Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Fault Diagnosis for Gas Turbine Rotor via Transfer Reinforcement Learning

As the central component of a significant power machine, the gas turbine's fault diagnosis accuracy is critical to the equipment's safety in service. However, the fault detection of the gas engine rotor system still faces challenges due to the difficulty of acquiring sensitive features and the lack of labeled data. To address these issues, we propose an improved DQN-based Transfer Reinforcement Learning method (Transfer-DQN) for robust gas turbine rotor fault diagnosis. The proposed method takes the collected one-dimensional raw vibration signal as input, with the fault sample set and fault category serving as the model environment and action. It uses a multi-scale one-dimensional wide convolutional neural network (M-WDCNN) with ∊-greedy strategy for Q-network fitting and decision making. Additionally, to consider the computational efficiency and differences between fault classes, Transfer-DQN uses multiple fault sample data as the source domain and a single fault class as the target domain, while performing the source-to-target domain transfer learning based on generative adversarial. Extensive experiments on the bearing dataset of Western Reserve University and our gas turbine test bench demonstrate the superiority of Transfer-DQN, achieving accuracies of 98.95% and 96.91 %, respectively. Compared with baseline approaches, our method breaks through the previous upper limit of 95 % to meet the need for robust and efficient fault diagnosis.
A High-precision Direction Finding Method for Aerial Search and Rescue Targets Based on Optimized Firefly Particle Filtering

Aviation search and rescue, as an important requirement for the development of military information construction, is of great significance for our military to carry out joint search and rescue to ensure the safety of personnel during wartime missions. Accurately and quickly obtaining the direction and location information of the target to be searched and rescued is the most crucial step in aviation search and rescue. However, in real-world search and rescue environments, airborne direction finding data often results in significant errors due to the obstruction of antennas by equipment such as landing gear and tires on the aircraft. This article proposes an optimized firefly particle filter algorithm that combines outlier detection ideas and obtains high-precision target direction and position information by integrating airborne GPS, airborne IMU, radio ranging data, and dual antenna direction finding data. The simulation results show that in complex environments, this algorithm outperforms other algorithms in terms of accuracy, convergence time, and applicability.
Prediction of Stress Levels using Low-Cost IoT-Based Health Parameters Measuring System

Human stress is a prime concern of health nowadays. Sometimes it affects the body state severely. When it is necessary to find or identify the stress level of anybody, patient or elderly person, continuous monitoring is required. So a low-cost system is required which can be used to measure different health parameters. For this, an IoT-based system has been developed to measure a few physical parameters such as temperature, pulse, and oxygen saturation (SpO2) using MAX30102 Pulse Oximeter and Heart Rate sensor which is subsequently interfaced with a node microcontroller unit for further processing. Whenever data is measured and collected using the implemented device, it has been observed that there are four types of people with stress levels: healthy, moderately stressed, highly stressed, and overstressed. Based on the received unknown data, several groups or clusters with similar data points and other groups or clusters with other dissimilar data points are formed using clustering-based approaches such as hierarchical-based agglomerative clustering, fuzzy-based fuzzy C-Means clustering, and centroid-based K-Means clustering. The selection of the number clusters is done by calculating the scores using the elbow method and the silhouette method. The obtained number of clusters is five using both methods, and this value has been kept the same for all clustering techniques. After this, the silhouette score is calculated for each clustering methods, and obtained the best score (0.693) from K-Means. Finally, the plot of means of each attribute has been analyzed for different cluster levels in each clustering algorithm.
Human-Robot Imitation Learning of Movement for Embodiment Gap

One of the problems in imitation between humans and robotd is embodiment gap. Therefore, in this paper, we propose a system that realizes movement level imitation between humans and robots for embodiment gap. We target embodiment gap arising from link length and number of joints, and estimate embodiment gap by solving not only the inverse kinematics but also the forward kinematics using the Levenbarg-Marquart method. By doing this, human movement is separated into end-effector-oriented movement and joint angle-oriented movement according to the robot's own embodiment. Then, based on the separated movement, we realize the imitation of movement using Behavior Cloning based Energy-Based Model. We applied the proposed method to a small humanoid robot and verified its effectiveness. As a result, we confirmed that the proposed method separates human movement dependent on the robot's embodiment. In addition, we realized the imitation learning of movement corresponding to embodiment gap estimated from the separated human movement.
Probability Conditional Guidance Fundus Image Enhancement

