153 research outputs found
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries
Accurate segmentation of surgical instrument tip is an important task for
enabling downstream applications in robotic surgery, such as surgical skill
assessment, tool-tissue interaction and deformation modeling, as well as
surgical autonomy. However, this task is very challenging due to the small
sizes of surgical instrument tips, and significant variance of surgical scenes
across different procedures. Although much effort has been made on visual-based
methods, existing segmentation models still suffer from low robustness thus not
usable in practice. Fortunately, kinematics data from the robotic system can
provide reliable prior for instrument location, which is consistent regardless
of different surgery types. To make use of such multi-modal information, we
propose a novel visual-kinematics graph learning framework to accurately
segment the instrument tip given various surgical procedures. Specifically, a
graph learning framework is proposed to encode relational features of
instrument parts from both image and kinematics. Next, a cross-modal
contrastive loss is designed to incorporate robust geometric prior from
kinematics to image for tip segmentation. We have conducted experiments on a
private paired visual-kinematics dataset including multiple procedures, i.e.,
prostatectomy, total mesorectal excision, fundoplication and distal gastrectomy
on cadaver, and distal gastrectomy on porcine. The leave-one-procedure-out
cross validation demonstrated that our proposed multi-modal segmentation method
significantly outperformed current image-based state-of-the-art approaches,
exceeding averagely 11.2% on Dice.Comment: Accepted to IROS 202
Evaluation and evolution analysis of water ecosystem service value in the yangtze river delta region based on meta-analysis
Rapid economic development, industrialization and urbanization lead to environmental pollution and damage the stability of regional ecosystems. The Yangtze River Delta region is an economically developed region in China, faces the problems of water environment pollution and water ecosystem service degradation. Reasonable assessment of water ecosystem service value (ESV) is of great significance for grasping the status of regional water ecosystem services, improving water ecological environment, and realizing regional sustainable development. This study collects 119 research literature about China, including 156 observations to establish a value transfer database; specially builds a Meta-analysis model including the variables of climate conditions, environmental pollution and environmental protection, then assesses the waters ESV in the Yangtze River Delta using the model and analyzes the changes from 2009 to 2018. The study finds that the location, population density, the area of the site, average annual precipitation, literature characteristics, landscape characteristics, wastewater discharge, environmental protection expenditure, and wastewater treatment costs can affect the water ESV significantly. Based on the meta-analysis benefit transfer model to evaluate the water ESV in Yangtze River Delta region is RMB 177,126 yuan/ hha/year and the growth rate is 27.18%. The place with the highest value per unit area is Shanghai, and the total value in Jiangsu Province is the highest. Economic development, waste water discharge and wastewater treatment costs are the main reasons for the changes and differences in the value of water ecosystem services in the Yangtze River Delta region. The contribution of this study to the field of water ESV assessment is that the meta-analysis model includes a broader set of influencing variables, including landscape, population density, climate change and environmental protection. It provides a practical reference for water ESV assessment on the local scale and a scientific basis for water area management related to the development of water area and ecological compensation, as well as promote the sustainable development of water ecosystems
Frequency-diverse MIMO metasurface antenna for computational imaging with aperture rotation technique
Metasurface antennas have been proposed for computational imaging (CI) systems, which can reconstruct images without using mechanical scanning or large antenna arrays. In a CI system based on metasurface antennas, a variety of different radiation fields, which can be applied to sample the objects, are generated by exciting different frequency points in broadband. According to the compressed sensing theory, the imaging performance of the system is mainly limited by frequency-diversity radiation modes. In general, it is difficult to achieve rich radiation modes; therefore, a special design of metasurface aperture is required. In this paper, we propose a frequency-diversity MIMO metasurface antenna that consists of 2 × 2 sub-apertures with randomly distributed surface impedance. By employing the aperture rotation technique (ART) which rotates the MIMO metasurface antenna around the panel axis, the pseudo-randomness of the radiation fields is utilized. The diversity of the radiation field is improved on the premise of ensuring the relatively low complexity of the system. The ART significantly improves the measurement richness at the cost of increasing the measurement time. The performance of the proposed method is evaluated through simulations and experiments, suggesting that the proposed 2 × 2 MIMO metasurface antenna and the ART are effective to reconstruct high-quality images
