Do We Really Need Deep Learning?: A Study on Play Identification using SEM Images

Deep learning has become an integral part of image classification and segmentation, especially with the use of convolutional neural networks (CNN) and their variants. Although computationally expensive and time-consuming, there are several promising applications to classify or segment SEM images, images of core and thin sections. But we have not really questioned the need for really deep networks in these applications? Can shallower networks be competitive in relation to deeper networks? Can a shallower network with a wider diversity of convolutional filters (breadth) do better than a deeper network? What image resolution and filter complexity do we need to achieve a high degree of accurate classification? In this thesis, I assess image classification using over 8000 SEM images acquired from 22 different unconventional plays to answer the questions posed above and provide guidelines to select an optimal depth and breadth for image classification. I evaluate several different CNN architectures systematically by changing the breadth (the number of filters within each layer) or the depth of the network (the number of layers) to relate classification accuracy and the complexity of the CNN. I also test the performance of the different CNN’s against different image resolutions to determine if there is a specific field-of-view that is necessary to obtain satisfactory play classification. For all image resolutions considered, surprisingly, the simplest and shallowest one-layer model performs remarkably well with even 22 different classes (plays) to identify. Despite the simplicity of the network, I achieve over 80% accuracy in play identification (with correspondingly high recall and precision). A moderate increase in depth to 2 layers advances the accuracy to beyond 90%, even with a modest number of filters. Deeper networks that lack filter width perform poorly, indicating the significance of filter diversity in each of the convolutional layers of a CNN. The results from this study show that deeper networks are probably not necessary for image classification of SEM images/core or thin-section images. The microstructural features within the samples probably necessitate a wider diversity of filters. Finally, although several studies have relied on transfer learning of ‘published’ or open-source CNNs for play identification and image segmentation, this study shows that the level of complexity required is far less, making training more efficient and reducing the likelihood of overfitting

A PC-Kriging-HDMR integrated with an adaptive sequential sampling strategy for high-dimensional approximate modeling

High-dimensional complex multi-parameter problems are prevalent in engineering, exceeding the capabilities of traditional surrogate models designed for low/medium-dimensional problems. These models face the curse of dimensionality, resulting in decreased modeling accuracy as the design parameter space expands. Furthermore, the lack of a parameter decoupling mechanism hinders the identification of couplings between design variables, particularly in highly nonlinear cases. To address these challenges and enhance prediction accuracy while reducing sample demand, this paper proposes a PC-Kriging-HDMR approximate modeling method within the framework of Cut-HDMR. The method leverages the precision of PC-Kriging and optimizes test point placement through a multi-stage adaptive sequential sampling strategy. This strategy encompasses a first-stage adaptive proportional sampling criterion and a second-stage central-based maximum entropy criterion. Numerical tests and a practical application involving a cantilever beam demonstrate the advantages of the proposed method. Key findings include: (1) The performance of traditional single-surrogate models, such as Kriging, significantly deteriorates in high-dimensional nonlinear problems compared to combined surrogate models under the Cut-HDMR framework (e.g., Kriging-HDMR, PCE-HDMR, SVR-HDMR, MLS-HDMR, and PC-Kriging-HDMR); (2) The number of samples required for PC-Kriging-HDMR modeling increases polynomially rather than exponentially as the parameter space expands, resulting in substantial computational cost reduction; (3) Among existing Cut-HDMR methods, no single approach outperforms the others in all aspects. However, PC-Kriging-HDMR exhibits improved modeling accuracy and efficiency within the desired improvement range compared to PCE-HDMR and Kriging-HDMR, demonstrating robustness.Comment: 17 pages with 7 figures and 9 table

qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers

We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This training is in an unsupervised way, without the need for labeled training data, and is thus referred to as pre-training. After the pre-training, the model can efficiently compute the likelihood of logical operators for any given syndrome, using maximum likelihood decoding. It can directly generate the most-likely logical operators with computational complexity $\mathcal O(2k)$ in the number of logical qubits $k$, which is significantly better than the conventional maximum likelihood decoding algorithms that require $\mathcal O(4^k)$ computation. Based on the pre-trained model, we further propose refinement to achieve more accurately the likelihood of logical operators for a given syndrome by directly sampling the stabilizer operators. We perform numerical experiments on stabilizer codes with small code distances, using both depolarizing error models and error models with correlated noise. The results show that our approach provides significantly better decoding accuracy than the minimum weight perfect matching and belief-propagation-based algorithms. Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes. Furthermore, it leverages the parallelization capabilities of GPUs, enabling simultaneous decoding of a large number of syndromes. Our approach sheds light on the efficient and accurate decoding of quantum error-correcting codes using generative artificial intelligence and modern computational power.Comment: Comments are welcom

Adaptive Performance Tuning for Voltage-Sourced Converters with Frequency Responses

Renewable generation brings both new energies and significant challenges to the evolving power system. To cope with the loss of inertia caused by inertialess power electronic interfaces (PEIs), the concept of the virtual synchronous generator (VSG) has been proposed. The PEIs under VSG control could mimic the external properties of the traditional synchronous generators. Therefore, the frequency stability of the entire system could be sustained against disturbances mainly caused by demand changes. Moreover, as the parameters in the emulation control processes are adjustable rather than fixed, the flexibility could be enhanced by proper tuning. This paper presents a parameter tuning method adaptive to the load deviations. First, the concept and implementation of the VSG algorithm performing an inertia response (IR) and primary frequency responses (PFR) are introduced. Then, the simplification of the transfer function of the dynamic system of the stand-alone VSG-PEI is completed according to the distributed poles and zeros. As a result, the performance indices during the IR and PFR stages are deduced by the inverse Laplace transformation. Then, the composite influences on the performances by different parameters (including the inertia constant, the speed droop, and the load deviations) are analyzed. Based on the composite influences and the time sequences, an adaptive parameter tuning method is presented. The feasibility of the proposed method is verified by simulation