Fundus images play a crucial role in diagnosing ocular and systemic vascular diseases in clinical settings. However, these images often suffer from issues like uneven brightness and transmission distortion, which adversely impact their quality. To address these challenges, this paper proposes a novel approach called the Probability Conditional Guidance Generative Adversarial Network (PCGGAN). This method leverages the high-frequency components of the images to enhance their quality. The hypothesis behind this approach is that retaining the content and pathological information associated with the high-frequency components is vital during the enhancement process. To achieve this, the fundus image is decomposed into high-frequency and low-frequency components, with the high-frequency components probabilistically used as conditional guidance during model training. This training strategy enables the model to learn the distinctions between unconditional and conditional constraints, thereby enhancing its capability for targeted generation and generalization. The effectiveness of the proposed method is validated through both quantitative and qualitative experiments.
Research on Defect Data of Distribution Transformers Based on Fusion Text Clustering Algorithm

Distribution transformers are important devices in power distribution networks. The mining of defect texts related to distribution transformers can provide valuable information that impacts the safe and stable operation of these transformers. However, due to the complexity and large volume of textual data in the power grid domain, the application of text mining is still in its early stages in China’s power system. This paper focuses on the research of a semantic recognition-based clustering algorithm for defect texts of distribution transformers. It aims to uncover potential patterns among these defect texts and contribute to the operation and maintenance of distribution networks.First, the semantic recognition of defect texts in distribution transformers is implemented to generate semantic vectors. Then, a clustering algorithm framework based on semantic vectors for defect texts in distribution transformers is proposed. Since the semantic vectors obtained from text representation models contain the main feature information of the texts and can be directly processed by computers, they can be inputted into different clustering algorithms to achieve clustering of defect texts in distribution transformers. Next, clustering experiments on defect texts in distribution transformers are conducted. Four different types of clustering algorithms are used to cluster the text feature vectors obtained from different text representation models. The experimental results are then compared and analyzed based on three clustering evaluation metrics.
A Review on Mathematical Modelling of Reactive Power Ancillary Service Market

Reactive power supports have been widely recognized as an ancillary service for power grid operation, but the research on reactive power markets is rather limited compared with other ancillary services. This paper firstly conducts an overview of state-of-art literature, and then systematically reviews the mathematical models for reactive power market modeling. Focusing on the settlement of reactive power markets in distribution networks, the mathematical formulations covering network operation, costs of various Var sources, and reactive power services from the main grid are summarized. Besides, the market-clearing mechanisms are classified into three categories. Finally, several open issues associated with the bidding price rule, market power and reactive power obligation, are also discussed. The value of this review paper is to provide a timely mathematical model library for designing the reactive power ancillary service market in distribution network level.
Primary Side Attributes based Control of DC Link Capacitor-Less Bi-directional Wireless Charger for Electric Vehicles

In recent times wireless charging systems for electric vehicles are gaining popularity mainly due to the convenience it offers to the end user and the electric grid. There are numerous Bi-directional AC-DC inductive charging systems and their controls have evolved in the recent past. These chargers conventionally use a bulky DC link capacitor and grid filters which hampers the power density of the charging system. In this connection, this work establishes a PWM technique to alleviate the dominant grid frequency harmonics and controls the power transfer bi-directionally for an LCC-LCC compensated DC link capacitor-less AC-DC charging system. The control is developed such that the grid-side filtering is necessary only for the resonant frequency harmonics, which reduces the size of grid side filter. The work also estimates the load current by sensing the primary side attributes to have all the control on the primary side to reduce the size of the off-board assembly. This established primary control for the converter system is tested for different values of bi-directional power flows and for variations in load and coupling changes as well. The developed control along with the PWM strategy is also evaluated for the grid current THD, estimation of the load currents, and the grid power factor for a wide range of current references and for their bidirectional flows.
Deep Reinforcement Learning Based Upper Limb Neuromusculoskeletal Simulator for Modelling Human Motor Control

The neuromusculoskeletal modeling and simulator (NMMS) have been widely utilized in various fields and applications. The deep reinforcement learning (DRL) algorithm is a promising method to study human motor controls and movement biomechanics via NMMS without experimental data. However, existing research lacks exploration of the DRL implementation for controlling neuromusculoskeletal simulators, and only a few have presented myoelectric control systems applied to the DRL-based NMMS. In this work, an off-policy DRL algorithm, Deep Deterministic Policy Gradient (DDPG), was implemented on an upper limb NMMS with two different types of action space - direct muscle activation output and PD-based internal model, and compared their control performance. In addition, we evaluated the performance of proportional myoelectric control systems implemented on the DRL-based upper limb NMMS. The results indicate that the DRL-based NMMS can execute upper limb movements accurately, and the proportional myoelectric control system reduced the muscle activation under both types of action space. Moreover, the PD-based internal model action space shows better learning and error-tracking performance than the direct muscle activation output action space.
Invited: Ring-Oscillator Physical Unclonable Function (RO-PUF) Based PRBS Generation as a Device Signature in Distributed Brain Implants

