A large number of publicly available documents, including patent documents and journal articles, can provide designers with creative stimuli, which could facilitate product innovation and collaborative design. As an important tacit knowledge, the acq...
Data-driven design is a process to reuse data sources and provide valuable information to provoke creative ideas in the stages of design. However, existing semantic networks for design creativity are built on data sources restricted to technological...
Not all knowledge is equally important in teaching and learning, yet identifying important knowledge remains a fundamental challenge in curriculum design. Traditional methods rely on expert judgment and lack systematic, reproducible criteria. This st...
Bridges are typically designed by reputable, specialized engineering and design companies with years of experience. In these firms, experienced engineers share and pass on their knowledge to younger colleagues. However, when these experts retire, som...
Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market chan...
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a la...
This paper provides a critical examination of the taken for granted nature of the codes/guidelines used towards the creation of designed spaces, their social relations with designers, and their agency in designing for people with disabilities. We con...
Knowledge management has become increasingly important in the era of knowledge economy. This study explores what is an optimal knowledge network for more efficient knowledge diffusion among strategic partners in order to provide insights on sustainab...
Children are often the most disadvantaged cohort during miserable situations of natural disaster, economic crisis, and environmental degradation. Meanwhile, children’s play is increasingly controlled, costly, and standardized with engineered st...
The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we dis...
A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge coll...
In open innovation platforms, users learn external knowledge through network interaction, and their position in the interactive network has an impact on the user’s sustainable knowledge contribution. Due to the gap in knowledge level, users&rsq...
Micromachining techniques have been applied widely to many industrial sectors, including aerospace, automotive, and precision instruments. However, due to their high-precision machining requirements, and the knowledge-intensive characteristics of min...
A fundamental goal of education is to inspire and instill deep, meaningful, and long-lasting conceptual change within the knowledge landscapes of students. This commentary posits that the tools of network science could be useful in helping educators...
This article aims to build a network for the exchange of knowledge between the government and production, community and university sectors for sustainable local development. To achieve this, the authors relied on the concepts of sustainable local dev...
Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of...
Neural network models for hyperspectral images classification are complex and therefore difficult to deploy directly onto mobile platforms. Neural network model compression methods can effectively optimize the storage space and inference time of the...
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...
This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Now...
Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensembl...
Scientific social networking sites like ResearchGate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to he...
Rainwater harvesting attracts growing interest from the field of municipal planning. When considering a rainwater harvesting system as a design object, questions include whether the system is designed for a single property or for a local water networ...
Knowledge distillation has been proven to be an important component in unsupervised image anomaly detection and localization. However, a significant challenge remains in how the student network can extract more accurate feature information from the t...
Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so conseq...
Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security i...
As an emerging product innovation model, open-source innovation has undergone rapid development in recent years. The sustainability and stability of the open-source product community (OSPC) is crucial for product innovation, and the effect that users...
The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfit...
As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher–student model, the poor quality of teacher network f...
Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and superv...
In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation...
Deep Learning (DL) networks are recent revolutionary developments in artificial intelligence research. Typical networks are stacked by groups of layers that are further composed of many convolutional kernels or neurons. In network design, many hyper-...
The process of agricultural robot design is a complex system requiring the cooperation and integration of agricultural, machinery, automation, and information technology. These demands create great challenges for the innovative design of agricultural...
The identification of microRNA (miRNA) biomarkers is crucial in advancing disease research and improving diagnostic precision. Network-based analysis methods are powerful for identifying disease-related biomarkers. However, it is a challenge to gener...
One of the greatest engineering feats in history is the construction of tunnels, and the management of tunnel safety depends heavily on the detection of tunnel defects. However, the real-time, portability, and accuracy issues with the present tunnel...
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizatio...
Future Wireless Sensor Networks (WSNs) will be ubiquitous, large-scale networks interconnected with the existing IP infrastructure. Autonomic functionalities have to be designed in order to reduce the complexity of their operation and management, and...
Industrial defect detection plays an important role in smart manufacturing and is widely used in various scenarios such as smart inspection and product quality control. Currently, although utilizing a framework for knowledge distillation to identify...
Model based definition (MBD) is the core of product digital research and development. The extension of the scope and content of the product model is an important way to improve the application ability of MBD methodology. In view of the demand of the...
This paper presents a measurement-based stability analysis of commercially available single-phase inverters in public low-voltage networks. In practice, manufacturers typically do not disclose the parameters of the inverter design, although interacti...
To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph...
The aim of this article is to verify what the relations are between entrepreneurial orientation and knowledge utilization in the internationalization of Polish firms. The article distinguishes four types of knowledge: market knowledge, network knowle...
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resourc...
In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification me...
Pre-trained models have played important roles in many tasks, such as domain adaptation and out-of-distribution generalization, by transferring matured knowledge. In this paper, we study Neural Architecture Search (NAS) in the feature space level and...
With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models, and it...
The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dile...
The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legit...
Most current pose estimation methods have a high resource cost that makes them unusable in some resource-limited devices. To address this problem, we propose an ultra-lightweight end-to-end pose distillation network, which applies some helpful techni...
In recent years, the methods based on convolutional neural networks have achieved significant progress in hyperspectral image super-resolution. However, existing methods still face two key challenges: (1) they fail to fully extract edge detail inform...
Existing full-order terminal sliding mode (FOTSM) control methods often require a priori knowledge of the system model. To tackle this problem, two novel neural-network-based FOTSM control methods were proposed. The first one was model based but did...
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