis (EDA) play an important role in building data‐driven processes because data scientists learn essential characteristics of the data in the context of the domain. Due to the complexity of production processes, it is usually difficult for data scientists to acquire this knowledge by themselves. Hence, they have to rely on continuous close collaboration with domain experts and their acquired domain expertise. However, direct communication does not promote documentation of the knowledge transfer from domain experts to data scientists. In this respect, changing team constellations, for example due to a change in personnel, result in a renewed high level of effort despite the same knowledge transfer problem. As a result, EDA is a cost‐intensive iterative process. We, therefore, investigate a system to extract information from the interactions that domain experts perform during EDA. Our approach relies on recording interactions and system states of an exploration tool and generating guided exploration sessions for domain novices. We implement our approach in a software tool and demonstrate its capabilities using two real‐world use cases from the manufacturing industry. We evaluate its feasibility in a user study to investigate whether domain novices can reproduce the most important insights from domain experts about the datasets of the use cases based on generated EDA sessions. From the results of this study, we conclude the feasibility of our system as participants are able to reproduce on average 86.5% of insights from domain experts.

Designing and Validation of e-module on Agriculture for Nutrition and Health (A4NH)

There is less awareness about health and nutrition problems and at the same time lack of availability of relevant and credible modules for ready reference for the needy people in India. This has aggravated the health and nutrition problems among rural women. The very low ratio of extension workers to farmers is aggravating the problem of knowledge transfer through the traditional system, so e-learning which is supported by growing connectivity and increased penetration of electronics in rural India can be used as an alternative solution to give faster and easily accessible information to the rural women, hence e module was developed using Microsoft Office PowerPoint 2013, e-learning module was created. The module was published as a CD-ROM. The module was validated through the sample group of researchers using the validation tool conceived by Vijayragavan and Singh [1] adapted for research purposes. Validation is done using four parameters, out of 4 parameters content and design were ranked first with a mean score of 3.88, followed by the satisfaction of felt information need with a mean score of 3.85. It was found that the utility of content and ease of understanding were scored low by the experts when compared to the content and satisfaction of the need for felt information. However, in absolute terms, all the parameters had a mean score of more than 3.5 indicating high acceptability of the complete module.

Reasoning by analogy and the transdisciplinarian’s circle: on the problem of knowledge transfer across cases in transdisciplinary research

In their 2018 paper, Carolina Adler, Gertrude Hirsch Hadorn, Thomas Breu, Urs Wiesmann, and Christian Pohl propose that transferability of knowledge across cases in transdisciplinary research should be thought of in terms of arguments by analogy. We aim to advance this discussion about transferability by examining it in the light of recent ideas about knowledge transfer, extrapolation, and external validity in the philosophy of science. We problematise Adler et al.’s proposal by identifying the ‘transdisciplinarian’s circle’, due to which even knowledge that could be of use in other projects may be identified too late for efficient knowledge transfer to take place. We then suggest that Steel’s ideas on overcoming the ‘extrapolator’s circle’ can serve as a source of inspiration for addressing the issue of the transdisciplinarian’s circle.

Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning☆

Sample efficiency, which refers to the number of samples required for a learning agent to attain a specific level of performance, is central to developing practical reinforcement learning (RL) for complex and large-scale decision-making problems. The ability to transfer and generalize knowledge gained from previous experiences to downstream tasks can significantly improve sample efficiency. Recent research indicates that successor feature (SF) RL algorithms enable knowledge generalization between tasks with different rewards but identical transition dynamics. It has recently been hypothesized that combining model-based (MB) methods with SF algorithms can alleviate the limitation of fixed transition dynamics. Furthermore, uncertainty-aware exploration is widely recognized as another appealing approach for improving sample efficiency. An agent can efficiently explore to better understand an environment by tracking uncertainty about the value of each available action. Putting together two ideas of hybrid model-based successor feature (MB-SF) and uncertainty leads to an approach to the problem of sample efficient uncertainty-aware knowledge transfer across tasks with different transition dynamics or/and reward functions. In this paper, the uncertainty of the value of each action is approximated by a Kalman filter (KF)-based multiple-model adaptive estimation. This KF-based framework treats the parameters of a model as random variables. To the best of our knowledge, this is the first attempt at formulating a hybrid MB-SF algorithm capable of generalizing knowledge across large or continuous state space tasks with various transition dynamics while requiring less computation at decision time than MB methods. We highlight why previous SF-based methods are constrained to knowledge generalization across same transition dynamics, present our novel approach on a firm theoretical foundation, and design a set of demonstration tasks to empirically validate the effectiveness of our proposed approach. The number of samples required to learn the tasks was compared to recent SF and MB baselines. The results show that our algorithm generalizes its knowledge across different transition dynamics, learns downstream tasks with significantly fewer samples than starting from scratch, and outperforms existing approaches. We believe that our proposed framework can account for the computationally efficient behavioural flexibilities observed in the empirical literature and can also serve as a solid theoretical foundation for future experimental work.

