Optimization of cone bit bearing seal based on failure analysis

Robust Bayesian Compressive Sensing for Signals in Structural Health Monitoring

The pick is the working part of the coal plow, the cutting performance of the pick is very important for the efficient production of the coal mine. In order to analyze the dynamic characteristics of the plow pick during the cutting process, a cutting test platform was built. The force of the plow pick and the vibration of the plow head were obtained through the cutting test, and the relationship between the vibration of the plow head and the cutting speed and cutting depth was analyzed. In order to study the influence of different alloy head shapes on the performance of plow pick, two kinds of pick were designed: a pick with chip bit and a pick with conical bit. The finite element method was used to establish the cutting model, and the numerical simulation was carried out. The research results show that the caving effect of pick with chip bit is better than that of pick with conical bit, but the cutting resistance of the pick with chip bit is greater than pick with conical bit. When the cutting angle is 60°, the force of the pick is the smallest and the force of the pick increases with the increase of the installation angle. The maximum error of the force of the pick obtained by the two methods is 8.41%. The cutting test verified the results of the numerical simulation. According to the research results, two types of pick were adopted in the coal plow, which were installed in different positions. Through the analysis of the actual working state of the plow, it is found that the damage form of the pick was consistent with the result of numerical simulation.

Being the core of the ejector refrigeration system, an ejector with a suitable mixer, conical-cylindrical or cylindrical, is key to high-energy-efficiency and low-carbon systems. To promote the scientific selection of mixers for ejectors based on the theoretical models that have been validated by experiments, the evolution laws of the entrainment ratios in the two types of ejectors are studied under various operating conditions. Furthermore, the influence mechanism of the mixer structures on the entrainment ratio of the ejector is elucidated by comparing the distribution characteristics of the entropy generation rate, pressure lift proportion, and entropy generation rate of the per-unit pressure lift in the two types of ejectors. The efficiencies of the conical-cylindrical mixer ejector and cylindrical mixer ejector exist a crossover, which makes the entrainment ratio of the conical-cylindrical mixer ejector smaller under small compression ratios but larger under large compression ratios. By changing the cylindrical mixer into a conical one, on the one hand, more pressure rise will be distributed in the diffuser, which helps to reduce the entropy increase rate in the pressurization process; on the other hand, the wall impulse effect of the conical mixer will lead to an increase in entropy generation rate of per-unit pressure lift, resulting in a growing entropy generation rate of boosting. The dominant roles are not the same with changing compression ratios, which leads to different relationships of entrainment ratio between the cylindrical and conical mixer ejectors.

3D point cloud presentation has been widely used in computer vision, automatic driving, augmented reality, smart cities and virtual reality. 3D point cloud compression method with higher compression ratio and tiny loss is the key to improve data transportation efficiency. In this paper, we propose a new 3D point cloud geometry compression method based on deep learning, also an auto-encoder performing better than other networks in detail reconstruction. It can reach much higher compression ratio than the state-of-art while keeping tolerable loss. It also supports parallel compressing multiple models by GPU, which can improve processing efficiency greatly. The compression process is composed of two parts. Firstly, Raw data is compressed into codeword by extracting feature of raw model with encoder. Then, the codeword is further compressed with sparse coding. Decompression process is implemented in reverse order. Codeword is recovered and fed into decoder to reconstruct point cloud. Detail reconstruction ability is improved by a hierarchical structure in our decoder. Latter outputs are grown from former fuzzier outputs. In this way, details are added to former output by latter layers step by step to make a more precise prediction. We compare our method with PCL compression and Draco compression on ShapeNet40 part dataset. Our method may be the first deep learning-based point cloud compression algorithm. The experiments demonstrate it is superior to former common compression algorithms with large compression ratio, which can also reserve original shapes with tiny loss.