Emulation Strategies and Economic Dispatch for Inverter-Based Renewable Generation under VSG Control Participating in Multiple Temporal Frequency Control

As the increasing penetration of inverter-based generation (IBG) and the consequent displacement of traditional synchronous generators (SGs), the system stability and reliability deteriorate for two reasons: first, the overall inertia decreases since the power electronic interfaces (PEIs) are almost inertia-less; second, renewable generation profiles are largely influenced by stochastic meteorological conditions. To strengthen power systems, the concept of the virtual synchronous generator (VSG) has been proposed, which controls the external characteristics of PEIs to emulate those of SGs. Currently, PEIs could perform short-term inertial and primary frequency responses through the VSG algorithm. For renewable energy sources (RES), deloading strategies enable the generation units to possess active power reserves for system frequency responses. Additionally, the deloading strategies could provide the potential for renewable generation to possess long-term frequency regulation abilities. This paper focuses on emulation strategies and economic dispatch for IBG units to perform multiple temporal frequency control. By referring to the well-established knowledge systems of generation and operation in conventional power systems, the current VSG algorithm is extended and complemented by the emulation of secondary and tertiary regulations. The reliability criteria are proposed, considering the loss of load probability (LOLP) and renewable spillage probability (RSP). The reliability criteria are presented in two scenarios, including the renewable units operated in maximum power point tracking (MPPT) and VSG modes. A LOLP-based economic dispatch (ED) approach is solved to acquire the generation and reserve schemes. The emulation strategies and the proposed approach are verified by simulation

Enhancement of Gas Sensing Characteristics of Multiwalled Carbon Nanotubes by CF4 Plasma Treatment for SF6 Decomposition Component Detection

H2S and SO2 are important gas components of decomposed SF6 of partial discharge generated by insulation defects in gas-insulated switchgear (GIS). Therefore, H2S and SO2 detection is important in the state evaluation and fault diagnosis of GIS. In this study, dielectric barrier discharge was used to generate CF4 plasma and modify multiwalled carbon nanotubes (MWNTs). The nanotubes were plasma-treated at optimum discharge conditions under different treatment times (0.5, 1, 2, 5, 8, 10, and 12 min). Pristine and treated MWNTs were used as gas sensors to detect H2S and SO2. The effects of treatment time on gas sensitivity were analyzed. Results showed that the sensitivity, response, and recovery time of modified MWNTs to H2S were improved, but the recovery time of SO2 was almost unchanged. At 10 min treatment time, the MWNTs showed good stability and reproducibility with better gas sensing properties compared with the other nanotubes

Effects of background gas on sulfur hexafluoride removal by atmospheric dielectric barrier discharge plasma

The effects of background gases (He, Ar, N2 and air) on SF6 removal in a dielectric barrier reactor were investigated at atmospheric pressure. A comparison among these background gases was performed in terms of discharge voltage, discharge power, mean electron energy, electron density, removal efficiency and energy yield for the destruction of SF6. Results showed that the discharge voltage of He and Ar was lower than that of N2 and air, but the difference of their discharge power was small. Compared with three other background gases, Ar had a relatively superior destruction and removal rate and energy yield since the mean electron energy and electron density in SF6/H2O/Ar plasma were both maintained at a high level. Complete removal of 2% SF6 could be achieved at a discharge power of 48.86 W with Ar and the corresponding energy yield can reach 4.8 g/kWh

Analysis of mechanism and optimal value of urban built environment resilience in response to stormwater flooding

The concept of urban resilience focuses on understanding the process and mechanisms of disaster occurrence, providing innovative approaches to address stormwater flooding. However, existing studies primarily concentrate on enhancing overall system resilience, with limited research examining the temporal progression from stormwater disturbance to flood generation. To fill this gap, this study categorizes the development process of stormwater flooding into three periods: disturbance resistance (DR), adjustment and adaptation (AA), and rapid recovery (RR). Using the SWMM (Storm Water Management Model) software, 27 representative parcels in the Beijing-Tianjin-Hebei region of China were simulated. By sequentially considering single-indicator control variables, resilience indicators that significantly impact the three periods were identified through the construction of a stormwater flooding resilience indicator library. Subsequently, resilience models for each disaster phase were constructed using the BP (Back Propagation) neural network, and genetic algorithms were employed to optimize the models and determine the optimal values of resilience indicators for each period. Finally, the research findings were summarized into a resilience design method for the built environment to address stormwater flooding, accompanied by a guide for improving stormwater flooding resilience.The study reveals the following key findings: (1) the influence of physical and spatial elements in the built environment on stormwater flooding formation varies across different stages of the disaster process; (2) distinct resilience indicators operate at different times and in different ways throughout the entire stormwater flooding resilience process; (3) enhancing stormwater flooding resilience in the built environment does not necessarily require setting specific threshold values for each influencing indicator; instead, an optimal single value emerges when multiple indicators interact. Moreover, when multiple indicators interact, an optimal combination module with the best value for a single indicator exists. This study investigates the complete cycle from storm disturbance to flood disaster formation, offering both solutions for cities to mitigate storm flood disasters and advancing theoretical research on urban storm flood resilience while fostering interdisciplinary integration