Some results of an empirical study on the willingness of university citizens to transfer knowledge

In 2021, a survey was conducted to map the willingness to share knowledge of several students studying at Hungarian universities. The authors of the study wanted to examine how students rate knowledge sharing in the organisation where they study, how open they are to share their knowledge, what informal forms of knowledge sharing exist in the educational institution for academic and non-academic knowledge sharing, with whom they are willing to share their knowledge the most and furthermore what are the most important knowledge transfer problems in their institutions. The respondents filled in a questionnaire during seminars and on the social media platforms. 552 university students participated in the survey. The results show that knowledge sharing is a fundamental feature of the institutional strategy and active knowledge transfer is practised in schools since this is one of the primary tasks of universities, however, it is less common to encourage or reward knowledge sharing. In the study women rated their institution's knowledge management practices stronger than men. Furthermore, the results showed that in institutions where knowledge sharing is an important part of the strategy, there is active knowledge sharing and the more distant the relationship between a student and a student is, the less open they are to sharing information. According to the findings, the closer the relationship is, the more sharing tends to occur within the participants of knowledge transfer. As the issue of knowledge management and knowledge sharing is an inevitable question in corporations’ and universities’ everyday life, preserving, developing, and transferring knowledge is a primary requirement for the organisations.

Unsupervised knowledge transfer for nonblind image deconvolution

• One of the few works for model adaption of nonblind image deconvolution. • Knowledge transfer to target domains done in an unsupervised learning manner. • Extensive experiments done on three applications of different fields. • Noticeable performance improvement achieved after model adaption. • Higher effectiveness than existing approaches. Nonblind image deconvolution restores the clear image from a blurred one under a known blur kernel, whose recent development has been boosted by supervised deep learning. Motivated by the inaccessibility of ground-truth images for supervised learning in many application domains, such as scientific imaging, this paper studies the unsupervised knowledge transfer problem for nonblind image deconvolution, which aims at adapting a deep model pre-trained on a source domain, to a ground-truth-scare target domain where image contents or blur kernels are distinct from that of the source domain. We propose to conduct the knowledge transfer regarding both images and kernels, by leveraging the model being adapted itself to generate pairs of a pseudo ground-truth image and a blurred image for self training. The proposed method neither accesses source-domain data, which avoids privacy issues, nor accesses target-domain ground-truths, which avoids ground-truth collection. Its effectiveness is demonstrated with the experiments on three deblurring tasks in different domains.

A Federated Transfer Learning Framework Based on Heterogeneous Domain Adaptation for Students’ Grades Classification