In 1970, Furth and Yoshikawa (1970 Phys. Fluids 13 2593–6) introduced the scalings of adiabatic plasma compression. Basically, if the shape of the external plasma boundary and the aspect ratio are preserved during the compression, then the density, kinetic pressure, beta and current scale respectively as n∼C3 , p∼C5 , B∼C2 , β∼C , It∼C , where C is the linear compression ratio, that is, the ratio between initial and final major radii. In this work, we show analytically, by expanding the Grad–Shafranov equation in terms of C, that the deviation to the Furth–Yoshikawa scaling is related to the Shafranov shift that arises when beta increases at large compression ratios. There is an obvious effect of the Shafranov shift because the axis is moved to a region with larger volume element, and an indirect effect, associated to the relation between flux and radius. The latter effect adds to the first, and is of the same order of magnitude. The result is that the pressure increases less than the C 5 scaling, which can have a significant impact on the fusion power achieved at maximum compression. The analytical results are backed up by equilibrium simulations carried out with the CHEASE code. Equilibria are obtained for different values of C, with conservation of the total fluxes, q profile, and entropy of the plasma. The agreement of the theory and simulations is very good when the boundary of the plasma is circular and the aspect ratio small. When the aspect ratio is close to 1, and/or the boundary not circular, the analytical result gives the gist of the reduction of compression. Finally, a pressure anisotropy (p⊥−p∥)/p approximately equal to the increase in normalized Shafranov shift is predicted.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Providing a desired visual quality of lossy compression is an important and interesting research direction, it has been studied intensively in recent years. Its purpose is to guarantee that lossy compression will not affect further image processing efficiency due to too large loss of information while providing a relatively large compression ratio (CR). A two-step method has been proposed recently, this method is able, in some conditions, to provide a desired image quality, but its use can result in unsatisfactory accuracy in other conditions, in particular, when it is desired to produce a relatively low PSNR value. In this paper, we improve the two-step method of lossy compression in cases of unsatisfactory performance. A proposed procedure quickly calculates a parameter characterizing complexity of an image to be compressed. This allows discriminating images into rather simple and quite complex ones in advance and to adapt coder parameters at both steps accordingly. Experiments have been conducted for AGU coder based on Discrete Cosine Transform (DCT). The result demonstrates that the accuracy of providing a desired quality according to a chosen quality metric can be significantly improved, in particular, in the cases of complex structure texture images. The method also provides considerable benefits for "super-simple" images, which shows that in extreme cases the proposed approach is still robust.

Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

Microarray image compression: SLOCO and the effect of information loss

The extensive utilization of the Snappy compression algorithm in digital devices such as smartphones, IoT, and digital cameras has played a crucial role in alleviating demands on network bandwidth and storage space. This paper presents an improved Snappy compression algorithm optimized for implementation on field programmable gate arrays (FPGAs). The proposed algorithm enhances the compression ratio by refining the encoding format of Snappy and introduces an innovative approach utilizing fingerprints within the dictionary to minimize storage space requirements. Additionally, the algorithm incorporates a pipeline structure to optimize performance. Experimental results demonstrate that the proposed algorithm achieves a throughput of 1.6 GB/s for eight hardware kernels. The average compression ratio is 2.27, representing a 6.1% improvement over the state-of-the-art Snappy FPGA implementation. Notably, the proposed algorithm architecture consumes fewer on-chip storage resources compared to other advanced algorithms, striking a balance between logic and storage resource utilization. This optimization leads to higher FPGA resource utilization efficiency. Our design addresses the growing demand for efficient lossless data compression solutions in consumer electronics, ultimately contributing to advancements in modern digital ecosystems.

Entropy encoder for low-power low-resources high-quality CFA image compression

Aims and Objective:The objective of this study was to evaluate the compression of renal dynamic (RD) study images using singular value decomposition (SVD) technique.Materials and Methods:4600 images of fifty RD study were compressed by using SVD technique. Two Nuclear Medicine (NM) Physicians compared compressed images with their corresponding input images and labeled these as acceptable or unacceptable. The SVD computation time and compression ratio were calculated for each image. The quality of compressed image was also assessed objectively using the following image quality metrics: Error, structural similarity (SSIM), Brightness, global contrast factor, contrast per pixel (CPP), and blur. The error in split function (i.e., the error between split function calculated from compressed image and split function calculated from original image) was computed for every RD study. Wilcoxon signed-rank test with continuity correction was applied to find a statistically significant difference in ROI counts on compressed and original image at.Results:As per NM physicians compressed image frames look identical to the original image frames. Objectively the compressed images were brighter, less noisy, and also have better CPP. Based on the visual assessment, time activity curve generated from original and compressed image frames was identical. There was insignificant difference of ROI counts between the input and compressed image frames of 99m-Tc LLEC RD Study. There was no significant difference between the split renal function estimated from original and its compressed RD study. The average SSIM value, average compression ratio, and SVD computation time were found to be 0.7521, 1.475, and 0.1200.Conclusions:Visually, compressed image was identical to the original image. The percentage compression achieved was found to be up to 58% (compression factor achieved = 1.57). The SVD computation time was approximately 0.12 s for 64 × 64 matrix size image frame.

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

For instruction decompression, techniques such as single buffering, double buffering and pipelining have been proposed. However, due to jumping penalty, these techniques incur more delays in pipeline or system has to be stopped to refill the cache buffers. A Pipeline with Back-up for Flushing (PBF) technique has been developed that incurs no delay and without stopping due to jumping. However, the first instruction of each basic block should not be compressed and be put in another ROM, and thus the compression ratio should be sacrificed. This paper improves the PBF technique such that a single program ROM needed only. The simulation results for several benchmarks show that the average compression ratio is decreased about 11%, and the hardware cost deceased about 8%.

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.