Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).
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http://dx.doi.org/10.3390/s23115069 | DOI Listing |
Sensors (Basel)
October 2024
Warsaw Institute of Sexology and Psychotherapy, 00-508 Warsaw, Poland.
This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals.
View Article and Find Full Text PDFMob DNA
October 2024
Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.
Sensors (Basel)
October 2024
College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China.
Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results.
View Article and Find Full Text PDFFront Psychiatry
September 2024
School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan.
Background: Peripheral inflammation is often associated with depressive disorders, and immunological biomarkers of depression remain a focus of investigation.
Methods: We performed RNA-seq analysis of RNA transcripts of human peripheral blood mononuclear cells from a case-control study including subjects with self-reported depression in the pre-symptomatic state of major depressive disorder and analyzed differentially expressed genes (DEGs) and the frequency of intron retention (IR) using rMATS.
Results: Among the statistically significant DEGs identified, the 651 upregulated DEGs were particularly enriched in the term "bacterial infection and phagocytosis", whereas the 820 downregulated DEGs were enriched in the terms "antigen presentation" and "T-cell proliferation and maturation".
Sensors (Basel)
July 2024
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics.
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