In the field of educational data mining, the classification of students’ grades is a subject that receives widespread attention. However, solving this problem based on machine learning algorithms and deep learning algorithms is usually limited by large datasets. The privacy problem of educational data platforms also limits the possibility of building an extensive dataset of students’ information and behavior by gathering small datasets and then carrying out the federated training model. Therefore, the balance of educational data and the inconsistency of feature distribution are the critical problems that need to be solved urgently in educational data mining. Federated learning technology enables multiple participants to continue machine learning and deep learning in protecting data privacy and meeting legal compliance requirements to solve the data island problem. However, these methods are only applicable to the data environment with common characteristics or common samples under the alliance. This results in domain transfer between nodes. Therefore, in this paper, we propose a framework based on federated transfer learning for student classification with privacy protection. This framework introduces the domain adaptation method and extends the domain adaptation to the constraint of federated learning. Through the feature extractor, this method matches the feature distribution of each party in the feature space. Then, labels and domains are classified on each side, the model is trained, and the target model is updated by gradient aggregation. The federated learning framework based on this method can effectively solve the federated transfer learning on heterogeneous datasets. We evaluated the performance of the proposed framework for student classification on the datasets of two courses. We simulated four scenarios according to different situations in reality. Then, the results of only source domain training, only target domain training, and federated migration training are compared. The experimental results show that the heterogeneous federated transfer framework based on domain adaptation can solve federated learning and knowledge transfer problems when there are little data at the data source and can be used for students’ grades classification in small datasets.

Cultural Semiotics in Strategy Conversation: Taking a Position on Strategy Through Knowledge Transfer

This study reviews knowledge management as a construct for analysing strategy discourse; identifying key strategy artefacts; and how they may be interpreted by key stakeholders engaged in strategy discourse. The importance of storytelling and narrative in the development of strategy is recognised. In developing the notion of strategy as a people orientated construct, this study provides a theoretical foundation for the determination of how stakeholders in strategy may take a position on strategy. This study defines an organisational model of the social interactions affecting knowledge transfer within organisations arising from problems of knowledge location, retention, and transfer. This ontological enquiry captures the epistemological characteristics of strategy artefacts interpreted by stakeholders in a senior management team. This paper goes on to underpin the value of a semiotic view as a diagnostic tool to determine the position that stakeholders take in the context of existing strategy discourse. From an etymological perspective this study posits a typology based upon a semiotic framework to help diagnose how stakeholders take a position based on their interpretation of key strategy artefacts; and to understand the nature of interpretation as a means of intervention by which the strategy narrative may be reshaped. What is of interest is how storytelling and narrative empowers individuals as they seek to disseminate and transfer knowledge from the past to shape the future. This study reveals the inflection that individuals may exert on knowledge artefacts; and the motivation of those who trade in knowledge assets, through storytelling and narrative, as players in the game of strategy search for coping strategies to adapt to the new reality. Ultimately this study provides new insight into the power of semiotics in the early stage; and constructivism in the later stages of the knowledge management continuum; and describes how participants in strategy adopt a position on strategy.

Evidence‐Informed Audit Standard Setting: Exploring Evidence Use and Knowledge Transfer*

ABSTRACTAcademics and practitioners agree that there are substantial barriers to systematically transferring audit research knowledge to policy‐makers. We adopt a design science approach to investigate the efficacy of employing a research synthesis, embedded in an interactive process with audit standard setters, to transfer such knowledge. We identify a standard‐setting issue that, we argue, is typical of the class of problems encountered by both parties when attempting knowledge transfer. Following design science prescriptions, we evaluate the pragmatic validity of our prototype research synthesis and its creation process. We provide initial evidence (“proof of concept”) that a research synthesis process can effectively and efficiently facilitate academic research knowledge transfer to inform audit standard setters' deliberations. Finally, we provide evidence that the problem of academic research knowledge transfer in accounting standard setting and evaluation continues. Our study and findings reflect how design science facilitates change in real‐world problem contexts through research‐based proofs of concept.

Connecting Biochemistry Knowledge to Patient Care in the Clinical Workplace: Senior Medical Students’ Perceptions about Facilitators and Barriers

