A novel method, named as modeling indicator determined (MID) method, based on two model evaluation parameters i.e., root mean square error of prediction (RMSEP) and ratio performance deviation (RPD), is proposed to employ high-level fusion for quantitative analysis. The two MID methods of root mean square error of prediction weighted (RMSEPW) method and ratio performance deviation weighted (RPDW) method are put forward on the basis of the model evaluation indicators from the individual models. Performance of RMSEPW method and RPDW method are evaluated in terms of the predictive ability of root mean square error of prediction for fusion (RMSEP) through the fused models. The two MID methods are applied to UV-visible (UV-vis), near infrared (NIR) and mid-infrared (MIR) spectral data of active ingredient in pesticide, and gas chromatography-mass spectrometer (GC-MS) and NIR spectral data of n-heptane in chemical complex for high-level fusion. Moreover, the results are compared with the individual methods. As a result, the overall results show that the two MID methods are promising with significant improvement of predictive performance for high-level fusion when executing quantitative analysis.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.saa.2019.04.022 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Study on the Transformation Process of Thyroid Fine-Needle Aspiration Liquid-Based Cytology to Whole-Slide Image.
Cytopathology
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Objective: Analyse and summarise the reasons for failure in the digital acquisition of thyroid liquid-based cytology (LBC) slides and the technical challenges, and explore methods to obtain reliable and reproducible whole digital slide images for clinical thyroid cytology.
Method: Use the glass slide scanning imaging system to acquire whole-slide image (WSI) of thyroid LBC in sdpc format through different. Statistical analysis was conducted on the different acquisition methods, the quality of the glass slides, clinical and pathological characteristics of the case, TBSRTC grading and the quality of WSI.
Cascaded Feature Fusion Grasping Network for Real-Time Robotic Systems.
Sensors (Basel)
December 2024
College of Engineering, Huaqiao University, Quanzhou 362021, China.
Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels.
View Article and Find Full Text PDFInfrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network.
Sensors (Basel)
December 2024
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features.
View Article and Find Full Text PDFSensor Fusion Method for Object Detection and Distance Estimation in Assisted Driving Applications.
Sensors (Basel)
December 2024
Center for Automotive Research and Sustainable Mobility (CARS@PoliTO), Politecnico di Torino, 10129 Torino, Italy.
The fusion of multiple sensors' data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors' attributes to plan the vehicle's speed according to real-time road occupation and distance from obstacles.
View Article and Find Full Text PDFIntegrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization.
Sensors (Basel)
December 2024
Department of Computer Science, Louisiana Tech University, 201 Mayfield Ave, Ruston, LA 71272, USA.
Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional 'olfaction-only' OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!