What are the most recent advances in the use of ultrasound to detect breast cancer?4 answersBreast ultrasound has seen significant technological advancements, including new imaging modalities, high-frequency transducers, elastography, contrast-enhanced US, and automated US. Computer-aided diagnostic techniques using ultrasound video sequences have been developed, such as the contrastive learning–guided multi-meta attention network (CLMAN), which outperforms existing methods for breast lesion diagnosis. Improved Unet and boundary-oriented network (BO-Net) algorithms have been proposed for ultrasound image segmentation of breast tumors, achieving improved accuracy and edge detail segmentation. Deep vision transformers, such as BEiT, CaiT, Swin, XCiT, and Vis-Former, have been adapted to extract robust radiomics for classifying breast tumors as benign or malignant, showing promising results for the detection of malignant breast tumors on ultrasound images.
How can multi-channel recording and beamforming be used to improve sound detection in noisy environments?5 answersMulti-channel recording and beamforming techniques can be used to improve sound detection in noisy environments. By using multiple microphones arranged in an array, the signals from different directions can be captured simultaneously, allowing for better separation of sound sources and suppression of background noise. Beamforming algorithms, such as steered-response power (SRP) and minimum-variance distortionless response (MVDR), can then be applied to enhance the desired sound source and attenuate interfering noise. These techniques improve the accuracy of direction of arrival (DOA) estimation and provide high positioning accuracy and strong spatial directivity. Additionally, deep learning-based approaches can be used to estimate factors of the beamformer and enhance the beamformed signal, further improving the perceptual quality of the detected sound. Overall, multi-channel recording and beamforming methods offer effective solutions for sound detection in noisy environments, enabling better localization and separation of sound sources.
How can we design a pipeline isolation plug-in for VR?5 answersA pipeline isolation plug-in for VR can be designed by incorporating three separate plug modules: a tug module, an isolation module, and a drag module. The plug can be transported along the pipe and activated by a fluid pressure differential in the pipe. The plug should have a quick connecting interface and a pipeline joint to ensure convenient and reliable connection with the pipeline. The interface should have an enlarged tube diameter and a circular clamp spring to securely seal the inserted pipeline. Additionally, pressure relief holes can be incorporated in the pipeline internal plug to easily connect with the threads of the pipe opening and avoid installation issues due to pressure in the pipeline. The design should also consider the use of an improved high-viscosity medium pipeline isolation device, which includes a liquid inlet pipe, a pressure gauge, and a conveying pipeline to detect leakage and minimize production impact.
How is sound sensor used in pipeline leakage detection?5 answersSound sensors are used in pipeline leakage detection by capturing acoustic emission signals generated by the leaks. These sensors can detect small gas pipeline leakages by measuring changes in low-frequency acoustic pressure. Different statistical measures and features extracted from the acoustic emission signals, such as kurtosis, skewness, mean value, and frequency spectrum, are used to train machine learning models for leak detection. The performance of the acoustic methods based on cross-correlation for pipeline leakage detection can be improved by using a secondary phase transform (PHAT) cross-correlation method. The proposed method calculates the secondary cross-correlation function and uses peak search to estimate time delay between sensor signals, resulting in accurate leakage detection. Additionally, a non-invasive online method for pipeline micro leakage detection and localization has been proposed, which successfully detects and locates micro leakages using sound sensors.
What are the different methods for monitoring pipeline integrity?5 answersDifferent methods for monitoring pipeline integrity include nonintrusive sensor systems, fiber optic sensing, and software systems. Nonintrusive sensor systems involve installing sensors on the exterior of pipelines to measure pressure and detect damages in real-time. Fiber optic sensing utilizes fiber optic cables installed inside pipelines to monitor various parameters such as temperature, corrosion, strain, and vibrations. Software systems model the linear part of a pipeline and include integrity monitoring systems for all objects within the pipeline, using sensors, logging systems, mathematical models, and algorithms for integrity checking. Another method involves using a measurement device to measure the electrical impedance of armor wires in the pipeline, with variations indicating defects. Additionally, a system and method for monitoring vessel coating layers can also be used to detect breaches and changes in coating condition.
What are the pipeline leakage detection technique?2 answersPipeline leakage detection techniques include infrared thermography (IRT) combined with Faster Region-based Convolutional Neural Network (Faster R-CNN), machine learning-based platform using acoustic emission (AE) sensor channel information, improved uniform-phase local characteristic-scale decomposition (IUPLCD) combined with grid search algorithm-optimized twin-bounded support vector machine (GS-TBSVM), secondary phase transform (PHAT) cross-correlation method, and parameter-optimized recurrent attention network (PRAN) with long short-term memory (LSTM) network and particle swarm optimization (PSO) algorithm. These techniques utilize different approaches such as image analysis, statistical measures, signal decomposition, and deep learning to detect pipeline leakages. They aim to improve the accuracy and efficiency of leakage detection, reduce false alarms, and provide reliable results for the implementation of leakage detection systems.