In this paper, we propose and evaluate a method of generating low-cost device signatures for distributed wireless brain implants, using a Pseudo-Random Binary Sequence (PRBS) Generator that utilizes a modified Ring-Oscillator-based Physical Unclonable Function (RO-PUF). The modified RO-PUF's output is used as a seed for the PRBS generator, which creates a multi-bit output that can be mapped to a time-slot when the implant is allowed to communicate with the external world using duty-cycled time-division multiplexing. A 9-bit PRBS generator is shown in hardware (with a TSMC 65 nm test chip implementation) that demonstrates < 100 nW Power consumption in measurement (72% lower power and 78% lower area than a traditional 9-bit RO-PUF implementation), which supports 26 implants with the probability of time-slot collision being < 50%. This potentially creates a pathway for low-cost device signature generation for highly resource-constrained scenarios such as wireless. distributed neural implants.
Learning Quantum Distributions Based on Normalizing Flow

Learning many-body quantum systems is of fundamental importance in quantum information processing, however, it is a challenging task which typically requires estimating quantum distributions of dimensionality exponentially scaling to the system size. As generative models have shown a great scalability to learn high-dimensional distributions and found wide applications in the domain of image and text, they can be a powerful tool to facilitate us to accomplish the challenging quantum tasks. In this work, we propose using normalizing flow (NF) models with fast sampling to learn discrete quantum distributions for quantum state tomography. Particularly, three NF models, including denoising flow, argmax flow, and tree flow, are first adapted to the task of explicit quantum probability density estimation. We then perform extensive experiments on a large scale of quantum systems, and our numerical results demonstrate that these discrete NFs admit an excellent sampling efficiency in the sense that they are insensitive to the system size to learn the high-dimensional quantum distributions, without compromising the learning performance. Finally, in comparison to the other generative models, such as autoregressive, the NFs avoid the problem of slow sequential sampling.
Phase Retrieval Based RIDNet Denoising Algorithm for Color Image

The phase retrieval problem refers to recovering the original signal from only amplitude measurements. The detector CCD can only seek the intensity information in the diffraction field, and the phase is generally lost. The phase retrieval problem is non-linear, and it is difficult to reconstruct the original from only the amplitude of the color image. The phase retrieval algorithm, based on RIDNet denoising(RIDNetPR) is proposed to deal with color image reconstruction. First, the features are extracted using RIDNet, and multiple Enhancement Attention Modules (EAM) are repeatedly stacked for color image denoising tasks to ensure the ability of the deep learning network for feature extraction. Regularization terms constrain the network by improving the recovery accuracy. Second, the Alternating Direction Method of Multipliers (ADMM) is utilized to reconstruct the original color images. Compared with traditional phase retrieval algorithms, the experimental results show that the proposed algorithm improves the Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure(SSIM) values at different Gaussian noise levels. The effectiveness of the proposed algorithm is illustrated for color image.
Research on Image-text Fusion Retrieval Methods for Picture Books

With the increasing development of social technology, the image data on the Internet shows an explosive growth. In a large number of complex and different databases, how to make better use of image features to achieve simple and efficient real-time retrieval is a new challenge we face. In recent years, image retrieval has begun to develop towards multi-feature fusion and specific application scenarios, but the addition of most text features relies on the text annotation of image information, and uses tags or description semantics for retrieval. In this way, the image information not mentioned in the image annotation will not be retrieved, and the requirements for the data set are relatively high. This paper proposes an image retrieval feature enhancement method fused with text features. Based on the spatial distance score weight, the text features are combined with the extracted VLAD (Vector of Locally Aggregated Descriptors) features to obtain the feature vector describing the image content. By adding text features contained in the image itself, the retrieval effect of images containing text information can be improved, and the retrieval accuracy of occluded images can also be improved to a certain extent, so that image retrieval achieves a relatively satisfactory effect.
Generating Predictive Models for Emerging Semiconductor Devices

