| 1. |
- Chen, Chao, et al.
(författare)
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Effects of Salt Tracer Amount, Concentration and Kind on the Fluid Flow Behavior in a Hydrodynamic Model of Continuous Casting Tundish
- 2012
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Ingår i: Steel Research International. - : John Wiley & Sons. - 1611-3683 .- 1869-344X. ; 83:12, s. 1141-1151
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Tidskriftsartikel (refereegranskat)abstract
- The hydrodynamic modeling method that widely used to simulate the fluid flow was reconsidered and discussed in this paper. The effects of injected salt tracer amount, concentration and kind on the fluid flow behavior in a hydrodynamic model tundish were investigated. The results were compared with the mathematical modeling calculation results, that the tracer density effect was eliminated. The residence time distribution (RTD) curve of tracer introduced deviated to the left side of the calculated curve, besides the deviation was increased as dimensionless tracer amount (the ratio of tracer amount to hydrodynamic model tundish volume) increased from 0.202 × 10−3 to 1.008 × 10−3. The results of tracer concentration study were similar, namely the deviation was increased with concentration increased; on the other hand, the deformation of a “stair-shape” RTD curve occurred when tracer concentration was much lower (at dimensionless tracer amount of 0.168 × 10−3 with converting to saturated solution). Besides, the effect of tracer kind on the accuracy of hydrodynamic modeling was also studied; the measurements of KCl solution with lower density than that of NaCl solution exhibited more of accuracy. Finally, the optimized tracer in hydrodynamic model tundish of present work is saturated KCl solution with dimensionless tracer amount of 0.202 × 10−3.
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| 2. |
- Cheng, Chunling, et al.
(författare)
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High speed data streams clustering algorithm based on improved SS tree
- 2012
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Ingår i: Innovative Computing Information and Control Express Letters, Part B. - : ICIC International. - 2185-2766. ; 3:1, s. 207-212
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Tidskriftsartikel (refereegranskat)abstract
- In high speed networks, data streams show rapid, bursting and continuous characteristics, which makes real-time clustering of data streams be a difficulty. An improved SS tree structure is designed in this paper to keep the summarized information of data streams. Then, a high speed data streams clustering algorithm based on improved SS tree is proposed. In order to process the bursting streams in time, caching and piggyback mechanisms are used. The chaining buffers in the improved SS tree are used to temporarily store the data stream objects which cannot be processed immediately, and then the contents in buffers will be piggybacked together with the following data. To meet high arrival of data streams, two-phase clustering framework is adopted. Pre-aggregation phase produces local micro-clusters. After that, local micro-clusters take part in the global clustering phase based on the improved SS tree. Experimental results show that the proposed algorithm has better clustering accuracy in high-speed networks. The improved SS tree can effectively cluster high speed data streams and has a good applicability.
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| 3. |
- Cheng, Haibo, et al.
(författare)
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ANN based Interwell Connectivity Analysis in Cyber-Physical Petroleum Systems
- 2019
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Ingår i: Proceedings. - : IEEE. ; , s. 199-205
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (O&G) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.
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| 4. |
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| 5. |
- Cheng, Haibo, et al.
(författare)
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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM
- 2020
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:19
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Tidskriftsartikel (refereegranskat)abstract
- Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.
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| 6. |
- Cheng, Haibo, et al.
(författare)
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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data
- 2023
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Ingår i: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.
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| 7. |
- Cheng, Haibo, et al.
(författare)
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LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data
- 2020
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 67289-67299
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Tidskriftsartikel (refereegranskat)abstract
- In petroleum production system, interwell connectivity evaluation is a significant process to understand reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas field. In this paper, a novel long short-term memory (LSTM) neural network based global sensitivity analysis (GSA) method is proposed to analyse injector-producer relationship. LSTM neural network is employed to build up the mapping relationship between production wells and surrounding injection wells using the massive historical injection and production fluctuation data of a synthetic reservoir model. Next, the extended Fourier amplitude sensitivity test (EFAST) based GSA approach is utilized to evaluate interwell connectivity on the basis of the generated LSTM model. Finally, the presented LSTM based EFAST sensitivity analysis method is applied to a benchmark test and a synthetic reservoir model. Experimental results show that the proposed technique is an efficient method for estimating interwell connectivity.
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| 8. |
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| 9. |
- Cheng, Liu, et al.
(författare)
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EEG-CLNet : Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal
- 2023
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 72
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Tidskriftsartikel (refereegranskat)abstract
- Sleep-stage and apnea-hypopnea index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is both time- and resource-consuming. In this work, we propose a novel single electroencephalogram (EEG)-based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multitask collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intragroups via both local parameter sharing and cross-task knowledge distillation (CTKD), rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1%-5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, the EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for a lightweight wearable sleep monitoring system in the future.
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| 10. |
- Cheng, Xiaogang, et al.
(författare)
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A Contactless Measuring Method of Skin Temperature based on the Skin Sensitivity Index and Deep Learning
- 2019
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Ingår i: Applied Sciences. - Switzerland : MDPI. - 2076-3417 .- 1454-5101. ; 9:7
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Tidskriftsartikel (refereegranskat)abstract
- Featured Application The NISDL method proposed in this paper can be used for real time contactless measuring of human skin temperature, which reflects human body thermal comfort status and can be used for control HVAC devices. Abstract In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there has not been any satisfactory solution for thermal comfort measurements until now. In this paper, a contactless measuring method based on a skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named the skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, a two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.62% and 52.25% error values (NISDL method I, II) are scattered at (0 degrees C, 0.25 degrees C), and the same error intervals distribution of NIPST is 35.39%.
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