As the most commonly used attack strategy by Botnets, the Domain Generation Algorithm (DGA) has strong invisibility and variability. Using deep learning models to detect different families of DGA domain names can improve the network defense ability a...
Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies. This paper introduces Adaptive S...
This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation...
In the field of management science and business administration, the case method is gaining ground in research and in teaching. Case studies support on the one hand exploratory research and on the other hand problem-based teaching. However, we find th...
In this paper, a novel bearing faulty prediction method based on federated transfer learning and knowledge distillation is proposed with three stages: (1) a “signal to image” conversion method based on the continuous wavelet transform is...
The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mi...
With the continuous improvement in oral health awareness, people’s demand for oral health diagnosis has also increased. Dental object detection is a key step in automated dental diagnosis; however, because of the particularity of medical data,...
Although contact-based vibration signal methods for mechanical equipment fault diagnosis demonstrate superior performance, their practical deployment faces significant limitations. In contrast, acoustic signals offer notable deployment flexibility du...
In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG)...
Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep lear...
Conventional independently trained models for distributed photovoltaic (DPV) power forecasting at the feeder, main-transformer, and regional-grid levels do not explicitly reuse spatiotemporal representations across the distribution-network hierarchy....
The growing popularity of social media data brings questions about its accuracy and usefulness for a wide array of cultural heritage projects, often lacking data sources crucial for better planning and implementation. In this paper, we are studying t...
The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert la...
The goal of this study was to investigate the impact of introducing methods used in other countries on student success. The results suggested that the introduction of specific practices to improve entrepreneurial competence would be more effective fo...
After performing a systematic review of the literature, it was noticeable that the discussion regarding Green Supply Chain Management (GSCM) along with Knowledge and Technology Transfer (KTT) and Innovation is scarce. To address this issue, the objec...
Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image class...
A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that...
Effects of traditional training methods are not obvious when transferring competent knowledge to construction workers to allow them to deal with new technology and intelligent equipment. The purpose of this study was to explore knowledge transfer pat...
Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer...
The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, problems such as knowledge loss still occur during...
Efficient emergency load transfer is crucial for ensuring the power system’s safe operation and reliable power supply. However, traditional load transfer methods that rely on human experience have limitations, such as slow response times and lo...
Background: Knowledge transfer has been an important aspect in supply chain systems; however, there are many factors that affect the impact of transfer effectiveness. In previous studies, less attention has been given to the process of knowledge tran...
This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement pro...
Multi-agent reinforcement learning (MARL) has proven to be effective and promising in team collaboration tasks. Knowledge transfer in MARL has also received increasing attention. Compared to knowledge transfer in single-agent tasks, knowledge transfe...
Knowledge transfer has become very popular in recent years, and it is either based on a one-way transfer method used with knowledge distillation or based on a two-way knowledge transfer implemented by deep mutual learning, while both of them adopt a...
Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to ac...
Domain adaptation has been used to transfer the knowledge from the source domain to the target domain where training data is insufficient in the target domain; thus, it can overcome the data shortage problem of power load forecasting effectively. Ins...
In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving...
Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based...
Remote-sensing image dehazing (RSID) is crucial for applications such as military surveillance and disaster assessment. However, current methods often rely on complex network architectures, compromising computational efficiency and scalability. Furth...
Knowledge and practice of medicinal plant use is embedded in the Maasai culture. However, it is not known how that knowledge and practices are acquired by children and transferred across generations. We assessed children’s knowledge of medicina...
Multi-channel electroencephalography (EEG) provides spatial information for emotion recognition but requires more complex signal acquisition than single-channel EEG. This study proposes a multi-channel-to-single-channel knowledge transfer framework t...
Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses o...
In this article, a novel cross-domain knowledge transfer method is implemented to optimize the tradeoff between energy consumption and information freshness for all pieces of equipment powered by heterogeneous energy sources within smart factory. Thr...
Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant p...
Community-based participatory research initiatives such as “hackAir”, “luftdaten.info”, “senseBox”, “CAPTOR”, “CurieuzeNeuzen Vlaanderen”, “communityAQ”, and “Healthy Air, Healthier Children” campaign among many others for mitigating short-lived clim...
This study focuses on exploring the nature of inter-organizational learning and the types of knowledge that are transferred/shared between foreign multinational companies (MNCs) and their local partner firms within international interfirm alliances i...
Background: The dynamics of supply chain networks have changed due to increasing complexities. Global expansions and knowledge transfer in supply chain networks bring efficiency and effectiveness to companies. However, the probability of supply chain...
Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment...
Real-world datasets often follow a long-tailed distribution, where a few majority (head) classes contain a large number of samples, while many minority (tail) classes contain significantly fewer samples. This imbalance creates an information disparit...
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incompl...
Low-resolution face recognition poses significant challenges in real-world applications, particularly for deployment on resource-constrained mobile terminals where both accuracy and efficiency are critical. While knowledge distillation has emerged as...
Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper,...
This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant pre...
The article deals with the design of virtual reality (VR) interactive training as a teaching method and its effect on knowledge transfer and retention of students. The first part presents the methodology of the VR interactive training design. The sec...
(1) Background: This paper examines the relationships between generational handover protocols, knowledge transfer behavior, and key organizational outcomes. (2) Methods: A quantitative design was applied, using non-parametric tests and partial least...
Transfer learning enables the leveraging of knowledge acquired from other piezoelectric actuators (PEAs) to facilitate the positioning control of a target PEA. However, blind knowledge transfer from datasets irrelevant to the target PEA often leads t...
A convolutional neural network (CNN)-based inter-floor noise source type classifier and locator with input from a single microphone was proposed in [Appl. Sci. 9, 3735 (2019)] and validated in a campus building experiment. In this study, the followin...
The present study aims to investigate how commercialisation and knowledge transfer between the SMEs of the tourism sector and the higher education institutions (HEIs) are made, as well as to find out whether the SMEs of the tourism sector are part of...
As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the numb...
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