Circuit design requires fast and scalable models which are compatible to modern electronic design automation tools. For this task typically analytical compact models are preferred. However, for emerging device concepts with altered conduction mechanisms like reconfigurable FETs, tunnel FETs or feedback FETs compact models are often not yet ready for circuit simulation environments. Table models help to bridge this gap, as they only require biasing values in form of current- and capacitance tables, which are then interpolated during circuit simulation. However, the conventional approach of generating the data set through DC ramps in physical simulation typically results in a large number of TCAD simulations and many redundant data points. To simplify model building, cut turnaround time and omit redundant data, this work proposes to restrict to a single transient TCAD simulation, where all required bias points for a fully functional predictive table model are provided by nesting harmonic functions. The mathematical reasoning for the selection of appropriate parameters such as frequencies and sampling rate are presented and their influence on the quality of the obtained table model is exposed. This work further presents implementation of the modeling method including validation and post-processing of the transient simulation result. The target device is an emerging reconfigurable FET (RFET) with planar structure. Finally, the performance of the obtained predictive table model of the RFET is demonstrated with circuit simulation of logic cells.
A Study on Image Classification Method for Lightweight Network Models Based on Adaptive Channel Number Transformation (ACT)

Significant strides have been made in the field of deep learning for computer vision, particularly in image classification, largely attributed to the widespread use of Convolutional Neural Networks (CNNs). Nevertheless, the deepening layers and increasing complexity of CNNs pose challenges in deploying these models due to substantial computational and storage costs. This impedes their application on smaller mobile devices like smartphones. This article delves into a strategy leveraging channel number transformation to mitigate the computational and storage expenses associated with CNNs, optimizing their performance in image classification tasks. An Adaptive Channel Number Transformation (ACT) framework is devised. In this framework, the fundamental concept is that, in image classification tasks, the regulations governing model channel number transformation should be jointly overseen by the classification number labels of the model and the classification task. These regulations are fine-tuned by the labels in the latter part of the model. The ACT framework is applied to the lightweight network model of the ShuffleNets series. Its objective is to curtail the deployment costs of CNNs, ensuring the network sustains accuracy while diminishing the count of model parameters and computational time through adaptive channel number transformation.
A Community Detection Algorithm Based on Probabilistic Graphical Model

Many community detection algorithms suffer from problems such as low accuracy and efficiency, overfitting or underfitting, and high time complexity. To address the issues in global optimization algorithms proposed so far, GOMK-BP, a community detection algorithm based on the probabilistic graph model, is proposed in this paper. It takes the fully connected Markov random field (MRF) as the probabilistic graph model, maps the node’s similarity and importance indexes to the energy function, which is then optimized to improve the performance of the model, and the combination of weighted sum and exponential function makes the algorithm achieve a better fit. To improve the efficiency and accuracy, the traditional message propagation algorithm is improved, and the convergence is accelerated by using compressed sensing. Because of the fully connected MRF may be affected by the high computational complexity, GOMK-BP adjusts the model structure, and introduces sparse connections to balance the computational efficiency and model performance. Finally, the community structure is obtained. The proposed algorithm is tested on some artificial networks and real networks, the results show that GOMK-BP can detect high-quality community structures.
Latent State Synchronization in Dyadic Partners using EEG

Emerging research has shown that interbrain synchronization can occur when individuals interact on a shared task or receive shared stimuli. While the mechanisms producing such effects are not fully understood, interbrain synchronization has been observed in multiple contexts. Analyses of such synchrony, however, are often limited to individual, bottom-up features such as activity at individual cortical sites and frequency bands, or cross-projections of the data into a common space that maximizes correlation. It is an open question whether synchronization can also be observed using more top-down notions of latent state composed of multiple, simultaneous features that are not fit to the individual or task at hand? Here, we investigate whether latent state estimation methods, borrowed from the domain generalization toolboxes of the brain-computer interface community can be used to assess interbrain synchrony. Domain generalization methods learn models of neural activity that transfer from one domain to another without the need for participant- or task-specific training data. We use data from single individuals in a controlled task to train a domain-generalized model to detect latent state changes previously described as relating to alertness or vigilance. We apply the model to a novel, target domain in which teams of two worked together to locate and defuse simulated improvised explosive devices. Our results revealed significantly greater correlation between the latent states of dyadic partners compared to randomly paired individuals, establishing that interbrain synchronization can be observed using top-down methods that are not fit to the individuals, or task, in question.