Design of Wind Turbine Vibration Monitoring System

In order to ensure safety of wind turbine operation and to reduce the occurrence of faults as well as to improve the reliability of wind turbine operation, a vibration monitoring for wind turbine is developed. In this paper, it analyses the enlargement of all the parts of the structure and the working mechanism, the research method of wind turbine operation vibration is introduced, with the focus being the use of the sensor principle. Finally the hardware design and software of this system is introduced and the main function of this system is described, which realizes condition monitoring of the work state of wind turbines

Putting Them under Microscope: A Fine-Grained Approach for Detecting Redundant Test Cases in Natural Language

Natural language (NL) documentation is the bridge between software managers and testers, and NL test cases are prevalent in system-level testing and other quality assurance activities. Due to reasons such as requirements redundancy, parallel testing, and tester turnover within long evolving history, there are inevitably lots of redundant test cases, which significantly increase the cost. Previous redundancy detection approaches typically treat the textual descriptions as a whole to compare their similarity and suffer from low precision. Our observation reveals that a test case can have explicit test-oriented entities, such as tested function Components, Constraints, etc; and there are also specific relations between these entities. This inspires us with a potential opportunity for accurate redundancy detection. In this paper, we first define five test-oriented entity categories and four associated relation categories and re-formulate the NL test case redundancy detection problem as the comparison of detailed testing content guided by the test-oriented entities and relations. Following that, we propose Tscope, a fine-grained approach for redundant NL test case detection by dissecting test cases into atomic test tuple(s) with the entities restricted by associated relations. To serve as the test case dissection, Tscope designs a context-aware model for the automatic entity and relation extraction. Evaluation on 3,467 test cases from ten projects shows Tscope could achieve 91.8% precision, 74.8% recall, and 82.4% F1, significantly outperforming state-of-the-art approaches and commonly-used classifiers. This new formulation of the NL test case redundant detection problem can motivate the follow-up studies to further improve this task and other related tasks involving NL descriptions.Comment: 12 pages, 6 figures, to be published in ESEC/FSE 2

Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective

A Simple LLM Framework for Long-Range Video Question-Answering

We present LLoVi, a language-based framework for long-range video question-answering (LVQA). Unlike prior long-range video understanding methods, which are often costly and require specialized long-range video modeling design (e.g., memory queues, state-space layers, etc.), our approach uses a frame/clip-level visual captioner (e.g., BLIP2, LaViLa, LLaVA) coupled with a Large Language Model (GPT-3.5, GPT-4) leading to a simple yet surprisingly effective LVQA framework. Specifically, we decompose short and long-range modeling aspects of LVQA into two stages. First, we use a short-term visual captioner to generate textual descriptions of short video clips (0.5-8s in length) densely sampled from a long input video. Afterward, an LLM aggregates the densely extracted short-term captions to perform long-range temporal reasoning needed to understand the whole video and answer a question. To analyze what makes our simple framework so effective, we thoroughly evaluate various components of our system. Our empirical analysis reveals that the choice of the visual captioner and LLM is critical for good LVQA performance. Furthermore, we show that a specialized prompt that asks the LLM first to summarize the noisy short-term visual captions and then answer a given input question leads to a significant LVQA performance boost. On EgoSchema, which is best known as a very long-form video question-answering benchmark, our method achieves 50.3% accuracy, outperforming the previous best-performing approach by 18.1% (absolute gain). In addition, our approach outperforms the previous state-of-the-art by 4.1% and 3.1% on NeXT-QA and IntentQA. We also extend LLoVi to grounded LVQA and show that it outperforms all prior methods on the NeXT-GQA dataset. We will release our code at https://github.com/CeeZh/LLoVi

Serum microRNA Profiles Serve as Novel Biomarkers for Autoimmune Diseases

Autoimmune diseases involve a complex dysregulation of immunity. Autoimmune diseases include many members [e.g., rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE)], and most of them are classified according to what organs and tissues are targeted by the damaging immune response. Many studies have focused on finding specific biomarkers for single autoimmune diseases, but so far, there are no universal biomarkers for detecting almost all autoimmune diseases. Serum miRNAs have served as potential biomarkers for detecting various diseases. The purpose of this study was to find a universal biomarker for diagnosing autoimmune diseases. Regulatory T cells (Tregs) play a crucial role in protecting an individual from autoimmunity, and depletion of Tregs in mice is considered a representative animal model of autoimmune disease. Two mouse models for Treg depletion, in which Treg was depleted by CD25mAb (in C57 mice) or by diphtheria toxin (DT) (in Foxp3DTR mice), were investigated, and 381 miRNAs were identified in the serum of mice with Treg depletion. A distinctive circulating miRNA profile was identified in Treg-depleted mice and in patients with autoimmune disease. QRT-PCR confirmation and ROC curve analysis determined that six miRNAs (miR-551b, miR-448, miR-9, miR-124, miR-148, and miR-34c) in the Treg-depleted mouse models and three miRNAs [miR-551b (specificity 73.5%, sensitivity 88.4%), miR-448 (specificity 82.4%, sensitivity 91.3%), and miR-124 (specificity 76.5%, sensitivity 91.3%)] in patients with RA, SLE, Sjogren's syndrome (SS), and ulcerative colitis (UC) could serve as valuable specific biomarkers. These circulating miRNAs may represent potential universal biomarkers for autoimmune diseases diagnosis and prognosis

Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Krasnosel'skii-Pokrovskii Model

As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator