Energy storage systems are a fundamental part of any efficient energy scheme. Because of this, different storage techniques may be adopted, depending on both the type of source and the characteristics of the source. In this investigation, present contribution highlights current developments on compressed air storage systems (CAES). The investigation explores both the operational mode of the system, and the health & safety issues regarding the storage systems for energy. The investigation also includes a detailed conclusion, which summarises the vast significance of novel energy storage technology. The investigation thoroughly evaluates the various types of compressed air energy storage systems, along with the advantages and disadvantages of each type. Different expanders ideal for various different compressed air energy storage systems are also analysed. Design of salt caverns and other underground and above compressed air storage systems were also discussed in terms of advantages and disadvantages.

Compressed air energy storage systems: Components and operating parameters – A review

In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is widely used in various industry fields, where the structural integrity of specimens is of vital importance. The estimation of defect size in specimen from the MFL measurements is a key and difficult problem. The traditional methods have low precision because feature extraction procedure relies on prior knowledge and the ability of designer. Inspired by the idea of convolutional neural network (CNN), a novel visual transformation CNN (VT-CNN) is proposed in this paper to overcome the limitation of traditional method in a feature extraction procedure. By adding a visual transformation layer according to the characteristics of the MFL measurements, the VT-CNN can distinguish the defect feature with different sizes more accurately. Moreover, since the VT-CNN method is designed based on the deep learning theory, more industrial big data with accurate label should be used to train the network. Due to the difficulty of making real industrial big data, a novel mesher magnetic dipole model is designed to simulate this industrial process. A large simulated MFL measurement of irregular defects produced by this model can increase the number of training samples and improve the robustness of the network. Experiments to estimate natural corrosion defects on real industrial pipelines are performed to validate the proposed framework. The experimental results are illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network

One of the largest movers of the world economy is the oil and gas industry. The industry generates billions of barrels of oil to match more than half the world’s energy demands. Production of energ...

https://journals.sagepub.com/doi/pdf/10.1177/1475921719837718

Inspection and monitoring systems subsea pipelines: A review paper:

Electrical energy storage has been recognised as an underpinning technology to meet the challenges in the power network arisen from the rapidly increasing penetration of renewable energy. Compressed Air Energy Storage (CAES) has gained substantial worldwide attention in recent years due to its low-cost and high-reliability in the large-scale energy storage systems. Air expander is one of the key components in a CAES system because its operational characteristics determine the power conversion efficiency and the power generation during the discharge period. The performance of the expander contributes heavily to the round trip efficiency of the whole system. This paper presents an up-to-date review of the CAES technology, and methods for modelling and selecting expanders for CAES systems. The focuses of selecting the appropriate expansion machines are identifying and analysing the characteristics of both CAES systems and expansion machines, and finding the matched expanders for the CAES system formulation (i.e. diabatic, adiabatic and isothermal CAES) and operational conditions (i.e. air pressure, temperature and flow rate). After all, recommendations and guidelines in selecting appropriate expanders and expansion stage numbers are formulated and discussed; this laid a step stone for choosing suitable expansion machines to achieve an overall CAES system high efficiency.

Optimal selection of air expansion machine in compressed air energy storage : a review

Impacts of compressed air energy storage plant on an electricity market with a large renewable energy portfolio

Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.

https://www.mdpi.com/1424-8220/15/12/29845/pdf

Theory and Application of Magnetic Flux Leakage Pipeline Detection

Cough is a common symptom of many respiratory diseases. Many medical literatures underline that a system for the automatic, objective, and reliable detection of cough events is important and very promising to detect pathology severity in chronic cough disease. In order to track the development status of an audio-based cough monitoring system, we briefly described the history of objective cough detection and then illustrated the cough sound generating principle. The probable endpoints of cough clinical studies, including cough frequency, intensity of coughing, and acoustic properties of cough sound, were analyzed in this paper. Finally, we introduce some successful cough monitoring equipment and their recognition algorithm in detail. It can be obtained that, firstly, acoustic variability of cough sounds within and between individuals makes it difficult to assess the intensity of coughing. Furthermore, now great progress in audio-based cough detection is being made. Moreover, accurate portable objective monitoring systems will be available and widely used in home care and clinical trials in the near future.

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Theory and application of audio-based assessment of cough

Motivation Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/detection-of-sputum-by-interpreting-the-time-frequency-33thdf27hr.pdf

Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.

In this paper, a method of characteristic extraction and recognition on lung sounds is given. Wavelet de-noised method is adopted to reduce noise of collected lung sounds and extract wavelet characteristic coefficients of the de-noised lung sounds by wavelet decomposition. Considering the problem that lung sounds characteristic vectors are of high dimensions after wavelet decomposition and reconstruction, a new method is proposed to transform the characteristic vectors from reconstructed signals into reconstructed signal energy. In addition, we use linear discriminant analysis (LDA) to reduce the dimension of characteristic vectors for comparison in order to obtain a more efficient way for recognition. Finally, we use BP neural network to carry out lung sounds recognition where comparatively high-dimensional characteristic vectors and low- dimensional vectors are set as input and lung sounds categories as output with a recognition accuracy of 82.5% and 92.5%.

/pdf/a-lung-sound-category-recognition-method-based-on-wavelet-10yv0g31bu.pdf

Yan Shi

Papers

Theory and Application of Magnetic Flux Leakage Pipeline Detection

Theory and application of audio-based assessment of cough

Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques.

A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network.

Energy conversion characteristics of a hydropneumatic transformer in a sustainable-energy vehicle