EB-OCT: a potential strategy on early diagnosis and treatment for lung cancer
Lung cancer is the leading cause of cancer-related death in China and the world, mainly attributed to delayed diagnosis, given that currently available early screening strategies exhibit limited value. Endobronchial optical coherence tomography (EB-OCT) has the characteristics of non-invasiveness, accuracy, and repeatability. Importantly, the combination of EB-OCT with existing technologies represents a potential approach for early screening and diagnosis. In this review, we introduce the structure and strengths of EB-OCT. Furthermore, we provide a comprehensive overview of the application of EB-OCT on early screening and diagnosis of lung cancer from in vivo experiments to clinical studies, including differential diagnosis of airway lesions, early screening for lung cancer, lung nodules, lymph node biopsy and localization and palliative treatment of lung cancer. Moreover, the bottlenecks and difficulties in developing and popularizing EB-OCT for diagnosis and treatment during clinical practice are analyzed. The characteristics of OCT images of normal and cancerous lung tissues were in good agreement with the results of pathology, which could be used to judge the nature of lung lesions in real time. In addition, EB-OCT can be used as an assistant to biopsy of pulmonary nodules and improve the success rate of biopsy. EB-OCT also plays an auxiliary role in the treatment of lung cancer. In conclusion, EB-OCT is non-invasive, safe and accurate in real-time. It is of great significance in the diagnosis of lung cancer and suitable for clinical application and is expected to become an important diagnostic method for lung cancer in the future
Remaining Useful Life Modelling with an Escalator Health Condition Analytic System
The refurbishment of an escalator is usually linked with its design life as
recommended by the manufacturer. However, the actual useful life of an
escalator should be determined by its operating condition which is affected by
the runtime, workload, maintenance quality, vibration, etc., rather than age
only. The objective of this project is to develop a comprehensive health
condition analytic system for escalators to support refurbishment decisions.
The analytic system consists of four parts: 1) online data gathering and
processing; 2) a dashboard for condition monitoring; 3) a health index model;
and 4) remaining useful life prediction. The results can be used for a)
predicting the remaining useful life of the escalators, in order to support
asset replacement planning and b) monitoring the real-time condition of
escalators; including alerts when vibration exceeds the threshold and signal
diagnosis, giving an indication of possible root cause (components) of the
alert signal.Comment: 14 pages, 12 figures, 7 table
An Algorithm for Modelling Escalator Fixed Loss Energy for PHM and sustainable energy usage
Prognostic Health Management (PHM) is designed to assess and monitor the
health status of systems, anticipate the onset of potential failure, and
prevent unplanned downtime. In recent decades, collecting massive amounts of
real-time sensor data enabled condition monitoring (CM) and consequently,
detection of abnormalities to support maintenance decision-making.
Additionally, the utilization of PHM techniques can support energy
sustainability efforts by optimizing energy usage and identifying opportunities
for energy-saving measures. Escalators are efficient machines for transporting
people and goods, and measuring energy consumption in time can facilitate PHM
of escalators. Fixed loss energy, or no-load energy, of escalators denotes the
energy consumption by an unloaded escalator. Fixed loss energy varies over time
indicating varying operating conditions. In this paper, we propose to use
escalators' fixed loss energy for PHM. We propose an approach to compute daily
fixed loss energy based on energy consumption sensor data. The proposed
approach is validated using a set of experimental data. The advantages and
disadvantages of each approach are also presented, and recommendations are
given. Finally, to illustrate PHM, we set up an EWMA chart for monitoring the
fixed loss over time and demonstrate the potential in reducing energy costs
associated with escalator operation
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