Ballast systems and ballast water dynamic allocation between ballast tanks are very important for ensuring the offshore operation efficiency and safety of the revolving floating crane (RFC). Its modeling and solving have multiple difficulties such as...
Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a...
Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning acro...
The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of t...
Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serv...
Large language models and retrieval-augmented generation (RAG) are widely applied in knowledge question-answering tasks. However, in knowledge-intensive domains such as agriculture, hallucination and insufficient retrieval accuracy remain challenging...
This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is ap...
1 December 2021
Although the use of artificial intelligence (AI) in healthcare is still in its early stages, it is important to understand the factors influencing its adoption. Using a qualitative multi-case study of three hospitals in China, we explored the researc...
To address the technical challenge of synergistic optimization between lightweight design and structural performance for mechanical locking drill pipes of rotary drilling rigs, this study takes such drill pipes as the research object. Seven typical o...
Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algori...
The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by...
Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of...
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for...
Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Vi...
Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land...
Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much i...
The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learn...
With the development of deep learning and sensors and sensor collection methods, computer vision inspection technology has developed rapidly. The deep-learning-based classification algorithm requires the acquisition of a model with superior generaliz...
As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be...
A reliable energy-efficient multi-level routing algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy, number of the neighbors and centrality of each node for cluster formation, which is critical for...
In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-b...
With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowled...
The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart manufacturing with capabilities for real-time monitoring, qualit...
The article presents the results of research on the importance of managing the energy transition in various economic sectors. The barriers and benefits of implementing a knowledge management and environmental management system in enterprises were con...
In this paper, the distributed unrelated parallel machines scheduling problem (DUPMSP) is studied and a hybrid imperialist competitive algorithm (HICA) is proposed to minimize total tardiness. All empires were categorized into three types: the strong...
Published gaining-sharing knowledge (GSK) variants adapt scalar parameters and donor/selection policy to a single operating point. Dimension-Tiered GSK (DT-GSK) selects its configuration by dimension: an adaptive scaffold serves every tier, a determi...
In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which...
We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method...
With the rapid evolution of next-generation mobile communications, semantic communication has emerged as an intelligent communication paradigm capable of surpassing the Shannon limit. A fundamental prerequisite for this paradigm is the synchronizatio...
Federated learning is a knowledge transmission and training process that occurs in turn between user models on edge devices and the training model in the central server. Due to privacy policies and concerns and heterogeneous data, this is a widesprea...
The no idle permutation flow shop scheduling problem (NIPFSP) is a popular NP-hard combinatorial optimization problem, which exists in several real world production processes. This study proposes a novel hybrid estimation of the distribution algorith...
Vertical integration, also known as make-or-buy, defines whether activities are conducted by company or provided by external parties. There are different models to support decision making for vertical integration in the literature. However, they igno...
This paper presents a hybrid knowledge-based expert system (KBES) designed to predict crack incubation and fatigue life in gear design, serving as both a research tool and an educational resource. While crack growth and initiation are well understood...
The Static Economic Load Dispatch (SELD) problem is a paramount optimization challenge in power engineering that seeks to optimize the allocation of power between generating units to meet imposed constraints while minimizing energy requirements. Rece...
Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advant...
Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), arti...
A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowle...
Knowledge-based NMR spectral prediction relies on the correlations between substructures and sub-spectra. To extract the correlations, a systematic substructure measurement has been developed to classify substructures according to their chemical shif...
Knowledge representation and manipulation in knowledge-based systems typically rely on ontologies. The aim of this work is to provide a novel weak unification-based method and an automatic tool for OWL ontology merging to ensure well-coordinated task...
In an uncertain situation, data may present in continuous form or discrete form. We have various techniques to deal with continuous data in a realistic situation. However, when data are in discrete form, the existing techniques are inadequate to deal...
Process innovation plays a vital role in the manufacture realization of increasingly complex new products, especially in the context of sustainable development and cleaner production. Knowledge-based innovation design can inspire designers’ creative...
Aiming at early detection of subsurface cracks induced by contact fatigue in rotating machinery, the knowledge-based data analysis algorithm is proposed for health condition monitoring through the analysis of acoustic emission (AE) time series. A rob...
Multimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problem...
Traditional library systems are gradually being replaced by digital libraries. Digital libraries are developing from simple database-based storage and retrieval to knowledge-based implementation. The fuzzy C-means (FCM) clustering algorithm is an exa...
The development of clinical decision support systems for treatment planning and monitoring faces significant challenges, such as high labor intensity, integration complexities, lack of universality, and insufficient consideration of individual patien...
Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rationa...
Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devic...
Fuzzy Petri net (FPN) is widely used to repre sent, model and analyse knowledge-based systems (KBSs). Meanwhile, a reachability tree is an important tool to fully represent the flow relationship of FPN and is widely applied to implement inference in...
High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC...
Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm....
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