Phenomenon : Medical students have difficulties applying knowledge about biomedical mechanisms learned before clerkships to patient care activities. Many studies frame this challenge as a problem of basic science knowledge transfer predominantly influenced by students’ individual cognitive processes. Social cognitive theory would support extending this framing to the interplay between the individual’s cognition, the environment, and their behaviors. This study investigates senior medical students’ experiences of biochemistry knowledge use during workplace learning and examines how their experiences were influenced by interactions with people and other elements of the clinical learning environment. Approach : The authors used a qualitative approach with a constructivist orientation. From September to November 2020 they conducted semi-structured interviews with 11 fourth-year medical students at one institution who had completed the pre-clerkship curriculum, core clinical clerkships, and the United States Medical Licensing Exam Step 1. The authors identified themes using thematic analysis. Findings : Participants reported that they infrequently used or connected to biochemistry knowledge in workplace patient care activities, yet all had examples of such connections that they found valuable to learning. Most participants felt the responsibility for making connections between biochemistry knowledge and activities in the clinical workplace should be shared between themselves and supervisors, but connections were often recognized and acted on only by the student. Connections that participants described prompted their effort to retrieve knowledge or fill a perceived learning gap. Participants identified multiple barriers and facilitators to connecting, including supervisors’ behaviors and perceived knowledge, and “patients seen” in clerkships. Participants also reported learning biochemistry during USMLE Step 1 study that did not connect to patient care activities, underscoring a perception of disconnect. Insights : This study identifies specific personal, social, and physical environmental elements that influence students’ perceived use of biochemistry during patient care activities. Though these findings may be most significant for biochemistry, they likely extend to other basic science disciplines. Students’ self-directed efforts to connect to their biochemistry knowledge could be augmented by increased social support from clinical supervisors, which in turn likely requires faculty development. Opportunities for connection could be enhanced by embedding into the environment instructional strategies or technologies that build on known authentic connections between biochemistry and “patients seen” in clerkships. These efforts could strengthen student learning, improve clinical supervisors’ self-efficacy, and better inform curriculum design.

Students Training Through Applied Activities at Department of Automation and Applied Informatics, University Politehnica Timișoara

AbstractThe paper is focused on the practical knowledge transfer problems in higher education engineering. The objective is to improve the knowledge transfer in order to facilitate rapid workplace integration. The curriculum and the practical training of students at University Politehnica Timisoara, Department of Automation and Applied Informatics are presented and discussed. The implication of companies in the students’ practical training is highlighted. A practical knowledge transfer system is proposed, based on logical distribution of the requirements for a single practical application, transversally, to the curriculum courses. The initial changes in the curriculum, introduced after the Bologna Declaration, are presented for the previously mentioned department. Ideas and questions were released which can be clarified, interpreted and criticized, providing a discussion base that can be benefic to the academic and industrial community. The presented changes were discussed with the economical zonal partners (companies: Continental Automotive, Hella, Nokia, Yazaki, Huf, Flextronics, etc.), being appreciated and sustained by the companies.

A novel framework of knowledge transfer system for construction projects based on knowledge graph and transfer learning

For construction enterprises, efficient knowledge sharing among projects not only effectively improves enterprise technology, level of management and competitiveness, but also promotes their sustainable development. Given the many benefits of knowledge management, enterprises have an urgent need for project knowledge sharing methods and tools. In this study, we build an automated and intelligent framework for a construction project knowledge transfer system based on knowledge graph and transfer learning. This framework aims to solve the problem of ineffective knowledge transfer that is encountered in the management of construction project knowledge sharing. First, to discover the relationship among knowledge and further obtain the relationships among projects, we design a domain knowledge graph ontology for construction projects and build an example. Then, based on this domain knowledge graph and combining construction data distance distribution with construction project knowledge background, we design a new construction project similarity measurement algorithm (PBG-MMD), which can guide the selection of the knowledge transfer source domain. Finally, a new transfer learning method is developed to automatically select the transfer source domain according to the domain context. The framework provides an effective answer for the problem of “what to transfer” in transfer learning and provides an effective solution to address the problem of “how to transfer” during knowledge transfer. Through the verification of practical case data, the proposed framework successfully realizes knowledge transfer among construction projects and provides an automated and intelligent knowledge sharing approach for construction enterprises.

Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection

In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this paper, we are interested in the problem of knowledge transfer between the artificially generated and existing handmade benchmark problems in the domain of continuous numerical optimization. That is, can an algorithm selection model trained purely on artificially generated problems correctly provide algorithm recommendations for existing handmade problems. We show that such a model produces low-quality results, and we also provide explanations about how the algorithm selection model works and show the differences between the problem data sets in order to explain the model’s performance.

