An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine

In this article, a new method of pattern recognition for machine working conditions is presented that is based on time-frequency image (TFI) feature extraction and support vector machines (SVMs). In this study, the Hilbert time-frequency spectrum (HTFS) is used to construct TFIs because of its good performance in non-stationary and non-linear signal analysis. Cyclostationarity signal analysis is a pre-processing method for improving the performance of the HTFS in the construction of TFIs. Feature extraction for TFIs is investigated in detail to construct a feature vector for pattern recognition. Gravity centre and information entropy of TFIs are used to construct the feature vector for pattern recognition. SVMs are used for different working conditions classification by the constructed feature vector because of its powerful performance even for small samples. In the end, rolling bearing pattern recognition is used as an example to testify the effectiveness of this method. According to the result analysis, it can be concluded that this method will contribute to the development of preventative maintenance

Research on Optimizing National Mental Health Literacy from the Perspective of Health in China: Significance and Approach

Improving the level of national mental health and strengthening the construction of social psychological service system are the necessary and important ways to promote the implementation of the Healthy China strategy. However, the gap between the requirements of Healthy China strategy for social mental health service system and the current situation of mental health service in China is still widespread. Promoting the public to form an effective demand for psychological help and establishing a new mental health service model is the key to bridge this gap. Optimizing mental health literacy is an important starting point and breakthrough to solve this problem. At present, the research on mental health literacy is undergoing a transformation from the paradigm of psycho-epidemiology to the paradigm of psychology and sociology. Taking contemporary China as the background, the reconceptualization of mental health literacy and the exploration of the current situation of Chinese mental health literacy in the new era is one of the core task of current psychological path research. The significance of this study is mainly reflected in: (1) promote the formation of effective demand for help and improve the level of public mental health; (2) enhance cultural selfconfidence and construct the theory of mental health literacy of contemporary Chinese people; (3) guide the supply side structural reform of mental health services and promote mental health equity; (4) reduce the burden of mental illness and help build a well-off society in an all-round way; (5) help build a social and psychological service system

Refinement of primary Si in hypereutectic Al-Si alloys by intensive melt shearing

Hypereutectic Al-Si based alloys are gaining popularity for applications where a combination of light weight and high wear resistance is required. The high wear resistance arising from the hard primary Si particles comes at the price of extremely poor machine tool life. To minimize machining problems while exploiting outstanding wear resistance, the primary Si particles must be controlled to a uniform small size and uniform spatial distribution. The current industrial means of refining primary Si chemically by the addition of phosphorous suffers from a number of problems. In the present paper an alternative, physical means of refining primary Si by intensive shearing of the melt prior to casting is investigated. Al-15wt%Si alloy has been solidified under varying casting conditions (cooling rate) and the resulting microstructures have been studied using microscopy and quantitative image analysis. Primary Si particles were finer, more compact in shape and more numerous with increasing cooling rate. Intensive melt shearing led to greater refinement and more enhanced nucleation of primary Si than was achieved by adding phosphorous. The mechanism of enhanced nucleation is discussed.EPSRC (grant EP/H026177/1)

An investigation into frequency resolution estimation model for impact signal analysis by using Hilbert spectrum and condition classification for marine diesel engine

In this paper, frequency resolution determination method is investigated according to Hilbert spectrum performance for impact signal analysis. A new constructed performance estimation model for the best frequency resolution is put forward in this research for the impact signal pattern recognition. Different parameters in the time-frequency distribution by using Hilbert spectrum are considered in this estimation model for the best frequency resolution determination. To verify the effectiveness of this estimation model, numerical simulation is used for Hilbert spectrum construction analysis. At the same time, different marine diesel engine working condition signals analysis are also used to illustrate the methodology developed in this research and verify the effectiveness. It can be concluded that this method can contribute the development for impact signal analysis by using Hilbert spectrum

Laboratory Study on Improving Recovery of Ultra-Heavy Oil Using High-Temperature-Resistant Foam

After multiple rounds of steam huff-and-puff processes, an ultra-heavy oil reservoir is prone to excessive steam injection pressure, large heat loss, small sweep range of steam, and steam channeling, thus severely affecting the effective utilization of the oil reservoir. To solve these problems, one-dimensional and three-dimensional (3D) physical simulation tools were used to study the plugging performance of high-temperature composite foams by adding tanning extract and alkali lignin under the influence of some factors such as the reservoir temperature, salinity of formation water, and injection methods. The ultra-heavy oil used in the experiment comes from Shengli Oilfield. Under the condition of surface degassing, the viscosity of ultra-heavy oil could reach 145169 mPa.s at 60 °C. The experimental results show that the foam can produce a synergistic effect with both gel systems, indicating that the gel increases the stability of the foam. The foam can transfer more gel into the high-permeability formation, which can efficiently control the foam. The 3D physical simulation experiments indicated that both the systems enhance the recovery of heavy oil reservoir and reduce its moisture content significantly using steam injection. The method involving tannin extract foam and steam injection increased the recovery by 20% compared to the foam involving only steam injection. The method involving alkali lignin foam and steam injection increased the recovery by 11%

An improved key-phase-free blade tip-timing technique for nonstationary test conditions and its application on large-scale centrifugal compressor blades

7partially_openopenHe, Changbo; Antoni, Jerome; Daga, Alessandro Paolo; Li, Hongkun; Chu, Ning; Lu, Siliang; Li, ZhixiongHe, Changbo; Antoni, Jerome; Daga, Alessandro Paolo; Li, Hongkun; Chu, Ning; Lu, Siliang; Li, Zhixion

Enable Language Models to Implicitly Learn Self-Improvement From Data

Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods.Comment: 28 pages, 5 figures, 4 table

Milling cutter condition reliability prediction based on state space model

Reliability analysis based on equipment's performance degradation characteristics is one of important research areas for reliability engineering. Many researcher work on multi-sample analysis, but it is limited for single equipment or small sample reliability prediction. Therefore, the method of reliability prediction based on state space model (SSM) is investigated in this research for small sample analysis. Firstly, signals about machine working conditions are collected based on-line monitoring technology. Secondly, wavelet packet energy parameters are determined based on the monitored signals. Frequency band energy is regarded as characteristic parameter. Then, the degradation characteristics of signal to noise ratio is improved by moving average filtering processing. In the end, SSM is established to predict degradation characteristics of probability density distribution, and the degree of reliability is determined. Milling cutter is used to demonstrate the rationality and effectiveness of this method. It can be concluded that this method is effective for milling cutter reliability estimation based on the data analysis. It also contributes to machine condition remaining useful life prediction