Review of guidance papers on regression modeling in statistical series of medical journals.

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

The problem of knowledge transfer between the scientific sphere and the national economy of the country

Scientific developments created in numerous research organizations have not always found their application in the national economy of the country. The intensive development of a network of distributed situation centers is designed to positively influence the relations of various organizations and their joint activities. The article tells about the problems of the past, ways to overcome them, promising changes, personal experience of the scientific institute in the framework of work with administrative authorities.

Педагогика в созвездии наук: методологический диалог и «перенос знаний»

According to representatives of the exact sciences, as well as philosophers and methodologists of the humanities, the epistemological status of pedagogy today is quite low. There are no verified regulations for the empirical, theoretical and contextual substantiation of conclusions and methods for obtaining them; the voices of the theoreticians of pedagogical science about the structure and component composition of pedagogical theory sound polyphonic. The differences between theory and concept, long recognized in the science of science, do not find the right understanding and response in pedagogical works. Pedagogy, being an integral member of the community of human sciences, must today enter into an active and meaningful scientific dialogue with methodologists, science scholars, humanists, who represent a “strong” version of human knowledge. De­scribed by the famous Russian philosopher L.A. Mikeshina’s methodological dia­logues prompted us to take up the urgently required concretization of the author’s interesting conclusions and arguments in relation to the methodology of pedagogy which L.A. Mikeshina presents as transfer of terms (extrapolation). Based on the analysis of the cited book and many works of philosophers and teachers, we man­aged to build a triad of criteria for correct extrapolation of terms: a) address cor­rectness; correspondence of the pedagogical sound of the term transferred to ped­agogy to that which is accepted in donor science; b) the creative focus of the transferred term; c) detachment from repetition of the well-known method of noise stylistic reproduction. As one of the examples of the transfer of knowledge into pedagogy, the author considers the absorption by the science of education of the stylistics of related sciences based on scientific metaphors. The second part of the article is devoted to the theoretical construction of pedagogy. A methodologi­cal screen of a particular pedagogical theory is presented, white spots of the topic are revealed, the idea of theoretical constructions in pedagogy, reflected qualita­tively and quantitatively, is proposed. The connection between the problem of knowledge transfer (1) and the theoretical construction of pedagogy (2) is shown.

CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin.

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

Lipschitz Lifelong Reinforcement Learning

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with respect to the tasks space. These theoretical results lead us to a value-transfer method for Lifelong RL, which we use to build a PAC-MDP algorithm with improved convergence rate. Further, we show the method to experience no negative transfer with high probability. We illustrate the benefits of the method in Lifelong RL experiments.

Transferable common feature space mining for fault diagnosis with imbalanced data

Many deep transfer learning methods for fault diagnosis have been proposed in this decade. Some of the existing methods focus on addressing the problem of fault data scarcity and fault knowledge transfer across different domains with different number of samples. There is still much room to improve considering the best performance so far on imbalanced and transfer fault diagnosis. The existing researches apply synthetic data generation, weighted sample or cost and transfer learning techniques to solve the problem. However, the synthetic samples might not follow the true fault data distribution or exploit excessively over the available small data which could lead to model bias or overfitting. In addition, the value of the abundant normal condition data has not been well explored which may carry essential information for fault discrimination. To address these problems, a novel two stage transferable common feature space mining method for fault diagnosis is developed which is termed as Common Feature and Compare Net (CFCNet). The fault diagnosis task has been divided into two stages, common feature learning and fault category diagnosis. In the first stage, CFCNet trains a weakly supervised domain adaptive convolutional Autoencoder to learn the common features underlying multi-domain data, which makes efficient use of all the available data and is termed as Common Feature Net. In the second stage, the trained Common Feature Net and a Unique Feature Net is combined to construct a dual-channel feature extraction and comparison architecture. CFCNet could mine both the transferable common features and unique features of different faults. Based on a feature concatenation and similarity computation structure, CFCNet enables an efficient similarity estimation mechanism for fault diagnosis. Training strategy of few shot learning is adopted to train CFCNet which can balance the training progress instead of the imbalanced data. Extensive experiments have verified the superior performance of the proposed method.